system

Abstract

The system according to the embodiment aims to efficiently manage multiple tasks and appropriately adjust priorities. [Solution] A system according to an embodiment includes a collection unit, a determination unit, a management unit, an adjustment unit, and a visualization unit. The collection unit collects task information. The determination unit determines the priority of each task based on the information collected by the collection unit. The management unit manages the progress of each task based on the priority determined by the determination unit. The adjustment unit readjusts the task priorities based on the progress managed by the management unit. The visualization unit visualizes the progress of the entire project based on the priority readjusted by the adjustment unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to efficiently manage multiple tasks and appropriately adjust their priorities.

[0005] The system according to the embodiment aims to efficiently manage multiple tasks and appropriately adjust priorities. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, a determination unit, a management unit, an adjustment unit, and a visualization unit. The collection unit collects task information. The determination unit determines the priority of each task based on the information collected by the collection unit. The management unit manages the progress of each task based on the priority determined by the determination unit. The adjustment unit readjusts the task priorities based on the progress managed by the management unit. The visualization unit visualizes the progress of the entire project based on the priority readjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage multiple tasks and appropriately adjust priorities. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A task management system according to an embodiment of the present invention uses a generation AI to prioritize and manage tasks in a work environment where multiple projects are being carried out simultaneously. This task management system first collects task information for each project, and then the generation AI analyzes this information to determine the priority of each task. Based on the generated priorities, the system manages the progress of each task and readjusts priorities as necessary. Furthermore, the system visualizes the progress of the entire project based on the progress of each task. This system enables efficient management of multiple projects and increases the project success rate without relying on the experience or skills of experts. For example, a task management system collects information such as the project start date, end date, deadlines for each task, and required resources. The generation AI then analyzes this information to determine the priority of each task. The generation AI generates optimal priorities based on the importance and urgency of each task, resource availability, and other factors. The progress of each task is managed based on the generated priorities. The generation AI monitors the progress of tasks in real time and readjusts task priorities as necessary. For example, if a task is delayed, the generation AI increases the priority of that task and adjusts the progress of other tasks. Furthermore, the generative AI visualizes the progress of the entire project based on the progress of each task. For example, it generates Gantt charts and progress reports, allowing the progress of the project to be understood at a glance. This allows multiple projects to be managed efficiently and increases the success rate of projects without relying on the experience and skills of experts. This allows task management systems to efficiently manage multiple projects and increases the success rate of projects without relying on the experience and skills of experts.

[0029] A task management system according to an embodiment includes a collection unit, a determination unit, a management unit, an adjustment unit, and a visualization unit. The collection unit collects task information. The task information includes, but is not limited to, the start and end dates of a project, the deadlines of each task, and required resources. For example, the collection unit obtains the start and end dates of a project from a calendar and collects the deadlines of each task from a project management tool. The collection unit can also obtain the required resources from a resource management system. For example, the collection unit obtains the start and end dates of a project using a calendar API and collects the deadlines of each task using the API of the project management tool. The collection unit can also obtain the required resource information from a database of the resource management system. The determination unit determines the priority of each task based on the information collected by the collection unit. The determination unit generates optimal priorities by taking into account, for example, the importance and urgency of each task, resource usage status, and the like. For example, the determination unit evaluates the importance of a task based on its impact on the project and the urgency based on how close the deadline is. The determination unit can also evaluate the resource utilization status based on the resource allocation status. For example, the determination unit evaluates the importance of a task based on its impact score on the project and the urgency based on the number of days remaining until the deadline. The determination unit can also evaluate the resource utilization status based on the resource allocation score. The management unit manages the progress of each task based on the priority determined by the determination unit. For example, the management unit monitors the progress of tasks in real time and readjusts the priority of tasks as needed. For example, the management unit obtains the progress of tasks from a project management tool and raises the priority of tasks that are delayed. The management unit can also obtain the progress of tasks from a resource management system and readjust the priority of tasks according to the resource utilization status. For example, the management unit obtains the progress of tasks using an API of the project management tool and raises the priority of tasks that are delayed.The management unit can also obtain resource usage status from the database of the resource management system and readjust task priorities according to the resource usage status. The adjustment unit readjusts task priorities based on the progress status managed by the management unit. The adjustment unit, for example, readjusts priorities according to task delays. For example, the adjustment unit raises the priority of a delayed task and adjusts the progress of other tasks. The adjustment unit can also readjust task priorities according to resource usage status. For example, the adjustment unit readjusts task priorities according to resource usage status to ensure efficient resource use. The visualization unit visualizes the progress of the entire project based on the priorities readjusted by the adjustment unit. The visualization unit generates, for example, a Gantt chart or a progress report to enable the progress of the project to be understood at a glance. For example, the visualization unit generates a Gantt chart to visually display the progress of each task. The visualization unit can also generate a progress report to provide a detailed report on the progress of the project. For example, the visualization unit generates a Gantt chart using an API of a project management tool to visually display the progress of each task. The visualization unit can also generate progress reports based on information acquired from the database of the project management tool, and provide detailed reports on the progress of the project. As a result, the task management system according to the embodiment can efficiently manage multiple projects and increase the success rate of projects without relying on the experience and skills of experts.

[0030] The collection unit can collect the project start date, end date, deadline for each task, required resources, and related information. For example, the collection unit obtains the project start date and end date from a calendar and collects the deadline for each task from a project management tool. The collection unit can also obtain the required resources from a resource management system. For example, the collection unit obtains the project start date and end date using a calendar API and collects the deadline for each task using the API of the project management tool. The collection unit can also obtain the required resource information from the database of the resource management system. This allows detailed project information to be collected, thereby enabling accurate task prioritization. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can obtain the project start date and end date using a calendar API and input them into the generation AI for analysis.

[0031] The determination unit can generate appropriate priorities by taking into account the importance and urgency of tasks and resource usage status. For example, the determination unit evaluates the importance of tasks based on their impact on the project and the urgency based on how close the deadline is. The determination unit can also evaluate resource usage status based on resource allocation status. For example, the determination unit evaluates the importance of tasks based on their impact score on the project and the urgency based on the number of days remaining until the deadline. The determination unit can also evaluate resource usage status based on resource allocation scores. This allows for optimal task prioritization, thereby streamlining project progress. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit can input the importance, urgency, and resource usage status of tasks into the generation AI, and the generation AI can generate optimal priorities.

[0032] The management unit can monitor the progress of tasks in real time. For example, the management unit can obtain the progress of tasks from a project management tool and raise the priority of a task that is experiencing delays. The management unit can also obtain the progress of tasks from a resource management system and readjust the priority of tasks according to the resource usage status. For example, the management unit can obtain the progress of tasks using an API of the project management tool and raise the priority of a task that is experiencing delays. The management unit can also obtain the resource usage status from the database of the resource management system and readjust the priority of tasks according to the resource usage status. In this way, by monitoring the progress of tasks in real time, delays and problems can be detected and addressed early. Some or all of the above-mentioned processing in the management unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the management unit can input the progress of tasks into the generation AI, and the generation AI can raise the priority of a task that is experiencing delays.

[0033] The adjustment unit can readjust the priority in accordance with task delays. For example, the adjustment unit raises the priority of a task that is experiencing delays and adjusts the progress of other tasks. The adjustment unit can also readjust the priority of tasks in accordance with resource usage. For example, the adjustment unit readjusts the priority of tasks in accordance with resource usage to ensure efficient resource use. In this way, by readjusting the priority in accordance with task delays, it is possible to maintain smooth progress of the project. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input information about a task that is experiencing delays into the generation AI and have the generation AI readjust the priority.

[0034] The visualization unit can generate a Gantt chart and a progress report to visualize the progress of the project. For example, the visualization unit can generate a Gantt chart to visually display the progress of each task. The visualization unit can also generate a progress report to provide a detailed report on the progress of the project. For example, the visualization unit can generate a Gantt chart using an API of a project management tool to visually display the progress of each task. The visualization unit can also generate a progress report based on information obtained from the database of the project management tool to provide a detailed report on the progress of the project. This allows the progress of the project to be visualized, allowing stakeholders to understand the progress at a glance. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input the progress of the project into the generation AI, which can then generate a Gantt chart and a progress report.

[0035] The collection unit can collect the progress status of each task in real time and optimize the collection method according to the progress status. For example, if the task is progressing smoothly, the collection unit performs regular collection. Furthermore, if the task is delayed, the collection unit can increase the collection frequency to collect detailed information. For example, if the task is nearing completion, the collection unit reduces the collection frequency to save resources. In this way, by optimizing the collection method according to the progress status of the task, information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the progress status of the task to the generation AI, and the generation AI can optimize the collection method.

[0036] The collection unit can collect risk factors for the project and prioritize collection of high-risk tasks. The collection unit, for example, collects detailed risk information for high-risk tasks. The collection unit can also frequently collect progress information for high-risk tasks. For example, the collection unit collects external factors (weather, economic conditions, etc.) related to high-risk tasks. This prioritizes collection of high-risk tasks, thereby improving project risk management. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using or without the generation AI. For example, the collection unit can input information on high-risk tasks into the generation AI, which can collect risk information.

[0037] When collecting task information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting task information related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting task information related to the user's destination. For example, when the user is working remotely, the collection unit prioritizes collecting task information accessible online. This allows for efficient collection of highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, and have the generation AI prioritize collecting highly relevant information.

[0038] The collection unit can analyze the user's social media activities and collect related information when collecting task information. For example, the collection unit collects related task information based on information shared by the user on social media. The collection unit can also adjust the collection timing taking into account the user's social media activity time. For example, the collection unit analyzes the user's interests on social media and prioritizes collecting related task information. This allows for efficient collection of related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can collect related information.

[0039] The determination unit can analyze the dependency relationships between tasks and determine priorities based on the dependency relationships. For example, the determination unit prioritizes tasks that have dependency relationships. The determination unit can also postpone tasks that do not have dependency relationships. For example, the determination unit prioritizes tasks that have strong dependency relationships, thereby smoothing the progress of the entire project. In this way, by taking the dependency relationships between tasks into consideration, the progress of the entire project can be smoothed. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit can input task dependency relationship data into a generation AI, and the generation AI can determine the priorities.

[0040] The decision unit can prioritize high-risk tasks by taking into account the risk factors of the tasks. For example, the decision unit prioritizes high-risk tasks to reduce the risk of the entire project. The decision unit can also postpone low-risk tasks. For example, the decision unit allocates additional resources to high-risk tasks to prioritize processing them. In this way, the priority processing of high-risk tasks reduces the risk of the entire project. Some or all of the above-mentioned processing in the decision unit may be performed using, or without, a generation AI. For example, the decision unit can input risk factor data into a generation AI and have the generation AI prioritize processing high-risk tasks.

[0041] When determining task priorities, the determination unit can generate optimal priorities by referring to the user's past task completion history. The determination unit generates optimal priorities, for example, based on the history of tasks previously completed by the user. The determination unit can also generate efficient priorities from the user's past task completion history. For example, the determination unit analyzes the user's past task completion history and generates the most effective priorities. This allows optimal priorities to be generated by referring to the user's past task completion history. Some or all of the above-described processing in the determination unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the user's past task completion history data into the generation AI and use the generation AI to generate optimal priorities.

[0042] When determining task priorities, the determination unit can adjust the priorities taking into account the user's current project status. For example, the determination unit adjusts the priorities taking into account the user's current project status. The determination unit can also generate optimal priorities based on the user's current project status. For example, the determination unit analyzes the user's current project status and generates efficient priorities. This allows efficient task management by taking into account the user's current project status. Some or all of the above-described processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's current project status data into the generation AI and adjust the priorities using the generation AI.

[0043] The management unit can monitor the progress of tasks in real time and adjust the management method according to the progress. For example, if the task is progressing smoothly, the management unit performs regular monitoring. In addition, if the task is delayed, the management unit can increase the monitoring frequency to collect more detailed information. For example, if the task is nearing completion, the management unit reduces the monitoring frequency to save resources. This allows tasks to be managed efficiently by optimizing the management method according to the progress of the task. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input task progress data into the generation AI, which can adjust the management method.

[0044] The management unit can optimize resource allocation based on the progress of a task. For example, if a task is progressing smoothly, the management unit reallocates resources to other tasks. In addition, if a task is delayed, the management unit can allocate additional resources to expedite progress. For example, if a task is nearing completion, the management unit saves resources and allocates them to other tasks. In this way, optimizing resource allocation based on the progress of a task allows for efficient resource utilization. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input task progress data into the generation AI, which then optimizes resource allocation.

[0045] When managing the progress of a task, the management unit can select the optimal management method by taking into account the user's geographical location information. For example, when the user is in a specific area, the management unit prioritizes managing task information related to that area. Furthermore, when the user is traveling, the management unit can also prioritize managing task information related to the user's destination. For example, when the user is working remotely, the management unit prioritizes managing task information accessible online. This allows tasks to be managed efficiently by taking the user's geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information into the generation AI, which can then select the optimal management method.

[0046] When managing the progress of a task, the management unit can analyze the user's social media activities and reflect related information in the management. For example, the management unit manages related task information based on information shared by the user on social media. The management unit can also adjust the management timing taking into account the user's social media activity time. For example, the management unit analyzes the user's interests on social media and prioritizes managing related task information. This allows for efficient management of related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's social media activity data into a generation AI and reflect related information from the generation AI in the management.

[0047] The adjustment unit can dynamically readjust the priority based on the progress of the task. For example, if the task is progressing smoothly, the adjustment unit maintains the priority. Furthermore, if the task is delayed, the adjustment unit can also raise the priority to expedite progress. For example, if the task is nearing completion, the adjustment unit lowers the priority and allocates resources to other tasks. In this way, tasks can be managed efficiently by dynamically readjusting the priority based on the progress of the task. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input task progress data into the generation AI and have the generation AI dynamically readjust the priority.

[0048] The adjustment unit can analyze task dependencies and readjust priorities based on the dependencies. For example, the adjustment unit prioritizes and readjusts tasks that have dependencies. The adjustment unit can also postpone tasks that do not have dependencies. For example, the adjustment unit prioritizes and readjusts tasks that have strong dependencies, thereby smoothing the progress of the entire project. In this way, by taking task dependencies into consideration, the progress of the entire project is smoothed. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input task dependency data into the generation AI and have the generation AI readjust the priorities.

[0049] When readjusting task priorities, the adjustment unit can perform optimal readjustment by referring to the user's past task completion history. The adjustment unit performs optimal readjustment, for example, based on the history of tasks previously completed by the user. The adjustment unit can also perform efficient readjustment from the user's past task completion history. For example, the adjustment unit analyzes the user's past task completion history and performs the most effective readjustment. In this way, optimal readjustment can be performed by referring to the user's past task completion history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's past task completion history data into the generation AI and use the generation AI to perform optimal readjustment.

[0050] When readjusting task priorities, the adjustment unit can perform the readjustment taking into account the user's current project status. The adjustment unit, for example, performs the readjustment taking into account the user's current project status. The adjustment unit can also perform optimal readjustment based on the user's current project status. For example, the adjustment unit analyzes the user's current project status and performs efficient readjustment. This allows efficient task management by taking into account the user's current project status. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's current project status data into the generation AI and perform readjustment using the generation AI.

[0051] The visualization unit can visualize the progress of the project in real time and optimize the visualization method according to the progress. For example, if the project is progressing smoothly, the visualization unit performs regular visualization. Furthermore, if the project is delayed, the visualization unit can increase the visualization frequency and provide more detailed information. For example, if the project is nearing completion, the visualization unit reduces the visualization frequency to save resources. This allows efficient progress management by optimizing the visualization method according to the progress of the project. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input project progress data into the generation AI and use the generation AI to optimize the visualization method.

[0052] The visualization unit can visualize risk factors for the project and highlight high-risk tasks. For example, the visualization unit can highlight high-risk tasks to visualize the risks of the entire project. The visualization unit can also postpone low-risk tasks. For example, the visualization unit can allocate additional resources to high-risk tasks to process them as a priority. This highlights high-risk tasks, thereby improving risk management for the entire project. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input risk factor data into a generation AI and have the generation AI highlight high-risk tasks.

[0053] When visualizing the progress of a project, the visualization unit can select the optimal display method by taking into account the user's geographical location information. For example, when the user is in a specific area, the visualization unit prioritizes visualizing task information related to that area. Furthermore, when the user is traveling, the visualization unit can also prioritize visualizing task information related to the user's destination. For example, when the user is working remotely, the visualization unit prioritizes visualizing task information accessible online. This allows for efficient progress management by taking the user's geographical location information into consideration. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's geographical location information into the generation AI, which can then select the optimal display method.

[0054] When visualizing the progress of a project, the visualization unit can analyze the user's social media activities and reflect related information in the visualization. For example, the visualization unit visualizes related task information based on information shared by the user on social media. The visualization unit can also adjust the timing of visualization taking into account the user's social media activity time. For example, the visualization unit analyzes the user's interests on social media and prioritizes visualizing related task information. This allows for efficient visualization of related information by analyzing the user's social media activities. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's social media activity data into the generation AI and have the generation AI reflect related information in the visualization.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The task management system may further include a notification unit. The notification unit may notify the user of changes in task progress and priority. For example, the notification unit may send an alert to the user when task progress is behind schedule. The notification unit may also notify the user when task priority has changed. The notification unit may also send a reminder when a task deadline is approaching. This allows the user to grasp task progress and priority changes in real time, enabling efficient task management.

[0057] The task management system may further include a feedback unit. The feedback unit may collect feedback from users and use it to improve the system. For example, the feedback unit may provide an interface that allows users to express their opinions on task priorities. The feedback unit may also provide a function that allows users to leave comments on the progress of tasks. The feedback unit may also analyze user feedback and provide data for improving the system's algorithms. This makes it possible to improve the system by reflecting user opinions, thereby providing a task management system that is easier to use.

[0058] The task management system may further include a learning unit. The learning unit can learn the user's past task management data and use it for future task management. For example, the learning unit may analyze what tasks the user has managed in the past and how. The learning unit may also learn the user's past task completion times and resource usage status and reflect this in future task management. Furthermore, the learning unit may suggest optimal task management methods based on the user's past task management data. This enables efficient task management that makes use of the user's past experience.

[0059] The task management system may further include a prediction unit. The prediction unit can predict future task progress based on task progress and resource usage. For example, the prediction unit can predict the completion time of a future task based on the progress of the current task. The prediction unit can also predict future resource shortages based on resource usage. Furthermore, the prediction unit can predict the progress of the entire project based on task progress and resource usage. This makes it possible to identify future problems in advance and take appropriate measures.

[0060] The task management system may further include a reward unit. The reward unit may provide a reward when the user completes a task. For example, the reward unit may award points when the user completes a task within a deadline. The reward unit may also provide a badge when the user completes a task efficiently. Furthermore, the reward unit may provide a reward every time the user completes a task to increase the user's motivation. This may motivate the user to complete tasks efficiently.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects task information. The task information includes the project start date, end date, deadline for each task, required resources, etc. The collection unit may, for example, use a calendar API to obtain the project start date and end date, and collect the deadline for each task using the API of the project management tool. It may also obtain the required resource information from the database of the resource management system. Step 2: The decision unit determines the priority of each task based on the information collected by the collection unit. The decision unit generates optimal priorities by taking into account the importance and urgency of the tasks, resource utilization status, etc. For example, the decision unit may evaluate the importance of a task based on its project impact score, and the urgency based on the number of days remaining until the deadline. Resource utilization status may also be evaluated based on the resource allocation score. Step 3: The management unit manages the progress of each task based on the priority determined by the determination unit. The management unit monitors the progress of tasks in real time and readjusts task priorities as necessary. For example, the management unit may obtain the progress of tasks using the API of a project management tool and raise the priority of tasks that are experiencing delays. It may also obtain resource usage status from the database of a resource management system and readjust task priorities according to the resource usage status. Step 4: The adjustment unit readjusts the priority of tasks based on the progress managed by the management unit. The adjustment unit readjusts the priority according to task delays and can also readjust the priority of tasks according to resource usage. For example, the adjustment unit raises the priority of a delayed task and adjusts the progress of other tasks. The adjustment unit also readjusts the priority of tasks according to resource usage, aiming for efficient resource use. Step 5: The visualization unit visualizes the progress of the entire project based on the priorities readjusted by the adjustment unit. The visualization unit generates Gantt charts, progress reports, etc., to enable the progress of the project to be understood at a glance. For example, a Gantt chart can be generated using the API of the project management tool to visually display the progress of each task. In addition, a progress report can be generated based on information obtained from the database of the project management tool to provide a detailed report on the progress of the project.

[0063] (Example 2) A task management system according to an embodiment of the present invention uses a generation AI to prioritize and manage tasks in a work environment where multiple projects are being carried out simultaneously. This task management system first collects task information for each project, and then the generation AI analyzes this information to determine the priority of each task. Based on the generated priorities, the system manages the progress of each task and readjusts priorities as necessary. Furthermore, the system visualizes the progress of the entire project based on the progress of each task. This system enables efficient management of multiple projects and increases the project success rate without relying on the experience or skills of experts. For example, a task management system collects information such as the project start date, end date, deadlines for each task, and required resources. The generation AI then analyzes this information to determine the priority of each task. The generation AI generates optimal priorities based on the importance and urgency of each task, resource availability, and other factors. The progress of each task is managed based on the generated priorities. The generation AI monitors the progress of tasks in real time and readjusts task priorities as necessary. For example, if a task is delayed, the generation AI increases the priority of that task and adjusts the progress of other tasks. Furthermore, the generative AI visualizes the progress of the entire project based on the progress of each task. For example, it generates Gantt charts and progress reports, allowing the progress of the project to be understood at a glance. This allows multiple projects to be managed efficiently and increases the success rate of projects without relying on the experience and skills of experts. This allows task management systems to efficiently manage multiple projects and increases the success rate of projects without relying on the experience and skills of experts.

[0064] A task management system according to an embodiment includes a collection unit, a determination unit, a management unit, an adjustment unit, and a visualization unit. The collection unit collects task information. The task information includes, but is not limited to, the start and end dates of a project, the deadlines of each task, and required resources. For example, the collection unit obtains the start and end dates of a project from a calendar and collects the deadlines of each task from a project management tool. The collection unit can also obtain the required resources from a resource management system. For example, the collection unit obtains the start and end dates of a project using a calendar API and collects the deadlines of each task using the API of the project management tool. The collection unit can also obtain the required resource information from a database of the resource management system. The determination unit determines the priority of each task based on the information collected by the collection unit. The determination unit generates optimal priorities by taking into account, for example, the importance and urgency of each task, resource usage status, and the like. For example, the determination unit evaluates the importance of a task based on its impact on the project and the urgency based on how close the deadline is. The determination unit can also evaluate the resource utilization status based on the resource allocation status. For example, the determination unit evaluates the importance of a task based on its impact score on the project and the urgency based on the number of days remaining until the deadline. The determination unit can also evaluate the resource utilization status based on the resource allocation score. The management unit manages the progress of each task based on the priority determined by the determination unit. For example, the management unit monitors the progress of tasks in real time and readjusts the priority of tasks as needed. For example, the management unit obtains the progress of tasks from a project management tool and raises the priority of tasks that are delayed. The management unit can also obtain the progress of tasks from a resource management system and readjust the priority of tasks according to the resource utilization status. For example, the management unit obtains the progress of tasks using an API of the project management tool and raises the priority of tasks that are delayed.The management unit can also obtain resource usage status from the database of the resource management system and readjust task priorities according to the resource usage status. The adjustment unit readjusts task priorities based on the progress status managed by the management unit. The adjustment unit, for example, readjusts priorities according to task delays. For example, the adjustment unit raises the priority of a delayed task and adjusts the progress of other tasks. The adjustment unit can also readjust task priorities according to resource usage status. For example, the adjustment unit readjusts task priorities according to resource usage status to ensure efficient resource use. The visualization unit visualizes the progress of the entire project based on the priorities readjusted by the adjustment unit. The visualization unit generates, for example, a Gantt chart or a progress report to enable the progress of the project to be understood at a glance. For example, the visualization unit generates a Gantt chart to visually display the progress of each task. The visualization unit can also generate a progress report to provide a detailed report on the progress of the project. For example, the visualization unit generates a Gantt chart using an API of a project management tool to visually display the progress of each task. The visualization unit can also generate progress reports based on information acquired from the database of the project management tool, and provide detailed reports on the progress of the project. As a result, the task management system according to the embodiment can efficiently manage multiple projects and increase the success rate of projects without relying on the experience and skills of experts.

[0065] The collection unit can collect the project start date, end date, deadline for each task, required resources, and related information. For example, the collection unit obtains the project start date and end date from a calendar and collects the deadline for each task from a project management tool. The collection unit can also obtain the required resources from a resource management system. For example, the collection unit obtains the project start date and end date using a calendar API and collects the deadline for each task using the API of the project management tool. The collection unit can also obtain the required resource information from the database of the resource management system. This allows detailed project information to be collected, thereby enabling accurate task prioritization. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can obtain the project start date and end date using a calendar API and input them into the generation AI for analysis.

[0066] The determination unit can generate appropriate priorities by taking into account the importance and urgency of tasks and resource usage status. For example, the determination unit evaluates the importance of tasks based on their impact on the project and the urgency based on how close the deadline is. The determination unit can also evaluate resource usage status based on resource allocation status. For example, the determination unit evaluates the importance of tasks based on their impact score on the project and the urgency based on the number of days remaining until the deadline. The determination unit can also evaluate resource usage status based on resource allocation scores. This allows for optimal task prioritization, thereby streamlining project progress. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit can input the importance, urgency, and resource usage status of tasks into the generation AI, and the generation AI can generate optimal priorities.

[0067] The management unit can monitor the progress of tasks in real time. For example, the management unit can obtain the progress of tasks from a project management tool and raise the priority of a task that is experiencing delays. The management unit can also obtain the progress of tasks from a resource management system and readjust the priority of tasks according to the resource usage status. For example, the management unit can obtain the progress of tasks using an API of the project management tool and raise the priority of a task that is experiencing delays. The management unit can also obtain the resource usage status from the database of the resource management system and readjust the priority of tasks according to the resource usage status. In this way, by monitoring the progress of tasks in real time, delays and problems can be detected and addressed early. Some or all of the above-mentioned processing in the management unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the management unit can input the progress of tasks into the generation AI, and the generation AI can raise the priority of a task that is experiencing delays.

[0068] The adjustment unit can readjust the priority in accordance with task delays. For example, the adjustment unit raises the priority of a task that is experiencing delays and adjusts the progress of other tasks. The adjustment unit can also readjust the priority of tasks in accordance with resource usage. For example, the adjustment unit readjusts the priority of tasks in accordance with resource usage to ensure efficient resource use. In this way, by readjusting the priority in accordance with task delays, it is possible to maintain smooth progress of the project. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input information about a task that is experiencing delays into the generation AI and have the generation AI readjust the priority.

[0069] The visualization unit can generate a Gantt chart and a progress report to visualize the progress of the project. For example, the visualization unit can generate a Gantt chart to visually display the progress of each task. The visualization unit can also generate a progress report to provide a detailed report on the progress of the project. For example, the visualization unit can generate a Gantt chart using an API of a project management tool to visually display the progress of each task. The visualization unit can also generate a progress report based on information obtained from the database of the project management tool to provide a detailed report on the progress of the project. This allows the progress of the project to be visualized, allowing stakeholders to understand the progress at a glance. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input the progress of the project into the generation AI, which can then generate a Gantt chart and a progress report.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting task information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect information. For example, if the user is in a hurry, the collection unit can immediately set the collection timing to quickly collect information. This adjusts the collection timing according to the user's emotions, reducing the user's burden and allowing information to be collected efficiently. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0071] The collection unit can collect the progress status of each task in real time and optimize the collection method according to the progress status. For example, if the task is progressing smoothly, the collection unit performs regular collection. Furthermore, if the task is delayed, the collection unit can increase the collection frequency to collect detailed information. For example, if the task is nearing completion, the collection unit reduces the collection frequency to save resources. In this way, by optimizing the collection method according to the progress status of the task, information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the progress status of the task to the generation AI, and the generation AI can optimize the collection method.

[0072] The collection unit can collect risk factors for the project and prioritize collection of high-risk tasks. The collection unit, for example, collects detailed risk information for high-risk tasks. The collection unit can also frequently collect progress information for high-risk tasks. For example, the collection unit collects external factors (weather, economic conditions, etc.) related to high-risk tasks. This prioritizes collection of high-risk tasks, thereby improving project risk management. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using or without the generation AI. For example, the collection unit can input information on high-risk tasks into the generation AI, which can collect risk information.

[0073] The collection unit can estimate the user's emotions and determine the priority of task information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit postpones collecting less important task information. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting more important task information. For example, if the user is in a hurry, the collection unit prioritizes collecting more urgent task information. This allows for efficient information collection by determining the priority of task information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI may determine the priority of task information.

[0074] When collecting task information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting task information related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting task information related to the user's destination. For example, when the user is working remotely, the collection unit prioritizes collecting task information accessible online. This allows for efficient collection of highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, and have the generation AI prioritize collecting highly relevant information.

[0075] The collection unit can analyze the user's social media activities and collect related information when collecting task information. For example, the collection unit collects related task information based on information shared by the user on social media. The collection unit can also adjust the collection timing taking into account the user's social media activity time. For example, the collection unit analyzes the user's interests on social media and prioritizes collecting related task information. This allows for efficient collection of related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can collect related information.

[0076] The determination unit can estimate the user's emotions and adjust task priorities based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit can lower the priority of low-importance tasks. Furthermore, if the user is relaxed, the determination unit can also increase the priority of high-importance tasks. For example, if the user is in a hurry, the determination unit can increase the priority of high-urgency tasks. This allows for efficient task management by adjusting task priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 processing in the determination unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI adjust the task priorities.

[0077] The determination unit can analyze the dependency relationships between tasks and determine priorities based on the dependency relationships. For example, the determination unit prioritizes tasks that have dependency relationships. The determination unit can also postpone tasks that do not have dependency relationships. For example, the determination unit prioritizes tasks that have strong dependency relationships, thereby smoothing the progress of the entire project. In this way, by taking the dependency relationships between tasks into consideration, the progress of the entire project can be smoothed. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit can input task dependency relationship data into a generation AI, and the generation AI can determine the priorities.

[0078] The decision unit can prioritize high-risk tasks by taking into account the risk factors of the tasks. For example, the decision unit prioritizes high-risk tasks to reduce the risk of the entire project. The decision unit can also postpone low-risk tasks. For example, the decision unit allocates additional resources to high-risk tasks to prioritize processing them. In this way, the priority processing of high-risk tasks reduces the risk of the entire project. Some or all of the above-mentioned processing in the decision unit may be performed using, or without, a generation AI. For example, the decision unit can input risk factor data into a generation AI and have the generation AI prioritize processing high-risk tasks.

[0079] The determination unit can estimate the user's emotions and adjust the priority determination method based on the estimated user emotions. For example, if the user is stressed, the determination unit can adopt a simple priority determination method. Alternatively, if the user is relaxed, the determination unit can adopt a detailed priority determination method. For example, if the user is in a hurry, the determination unit can adopt a quick priority determination method. This allows for efficient task management by adjusting the priority determination method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the determination unit can input the user's emotion data into the generation AI and adjust the priority determination method using the generation AI.

[0080] When determining task priorities, the determination unit can generate optimal priorities by referring to the user's past task completion history. The determination unit generates optimal priorities, for example, based on the history of tasks previously completed by the user. The determination unit can also generate efficient priorities from the user's past task completion history. For example, the determination unit analyzes the user's past task completion history and generates the most effective priorities. This allows optimal priorities to be generated by referring to the user's past task completion history. Some or all of the above-described processing in the determination unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the user's past task completion history data into the generation AI and use the generation AI to generate optimal priorities.

[0081] When determining task priorities, the determination unit can adjust the priorities taking into account the user's current project status. For example, the determination unit adjusts the priorities taking into account the user's current project status. The determination unit can also generate optimal priorities based on the user's current project status. For example, the determination unit analyzes the user's current project status and generates efficient priorities. This allows efficient task management by taking into account the user's current project status. Some or all of the above-described processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's current project status data into the generation AI and adjust the priorities using the generation AI.

[0082] The management unit can estimate the user's emotions and manage the task progress based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can reduce the frequency of progress reports. Furthermore, if the user is relaxed, the management unit can also increase the frequency of progress reports. For example, if the user is in a hurry, the management unit can immediately report the progress. This allows the task progress to be managed according to the user's emotions, thereby efficiently progressing the task. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the management unit can input the user's emotion data into the generation AI and manage the task progress using the generation AI.

[0083] The management unit can monitor the progress of tasks in real time and adjust the management method according to the progress. For example, if the task is progressing smoothly, the management unit performs regular monitoring. In addition, if the task is delayed, the management unit can increase the monitoring frequency to collect more detailed information. For example, if the task is nearing completion, the management unit reduces the monitoring frequency to save resources. This allows tasks to be managed efficiently by optimizing the management method according to the progress of the task. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input task progress data into the generation AI, which can adjust the management method.

[0084] The management unit can optimize resource allocation based on the progress of a task. For example, if a task is progressing smoothly, the management unit reallocates resources to other tasks. In addition, if a task is delayed, the management unit can allocate additional resources to expedite progress. For example, if a task is nearing completion, the management unit saves resources and allocates them to other tasks. In this way, optimizing resource allocation based on the progress of a task allows for efficient resource utilization. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input task progress data into the generation AI, which then optimizes resource allocation.

[0085] The management unit can estimate the user's emotions and adjust the display method of the task progress status based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the management unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the management unit can provide a display method that focuses on the main points. This allows for highly visible display by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the management unit can input the user's emotion data into the generation AI, and the generation AI can adjust the display method.

[0086] When managing the progress of a task, the management unit can select the optimal management method by taking into account the user's geographical location information. For example, when the user is in a specific area, the management unit prioritizes managing task information related to that area. Furthermore, when the user is traveling, the management unit can also prioritize managing task information related to the user's destination. For example, when the user is working remotely, the management unit prioritizes managing task information accessible online. This allows tasks to be managed efficiently by taking the user's geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information into the generation AI, which can then select the optimal management method.

[0087] When managing the progress of a task, the management unit can analyze the user's social media activities and reflect related information in the management. For example, the management unit manages related task information based on information shared by the user on social media. The management unit can also adjust the management timing taking into account the user's social media activity time. For example, the management unit analyzes the user's interests on social media and prioritizes managing related task information. This allows for efficient management of related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's social media activity data into a generation AI and reflect related information from the generation AI in the management.

[0088] The adjustment unit can estimate the user's emotions and readjust task priorities based on the estimated user emotions. For example, when the user is feeling stressed, the adjustment unit can lower the priority of low-importance tasks. Furthermore, when the user is relaxed, the adjustment unit can also raise the priority of high-importance tasks. For example, when the user is in a hurry, the adjustment unit can raise the priority of high-urgency tasks. This allows for efficient task management by readjusting task priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI readjust the task priorities.

[0089] The adjustment unit can dynamically readjust the priority based on the progress of the task. For example, if the task is progressing smoothly, the adjustment unit maintains the priority. Furthermore, if the task is delayed, the adjustment unit can also raise the priority to expedite progress. For example, if the task is nearing completion, the adjustment unit lowers the priority and allocates resources to other tasks. In this way, tasks can be managed efficiently by dynamically readjusting the priority based on the progress of the task. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input task progress data into the generation AI and have the generation AI dynamically readjust the priority.

[0090] The adjustment unit can analyze task dependencies and readjust priorities based on the dependencies. For example, the adjustment unit prioritizes and readjusts tasks that have dependencies. The adjustment unit can also postpone tasks that do not have dependencies. For example, the adjustment unit prioritizes and readjusts tasks that have strong dependencies, thereby smoothing the progress of the entire project. In this way, by taking task dependencies into consideration, the progress of the entire project is smoothed. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input task dependency data into the generation AI and have the generation AI readjust the priorities.

[0091] The adjustment unit can estimate the user's emotions and adjust the priority readjustment method based on the estimated user's emotions. For example, the adjustment unit can adopt a simple readjustment method when the user is stressed. The adjustment unit can also adopt a detailed readjustment method when the user is relaxed. For example, the adjustment unit can adopt a quick readjustment method when the user is in a hurry. This allows efficient task management by adjusting the readjustment method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data into the generation AI and adjust the readjustment method using the generation AI.

[0092] When readjusting task priorities, the adjustment unit can perform optimal readjustment by referring to the user's past task completion history. The adjustment unit performs optimal readjustment, for example, based on the history of tasks previously completed by the user. The adjustment unit can also perform efficient readjustment from the user's past task completion history. For example, the adjustment unit analyzes the user's past task completion history and performs the most effective readjustment. In this way, optimal readjustment can be performed by referring to the user's past task completion history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's past task completion history data into the generation AI and use the generation AI to perform optimal readjustment.

[0093] When readjusting task priorities, the adjustment unit can perform the readjustment taking into account the user's current project status. The adjustment unit, for example, performs the readjustment taking into account the user's current project status. The adjustment unit can also perform optimal readjustment based on the user's current project status. For example, the adjustment unit analyzes the user's current project status and performs efficient readjustment. This allows efficient task management by taking into account the user's current project status. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's current project status data into the generation AI and perform readjustment using the generation AI.

[0094] The visualization unit can estimate the user's emotions and visualize the project progress based on the estimated user emotions. For example, if the user is feeling stressed, the visualization unit provides a simple, highly visible visualization method. Furthermore, if the user is relaxed, the visualization unit can also provide a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit provides a visualization method that focuses on the main points. This enables highly visible display by visualizing the project progress according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 processing in the visualization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's emotion data into the generation AI, which then visualizes the project progress.

[0095] The visualization unit can visualize the progress of the project in real time and optimize the visualization method according to the progress. For example, if the project is progressing smoothly, the visualization unit performs regular visualization. Furthermore, if the project is delayed, the visualization unit can increase the visualization frequency and provide more detailed information. For example, if the project is nearing completion, the visualization unit reduces the visualization frequency to save resources. This allows efficient progress management by optimizing the visualization method according to the progress of the project. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input project progress data into the generation AI and use the generation AI to optimize the visualization method.

[0096] The visualization unit can visualize risk factors for the project and highlight high-risk tasks. For example, the visualization unit can highlight high-risk tasks to visualize the risks of the entire project. The visualization unit can also postpone low-risk tasks. For example, the visualization unit can allocate additional resources to high-risk tasks to process them as a priority. This highlights high-risk tasks, thereby improving risk management for the entire project. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input risk factor data into a generation AI and have the generation AI highlight high-risk tasks.

[0097] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user's emotions. For example, when the user is stressed, the visualization unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the visualization unit can also provide a display method including detailed information. For example, when the user is in a hurry, the visualization unit provides a display method that focuses on the main points. This enables highly visible display by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the visualization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0098] When visualizing the progress of a project, the visualization unit can select the optimal display method by taking into account the user's geographical location information. For example, when the user is in a specific area, the visualization unit prioritizes visualizing task information related to that area. Furthermore, when the user is traveling, the visualization unit can also prioritize visualizing task information related to the user's destination. For example, when the user is working remotely, the visualization unit prioritizes visualizing task information accessible online. This allows for efficient progress management by taking the user's geographical location information into consideration. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's geographical location information into the generation AI, which can then select the optimal display method.

[0099] When visualizing the progress of a project, the visualization unit can analyze the user's social media activities and reflect related information in the visualization. For example, the visualization unit visualizes related task information based on information shared by the user on social media. The visualization unit can also adjust the timing of visualization taking into account the user's social media activity time. For example, the visualization unit analyzes the user's interests on social media and prioritizes visualizing related task information. This allows for efficient visualization of related information by analyzing the user's social media activities. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's social media activity data into the generation AI and have the generation AI reflect related information in the visualization. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, determination unit, management unit, adjustment unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The management unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visualization unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, determination unit, management unit, adjustment unit, and visualization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The management unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, determination unit, management unit, adjustment unit, and visualization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The management unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visualization unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, decision unit, management unit, adjustment unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The decision unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The management unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visualization unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The task management system may further include a notification unit. The notification unit may notify the user of changes in task progress and priority. For example, the notification unit may send an alert to the user when task progress is behind schedule. The notification unit may also notify the user when task priority has changed. The notification unit may also send a reminder when a task deadline is approaching. This allows the user to grasp task progress and priority changes in real time, enabling efficient task management.

[0102] The task management system may further include a feedback unit. The feedback unit may collect feedback from users and use it to improve the system. For example, the feedback unit may provide an interface that allows users to express their opinions on task priorities. The feedback unit may also provide a function that allows users to leave comments on the progress of tasks. The feedback unit may also analyze user feedback and provide data for improving the system's algorithms. This makes it possible to improve the system by reflecting user opinions, thereby providing a task management system that is easier to use.

[0103] The task management system may further include a learning unit. The learning unit can learn the user's past task management data and use it for future task management. For example, the learning unit may analyze what tasks the user has managed in the past and how. The learning unit may also learn the user's past task completion times and resource usage status and reflect this in future task management. Furthermore, the learning unit may suggest optimal task management methods based on the user's past task management data. This enables efficient task management that makes use of the user's past experience.

[0104] The task management system may further include a prediction unit. The prediction unit can predict future task progress based on task progress and resource usage. For example, the prediction unit can predict the completion time of a future task based on the progress of the current task. The prediction unit can also predict future resource shortages based on resource usage. Furthermore, the prediction unit can predict the progress of the entire project based on task progress and resource usage. This makes it possible to identify future problems in advance and take appropriate measures.

[0105] The task management system may further include a reward unit. The reward unit may provide a reward when the user completes a task. For example, the reward unit may award points when the user completes a task within a deadline. The reward unit may also provide a badge when the user completes a task efficiently. Furthermore, the reward unit may provide a reward every time the user completes a task to increase the user's motivation. This may motivate the user to complete tasks efficiently.

[0106] The task management system may further include a sentiment analysis unit. The sentiment analysis unit may analyze the user's sentiment and reflect it in task management. For example, the sentiment analysis unit may analyze the user's text input or voice input to estimate the user's sentiment. The sentiment analysis unit may also adjust the priority of tasks based on the user's sentiment. Furthermore, the sentiment analysis unit may change the way task progress is displayed depending on the user's sentiment. This enables task management that takes the user's sentiment into consideration.

[0107] The task management system may further include a stress management unit. The stress management unit may monitor the user's stress level and reflect it in task management. For example, the stress management unit may collect the user's biometric information and estimate the stress level. The stress management unit may also adjust task priorities when the user's stress level is high. Furthermore, the stress management unit may change the way task progress is displayed depending on the user's stress level. This allows for efficient task management while reducing the user's stress.

[0108] The task management system may further include a motivation management unit. The motivation management unit may monitor the user's motivation and reflect it in task management. For example, the motivation management unit may collect user behavior data and estimate the user's motivation level. The motivation management unit may also adjust task priorities when the user's motivation level is low. Furthermore, the motivation management unit may change the way task progress is displayed depending on the user's motivation level. This allows for efficient task management while maintaining the user's motivation.

[0109] The task management system may further include a relaxation unit. The relaxation unit may monitor the user's state of relaxation and reflect the result in task management. For example, the relaxation unit may collect biometric information of the user and estimate the state of relaxation. The relaxation unit may also adjust the priority of tasks when the user is relaxed. Furthermore, the relaxation unit may change the method of displaying the progress of tasks depending on the user's state of relaxation. This allows tasks to be managed efficiently while maintaining the user's state of relaxation.

[0110] The task management system may further include an emotion feedback unit. The emotion feedback unit may provide feedback based on the user's emotion. For example, the emotion feedback unit may provide advice to relax when the user is feeling stressed. The emotion feedback unit may also provide advice to increase motivation when the user is relaxed. Furthermore, the emotion feedback unit may change the way in which the task progress is displayed depending on the user's emotion. This allows for providing feedback that takes the user's emotion into consideration, thereby enabling efficient task management.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects task information. The task information includes the project start date, end date, deadline for each task, required resources, etc. The collection unit may, for example, use a calendar API to obtain the project start date and end date, and collect the deadline for each task using the API of the project management tool. It may also obtain the required resource information from the database of the resource management system. Step 2: The decision unit determines the priority of each task based on the information collected by the collection unit. The decision unit generates optimal priorities by taking into account the importance and urgency of the tasks, resource utilization status, etc. For example, the decision unit may evaluate the importance of a task based on its project impact score, and the urgency based on the number of days remaining until the deadline. Resource utilization status may also be evaluated based on the resource allocation score. Step 3: The management unit manages the progress of each task based on the priority determined by the determination unit. The management unit monitors the progress of tasks in real time and readjusts task priorities as necessary. For example, the management unit may obtain the progress of tasks using the API of a project management tool and raise the priority of tasks that are experiencing delays. It may also obtain resource usage status from the database of a resource management system and readjust task priorities according to the resource usage status. Step 4: The adjustment unit readjusts the priority of tasks based on the progress managed by the management unit. The adjustment unit readjusts the priority according to task delays and can also readjust the priority of tasks according to resource usage. For example, the adjustment unit raises the priority of a delayed task and adjusts the progress of other tasks. The adjustment unit also readjusts the priority of tasks according to resource usage, aiming for efficient resource use. Step 5: The visualization unit visualizes the progress of the entire project based on the priorities readjusted by the adjustment unit. The visualization unit generates Gantt charts, progress reports, etc., to enable the progress of the project to be understood at a glance. For example, a Gantt chart can be generated using the API of the project management tool to visually display the progress of each task. In addition, a progress report can be generated based on information obtained from the database of the project management tool to provide a detailed report on the progress of the project.

[0113] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects task information; a determination unit that determines a priority of each task based on the information collected by the collection unit; a management unit that manages the progress of each task based on the priority determined by the determination unit; an adjustment unit that readjusts the priority of tasks based on the progress managed by the management unit; a visualization unit that visualizes the progress of the entire project based on the priority readjusted by the adjustment unit; Equipped with A system characterized by:

2. The collecting unit Collect the project start date, end date, deadlines for each task, required resources, and related information The system of claim 1 .

3. The determination unit Generate appropriate priorities by considering the importance and urgency of tasks and resource availability The system of claim 1 .

4. The management unit Monitor task progress in real time The system of claim 1 .

5. The adjustment unit Re-prioritize tasks as they get delayed The system of claim 1 .

6. The visualization unit Generate Gantt charts and progress reports to visualize project progress The system of claim 1 .

7. The collecting unit Estimates user emotions and adjusts the timing of collecting task information based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Collect progress on each task in real time and adjust collection methods accordingly The system of claim 1 .

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