system

Abstract

The system according to the embodiment aims to propose a method of performing tasks that is optimized for the user's working style. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes how to proceed with a task based on the analysis results obtained by the analysis unit. The update unit updates the task list proposed by the proposal 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 technologies have had the problem that users' task prioritization is not optimized for their individual work styles, making efficient task management difficult.

[0005] The system according to the embodiment aims to propose a method of performing tasks that is optimized for the user's working style. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes how to proceed with a task based on the analysis results obtained by the analysis unit. The update unit updates the task list proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose a method for progressing with a task that is optimized for the user's working style. [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 learning-based task optimization system according to an embodiment of the present invention learns from a user's past work prioritization actions and decisions and provides advice to each individual user on the optimal way to complete tasks. The learning-based task optimization system collects and analyzes the user's past task management data, proposes optimal ways to complete tasks, and updates the task list. For example, the learning-based task optimization system collects the user's past task management data and analyzes information such as the order in which tasks were performed, the amount of time each task took, and the results each task produced. Based on the analysis results, the learning-based task optimization system identifies the user's task management patterns and proposes optimal ways to complete tasks. For example, if a user has a pattern of preparing important tasks periodically, the learning-based task optimization system learns this pattern and suggests prioritizing the next similar task. Furthermore, if a user tends to spend too much time on a particular task, the learning-based task optimization system provides advice on how to efficiently complete that task. Furthermore, the learning-based task optimization system monitors the user's task progress in real time and updates the task list as needed. For example, if the user completes a task earlier than planned, the system suggests starting the next task earlier. Additionally, if the user is behind schedule, the system suggests postponing lower-priority tasks. This allows the learning task optimization system to provide a task list optimized for each user's individual work style, improving work efficiency. This allows the learning task optimization system to efficiently advance the user's work, improving work productivity. For example, the system can suggest optimal ways to proceed with various tasks, such as the user's daily routine tasks and tasks related to specific projects.

[0029] A learning-based task optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects a user's past task management data. The user's past task management data includes, for example, information on which tasks were performed in what order, how long each task took, and what results each task produced, but is not limited to these examples. The collection unit acquires data from the user's task management application, for example. The collection unit can also collect data manually entered by the user. The collection unit can also acquire the user's past task management data from cloud storage. For example, the collection unit acquires data from the user's task management application via an API. The manually entered data collects task information directly entered by the user. The data acquisition from cloud storage collects past task data saved by the user. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the order and time of task execution using statistical analysis. The analysis unit can also predict task results using a machine learning algorithm. The analysis unit can also extract task patterns using data mining technology. For example, statistical analysis analyzes the order and time of task execution as numerical data. Machine learning algorithms predict task results based on past data. Data mining technology extracts useful patterns from large amounts of data. The suggestion unit proposes how to proceed with the task based on the analysis results obtained by the analysis unit. The suggestion is made based on, for example, a prioritization method or a step-by-step procedure, but is not limited to such examples. For example, the suggestion unit suggests performing a high-priority task first. The suggestion unit can also propose the next step depending on the progress of the task. The suggestion unit can also advise on how to proceed with the task efficiently. For example, prioritization is made based on importance and urgency. The step-by-step procedure suggests the next step depending on the progress of the task.Advice on efficient progress is provided based on time management and resource optimization for tasks. The update unit updates the task list proposed by the suggestion unit. The update is performed, for example, in real time, but is not limited to this example. For example, the update unit updates the task list when the user completes a task. The update unit can also update the task list when the user adds a new task. The update unit can also automatically update the task list according to the user's progress with the task. For example, real-time update updates the task list immediately when the user completes a task. New task addition updates the task list when the user adds a new task. Automatic update automatically updates the task list according to the user's progress with the task. This allows the learning-based task optimization system according to the embodiment to learn from the user's past actions and decisions regarding work prioritization and advise each user on the optimal way to progress with tasks. Some or all of the above-described processing by the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the task list using an AI model that receives the task list proposed by the suggestion unit as input and outputs a task list.

[0030] The learning-based task optimization system includes an advice unit that provides advice for specific tasks. The advice unit provides advice for the specific tasks. Examples of specific tasks include, but are not limited to, project tasks and daily work tasks. For example, the advice unit provides advice on how to proceed with project tasks and how to allocate resources. For daily work tasks, the advice unit can also provide advice on efficient ways to proceed and time management. The advice unit can also provide advice on how to prioritize specific tasks. For example, advice for project tasks is provided based on how to proceed with tasks and how to allocate resources. Advice for daily work tasks is provided based on efficient ways to proceed and time management. Advice on how to prioritize tasks is provided based on importance and urgency. As a result, providing advice for specific tasks enables the user to manage tasks more efficiently. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can provide advice using an AI model that receives data related to the specific task as input and outputs advice.

[0031] The learning-based task optimization system includes a monitoring unit that monitors the progress of a task. The monitoring unit monitors the progress of the task. The progress of the task includes, but is not limited to, for example, a progress rate and a completion degree. For example, the monitoring unit monitors the progress rate of the task and notifies the user of the progress. The monitoring unit can also monitor the completion degree of the task and notify the user of the completion degree. The monitoring unit can also suggest a next step depending on the progress of the task. For example, the progress rate is monitored by monitoring the progress of the task as numerical data. The completion degree is monitored by monitoring the completion degree of the task as numerical data. The next step is suggested depending on the progress of the task. In this way, by monitoring the progress of the task, the task list can be updated in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can monitor the progress of the task using an AI model that inputs data related to the progress of the task and outputs the progress.

[0032] The learning-based task optimization system includes an adjustment unit that adjusts priorities. The adjustment unit adjusts the priorities. Examples of priorities include, but are not limited to, importance and urgency. For example, the adjustment unit adjusts the priorities so that tasks with higher importance are executed first. The adjustment unit can also adjust the priorities so that tasks with higher urgency are executed first. The adjustment unit can also adjust the priorities according to the progress of the user's tasks. For example, the adjustment of importance is performed based on the importance of the task. The adjustment of urgency is performed based on the urgency of the task. The adjustment according to the progress is performed based on the progress of the user's tasks. This allows the user to manage tasks more flexibly by adjusting the priorities. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the priorities using an AI model that inputs data related to the importance and urgency of tasks and outputs priorities.

[0033] The collection unit can analyze the user's past task management data and select the optimal collection method. For example, the collection unit prioritizes the selection of a data collection method that the user frequently used in the past. The collection unit can also analyze a method that the user used to efficiently collect data in the past and apply that method again. The collection unit can also select the optimal collection method for a specific time period from the user's past task management data. For example, the collection unit prioritizes the selection of a data collection method that the user frequently used in the past. The collection unit analyzes a method that the user used to efficiently collect data in the past and applies that method again. The collection unit selects the optimal collection method for a specific time period from the user's past task management data. In this way, the optimal collection method can be selected by analyzing the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past task management data into a generation AI and have the generation AI select the optimal collection method.

[0034] When collecting data, the collection unit may filter the data based on the user's current project or areas of interest. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may also preferentially collect highly relevant data based on the user's areas of interest. The collection unit may also filter and collect data related to areas in which the user has previously shown interest. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may preferentially collect highly relevant data based on the user's areas of interest. The collection unit may filter and collect data related to areas in which the user has previously shown interest. In this way, highly relevant data can be collected by filtering data based on the user's current project or areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to the user's projects and areas of interest to the generation AI and have the generation AI perform the filtering.

[0035] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. If the user prefers text input, the collection unit can prioritize collecting text data. If the user prefers image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data regarding the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data 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 data related to that area. Furthermore, when the user is on the move, the collection unit can also collect data related to the user's current location in real time. Furthermore, when the user frequently visits a specific location, the collection unit can prioritize collecting data related to that location. For example, when the user is in a specific area, data related to that area is prioritized. When the user is on the move, data related to the user's current location is collected in real time. When the user frequently visits a specific location, data related to that location is prioritized. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0037] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can analyze the user's social media activity patterns and collect related data. The collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data regarding the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0038] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also customize the collection timing and means by reflecting the user's feedback. For example, the collection method can be improved based on feedback provided by the user in the past. The optimal collection means can be selected based on the user's past feedback. The collection timing and means can be customized by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data regarding the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with high importance. The analysis unit can also perform a simplified analysis on a task with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance. For example, a detailed analysis is performed on a task with high importance. A simplified analysis is performed on a task with low importance. The depth and scope of the analysis are adjusted according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the task. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the task category. For example, the analysis unit applies an analysis algorithm that emphasizes creativity to a creative task. The analysis unit can also apply a data-focused analysis algorithm to an analytical task. The analysis unit can also apply an efficiency-focused analysis algorithm to a routine task. For example, the analysis unit applies an analysis algorithm that emphasizes creativity to a creative task. The analysis unit applies a data-focused analysis algorithm to an analytical task. The analysis unit applies an efficiency-focused analysis algorithm to a routine task. In this way, by applying an appropriate analysis algorithm depending on the task category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the task category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis patterns and apply the optimal analysis method. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can analyze the user's past analysis patterns and apply the optimal analysis method. The analysis unit can correct errors based on the user's past analysis results to improve accuracy. In this way, the analysis accuracy is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the tasks. For example, the analysis unit prioritizes analysis of tasks with an upcoming submission deadline. The analysis unit can also postpone analysis of tasks with a distant submission deadline. The analysis unit can also adjust the order of analysis according to the submission time. For example, the analysis unit prioritizes analysis of tasks with an upcoming submission deadline. The analysis unit can postpone analysis of tasks with a distant submission deadline. The analysis unit adjusts the order of analysis according to the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of tasks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission time of tasks to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. The analysis unit can also adjust the order of analysis according to the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit postpones analysis of less relevant tasks. The analysis unit adjusts the order of analysis according to the relevance of the tasks. This enables efficient analysis by adjusting the order of analysis based on the relevance of the tasks. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit provides analysis results in simple language. The way in which the analysis results are presented can be adjusted according to the user's level of expertise. This allows for a deeper understanding of the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way in which the analysis results are presented.

[0045] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. For example, the suggestion unit makes a detailed proposal for a task with high importance. The suggestion unit can also make a simplified proposal for a task with low importance. The suggestion unit can also adjust the depth and scope of the proposal according to the importance. For example, a detailed proposal is made for a task with high importance. A simplified proposal is made for a task with low importance. The depth and scope of the proposal are adjusted according to the importance. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the task. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0046] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the task category. For example, the suggestion unit can apply a proposal algorithm that emphasizes creativity to a creative task. The suggestion unit can also apply a proposal algorithm that emphasizes data to an analytical task. The suggestion unit can also apply a proposal algorithm that emphasizes efficiency to a routine task. For example, the suggestion unit can apply a proposal algorithm that emphasizes creativity to a creative task. The suggestion unit can apply a proposal algorithm that emphasizes data to an analytical task. The suggestion unit can apply a proposal algorithm that emphasizes efficiency to a routine task. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the task category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data regarding the task category to a generation AI and cause the generation AI to apply a proposal algorithm.

[0047] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal patterns and apply an optimal suggestion method. The suggestion unit can also correct errors and improve accuracy based on the user's past proposal results. For example, the suggestion unit can improve the accuracy of the current proposal based on the user's past proposal results. The user's past proposal patterns can be analyzed and an optimal suggestion method can be applied. Errors can be corrected and accuracy can be improved based on the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0048] The suggestion unit can determine the priority of the proposals based on the submission dates of the tasks when making the proposals. For example, the suggestion unit prioritizes proposals for tasks with upcoming submission deadlines. The suggestion unit can also postpone proposals for tasks with more distant submission deadlines. The suggestion unit can also adjust the order of proposals according to the submission dates. For example, the suggestion unit prioritizes proposals for tasks with upcoming submission deadlines. The suggestion unit can postpone proposals for tasks with more distant submission deadlines. The suggestion unit adjusts the order of proposals according to the submission dates. This enables efficient proposals by determining the priority of proposals based on the submission dates of tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the submission dates of tasks to the generation AI and cause the generation AI to determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the tasks when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can also postpone suggesting less relevant tasks. The suggestion unit can also adjust the order of suggestions based on the relevance of the tasks. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can postpone suggesting less relevant tasks. The suggestion unit adjusts the order of suggestions based on the relevance of the tasks. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of suggestions.

[0050] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. If the user does not have technical expertise, the suggestion unit provides a proposal in simple language. The way the proposal is expressed is adjusted according to the user's level of expertise. In this way, adjusting the use of technical terminology in the proposal according to the user's level of expertise deepens the understanding of the proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the proposal is expressed.

[0051] When updating the task list, the update unit can select an optimal update method by referring to the user's past task completion history. The update unit updates the task list, for example, based on how the user efficiently completed tasks in the past. The update unit can also select an optimal update timing from the user's past task completion history. The update unit can also analyze the user's past task completion patterns and apply an optimal update method. For example, the task list is updated based on how the user efficiently completed tasks in the past. The optimal update timing is selected from the user's past task completion history. The user's past task completion patterns are analyzed and an optimal update method is applied. In this way, the optimal update method can be selected by referring to the past task completion history. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's past task completion history to the generation AI and cause the generation AI to select an optimal update method.

[0052] When updating the task list, the update unit can customize the update content based on the user's current task progress. For example, the update unit prioritizes updating information related to the user's current task. The update unit can also provide optimal update content based on the user's current task progress. The update unit can also prioritize updating tasks for which the user is behind. For example, the update unit prioritizes updating information related to the user's current task. The update unit provides optimal update content based on the user's current task progress. The update unit prioritizes updating tasks for which the user is behind. This enables efficient task management by customizing the update content based on the current task progress. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the update content.

[0053] The update unit can improve the update method by reflecting user feedback when updating the task list. The update unit improves the task list update method, for example, based on feedback provided by the user. The update unit can also select an optimal update means based on the user feedback. The update unit can also customize the update timing and means by reflecting the user feedback. For example, the update unit improves the task list update method based on feedback provided by the user. The optimal update means is selected based on the user feedback. The update timing and means are customized by reflecting the user feedback. In this way, the update method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input data related to user feedback into the generation AI and cause the generation AI to improve the update method.

[0054] When updating the task list, the update unit can select the optimal update method by taking into account the user's geographical location information. For example, if the user is in a specific area, the update unit prioritizes updating tasks related to that area. Furthermore, if the user is traveling, the update unit can also update tasks related to the user's current location in real time. Furthermore, if the user frequently visits a specific location, the update unit can also prioritize updating tasks related to that location. For example, if the user is in a specific area, the update unit prioritizes updating tasks related to that area. If the user is traveling, the update unit updates tasks related to the user's current location in real time. If the user frequently visits a specific location, the update unit prioritizes updating tasks related to that location. This allows highly relevant tasks to be managed preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal update method.

[0055] When updating the task list, the update unit can analyze the user's social media activity and suggest update content. The update unit, for example, updates related tasks based on information shared by the user on social media. The update unit can also analyze the user's social media activity pattern and update related tasks. The update unit can also update related tasks by referring to the activities of the user's friends on social media. For example, the update unit updates related tasks based on information shared by the user on social media. The update unit analyzes the user's social media activity pattern and updates related tasks. The update unit can update related tasks by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's social media activity to the generation AI and cause the generation AI to suggest update content.

[0056] When updating the task list, the update unit can customize the update method by reflecting the user's past feedback. The update unit, for example, improves the update method based on feedback provided by the user in the past. The update unit can also select an optimal update means based on the user's past feedback. The update unit can also customize the update timing and means by reflecting the user's feedback. For example, the update unit improves the update method based on feedback provided by the user in the past. The optimal update means is selected based on the user's past feedback. The update timing and means are customized by reflecting the user's feedback. In this way, the update method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's feedback to the generation AI and cause the generation AI to customize the update method.

[0057] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the task. For example, the advice unit provides detailed advice for a task with high importance. The advice unit can also provide simplified advice for a task with low importance. The advice unit can also adjust the depth and scope of the advice according to the importance. For example, detailed advice is provided for a task with high importance. Simplified advice is provided for a task with low importance. The depth and scope of the advice are adjusted according to the importance. In this way, by adjusting the level of detail of the advice according to the importance of the task, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0058] When providing advice, the advice unit can apply different advice algorithms depending on the task category. For example, the advice unit applies an advice algorithm that emphasizes creativity to a creative task. The advice unit can also apply a data-focused advice algorithm to an analytical task. The advice unit can also apply an efficiency-focused advice algorithm to a routine task. For example, the advice unit applies an advice algorithm that emphasizes creativity to a creative task. The advice unit applies a data-focused advice algorithm to an analytical task. The advice unit applies an efficiency-focused advice algorithm to a routine task. This improves the accuracy of advice by applying an appropriate advice algorithm depending on the task category. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data related to the task category to the generation AI and cause the generation AI to apply the advice algorithm.

[0059] When giving advice, the advice unit can improve the accuracy of the advice by referring to the results of past advice given to the user. The advice unit, for example, improves the accuracy of current advice based on the results of advice given to the user in the past. The advice unit can also analyze the user's past advice patterns and apply the optimal advice method. The advice unit can also correct errors and improve accuracy based on the user's past advice results. For example, the advice unit improves the accuracy of current advice based on the results of advice given to the user in the past. The user's past advice patterns are analyzed and the optimal advice method is applied. Errors are corrected and accuracy is improved based on the user's past advice results. In this way, the accuracy of advice is improved by referring to the results of past advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the results of past advice given to the user into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0060] When giving advice, the advice unit can determine the priority of the advice based on the submission time of the task. For example, the advice unit gives priority to advice for tasks with an upcoming submission deadline. The advice unit can also postpone giving advice for tasks with a distant submission deadline. The advice unit can also adjust the order of advice according to the submission time. For example, advice is given priority to tasks with an upcoming submission deadline. Advice is given later for tasks with a distant submission deadline. The order of advice is adjusted according to the submission time. In this way, by determining the priority of advice based on the submission time of the task, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the submission time of the task to the generation AI and cause the generation AI to determine the priority of advice.

[0061] When giving advice, the advice unit can adjust the order of advice based on the relevance of the tasks. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can also postpone giving advice on less relevant tasks. The advice unit can also adjust the order of advice based on the relevance of the tasks. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can postpone giving advice on less relevant tasks. The advice unit adjusts the order of advice based on the relevance of the tasks. This enables efficient advice to be provided by adjusting the order of advice based on the relevance of the tasks. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of advice.

[0062] When giving advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has technical expertise, the advice unit provides advice that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the advice unit can provide advice in simple language. Furthermore, the advice unit can adjust the way the advice is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the advice unit provides advice that uses a lot of technical terms. If the user does not have technical expertise, the advice unit provides advice in simple language. The way the advice is expressed is adjusted depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the advice depending on the user's level of expertise, the advice can be more easily understood. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0063] During monitoring, the monitoring unit can select an optimal monitoring method by referring to the user's past task progress. For example, the monitoring unit selects a monitoring method based on how the user has efficiently progressed tasks in the past. The monitoring unit can also select an optimal monitoring timing based on the user's past task progress. The monitoring unit can also analyze the user's past task progress patterns and apply an optimal monitoring method. For example, the monitoring unit selects a monitoring method based on how the user has efficiently progressed tasks in the past. The optimal monitoring timing is selected based on the user's past task progress. The user's past task progress patterns are analyzed and an optimal monitoring method is applied. In this way, the optimal monitoring method can be selected by referring to the past task progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's past task progress to the generation AI and cause the generation AI to select an optimal monitoring method.

[0064] During monitoring, the monitoring unit can customize the monitoring content based on the user's current task progress. For example, the monitoring unit prioritizes monitoring information related to the task currently being performed by the user. The monitoring unit can also provide optimal monitoring content based on the user's current task progress. The monitoring unit can also prioritize monitoring of tasks for which the user is behind. For example, the monitoring unit prioritizes monitoring information related to the task currently being performed by the user. The monitoring unit provides optimal monitoring content based on the user's current task progress. The monitoring unit prioritizes monitoring of tasks for which the user is behind. This enables efficient task management by customizing the monitoring content based on the current task progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the monitoring content.

[0065] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user. The monitoring unit can also select the optimal monitoring means from the user feedback. The monitoring unit can also customize the monitoring timing and means by reflecting user feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user. The optimal monitoring means is selected from user feedback. The monitoring timing and means are customized by reflecting user feedback. In this way, the monitoring method can be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data regarding user feedback into a generation AI and cause the generation AI to improve the monitoring method.

[0066] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring tasks related to that area. Furthermore, if the user is traveling, the monitoring unit can also monitor tasks related to the user's current location in real time. Furthermore, if the user frequently visits a specific location, the monitoring unit can also prioritize monitoring tasks related to that location. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring tasks related to that area. If the user is traveling, the monitoring unit monitors tasks related to the user's current location in real time. If the user frequently visits a specific location, the monitoring unit prioritizes monitoring tasks related to that location. This allows for prioritized management of highly relevant tasks by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal monitoring method.

[0067] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring content. The monitoring unit, for example, monitors related tasks based on information shared by the user on social media. The monitoring unit can also analyze the user's social media activity patterns and monitor related tasks. The monitoring unit can also monitor related tasks by referring to the activities of the user's friends on social media. For example, the monitoring unit can monitor related tasks based on information shared by the user on social media. The monitoring unit can analyze the user's social media activity patterns and monitor related tasks. The monitoring unit can monitor related tasks by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's social media activity into a generation AI and have the generation AI execute a suggested monitoring content.

[0068] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, improves the monitoring method based on feedback provided by the user in the past. The monitoring unit can also select the optimal monitoring means based on the user's past feedback. The monitoring unit can also customize the monitoring timing and means by reflecting the user's feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user in the past. The optimal monitoring means is selected based on the user's past feedback. The monitoring timing and means are customized by reflecting the user's feedback. In this way, the monitoring method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's feedback to the generation AI and have the generation AI customize the monitoring method.

[0069] When adjusting priorities, the adjustment unit can select an optimal adjustment method by referring to the user's past task completion history. The adjustment unit adjusts priorities, for example, based on how the user efficiently completed tasks in the past. The adjustment unit can also select an optimal adjustment timing from the user's past task completion history. The adjustment unit can also analyze the user's past task completion patterns and apply an optimal adjustment method. For example, the adjustment unit adjusts priorities based on how the user efficiently completed tasks in the past. The optimal adjustment timing is selected from the user's past task completion history. The user's past task completion patterns are analyzed and an optimal adjustment method is applied. In this way, the optimal adjustment method can be selected by referring to the past task completion history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's past task completion history to the generation AI and cause the generation AI to select an optimal adjustment method.

[0070] When adjusting the priority, the adjustment unit can customize the adjustment content based on the user's current task progress. For example, the adjustment unit prioritizes adjusting information related to the task currently in progress by the user. The adjustment unit can also provide optimal adjustment content based on the user's current task progress. The adjustment unit can also prioritize adjustment for tasks for which the user is behind. For example, the adjustment unit prioritizes adjusting information related to the task currently in progress by the user. The adjustment unit provides optimal adjustment content based on the user's current task progress. The adjustment unit prioritizes adjustment for tasks for which the user is behind. This enables efficient task management by customizing the adjustment content based on the current task progress. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the adjustment content.

[0071] The adjustment unit can improve the adjustment method by reflecting user feedback when adjusting the priority. The adjustment unit improves the priority adjustment method, for example, based on feedback provided by the user. The adjustment unit can also select an optimal adjustment means from the user feedback. The adjustment unit can also customize the adjustment timing and means by reflecting the user feedback. For example, the adjustment method is improved based on feedback provided by the user. The optimal adjustment means is selected from the user feedback. The adjustment timing and means are customized by reflecting the user feedback. In this way, the adjustment method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data regarding user feedback into the generation AI and cause the generation AI to improve the adjustment method.

[0072] When adjusting the priority, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. For example, when the user is in a specific area, the adjustment unit prioritizes adjusting tasks related to that area. Furthermore, when the user is traveling, the adjustment unit can also prioritize adjusting tasks related to the user's current location in real time. Furthermore, when the user frequently visits a specific location, the adjustment unit can also prioritize adjusting tasks related to that location. For example, when the user is in a specific area, the adjustment unit prioritizes adjusting tasks related to that area. When the user is traveling, the adjustment unit adjusts tasks related to the user's current location in real time. When the user frequently visits a specific location, the adjustment unit prioritizes adjusting tasks related to that location. In this way, by taking the user's geographical location information into consideration, highly relevant tasks can be prioritized and managed. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal adjustment method.

[0073] When adjusting priorities, the adjustment unit can analyze the user's social media activity and suggest adjustment details. The adjustment unit, for example, adjusts related tasks based on information shared by the user on social media. The adjustment unit can also analyze the user's social media activity patterns and adjust related tasks. The adjustment unit can also adjust related tasks with reference to the activities of the user's friends on social media. For example, the adjustment unit adjusts related tasks based on information shared by the user on social media. The adjustment unit adjusts related tasks with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's social media activity into the generation AI and cause the generation AI to execute a suggestion of adjustment details.

[0074] When adjusting the priority, the adjustment unit can customize the adjustment method by reflecting the user's past feedback. The adjustment unit, for example, improves the adjustment method based on feedback provided by the user in the past. The adjustment unit can also select an optimal adjustment means from the user's past feedback. The adjustment unit can also customize the adjustment timing and means by reflecting the user's feedback. For example, the adjustment method can be improved based on feedback provided by the user in the past. The optimal adjustment means can be selected from the user's past feedback. The adjustment timing and means can be customized by reflecting the user's feedback. In this way, the adjustment method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's feedback to the generation AI and cause the generation AI to customize the adjustment method.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The learning-based task optimization system can predict task progress based on the user's past task completion history and optimize task progress based on the prediction results. For example, if a user has taken a long time to complete a particular task in the past, the system can suggest that task early to support time management. It can also promote smooth work progress by prioritizing tasks that the user has completed efficiently in the past. It can also analyze the user's past task completion patterns and suggest the optimal task order. This enables efficient task management using past data.

[0077] The learning-based task optimization system can optimize task progress by taking into account the user's geographic location information. For example, if the user is in a specific area, it will prioritize tasks related to that area. Also, if the user is on the move, it can suggest tasks that can be completed while traveling, supporting efficient time management. Furthermore, if the user frequently visits a specific location, it can prioritize tasks related to that location. This allows for flexible task management using geographic location information.

[0078] The learning task optimization system can analyze a user's social media activity and suggest related tasks. For example, it can prioritize suggesting related tasks based on information the user has shared on social media. It can also analyze the user's social media activity patterns and suggest optimal ways to complete tasks. It can also suggest related tasks based on the activity of the user's friends on social media. This allows for efficient task management that utilizes social media activity.

[0079] The learning-based task optimization system can optimize task progress by reflecting the user's past feedback. For example, it can improve the task progress method based on the user's past feedback. It can also select the optimal task progress timing based on the user's past feedback. It can also customize the task progress content by reflecting the user's feedback. This allows for efficient task management that utilizes past feedback.

[0080] The learning-based task optimization system can optimize task progress by taking into account the user's geographic location information. For example, if the user is in a specific area, it will prioritize tasks related to that area. Also, if the user is on the move, it can suggest tasks that can be completed while traveling, supporting efficient time management. Furthermore, if the user frequently visits a specific location, it can prioritize tasks related to that location. This allows for flexible task management using geographic location information.

[0081] The processing flow of the first embodiment will be briefly explained below.

[0082] Step 1: The collection unit collects the user's past task management data. The user's past task management data includes information such as which tasks were performed in what order, how long each task took, and what results each task produced. The collection unit obtains data from the user's task management application, as well as manually entered data and data from cloud storage. Step 2: The analysis unit analyzes the data collected by the collection unit. This analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit analyzes the order and time of task execution as numerical data, predicts task results based on past data, and extracts useful patterns from large amounts of data. Step 3: The proposal unit proposes how to proceed with the tasks based on the analysis results obtained by the analysis unit. The proposals are based on advice on how to prioritize, step-by-step procedures, and efficient ways to proceed. For example, it suggests performing high-priority tasks first, suggests next steps based on the progress of the task, and provides advice on efficient ways to proceed based on time management and resource optimization for the task. Step 4: The update unit updates the task list proposed by the suggestion unit. The update is performed in real time, and the task list is updated when the user completes a task or adds a new task. The task list can also be updated automatically according to the progress of the user's tasks. The update unit can also update the task list using an AI model that takes the task list proposed by the suggestion unit as input and outputs a task list.

[0083] (Example 2) A learning-based task optimization system according to an embodiment of the present invention learns from a user's past work prioritization actions and decisions and provides advice to each individual user on the optimal way to complete tasks. The learning-based task optimization system collects and analyzes the user's past task management data, proposes optimal ways to complete tasks, and updates the task list. For example, the learning-based task optimization system collects the user's past task management data and analyzes information such as the order in which tasks were performed, the amount of time each task took, and the results each task produced. Based on the analysis results, the learning-based task optimization system identifies the user's task management patterns and proposes optimal ways to complete tasks. For example, if a user has a pattern of preparing important tasks periodically, the learning-based task optimization system learns this pattern and suggests prioritizing the next similar task. Furthermore, if a user tends to spend too much time on a particular task, the learning-based task optimization system provides advice on how to efficiently complete that task. Furthermore, the learning-based task optimization system monitors the user's task progress in real time and updates the task list as needed. For example, if the user completes a task earlier than planned, the system suggests starting the next task earlier. Additionally, if the user is behind schedule, the system suggests postponing lower-priority tasks. This allows the learning task optimization system to provide a task list optimized for each user's individual work style, improving work efficiency. This allows the learning task optimization system to efficiently advance the user's work, improving work productivity. For example, the system can suggest optimal ways to proceed with various tasks, such as the user's daily routine tasks and tasks related to specific projects.

[0084] A learning-based task optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an update unit. The collection unit collects a user's past task management data. The user's past task management data includes, for example, information on which tasks were performed in what order, how long each task took, and what results each task produced, but is not limited to these examples. The collection unit acquires data from the user's task management application, for example. The collection unit can also collect data manually entered by the user. The collection unit can also acquire the user's past task management data from cloud storage. For example, the collection unit acquires data from the user's task management application via an API. The manually entered data collects task information directly entered by the user. The data acquisition from cloud storage collects past task data saved by the user. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the order and time of task execution using statistical analysis. The analysis unit can also predict task results using a machine learning algorithm. The analysis unit can also extract task patterns using data mining technology. For example, statistical analysis analyzes the order and time of task execution as numerical data. Machine learning algorithms predict task results based on past data. Data mining technology extracts useful patterns from large amounts of data. The suggestion unit proposes how to proceed with the task based on the analysis results obtained by the analysis unit. The suggestion is made based on, for example, a prioritization method or a step-by-step procedure, but is not limited to such examples. For example, the suggestion unit suggests performing a high-priority task first. The suggestion unit can also propose the next step depending on the progress of the task. The suggestion unit can also advise on how to proceed with the task efficiently. For example, prioritization is made based on importance and urgency. The step-by-step procedure suggests the next step depending on the progress of the task.Advice on efficient progress is provided based on time management and resource optimization for tasks. The update unit updates the task list proposed by the suggestion unit. The update is performed, for example, in real time, but is not limited to this example. For example, the update unit updates the task list when the user completes a task. The update unit can also update the task list when the user adds a new task. The update unit can also automatically update the task list according to the user's progress with the task. For example, real-time update updates the task list immediately when the user completes a task. New task addition updates the task list when the user adds a new task. Automatic update automatically updates the task list according to the user's progress with the task. This allows the learning-based task optimization system according to the embodiment to learn from the user's past actions and decisions regarding work prioritization and advise each user on the optimal way to progress with tasks. Some or all of the above-described processing by the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the task list using an AI model that receives the task list proposed by the suggestion unit as input and outputs a task list.

[0085] The learning-based task optimization system includes an advice unit that provides advice for specific tasks. The advice unit provides advice for the specific tasks. Examples of specific tasks include, but are not limited to, project tasks and daily work tasks. For example, the advice unit provides advice on how to proceed with project tasks and how to allocate resources. For daily work tasks, the advice unit can also provide advice on efficient ways to proceed and time management. The advice unit can also provide advice on how to prioritize specific tasks. For example, advice for project tasks is provided based on how to proceed with tasks and how to allocate resources. Advice for daily work tasks is provided based on efficient ways to proceed and time management. Advice on how to prioritize tasks is provided based on importance and urgency. As a result, providing advice for specific tasks enables the user to manage tasks more efficiently. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can provide advice using an AI model that receives data related to the specific task as input and outputs advice.

[0086] The learning-based task optimization system includes a monitoring unit that monitors the progress of a task. The monitoring unit monitors the progress of the task. The progress of the task includes, but is not limited to, for example, a progress rate and a completion degree. For example, the monitoring unit monitors the progress rate of the task and notifies the user of the progress. The monitoring unit can also monitor the completion degree of the task and notify the user of the completion degree. The monitoring unit can also suggest a next step depending on the progress of the task. For example, the progress rate is monitored by monitoring the progress of the task as numerical data. The completion degree is monitored by monitoring the completion degree of the task as numerical data. The next step is suggested depending on the progress of the task. In this way, by monitoring the progress of the task, the task list can be updated in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can monitor the progress of the task using an AI model that inputs data related to the progress of the task and outputs the progress.

[0087] The learning-based task optimization system includes an adjustment unit that adjusts priorities. The adjustment unit adjusts the priorities. Examples of priorities include, but are not limited to, importance and urgency. For example, the adjustment unit adjusts the priorities so that tasks with higher importance are executed first. The adjustment unit can also adjust the priorities so that tasks with higher urgency are executed first. The adjustment unit can also adjust the priorities according to the progress of the user's tasks. For example, the adjustment of importance is performed based on the importance of the task. The adjustment of urgency is performed based on the urgency of the task. The adjustment according to the progress is performed based on the progress of the user's tasks. This allows the user to manage tasks more flexibly by adjusting the priorities. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the priorities using an AI model that inputs data related to the importance and urgency of tasks and outputs priorities.

[0088] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit temporarily delays data collection and resumes collection when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can collect data at that timing so as not to interrupt the user's work. Furthermore, if the user is tired, the collection unit can minimize data collection and resume collection after the user has rested. For example, if the user is feeling stressed, the collection unit estimates the user's emotions using an emotion estimation algorithm and adjusts the timing of data collection. If the user is concentrating, the collection unit collects data at that timing so as not to interrupt the user's work. If the user is tired, the collection unit minimizes data collection and resumes collection after the user has rested. In this way, adjusting the timing of data collection according to the user's emotions reduces the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The collection unit can analyze the user's past task management data and select the optimal collection method. For example, the collection unit prioritizes the selection of a data collection method that the user frequently used in the past. The collection unit can also analyze a method that the user used to efficiently collect data in the past and apply that method again. The collection unit can also select the optimal collection method for a specific time period from the user's past task management data. For example, the collection unit prioritizes the selection of a data collection method that the user frequently used in the past. The collection unit analyzes a method that the user used to efficiently collect data in the past and applies that method again. The collection unit selects the optimal collection method for a specific time period from the user's past task management data. In this way, the optimal collection method can be selected by analyzing the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past task management data into a generation AI and have the generation AI select the optimal collection method.

[0090] When collecting data, the collection unit may filter the data based on the user's current project or areas of interest. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may also preferentially collect highly relevant data based on the user's areas of interest. The collection unit may also filter and collect data related to areas in which the user has previously shown interest. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may preferentially collect highly relevant data based on the user's areas of interest. The collection unit may filter and collect data related to areas in which the user has previously shown interest. In this way, highly relevant data can be collected by filtering data based on the user's current project or areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data related to the user's projects and areas of interest to the generation AI and have the generation AI perform the filtering.

[0091] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. If the user prefers text input, the collection unit can prioritize collecting text data. If the user prefers image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data regarding the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0092] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit postpones less important data and prioritizes collecting more important data. Furthermore, when the user is relaxed, the collection unit can collect all data equally. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important data. For example, when the user is stressed, the collection unit estimates the user's emotions using an emotion estimation algorithm and determines the priority of data to be collected. When the user is relaxed, the collection unit collects all data equally. When the user is in a hurry, the collection unit prioritizes collecting the most important data. Thus, by prioritizing data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and have the generation AI determine the priority of the data to be collected.

[0093] When collecting data, the collection unit can prioritize collecting highly relevant data 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 data related to that area. Furthermore, when the user is on the move, the collection unit can also collect data related to the user's current location in real time. Furthermore, when the user frequently visits a specific location, the collection unit can prioritize collecting data related to that location. For example, when the user is in a specific area, data related to that area is prioritized. When the user is on the move, data related to the user's current location is collected in real time. When the user frequently visits a specific location, data related to that location is prioritized. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0094] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can analyze the user's social media activity patterns and collect related data. The collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data regarding the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0095] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also customize the collection timing and means by reflecting the user's feedback. For example, the collection method can be improved based on feedback provided by the user in the past. The optimal collection means can be selected based on the user's past feedback. The collection timing and means can be customized by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data regarding the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, if the user is nervous, the analysis unit uses an emotion estimation algorithm to estimate the user's emotions and provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a summary analysis result. This allows the analysis result to be better understood by adjusting the way the analysis is presented based on the user's emotions. 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with high importance. The analysis unit can also perform a simplified analysis on a task with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance. For example, a detailed analysis is performed on a task with high importance. A simplified analysis is performed on a task with low importance. The depth and scope of the analysis are adjusted according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the task. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the task category. For example, the analysis unit applies an analysis algorithm that emphasizes creativity to a creative task. The analysis unit can also apply a data-focused analysis algorithm to an analytical task. The analysis unit can also apply an efficiency-focused analysis algorithm to a routine task. For example, the analysis unit applies an analysis algorithm that emphasizes creativity to a creative task. The analysis unit applies a data-focused analysis algorithm to an analytical task. The analysis unit applies an efficiency-focused analysis algorithm to a routine task. In this way, by applying an appropriate analysis algorithm depending on the task category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the task category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis patterns and apply the optimal analysis method. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can analyze the user's past analysis patterns and apply the optimal analysis method. The analysis unit can correct errors based on the user's past analysis results to improve accuracy. In this way, the analysis accuracy is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is in a hurry, the analysis unit can estimate the user's emotions using an emotion estimation algorithm and provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. Adjusting the length of the analysis according to the user's emotions allows for a deeper understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the tasks. For example, the analysis unit prioritizes analysis of tasks with an upcoming submission deadline. The analysis unit can also postpone analysis of tasks with a distant submission deadline. The analysis unit can also adjust the order of analysis according to the submission time. For example, the analysis unit prioritizes analysis of tasks with an upcoming submission deadline. The analysis unit can postpone analysis of tasks with a distant submission deadline. The analysis unit adjusts the order of analysis according to the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of tasks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission time of tasks to the generation AI and have the generation AI determine the analysis priority.

[0102] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. The analysis unit can also adjust the order of analysis according to the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit postpones analysis of less relevant tasks. The analysis unit adjusts the order of analysis according to the relevance of the tasks. This enables efficient analysis by adjusting the order of analysis based on the relevance of the tasks. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0103] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit provides analysis results in simple language. The way in which the analysis results are presented can be adjusted according to the user's level of expertise. This allows for a deeper understanding of the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way in which the analysis results are presented.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. For example, if the user is nervous, the suggestion unit can estimate the user's emotions using an emotion estimation algorithm and provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows the suggestion to be presented in a way that is tailored to the user's emotions, thereby enhancing understanding of the suggestion. 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0105] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. For example, the suggestion unit makes a detailed proposal for a task with high importance. The suggestion unit can also make a simplified proposal for a task with low importance. The suggestion unit can also adjust the depth and scope of the proposal according to the importance. For example, a detailed proposal is made for a task with high importance. A simplified proposal is made for a task with low importance. The depth and scope of the proposal are adjusted according to the importance. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the task. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0106] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the task category. For example, the suggestion unit can apply a proposal algorithm that emphasizes creativity to a creative task. The suggestion unit can also apply a proposal algorithm that emphasizes data to an analytical task. The suggestion unit can also apply a proposal algorithm that emphasizes efficiency to a routine task. For example, the suggestion unit can apply a proposal algorithm that emphasizes creativity to a creative task. The suggestion unit can apply a proposal algorithm that emphasizes data to an analytical task. The suggestion unit can apply a proposal algorithm that emphasizes efficiency to a routine task. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the task category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data regarding the task category to a generation AI and cause the generation AI to apply a proposal algorithm.

[0107] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal patterns and apply an optimal suggestion method. The suggestion unit can also correct errors and improve accuracy based on the user's past proposal results. For example, the suggestion unit can improve the accuracy of the current proposal based on the user's past proposal results. The user's past proposal patterns can be analyzed and an optimal suggestion method can be applied. Errors can be corrected and accuracy can be improved based on the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0108] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, if the user is in a hurry, the suggestion unit can estimate the user's emotions using an emotion estimation algorithm and provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. Adjusting the length of the suggestions according to the user's emotions enhances their understanding. The emotion estimation is achieved using an emotion estimation function, for example, 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0109] The suggestion unit can determine the priority of the proposals based on the submission dates of the tasks when making the proposals. For example, the suggestion unit prioritizes proposals for tasks with upcoming submission deadlines. The suggestion unit can also postpone proposals for tasks with more distant submission deadlines. The suggestion unit can also adjust the order of proposals according to the submission dates. For example, the suggestion unit prioritizes proposals for tasks with upcoming submission deadlines. The suggestion unit can postpone proposals for tasks with more distant submission deadlines. The suggestion unit adjusts the order of proposals according to the submission dates. This enables efficient proposals by determining the priority of proposals based on the submission dates of tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the submission dates of tasks to the generation AI and cause the generation AI to determine the priority of proposals.

[0110] The suggestion unit can adjust the order of suggestions based on the relevance of the tasks when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can also postpone suggesting less relevant tasks. The suggestion unit can also adjust the order of suggestions based on the relevance of the tasks. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can postpone suggesting less relevant tasks. The suggestion unit adjusts the order of suggestions based on the relevance of the tasks. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of suggestions.

[0111] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. If the user does not have technical expertise, the suggestion unit provides a proposal in simple language. The way the proposal is expressed is adjusted according to the user's level of expertise. In this way, adjusting the use of technical terminology in the proposal according to the user's level of expertise deepens the understanding of the proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the proposal is expressed.

[0112] The update unit can estimate the user's emotions and adjust the update frequency of the task list based on the estimated user emotions. For example, if the user is feeling stressed, the update unit can reduce the update frequency of the task list to reduce the burden on the user. Furthermore, if the user is relaxed, the update unit can increase the update frequency of the task list to provide the latest information. Furthermore, if the user is in a hurry, the update unit can prioritize updating only important tasks. For example, if the user is feeling stressed, the update unit can estimate the user's emotions using an emotion estimation algorithm and adjust the update frequency of the task list. If the user is relaxed, the update unit can increase the update frequency of the task list to provide the latest information. If the user is in a hurry, the update unit prioritizes updating only important tasks. This reduces the burden on the user by adjusting the update frequency of the task list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit may input user emotion data to the generation AI and cause the generation AI to adjust the update frequency of the task list.

[0113] When updating the task list, the update unit can select an optimal update method by referring to the user's past task completion history. The update unit updates the task list, for example, based on how the user efficiently completed tasks in the past. The update unit can also select an optimal update timing from the user's past task completion history. The update unit can also analyze the user's past task completion patterns and apply an optimal update method. For example, the task list is updated based on how the user efficiently completed tasks in the past. The optimal update timing is selected from the user's past task completion history. The user's past task completion patterns are analyzed and an optimal update method is applied. In this way, the optimal update method can be selected by referring to the past task completion history. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's past task completion history to the generation AI and cause the generation AI to select an optimal update method.

[0114] When updating the task list, the update unit can customize the update content based on the user's current task progress. For example, the update unit prioritizes updating information related to the user's current task. The update unit can also provide optimal update content based on the user's current task progress. The update unit can also prioritize updating tasks for which the user is behind. For example, the update unit prioritizes updating information related to the user's current task. The update unit provides optimal update content based on the user's current task progress. The update unit prioritizes updating tasks for which the user is behind. This enables efficient task management by customizing the update content based on the current task progress. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the update content.

[0115] The update unit can improve the update method by reflecting user feedback when updating the task list. The update unit improves the task list update method, for example, based on feedback provided by the user. The update unit can also select an optimal update means based on the user feedback. The update unit can also customize the update timing and means by reflecting the user feedback. For example, the update unit improves the task list update method based on feedback provided by the user. The optimal update means is selected based on the user feedback. The update timing and means are customized by reflecting the user feedback. In this way, the update method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input data related to user feedback into the generation AI and cause the generation AI to improve the update method.

[0116] The update unit can estimate the user's emotions and prioritize the task list based on the estimated user's emotions. For example, when the user is feeling stressed, the update unit postpones less important tasks and prioritizes updating more important tasks. Furthermore, when the user is relaxed, the update unit can update all tasks evenly. Furthermore, when the user is in a hurry, the update unit can prioritize updating the most important tasks. For example, when the user is feeling stressed, the update unit estimates the user's emotions using an emotion estimation algorithm and prioritizes the task list. When the user is relaxed, the update unit updates all tasks evenly. When the user is in a hurry, the update unit prioritizes updating the most important tasks. This prioritizes the task list based on the user's emotions, allowing important tasks to be prioritized. 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 update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit may input user emotion data to the generation AI and have the generation AI determine the priority of the task list.

[0117] When updating the task list, the update unit can select the optimal update method by taking into account the user's geographical location information. For example, if the user is in a specific area, the update unit prioritizes updating tasks related to that area. Furthermore, if the user is traveling, the update unit can also update tasks related to the user's current location in real time. Furthermore, if the user frequently visits a specific location, the update unit can also prioritize updating tasks related to that location. For example, if the user is in a specific area, the update unit prioritizes updating tasks related to that area. If the user is traveling, the update unit updates tasks related to the user's current location in real time. If the user frequently visits a specific location, the update unit prioritizes updating tasks related to that location. This allows highly relevant tasks to be managed preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal update method.

[0118] When updating the task list, the update unit can analyze the user's social media activity and suggest update content. The update unit, for example, updates related tasks based on information shared by the user on social media. The update unit can also analyze the user's social media activity pattern and update related tasks. The update unit can also update related tasks by referring to the activities of the user's friends on social media. For example, the update unit updates related tasks based on information shared by the user on social media. The update unit analyzes the user's social media activity pattern and updates related tasks. The update unit can update related tasks by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's social media activity to the generation AI and cause the generation AI to suggest update content.

[0119] When updating the task list, the update unit can customize the update method by reflecting the user's past feedback. The update unit, for example, improves the update method based on feedback provided by the user in the past. The update unit can also select an optimal update means based on the user's past feedback. The update unit can also customize the update timing and means by reflecting the user's feedback. For example, the update unit improves the update method based on feedback provided by the user in the past. The optimal update means is selected based on the user's past feedback. The update timing and means are customized by reflecting the user's feedback. In this way, the update method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data regarding the user's feedback to the generation AI and cause the generation AI to customize the update method.

[0120] The advice unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is nervous, the advice unit provides simple, highly visible advice. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide advice that focuses on the main points. For example, if the user is nervous, the advice unit estimates the user's emotions using an emotion estimation algorithm and provides simple, highly visible advice. If the user is relaxed, the advice unit provides detailed advice. If the user is in a hurry, the advice unit provides advice that focuses on the main points. This allows the advice to be better understood by adjusting the way the advice is presented based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0121] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the task. For example, the advice unit provides detailed advice for a task with high importance. The advice unit can also provide simplified advice for a task with low importance. The advice unit can also adjust the depth and scope of the advice according to the importance. For example, detailed advice is provided for a task with high importance. Simplified advice is provided for a task with low importance. The depth and scope of the advice are adjusted according to the importance. In this way, by adjusting the level of detail of the advice according to the importance of the task, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the importance of the task to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0122] When providing advice, the advice unit can apply different advice algorithms depending on the task category. For example, the advice unit applies an advice algorithm that emphasizes creativity to a creative task. The advice unit can also apply a data-focused advice algorithm to an analytical task. The advice unit can also apply an efficiency-focused advice algorithm to a routine task. For example, the advice unit applies an advice algorithm that emphasizes creativity to a creative task. The advice unit applies a data-focused advice algorithm to an analytical task. The advice unit applies an efficiency-focused advice algorithm to a routine task. This improves the accuracy of advice by applying an appropriate advice algorithm depending on the task category. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data related to the task category to the generation AI and cause the generation AI to apply the advice algorithm.

[0123] When giving advice, the advice unit can improve the accuracy of the advice by referring to the results of past advice given to the user. The advice unit, for example, improves the accuracy of current advice based on the results of advice given to the user in the past. The advice unit can also analyze the user's past advice patterns and apply the optimal advice method. The advice unit can also correct errors and improve accuracy based on the user's past advice results. For example, the advice unit improves the accuracy of current advice based on the results of advice given to the user in the past. The user's past advice patterns are analyzed and the optimal advice method is applied. Errors are corrected and accuracy is improved based on the user's past advice results. In this way, the accuracy of advice is improved by referring to the results of past advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the results of past advice given to the user into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0124] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is in a hurry, the advice unit can provide short, concise advice. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. For example, if the user is in a hurry, the advice unit can estimate the user's emotions using an emotion estimation algorithm and provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice. If the user is excited, the advice unit can provide advice with visually stimulating effects. By adjusting the length of the advice according to the user's emotions, the advice can be more easily understood. The emotion estimation is realized using an emotion estimation function, for example, 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 advice unit can be performed using, for example, an AI, or without an AI. For example, the advice unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the advice.

[0125] When giving advice, the advice unit can determine the priority of the advice based on the submission time of the task. For example, the advice unit gives priority to advice for tasks with an upcoming submission deadline. The advice unit can also postpone giving advice for tasks with a distant submission deadline. The advice unit can also adjust the order of advice according to the submission time. For example, advice is given priority to tasks with an upcoming submission deadline. Advice is given later for tasks with a distant submission deadline. The order of advice is adjusted according to the submission time. In this way, by determining the priority of advice based on the submission time of the task, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the submission time of the task to the generation AI and cause the generation AI to determine the priority of advice.

[0126] When giving advice, the advice unit can adjust the order of advice based on the relevance of the tasks. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can also postpone giving advice on less relevant tasks. The advice unit can also adjust the order of advice based on the relevance of the tasks. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can postpone giving advice on less relevant tasks. The advice unit adjusts the order of advice based on the relevance of the tasks. This enables efficient advice to be provided by adjusting the order of advice based on the relevance of the tasks. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input data regarding the relevance of tasks to the generation AI and cause the generation AI to adjust the order of advice.

[0127] When giving advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has technical expertise, the advice unit provides advice that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the advice unit can provide advice in simple language. Furthermore, the advice unit can adjust the way the advice is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the advice unit provides advice that uses a lot of technical terms. If the user does not have technical expertise, the advice unit provides advice in simple language. The way the advice is expressed is adjusted depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the advice depending on the user's level of expertise, the advice can be more easily understood. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0128] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency to reduce the user's burden. Furthermore, if the user is relaxed, the monitoring unit can increase the monitoring frequency and provide the latest information. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring only important tasks. For example, if the user is stressed, the monitoring unit can estimate the user's emotions using an emotion estimation algorithm and adjust the monitoring frequency. If the user is relaxed, the monitoring unit can increase the monitoring frequency and provide the latest information. If the user is in a hurry, the monitoring unit can prioritize monitoring only important tasks. This reduces the user's burden by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data to the generation AI and have the generation AI adjust the monitoring frequency.

[0129] During monitoring, the monitoring unit can select an optimal monitoring method by referring to the user's past task progress. For example, the monitoring unit selects a monitoring method based on how the user has efficiently progressed tasks in the past. The monitoring unit can also select an optimal monitoring timing based on the user's past task progress. The monitoring unit can also analyze the user's past task progress patterns and apply an optimal monitoring method. For example, the monitoring unit selects a monitoring method based on how the user has efficiently progressed tasks in the past. The optimal monitoring timing is selected based on the user's past task progress. The user's past task progress patterns are analyzed and an optimal monitoring method is applied. In this way, the optimal monitoring method can be selected by referring to the past task progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's past task progress to the generation AI and cause the generation AI to select an optimal monitoring method.

[0130] During monitoring, the monitoring unit can customize the monitoring content based on the user's current task progress. For example, the monitoring unit prioritizes monitoring information related to the task currently being performed by the user. The monitoring unit can also provide optimal monitoring content based on the user's current task progress. The monitoring unit can also prioritize monitoring of tasks for which the user is behind. For example, the monitoring unit prioritizes monitoring information related to the task currently being performed by the user. The monitoring unit provides optimal monitoring content based on the user's current task progress. The monitoring unit prioritizes monitoring of tasks for which the user is behind. This enables efficient task management by customizing the monitoring content based on the current task progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the monitoring content.

[0131] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user. The monitoring unit can also select the optimal monitoring means from the user feedback. The monitoring unit can also customize the monitoring timing and means by reflecting user feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user. The optimal monitoring means is selected from user feedback. The monitoring timing and means are customized by reflecting user feedback. In this way, the monitoring method can be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data regarding user feedback into a generation AI and cause the generation AI to improve the monitoring method.

[0132] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user's emotions. For example, when the user is stressed, the monitoring unit postpones less important tasks and prioritizes monitoring more important tasks. Furthermore, when the user is relaxed, the monitoring unit can monitor all tasks equally. Furthermore, when the user is in a hurry, the monitoring unit can prioritize monitoring the most important tasks. For example, when the user is stressed, the monitoring unit estimates the user's emotions using an emotion estimation algorithm and determines monitoring priorities. When the user is relaxed, the monitoring unit monitors all tasks equally. When the user is in a hurry, the monitoring unit prioritizes monitoring the most important tasks. Thus, by determining monitoring priorities according to the user's emotions, important tasks can be prioritized and managed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI determine the monitoring priorities.

[0133] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring tasks related to that area. Furthermore, if the user is traveling, the monitoring unit can also monitor tasks related to the user's current location in real time. Furthermore, if the user frequently visits a specific location, the monitoring unit can also prioritize monitoring tasks related to that location. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring tasks related to that area. If the user is traveling, the monitoring unit monitors tasks related to the user's current location in real time. If the user frequently visits a specific location, the monitoring unit prioritizes monitoring tasks related to that location. This allows for prioritized management of highly relevant tasks by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal monitoring method.

[0134] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring content. The monitoring unit, for example, monitors related tasks based on information shared by the user on social media. The monitoring unit can also analyze the user's social media activity patterns and monitor related tasks. The monitoring unit can also monitor related tasks by referring to the activities of the user's friends on social media. For example, the monitoring unit can monitor related tasks based on information shared by the user on social media. The monitoring unit can analyze the user's social media activity patterns and monitor related tasks. The monitoring unit can monitor related tasks by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's social media activity into a generation AI and have the generation AI execute a suggested monitoring content.

[0135] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, improves the monitoring method based on feedback provided by the user in the past. The monitoring unit can also select the optimal monitoring means based on the user's past feedback. The monitoring unit can also customize the monitoring timing and means by reflecting the user's feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user in the past. The optimal monitoring means is selected based on the user's past feedback. The monitoring timing and means are customized by reflecting the user's feedback. In this way, the monitoring method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data regarding the user's feedback to the generation AI and have the generation AI customize the monitoring method.

[0136] The adjustment unit can estimate the user's emotions and change the priority adjustment method based on the estimated user's emotions. For example, when the user is feeling stressed, the adjustment unit postpones less important tasks and prioritizes more important tasks. Furthermore, when the user is relaxed, the adjustment unit can adjust all tasks equally. Furthermore, when the user is in a hurry, the adjustment unit can prioritize the most important tasks. For example, when the user is feeling stressed, the adjustment unit estimates the user's emotions using an emotion estimation algorithm and changes the priority adjustment method. When the user is relaxed, the adjustment unit adjusts all tasks equally. When the user is in a hurry, the adjustment unit prioritizes the most important tasks. This allows important tasks to be prioritized by changing the priority adjustment method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using 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 adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data to the generation AI and cause the generation AI to change the priority adjustment method.

[0137] When adjusting priorities, the adjustment unit can select an optimal adjustment method by referring to the user's past task completion history. The adjustment unit adjusts priorities, for example, based on how the user efficiently completed tasks in the past. The adjustment unit can also select an optimal adjustment timing from the user's past task completion history. The adjustment unit can also analyze the user's past task completion patterns and apply an optimal adjustment method. For example, the adjustment unit adjusts priorities based on how the user efficiently completed tasks in the past. The optimal adjustment timing is selected from the user's past task completion history. The user's past task completion patterns are analyzed and an optimal adjustment method is applied. In this way, the optimal adjustment method can be selected by referring to the past task completion history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's past task completion history to the generation AI and cause the generation AI to select an optimal adjustment method.

[0138] When adjusting the priority, the adjustment unit can customize the adjustment content based on the user's current task progress. For example, the adjustment unit prioritizes adjusting information related to the task currently in progress by the user. The adjustment unit can also provide optimal adjustment content based on the user's current task progress. The adjustment unit can also prioritize adjustment for tasks for which the user is behind. For example, the adjustment unit prioritizes adjusting information related to the task currently in progress by the user. The adjustment unit provides optimal adjustment content based on the user's current task progress. The adjustment unit prioritizes adjustment for tasks for which the user is behind. This enables efficient task management by customizing the adjustment content based on the current task progress. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data related to the user's current task progress to the generation AI and cause the generation AI to customize the adjustment content.

[0139] The adjustment unit can improve the adjustment method by reflecting user feedback when adjusting the priority. The adjustment unit improves the priority adjustment method, for example, based on feedback provided by the user. The adjustment unit can also select an optimal adjustment means from the user feedback. The adjustment unit can also customize the adjustment timing and means by reflecting the user feedback. For example, the adjustment method is improved based on feedback provided by the user. The optimal adjustment means is selected from the user feedback. The adjustment timing and means are customized by reflecting the user feedback. In this way, the adjustment method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data regarding user feedback into the generation AI and cause the generation AI to improve the adjustment method.

[0140] The adjustment unit can estimate the user's emotions and determine the frequency of priority adjustment based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit can reduce the frequency of priority adjustment to reduce the user's burden. Furthermore, if the user is relaxed, the adjustment unit can increase the frequency of priority adjustment and provide the latest information. Furthermore, if the user is in a hurry, the adjustment unit can prioritize and adjust only important tasks. For example, if the user is feeling stressed, the adjustment unit can estimate the user's emotions using an emotion estimation algorithm and determine the frequency of priority adjustment. If the user is relaxed, the adjustment unit can increase the frequency of priority adjustment and provide the latest information. If the user is in a hurry, the adjustment unit can prioritize and adjust only important tasks. Thus, by determining the frequency of priority adjustment according to the user's emotions, important tasks can be prioritized and managed. The emotion estimation is realized using an emotion estimation function, for example, using 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 adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data to the generation AI and have the generation AI determine the frequency of priority adjustment.

[0141] When adjusting the priority, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. For example, when the user is in a specific area, the adjustment unit prioritizes adjusting tasks related to that area. Furthermore, when the user is traveling, the adjustment unit can also prioritize adjusting tasks related to the user's current location in real time. Furthermore, when the user frequently visits a specific location, the adjustment unit can also prioritize adjusting tasks related to that location. For example, when the user is in a specific area, the adjustment unit prioritizes adjusting tasks related to that area. When the user is traveling, the adjustment unit adjusts tasks related to the user's current location in real time. When the user frequently visits a specific location, the adjustment unit prioritizes adjusting tasks related to that location. In this way, by taking the user's geographical location information into consideration, highly relevant tasks can be prioritized and managed. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal adjustment method.

[0142] When adjusting priorities, the adjustment unit can analyze the user's social media activity and suggest adjustment details. The adjustment unit, for example, adjusts related tasks based on information shared by the user on social media. The adjustment unit can also analyze the user's social media activity patterns and adjust related tasks. The adjustment unit can also adjust related tasks with reference to the activities of the user's friends on social media. For example, the adjustment unit adjusts related tasks based on information shared by the user on social media. The adjustment unit adjusts related tasks with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant tasks can be managed preferentially. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's social media activity into the generation AI and cause the generation AI to execute a suggestion of adjustment details.

[0143] When adjusting the priority, the adjustment unit can customize the adjustment method by reflecting the user's past feedback. The adjustment unit, for example, improves the adjustment method based on feedback provided by the user in the past. The adjustment unit can also select an optimal adjustment means from the user's past feedback. The adjustment unit can also customize the adjustment timing and means by reflecting the user's feedback. For example, the adjustment method can be improved based on feedback provided by the user in the past. The optimal adjustment means can be selected from the user's past feedback. The adjustment timing and means can be customized by reflecting the user's feedback. In this way, the adjustment method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data regarding the user's feedback to the generation AI and cause the generation AI to customize the adjustment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, update unit, advice unit, monitoring unit, and adjustment 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 and acquires data from the user's task management application. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes how to proceed with a task based on the analysis results. The update unit is realized by the control unit 46A of the smart device 14 and updates the proposed task list. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice for specific tasks. The monitoring unit is realized by the control unit 46A of the smart device 14 and monitors the progress of tasks. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts priorities. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, update unit, advice unit, monitoring unit, and adjustment unit, described above, 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 and acquires data from the user's task management application. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes how to proceed with a task based on the analysis results. The update unit is realized by the control unit 46A of the smart glasses 214 and updates the proposed task list. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice for a specific task. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and monitors the progress of tasks. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts priorities. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, update unit, advice unit, monitoring unit, and adjustment unit, described above, 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 and acquires data from the user's task management application. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes how to proceed with the task based on the analysis results. The update unit is realized by the control unit 46A of the headset type terminal 314 and updates the proposed task list. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice for specific tasks. The monitoring unit is realized by the control unit 46A of the headset type terminal 314 and monitors the progress of tasks. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts priorities. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, update unit, advice unit, monitoring unit, and adjustment unit, described above, 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 and acquires data from the user's task management application. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes how to proceed with the task based on the analysis results. The update unit is realized by the control unit 46A of the robot 414 and updates the proposed task list. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice for specific tasks. The monitoring unit is realized by the control unit 46A of the robot 414 and monitors the progress of tasks. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts priorities.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] The learning-based task optimization system can estimate the user's emotions and adjust task progress in real time based on the estimated emotions. For example, if the user is feeling stressed, the system can reevaluate task priorities and suggest easier tasks first to reduce stress. Also, if the user is relaxed, the system can prioritize more difficult tasks. Furthermore, if the user is concentrating, the system can suggest successive tasks to support efficient work. This enables flexible task management according to the user's emotions and improves work efficiency.

[0146] The learning-based task optimization system can predict task progress based on the user's past task completion history and optimize task progress based on the prediction results. For example, if a user has taken a long time to complete a particular task in the past, the system can suggest that task early to support time management. It can also promote smooth work progress by prioritizing tasks that the user has completed efficiently in the past. It can also analyze the user's past task completion patterns and suggest the optimal task order. This enables efficient task management using past data.

[0147] The learning-based task optimization system can estimate a user's emotions and visually display task progress based on the estimated emotions. For example, if a user is feeling stressed, the system can display task progress in a simple, highly visible format, helping to reduce stress. Also, if the user is relaxed, detailed progress can be displayed, making it easier to grasp the overall picture of the task. Furthermore, if the user is concentrating, progress can be updated in real time to support efficient work. This provides visual feedback according to the user's emotions, making task management more effective.

[0148] The learning-based task optimization system can optimize task progress by taking into account the user's geographic location information. For example, if the user is in a specific area, it will prioritize tasks related to that area. Also, if the user is on the move, it can suggest tasks that can be completed while traveling, supporting efficient time management. Furthermore, if the user frequently visits a specific location, it can prioritize tasks related to that location. This allows for flexible task management using geographic location information.

[0149] The learning-based task optimization system can estimate the user's emotions and notify the user of the task progress status through voice based on the estimated emotions. For example, if the user is feeling stressed, the system will notify the user of the task progress status in a calm voice, reducing stress. Also, if the user is relaxed, the system can notify the user of the task progress status in detail through voice, making it easier to grasp the overall picture of the task. Furthermore, if the user is concentrating, the system can notify the user of the progress status in real time through voice, supporting efficient work. This provides voice notifications according to the user's emotions, making task management more effective.

[0150] The learning task optimization system can analyze a user's social media activity and suggest related tasks. For example, it can prioritize suggesting related tasks based on information the user has shared on social media. It can also analyze the user's social media activity patterns and suggest optimal ways to complete tasks. It can also suggest related tasks based on the activity of the user's friends on social media. This allows for efficient task management that utilizes social media activity.

[0151] The learning-based task optimization system can estimate a user's emotions and customize task progress based on the estimated emotions. For example, if a user is feeling stressed, the system can display task progress in a simple, highly visible format to reduce stress. Also, if the user is relaxed, detailed progress can be displayed to help the user grasp the overall picture of the task. Furthermore, if the user is concentrating, progress can be updated in real time to support efficient work. This provides visual feedback according to the user's emotions, making task management more effective.

[0152] The learning-based task optimization system can optimize task progress by reflecting the user's past feedback. For example, it can improve the task progress method based on the user's past feedback. It can also select the optimal task progress timing based on the user's past feedback. It can also customize the task progress content by reflecting the user's feedback. This allows for efficient task management that utilizes past feedback.

[0153] The learning-based task optimization system can estimate a user's emotions and visually display task progress based on the estimated emotions. For example, if a user is feeling stressed, the system can display task progress in a simple, highly visible format, helping to reduce stress. Also, if the user is relaxed, detailed progress can be displayed, making it easier to grasp the overall picture of the task. Furthermore, if the user is concentrating, progress can be updated in real time to support efficient work. This provides visual feedback according to the user's emotions, making task management more effective.

[0154] The learning-based task optimization system can optimize task progress by taking into account the user's geographic location information. For example, if the user is in a specific area, it will prioritize tasks related to that area. Also, if the user is on the move, it can suggest tasks that can be completed while traveling, supporting efficient time management. Furthermore, if the user frequently visits a specific location, it can prioritize tasks related to that location. This allows for flexible task management using geographic location information.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The collection unit collects the user's past task management data. The user's past task management data includes information such as which tasks were performed in what order, how long each task took, and what results each task produced. The collection unit obtains data from the user's task management application, as well as manually entered data and data from cloud storage. Step 2: The analysis unit analyzes the data collected by the collection unit. This analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit analyzes the order and time of task execution as numerical data, predicts task results based on past data, and extracts useful patterns from large amounts of data. Step 3: The proposal unit proposes how to proceed with the tasks based on the analysis results obtained by the analysis unit. The proposals are based on advice on how to prioritize, step-by-step procedures, and efficient ways to proceed. For example, it suggests performing high-priority tasks first, suggests next steps based on the progress of the task, and provides advice on efficient ways to proceed based on time management and resource optimization for the task. Step 4: The update unit updates the task list proposed by the suggestion unit. The update is performed in real time, and the task list is updated when the user completes a task or adds a new task. The task list can also be updated automatically according to the progress of the user's tasks. The update unit can also update the task list using an AI model that takes the task list proposed by the suggestion unit as input and outputs a task list.

[0157] 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.

[0158] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0178] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Explanation of symbols]

[0229] 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 data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes how to proceed with a task based on the analysis result obtained by the analysis unit; an update unit that updates the task list proposed by the suggestion unit; Equipped with A system characterized by:

2. It has an advice section that provides advice for specific tasks.

2. The system of claim 1.

3. Equipped with a monitoring unit that monitors the progress of tasks 2. The system of claim 1.

4. Equipped with an adjustment unit that adjusts the priority 2. The system of claim 1.

5. The collecting unit The system estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze users' past task management data and select the optimal collection method 2. The system of claim 1.

7. The collecting unit As data is collected, it is filtered based on the user's current projects and areas of interest.

2. The system of claim 1.

8. The collecting unit When collecting data, select the optimal collection method according to the user's input method.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions.

2. The system of claim 1.

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