system

Abstract

The system according to the embodiment aims to support a user in managing their time effectively. [Solution] A system according to an embodiment includes a reception unit, a priority setting unit, an estimation unit, a schedule creation unit, and a reminder unit. The reception unit receives task input. The priority setting unit sets the priority of each task based on the task information received by the reception unit. The estimation unit estimates the time for each task based on the priority set by the priority setting unit. The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The reminder unit provides reminders based on the schedule created by the schedule creation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not sufficiently support users in managing their time effectively, and there is room for improvement.

[0005] The system according to the embodiment aims to support a user in managing their time effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a priority setting unit, an estimation unit, a schedule creation unit, and a reminder unit. The reception unit receives task input. The priority setting unit sets the priority of each task based on the task information received by the reception unit. The estimation unit estimates the time for each task based on the priority set by the priority setting unit. The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The reminder unit provides reminders based on the schedule created by the schedule creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist the user in effective time management. [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 time management support system according to an embodiment of the present invention utilizes a generation AI to help users manage their time effectively. In this system, a user inputs tasks, and a generation AI analyzes them, prioritizes them, estimates their time, creates a schedule, and provides reminders. For example, a user can input tasks via voice or by extracting tasks from photos. The generation AI analyzes the input tasks and prioritizes them based on their importance, deadlines, and the user's past behavioral patterns. Furthermore, the generation AI estimates the time required for each task based on past data and task content. The generation AI then proposes an optimal schedule, taking into account the user's available time and priorities. Finally, the generation AI sends timely reminders to ensure the user does not forget to complete tasks. Furthermore, the generation AI monitors task progress in real time and adjusts the schedule as necessary. It also provides feedback on completed tasks to help users manage their tasks more efficiently. This system enables users to manage their time effectively and is expected to lead to more efficient task completion. This allows the time management support system to help the user manage their time effectively and perform tasks efficiently.

[0029] A time management support system according to an embodiment includes a reception unit, a priority setting unit, an estimation unit, a schedule creation unit, and a reminder unit. The reception unit is a unit through which a user inputs tasks. When a user inputs tasks, tasks can be extracted from, for example, voice input or photos. The reception unit converts the user's voice into text using, for example, voice recognition technology. The reception unit can also extract tasks from photos using image recognition technology. For example, tasks can be extracted from photos taken by a user with a smartphone camera using OCR technology. The priority setting unit is a unit that sets the priority of each task based on the task information received by the reception unit. The priority setting unit determines the priority by taking into account, for example, the importance and deadline of the task, and the user's past behavioral patterns. For example, the priority setting unit prioritizes tasks with high importance. The priority setting unit can also prioritize tasks with upcoming deadlines. Furthermore, the priority setting unit can learn the user's past behavioral patterns and set optimal priorities. The estimation unit is a unit that estimates the time required for each task based on the priorities set by the priority setting unit. The estimation unit estimates the time required for each task based on, for example, past data and the content of the task. For example, the estimation unit makes an estimate by referring to the time required for similar tasks in the past. The estimation unit can also apply different estimation methods depending on the content and complexity of the task. The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The schedule creation unit proposes an optimal schedule by taking into account, for example, the user's free time and priority. For example, the schedule creation unit references the user's calendar information and assigns tasks to free time. The schedule creation unit can also dynamically change the schedule by taking into account the importance and deadline of the task. The reminder unit provides reminders based on the schedule created by the schedule creation unit. For example, the reminder unit sends reminders at appropriate times to help the user remember tasks.For example, the reminder unit may send a notification when a task deadline is approaching. The reminder unit may also estimate a user's emotions and adjust the timing of sending reminders based on the emotions. This allows the time management support system according to the embodiment to support the user in effective time management and efficient task completion.

[0030] The time management support system includes an input support unit that extracts tasks from voice input or photos. The input support unit is a component that allows a user to extract tasks from voice input or photos. For example, the input support unit converts the user's voice into text using voice recognition technology. For example, when a user vocally inputs tasks using a smartphone microphone, the voice recognition technology can analyze the voice and convert it into text data. The input support unit can also extract tasks from photos using image recognition technology. For example, tasks can be extracted from photos taken by a user with a smartphone camera using OCR technology. For example, when a user photographs handwritten notes, the OCR technology can analyze the notes and convert them into text data. This allows a user to input tasks from voice or photos. Some or all of the above-described processing in the input support unit may be performed using or without a generation AI. For example, the input support unit can input voice data or image data into a generation AI, which then analyzes the data and extracts tasks.

[0031] The time management support system includes a learning unit that learns a user's past behavioral patterns. The learning unit is a component for learning a user's past behavioral patterns. For example, the learning unit collects a history of tasks performed by the user in the past and learns behavioral patterns based on the data. For example, the learning unit can record what tasks the user performed in the past and at what time of day, and analyze the data. The learning unit can also use a machine learning algorithm to learn the user's behavioral patterns. For example, the learning unit can input the user's past task history as input data into a machine learning algorithm to learn behavioral patterns. This allows the system to learn the user's past behavioral patterns and provide more appropriate task management. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past task history into a generation AI, which can then analyze the data to learn behavioral patterns.

[0032] The time management support system includes a monitoring unit that monitors the progress of a task in real time. The monitoring unit is a component for monitoring the progress of a task in real time. For example, the monitoring unit monitors the progress of a task being performed by a user in real time and collects the data. For example, the monitoring unit can record the time when the user starts and finishes the task and monitor the progress based on the data. The monitoring unit can also display the progress of the user while the task is being performed in real time. For example, the monitoring unit can display the progress as a graph or chart while the user is performing the task, allowing the user to visually confirm how far the user has progressed. This allows the progress of the task to be monitored in real time. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's progress data into the generation AI, which can then analyze the data and monitor the progress.

[0033] The time management support system includes a readjustment unit that readjusts a schedule. The readjustment unit is a component for readjusting a schedule. For example, the readjustment unit readjusts the schedule in accordance with the progress of a user's tasks or changes in priority. For example, if a user is unable to complete a scheduled task, the readjustment unit can reschedule the task to a different time. The readjustment unit can also readjust the schedule in accordance with changes in the user's priority. For example, if a user adds a new important task, the readjustment unit can prioritize the task and incorporate it into the schedule. This allows the schedule to be readjusted as needed. Some or all of the above-described processing in the readjustment unit may be performed using or without a generation AI. For example, the readjustment unit can input the user's task data into the generation AI, which can then analyze the data and readjust the schedule.

[0034] The time management support system includes a feedback unit that provides feedback for completed tasks. The feedback unit is a component for providing feedback for completed tasks. For example, the feedback unit provides feedback based on the content and progress of the task completed by the user. For example, when the user completes a task, the feedback unit can evaluate the degree of completion and efficiency of the task and provide feedback. The feedback unit can also provide advice and improvements for the task completed by the user. For example, the feedback unit can provide advice to help the user perform the task more efficiently. This makes it possible to provide feedback for the completed task. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's task data into the generation AI, which can analyze the data and provide feedback.

[0035] The reception unit can analyze the user's past task input history and provide an optimal input interface. For example, the reception unit can automatically display tasks that the user has frequently input in the past as candidates. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks to be used in a specific time period based on the user's past input history. This makes it possible to provide an optimal input interface based on the user's past task input history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past input data into the generation AI, which can then analyze the data and provide an optimal input interface.

[0036] The reception unit can provide input assistance based on the user's current situation and environment when entering a task. For example, when the user is on the move, the reception unit can prioritize voice input to allow the user to easily enter a task. For example, when the user is in a meeting, the reception unit can prioritize text input to support input in a quiet environment. Furthermore, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. This allows input assistance to be provided according to the user's current situation and environment. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data about the user's current situation and environment into the generation AI, which can then analyze the data and provide input assistance.

[0037] When a task is input, the reception unit can suggest highly relevant tasks taking into account the user's geographical location information. For example, when the user is in the office, the reception unit can prioritize suggesting office-related tasks. For example, when the user is at home, the reception unit can prioritize suggesting tasks to be performed at home. Furthermore, when the user is out, the reception unit can also prioritize suggesting tasks to be performed while away from home. This makes it possible to suggest highly relevant tasks based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, and the generation AI can analyze the data and suggest highly relevant tasks.

[0038] The reception unit can analyze the user's social media activity when inputting a task and automatically input related tasks. For example, if the user indicates on social media that they will participate in an event, the reception unit can automatically input tasks related to the event. For example, if the user mentions a specific project on social media, the reception unit can automatically input tasks related to the project. Furthermore, if the user indicates on social media that they will be going to a specific place, the reception unit can automatically input tasks related to the place. In this way, related tasks can be automatically input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media data into the generation AI, which can then analyze the data and automatically input related tasks.

[0039] The priority setting unit can dynamically change the priority based on the importance and deadline of the task when setting the priority. For example, the priority setting unit can raise the priority when the importance of the task is high. For example, the priority setting unit can raise the priority when the deadline of the task is approaching. The priority setting unit can also set an optimal priority by taking into account both the importance and deadline of the task. This makes it possible to dynamically change the priority based on the importance and deadline of the task. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without the generation AI. For example, the priority setting unit can input task importance and deadline data to the generation AI, and the generation AI can analyze the data and dynamically change the priority.

[0040] When setting priorities, the priority setting unit can set optimal priorities taking into account the user's past behavioral patterns. The priority setting unit, for example, can prioritize tasks that the user frequently performed in the past. For example, the priority setting unit can prioritize tasks that the user considered important in the past. The priority setting unit can also analyze the user's past behavioral patterns and set optimal priorities. This makes it possible to set optimal priorities based on the user's past behavioral patterns. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without the generation AI. For example, the priority setting unit can input the user's past behavioral data into the generation AI, and the generation AI can analyze the data to set optimal priorities.

[0041] The priority setting unit can apply different priority setting algorithms depending on the task category when setting priorities. For example, the priority setting unit can apply a priority setting algorithm dedicated to work to work-related tasks. For example, the priority setting unit can apply a priority setting algorithm dedicated to housework to housework-related tasks. Furthermore, the priority setting unit can also apply a priority setting algorithm dedicated to hobbies to hobby-related tasks. This makes it possible to apply different priority setting algorithms depending on the task category. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without using the generation AI. For example, the priority setting unit can input task category data into the generation AI, which can analyze the data and apply the optimal priority setting algorithm.

[0042] The priority setting unit can automatically adjust the priorities based on the relevance of tasks when setting priorities. For example, the priority setting unit can raise the priority when there are many related tasks. For example, the priority setting unit can lower the priority when there are few related tasks. The priority setting unit can also analyze the relevance of tasks and set optimal priorities. This makes it possible to automatically adjust the priorities based on the relevance of tasks. Some or all of the above-mentioned processing in the priority setting unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the priority setting unit can input task relevance data into a generation AI, which can analyze the data and automatically adjust the priorities.

[0043] The estimation unit can improve the accuracy of the estimate by referring to past data when estimating time. The estimation unit can improve the accuracy of the estimate, for example, based on past task execution times. For example, the estimation unit can analyze past data to minimize estimation errors. The estimation unit can also provide an optimal estimate by referring to past data. This can improve the accuracy of the estimate by referring to past data. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input past task data into the generation AI, and the generation AI can analyze the data to improve the accuracy of the estimate.

[0044] The estimation unit can apply different estimation methods depending on the content and complexity of the task when estimating time. For example, the estimation unit can apply a simple estimation method to a simple task. For example, the estimation unit can apply a detailed estimation method to a complex task. The estimation unit can also select the optimal estimation method depending on the content of the task. This makes it possible to apply different estimation methods depending on the content and complexity of the task. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task content and complexity data into the generation AI, which can analyze the data and apply the optimal estimation method.

[0045] The estimation unit can determine the priority of estimates based on the submission dates of tasks when estimating time. For example, the estimation unit prioritizes tasks with upcoming submission deadlines. For example, the estimation unit can postpone tasks with more distant submission deadlines. The estimation unit can also dynamically adjust the priority of estimates based on the submission dates. This makes it possible to determine the priority of estimates based on the submission dates of tasks. Some or all of the above-described processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task submission date data into the generation AI, and the generation AI can analyze the data to determine the priority of estimates.

[0046] The estimation unit can adjust the order of estimation based on task relevance when estimating time. For example, if there are many related tasks, the estimation unit can prioritize the order of estimation. For example, if there are few related tasks, the estimation unit can postpone the order of estimation. The estimation unit can also analyze task relevance and set an optimal estimation order. This makes it possible to adjust the order of estimation based on task relevance. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task relevance data into the generation AI, and the generation AI can analyze the data and adjust the order of estimation.

[0047] When creating a schedule, the schedule creation unit can propose an optimal schedule by referring to the user's past schedule history. The schedule creation unit proposes an optimal schedule, for example, based on schedules the user has made in the past. For example, the schedule creation unit can propose an efficient schedule from the user's past schedule history. The schedule creation unit can also analyze the user's past schedule history and propose an optimal schedule. This makes it possible to propose an optimal schedule based on the user's past schedule history. Some or all of the above-mentioned processing in the schedule creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the schedule creation unit can input the user's past schedule data into the generation AI, which can then analyze the data and propose an optimal schedule.

[0048] The schedule creation unit can dynamically change the schedule when creating a schedule, taking into account the importance and deadline of the task. For example, the schedule creation unit can prioritize the schedule when the importance of the task is high. For example, the schedule creation unit can prioritize the schedule when the deadline of the task is approaching. The schedule creation unit can also set an optimal schedule by taking into account both the importance and deadline of the task. This makes it possible to dynamically change the schedule based on the importance and deadline of the task. Some or all of the above-mentioned processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input task importance and deadline data into the generation AI, and the generation AI can analyze the data and dynamically change the schedule.

[0049] The schedule creation unit can propose an optimal schedule by taking into consideration the user's geographical location information when creating a schedule. For example, when the user is in the office, the schedule creation unit prioritizes office-related tasks in the schedule. For example, when the user is at home, the schedule creation unit can prioritize tasks to be performed at home in the schedule. Furthermore, when the user is out, the schedule creation unit can also prioritize tasks to be performed while away from home in the schedule. This makes it possible to propose an optimal schedule based on the user's geographical location information. Some or all of the above-described processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input the user's geographical location information into the generation AI, which can then analyze the data and propose an optimal schedule.

[0050] The schedule creation unit can analyze the user's social media activity and automatically adjust the schedule when creating the schedule. For example, if the user indicates on social media that they will be attending an event, the schedule creation unit can incorporate tasks related to the event into the schedule. For example, if the user mentions a specific project on social media, the schedule creation unit can incorporate tasks related to the project into the schedule. Furthermore, if the user indicates on social media that they will be going to a specific place, the schedule creation unit can also incorporate tasks related to the place into the schedule. This makes it possible to automatically adjust the schedule based on the user's social media activity. Some or all of the above-mentioned processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input the user's social media data into the generation AI, which can then analyze the data and automatically adjust the schedule.

[0051] When sending a reminder, the reminder unit can select the optimal sending method by referring to the user's past reminder history. For example, the reminder unit preferentially selects a reminder sending method that the user has used favorably in the past. For example, the reminder unit can select the optimal sending method from the user's past reminder history. The reminder unit can also analyze the user's past reminder history and select the optimal sending method. This makes it possible to select the optimal sending method based on the user's past reminder history. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's past reminder history data into the generation AI, which can then analyze the data and select the optimal sending method.

[0052] When sending a reminder, the reminder unit can customize the content of the reminder by taking into account the importance and deadline of the task. For example, if the importance of the task is high, the reminder unit can make the content of the reminder more detailed. For example, if the deadline of the task is approaching, the reminder unit can emphasize the content of the reminder. The reminder unit can also customize the content of the reminder by taking into account both the importance and deadline of the task. This makes it possible to customize the content of the reminder based on the importance and deadline of the task. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input task importance and deadline data into the generation AI, which can then analyze the data and customize the content of the reminder.

[0053] When sending a reminder, the reminder unit can send the most appropriate reminder by taking into account the user's geographical location information. For example, when the user is in the office, the reminder unit can prioritize sending office-related reminders. For example, when the user is at home, the reminder unit can prioritize sending reminders for tasks to be done at home. Furthermore, when the user is out, the reminder unit can also prioritize sending reminders for tasks to be done away from home. This makes it possible to send the most appropriate reminder based on the user's geographical location information. Some or all of the above-described processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's geographical location information into the generation AI, which can analyze the data and send the most appropriate reminder.

[0054] When sending a reminder, the reminder unit can analyze the user's social media activity and automatically adjust the content of the reminder. For example, if a user indicates on social media that they will be attending an event, the reminder unit can send a reminder related to the event. For example, if a user mentions a specific project on social media, the reminder unit can send a reminder related to the project. Also, if a user indicates on social media that they will be going to a specific place, the reminder unit can send a reminder related to the place. This makes it possible to automatically adjust the content of the reminder based on the user's social media activity. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's social media data into the generation AI, which can analyze the data and automatically adjust the content of the reminder.

[0055] When extracting tasks from voice input or photos, the input support unit can select the optimal extraction method by referring to the user's past input history. For example, the input support unit can preferentially provide a voice input method that the user has frequently used in the past. For example, the input support unit can select the optimal voice input method from the user's past input history. The input support unit can also analyze the user's past input history and provide the optimal voice input method. This makes it possible to select the optimal extraction method based on the user's past input history. Some or all of the above-mentioned processing in the input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's past input history data into the generation AI, which can then analyze the data and select the optimal extraction method.

[0056] When extracting tasks from voice input or photos, the input support unit can extract highly relevant tasks by taking into account the user's geographical location information. For example, when the user is in the office, the input support unit prioritizes extracting office-related tasks. For example, when the user is at home, the input support unit can prioritize extracting tasks to be performed at home. Furthermore, when the user is out, the input support unit can also prioritize extracting tasks to be performed while away from home. This makes it possible to extract highly relevant tasks based on the user's geographical location information. Some or all of the above-described processing in the input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's geographical location information to the generation AI, which can then analyze the data to extract highly relevant tasks.

[0057] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and improve the accuracy of the learning algorithm. The learning unit can also select an optimal learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into a generation AI, which then analyzes the data to optimize the learning algorithm.

[0058] During learning, the learning unit can weight the learning data based on the submission time of the task. For example, the learning unit weights the learning data of a task with an upcoming submission deadline. For example, the learning unit can lighten the weight of the learning data of a task with a distant submission deadline. The learning unit can also dynamically adjust the weighting of the learning data based on the submission time. This allows the learning data to be weighted based on the submission time of the task. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input task submission time data into the generation AI, and the generation AI can analyze the data and weight the learning data.

[0059] When monitoring the progress of a task, the monitoring unit can select the optimal monitoring method by referring to the user's past progress history. For example, the monitoring unit preferentially selects a progress monitoring method that the user has used favorably in the past. For example, the monitoring unit can select the optimal monitoring method from the user's past progress history. The monitoring unit can also analyze the user's past progress history and select the optimal monitoring method. This makes it possible to select the optimal monitoring method based on the user's past progress history. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's past progress history data into the generation AI, which can then analyze the data and select the optimal monitoring method.

[0060] When monitoring the progress of a task, the monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. For example, when the user is in the office, the monitoring unit can prioritize monitoring the progress of office-related tasks. For example, when the user is at home, the monitoring unit can prioritize monitoring the progress of tasks performed at home. Furthermore, when the user is out, the monitoring unit can also prioritize monitoring the progress of tasks performed away from home. This makes it possible to select an optimal monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's geographical location information into the generation AI, which can then analyze the data to select an optimal monitoring method.

[0061] When readjusting a schedule, the readjustment unit can select an optimal readjustment method by referring to the user's past schedule history. For example, the readjustment unit preferentially selects a readjustment method that the user has used favorably in the past. For example, the readjustment unit can select an optimal readjustment method from the user's past schedule history. The readjustment unit can also analyze the user's past schedule history and select an optimal readjustment method. This makes it possible to select an optimal readjustment method based on the user's past schedule history. Some or all of the above-described processing in the readjustment unit may be performed using or without the generation AI. For example, the readjustment unit can input the user's past schedule history data into the generation AI, and the generation AI can analyze the data to select an optimal readjustment method.

[0062] When readjusting a schedule, the readjustment unit can select an optimal readjustment method taking into account the user's geographical location information. For example, when the user is in the office, the readjustment unit prioritizes readjustment of office-related tasks. For example, when the user is at home, the readjustment unit can prioritize readjustment of tasks to be performed at home. Furthermore, when the user is out, the readjustment unit can also prioritize readjustment of tasks to be performed while away from home. This makes it possible to select an optimal readjustment method based on the user's geographical location information. Some or all of the above-described processing in the readjustment unit may be performed using or without the generation AI. For example, the readjustment unit can input the user's geographical location information into the generation AI, which can then analyze the data to select an optimal readjustment method.

[0063] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, the feedback unit can preferentially provide a form of feedback that the user has preferred in the past. For example, the feedback unit can provide optimal feedback from the user's past feedback history. The feedback unit can also analyze the user's past feedback history and provide optimal feedback. This makes it possible to provide optimal feedback based on the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI, which can then analyze the data and provide optimal feedback.

[0064] When providing feedback, the feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, when the user is in the office, the feedback unit can prioritize providing office-related feedback. For example, when the user is at home, the feedback unit can prioritize providing feedback related to tasks to be performed at home. Furthermore, when the user is out, the feedback unit can also prioritize providing feedback related to tasks to be performed while out. This makes it possible to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI, which can then analyze the data to provide optimal feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The time management support system can acquire the user's health data and adjust task priorities and schedules. For example, if the user's heart rate and sleep data indicate a high level of fatigue, the system can reduce the task load. Also, if the user's health condition is good, the system can assign more tasks. Furthermore, the system can suggest appropriate rest times based on the health data. This allows for flexible time management according to the user's health condition.

[0067] The time management support system can suggest tasks based on the user's hobbies and interests. For example, if the user likes reading, it will preferentially suggest tasks related to reading. If the user likes sports, it can suggest exercise-related tasks. It can also suggest tasks for refreshing yourself based on the user's hobbies and interests. This allows for effective time management while increasing the user's motivation.

[0068] The time management support system can analyze a user's social network data and suggest collaborative tasks with other users. For example, it can synchronize the tasks of users participating in the same project. It can also suggest collaborative work by referring to tasks being performed by the user's friends and colleagues. Furthermore, it can suggest tasks that are effective for the user to do together with other users based on the social network data. This enables collaborative time management.

[0069] The time management support system can dynamically adjust the estimated time for a task based on the user's past task completion times. For example, if a similar task was completed in a short time in the past, the estimated time can be shortened. Also, for tasks that took a long time in the past, the estimated time can be extended. Furthermore, if the user's work efficiency improves, the estimated time can be adjusted appropriately. This allows for more accurate time estimation.

[0070] The time management support system can suggest the optimal task order based on the user's geographic location information. For example, if the user is out, nearby tasks can be suggested with priority. Also, if the user is at home, tasks to be done at home can be suggested with priority. Furthermore, the system can suggest an efficient task order taking into account the user's travel route. This enables effective time management with minimal travel time.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit is a section where the user inputs tasks. When the user inputs a task, it is possible to extract tasks from voice input or photos, for example. The reception unit can convert the user's voice into text using voice recognition technology and extract tasks from photos using image recognition technology. For example, it can extract tasks from photos taken by the user with a smartphone camera using OCR technology. Step 2: The priority setting unit sets the priority of each task based on the task information received by the receiving unit. The priority setting unit determines the priority taking into consideration the importance and deadline of the task, and the user's past behavioral patterns. For example, it sets priority to tasks with high importance or upcoming deadlines, and sets optimal priorities by learning the user's past behavioral patterns. Step 3: The estimation unit estimates the time required for each task based on the priorities set by the priority setting unit. The estimation unit estimates the time required for each task based on past data and the content of the task. For example, the estimation is based on the time required for similar tasks in the past, and different estimation methods are applied depending on the content and complexity of the task. Step 4: The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The schedule creation unit proposes an optimal schedule taking into account the user's free time and priorities. For example, it references the user's calendar information, assigns tasks to free time, and dynamically changes the schedule taking into account the importance and deadlines of tasks. Step 5: The reminder unit provides reminders based on the schedule created by the schedule creation unit. The reminder unit sends reminders at appropriate times to ensure the user does not forget to complete tasks. For example, the reminder unit can send notifications when a task deadline is approaching and can estimate the user's emotions and adjust the timing of reminder sending based on the emotions.

[0073] (Example 2) A time management support system according to an embodiment of the present invention utilizes a generation AI to help users manage their time effectively. In this system, a user inputs tasks, and a generation AI analyzes them, prioritizes them, estimates their time, creates a schedule, and provides reminders. For example, a user can input tasks via voice or by extracting tasks from photos. The generation AI analyzes the input tasks and prioritizes them based on their importance, deadlines, and the user's past behavioral patterns. Furthermore, the generation AI estimates the time required for each task based on past data and task content. The generation AI then proposes an optimal schedule, taking into account the user's available time and priorities. Finally, the generation AI sends timely reminders to ensure the user does not forget to complete tasks. Furthermore, the generation AI monitors task progress in real time and adjusts the schedule as necessary. It also provides feedback on completed tasks to help users manage their tasks more efficiently. This system enables users to manage their time effectively and is expected to lead to more efficient task completion. This allows the time management support system to help the user manage their time effectively and perform tasks efficiently.

[0074] A time management support system according to an embodiment includes a reception unit, a priority setting unit, an estimation unit, a schedule creation unit, and a reminder unit. The reception unit is a unit through which a user inputs tasks. When a user inputs tasks, tasks can be extracted from, for example, voice input or photos. The reception unit converts the user's voice into text using, for example, voice recognition technology. The reception unit can also extract tasks from photos using image recognition technology. For example, tasks can be extracted from photos taken by a user with a smartphone camera using OCR technology. The priority setting unit is a unit that sets the priority of each task based on the task information received by the reception unit. The priority setting unit determines the priority by taking into account, for example, the importance and deadline of the task, and the user's past behavioral patterns. For example, the priority setting unit prioritizes tasks with high importance. The priority setting unit can also prioritize tasks with upcoming deadlines. Furthermore, the priority setting unit can learn the user's past behavioral patterns and set optimal priorities. The estimation unit is a unit that estimates the time required for each task based on the priorities set by the priority setting unit. The estimation unit estimates the time required for each task based on, for example, past data and the content of the task. For example, the estimation unit makes an estimate by referring to the time required for similar tasks in the past. The estimation unit can also apply different estimation methods depending on the content and complexity of the task. The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The schedule creation unit proposes an optimal schedule by taking into account, for example, the user's free time and priority. For example, the schedule creation unit references the user's calendar information and assigns tasks to free time. The schedule creation unit can also dynamically change the schedule by taking into account the importance and deadline of the task. The reminder unit provides reminders based on the schedule created by the schedule creation unit. For example, the reminder unit sends reminders at appropriate times to help the user remember tasks.For example, the reminder unit may send a notification when a task deadline is approaching. The reminder unit may also estimate a user's emotions and adjust the timing of sending reminders based on the emotions. This allows the time management support system according to the embodiment to support the user in effective time management and efficient task completion.

[0075] The time management support system includes an input support unit that extracts tasks from voice input or photos. The input support unit is a component that allows a user to extract tasks from voice input or photos. For example, the input support unit converts the user's voice into text using voice recognition technology. For example, when a user vocally inputs tasks using a smartphone microphone, the voice recognition technology can analyze the voice and convert it into text data. The input support unit can also extract tasks from photos using image recognition technology. For example, tasks can be extracted from photos taken by a user with a smartphone camera using OCR technology. For example, when a user photographs handwritten notes, the OCR technology can analyze the notes and convert them into text data. This allows a user to input tasks from voice or photos. Some or all of the above-described processing in the input support unit may be performed using or without a generation AI. For example, the input support unit can input voice data or image data into a generation AI, which then analyzes the data and extracts tasks.

[0076] The time management support system includes a learning unit that learns a user's past behavioral patterns. The learning unit is a component for learning a user's past behavioral patterns. For example, the learning unit collects a history of tasks performed by the user in the past and learns behavioral patterns based on the data. For example, the learning unit can record what tasks the user performed in the past and at what time of day, and analyze the data. The learning unit can also use a machine learning algorithm to learn the user's behavioral patterns. For example, the learning unit can input the user's past task history as input data into a machine learning algorithm to learn behavioral patterns. This allows the system to learn the user's past behavioral patterns and provide more appropriate task management. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past task history into a generation AI, which can then analyze the data to learn behavioral patterns.

[0077] The time management support system includes a monitoring unit that monitors the progress of a task in real time. The monitoring unit is a component for monitoring the progress of a task in real time. For example, the monitoring unit monitors the progress of a task being performed by a user in real time and collects the data. For example, the monitoring unit can record the time when the user starts and finishes the task and monitor the progress based on the data. The monitoring unit can also display the progress of the user while the task is being performed in real time. For example, the monitoring unit can display the progress as a graph or chart while the user is performing the task, allowing the user to visually confirm how far the user has progressed. This allows the progress of the task to be monitored in real time. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's progress data into the generation AI, which can then analyze the data and monitor the progress.

[0078] The time management support system includes a readjustment unit that readjusts a schedule. The readjustment unit is a component for readjusting a schedule. For example, the readjustment unit readjusts the schedule in accordance with the progress of a user's tasks or changes in priority. For example, if a user is unable to complete a scheduled task, the readjustment unit can reschedule the task to a different time. The readjustment unit can also readjust the schedule in accordance with changes in the user's priority. For example, if a user adds a new important task, the readjustment unit can prioritize the task and incorporate it into the schedule. This allows the schedule to be readjusted as needed. Some or all of the above-described processing in the readjustment unit may be performed using or without a generation AI. For example, the readjustment unit can input the user's task data into the generation AI, which can then analyze the data and readjust the schedule.

[0079] The time management support system includes a feedback unit that provides feedback for completed tasks. The feedback unit is a component for providing feedback for completed tasks. For example, the feedback unit provides feedback based on the content and progress of the task completed by the user. For example, when the user completes a task, the feedback unit can evaluate the degree of completion and efficiency of the task and provide feedback. The feedback unit can also provide advice and improvements for the task completed by the user. For example, the feedback unit can provide advice to help the user perform the task more efficiently. This makes it possible to provide feedback for the completed task. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's task data into the generation AI, which can analyze the data and provide feedback.

[0080] The reception unit can estimate the user's emotions and adjust the task input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick task input. This allows the task input method to be adjusted according to the user's emotions. 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 reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit can input the user's voice data or image data into the generation AI, which can analyze the data to estimate the user's emotions and adjust the input method.

[0081] The reception unit can analyze the user's past task input history and provide an optimal input interface. For example, the reception unit can automatically display tasks that the user has frequently input in the past as candidates. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks to be used in a specific time period based on the user's past input history. This makes it possible to provide an optimal input interface based on the user's past task input history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past input data into the generation AI, which can then analyze the data and provide an optimal input interface.

[0082] The reception unit can provide input assistance based on the user's current situation and environment when entering a task. For example, when the user is on the move, the reception unit can prioritize voice input to allow the user to easily enter a task. For example, when the user is in a meeting, the reception unit can prioritize text input to support input in a quiet environment. Furthermore, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. This allows input assistance to be provided according to the user's current situation and environment. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data about the user's current situation and environment into the generation AI, which can then analyze the data and provide input assistance.

[0083] The reception unit can estimate the user's emotions and automatically prioritize the input tasks based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize tasks with high importance. For example, if the user is relaxed, the reception unit can prioritize long-term tasks. Furthermore, if the user is in a hurry, the reception unit can prioritize tasks that can be completed in a short time. This allows the prioritization of tasks to be automatically set based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which then analyzes the data and sets the priority of the tasks.

[0084] When a task is input, the reception unit can suggest highly relevant tasks taking into account the user's geographical location information. For example, when the user is in the office, the reception unit can prioritize suggesting office-related tasks. For example, when the user is at home, the reception unit can prioritize suggesting tasks to be performed at home. Furthermore, when the user is out, the reception unit can also prioritize suggesting tasks to be performed while away from home. This makes it possible to suggest highly relevant tasks based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, and the generation AI can analyze the data and suggest highly relevant tasks.

[0085] The reception unit can analyze the user's social media activity when inputting a task and automatically input related tasks. For example, if the user indicates on social media that they will participate in an event, the reception unit can automatically input tasks related to the event. For example, if the user mentions a specific project on social media, the reception unit can automatically input tasks related to the project. Furthermore, if the user indicates on social media that they will be going to a specific place, the reception unit can automatically input tasks related to the place. In this way, related tasks can be automatically input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media data into the generation AI, which can then analyze the data and automatically input related tasks.

[0086] The priority setting unit can estimate the user's emotions and adjust the priority of tasks based on the estimated user emotions. For example, if the user is feeling stressed, the priority setting unit can prioritize tasks with high importance. For example, if the user is relaxed, the priority setting unit can prioritize long-term tasks. Furthermore, if the user is in a hurry, the priority setting unit can prioritize tasks that can be completed in a short time. This allows the priorities of tasks to be adjusted based on the user's emotions. 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 priority setting unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the priority setting unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the task priorities.

[0087] The priority setting unit can dynamically change the priority based on the importance and deadline of the task when setting the priority. For example, the priority setting unit can raise the priority when the importance of the task is high. For example, the priority setting unit can raise the priority when the deadline of the task is approaching. The priority setting unit can also set an optimal priority by taking into account both the importance and deadline of the task. This makes it possible to dynamically change the priority based on the importance and deadline of the task. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without the generation AI. For example, the priority setting unit can input task importance and deadline data to the generation AI, and the generation AI can analyze the data and dynamically change the priority.

[0088] When setting priorities, the priority setting unit can set optimal priorities taking into account the user's past behavioral patterns. The priority setting unit, for example, can prioritize tasks that the user frequently performed in the past. For example, the priority setting unit can prioritize tasks that the user considered important in the past. The priority setting unit can also analyze the user's past behavioral patterns and set optimal priorities. This makes it possible to set optimal priorities based on the user's past behavioral patterns. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without the generation AI. For example, the priority setting unit can input the user's past behavioral data into the generation AI, and the generation AI can analyze the data to set optimal priorities.

[0089] The priority setting unit can estimate the user's emotions and adjust the display method of the priorities based on the estimated user emotions. For example, if the user is feeling stressed, the priority setting unit can provide a simple, highly visible display method. For example, if the user is relaxed, the priority setting unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the priority setting unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the priorities based on the user's emotions. 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-mentioned processing in the priority setting unit can be performed using or without the generation AI. For example, the priority setting unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the display method of the priorities.

[0090] The priority setting unit can apply different priority setting algorithms depending on the task category when setting priorities. For example, the priority setting unit can apply a priority setting algorithm dedicated to work to work-related tasks. For example, the priority setting unit can apply a priority setting algorithm dedicated to housework to housework-related tasks. Furthermore, the priority setting unit can also apply a priority setting algorithm dedicated to hobbies to hobby-related tasks. This makes it possible to apply different priority setting algorithms depending on the task category. Some or all of the above-mentioned processing in the priority setting unit may be performed using or without using the generation AI. For example, the priority setting unit can input task category data into the generation AI, which can analyze the data and apply the optimal priority setting algorithm.

[0091] The priority setting unit can automatically adjust the priorities based on the relevance of tasks when setting priorities. For example, the priority setting unit can raise the priority when there are many related tasks. For example, the priority setting unit can lower the priority when there are few related tasks. The priority setting unit can also analyze the relevance of tasks and set optimal priorities. This makes it possible to automatically adjust the priorities based on the relevance of tasks. Some or all of the above-mentioned processing in the priority setting unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the priority setting unit can input task relevance data into a generation AI, which can analyze the data and automatically adjust the priorities.

[0092] The estimation unit can estimate the user's emotions and adjust the accuracy of the time estimate based on the estimated user's emotions. For example, if the user is feeling stressed, the estimation unit can set a shorter time estimate. For example, if the user is relaxed, the estimation unit can set a longer time estimate. Furthermore, if the user is in a hurry, the estimation unit can quickly estimate the time. This allows the accuracy of the time estimate to be adjusted based on the user's emotions. 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-mentioned processing in the estimation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the estimation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the accuracy of the time estimate.

[0093] The estimation unit can improve the accuracy of the estimate by referring to past data when estimating time. The estimation unit can improve the accuracy of the estimate, for example, based on past task execution times. For example, the estimation unit can analyze past data to minimize estimation errors. The estimation unit can also provide an optimal estimate by referring to past data. This can improve the accuracy of the estimate by referring to past data. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input past task data into the generation AI, and the generation AI can analyze the data to improve the accuracy of the estimate.

[0094] The estimation unit can apply different estimation methods depending on the content and complexity of the task when estimating time. For example, the estimation unit can apply a simple estimation method to a simple task. For example, the estimation unit can apply a detailed estimation method to a complex task. The estimation unit can also select the optimal estimation method depending on the content of the task. This makes it possible to apply different estimation methods depending on the content and complexity of the task. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task content and complexity data into the generation AI, which can analyze the data and apply the optimal estimation method.

[0095] The estimation unit can estimate the user's emotions and adjust the display method of the estimation result based on the estimated user's emotions. For example, if the user is feeling stressed, the estimation unit can provide a simple, highly visible display method. For example, if the user is relaxed, the estimation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the estimation unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the estimation result based on the user's emotions. 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-mentioned processing in the estimation unit can be performed using or without the generation AI. For example, the estimation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the display method of the estimation result.

[0096] The estimation unit can determine the priority of estimates based on the submission dates of tasks when estimating time. For example, the estimation unit prioritizes tasks with upcoming submission deadlines. For example, the estimation unit can postpone tasks with more distant submission deadlines. The estimation unit can also dynamically adjust the priority of estimates based on the submission dates. This makes it possible to determine the priority of estimates based on the submission dates of tasks. Some or all of the above-described processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task submission date data into the generation AI, and the generation AI can analyze the data to determine the priority of estimates.

[0097] The estimation unit can adjust the order of estimation based on task relevance when estimating time. For example, if there are many related tasks, the estimation unit can prioritize the order of estimation. For example, if there are few related tasks, the estimation unit can postpone the order of estimation. The estimation unit can also analyze task relevance and set an optimal estimation order. This makes it possible to adjust the order of estimation based on task relevance. Some or all of the above-mentioned processing in the estimation unit may be performed using or without the generation AI. For example, the estimation unit can input task relevance data into the generation AI, and the generation AI can analyze the data and adjust the order of estimation.

[0098] The schedule creation unit can estimate the user's emotions and adjust the schedule creation method based on the estimated user emotions. For example, if the user is feeling stressed, the schedule creation unit can provide a simple, highly visible schedule. For example, if the user is relaxed, the schedule creation unit can provide a detailed schedule. Furthermore, if the user is in a hurry, the schedule creation unit can quickly create a schedule. This allows the schedule creation method to be adjusted based on the user's emotions. 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 such examples. Some or all of the above-mentioned processing in the schedule creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the schedule creation unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the schedule creation method.

[0099] When creating a schedule, the schedule creation unit can propose an optimal schedule by referring to the user's past schedule history. The schedule creation unit proposes an optimal schedule, for example, based on schedules the user has made in the past. For example, the schedule creation unit can propose an efficient schedule from the user's past schedule history. The schedule creation unit can also analyze the user's past schedule history and propose an optimal schedule. This makes it possible to propose an optimal schedule based on the user's past schedule history. Some or all of the above-mentioned processing in the schedule creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the schedule creation unit can input the user's past schedule data into the generation AI, which can then analyze the data and propose an optimal schedule.

[0100] The schedule creation unit can dynamically change the schedule when creating a schedule, taking into account the importance and deadline of the task. For example, the schedule creation unit can prioritize the schedule when the importance of the task is high. For example, the schedule creation unit can prioritize the schedule when the deadline of the task is approaching. The schedule creation unit can also set an optimal schedule by taking into account both the importance and deadline of the task. This makes it possible to dynamically change the schedule based on the importance and deadline of the task. Some or all of the above-mentioned processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input task importance and deadline data into the generation AI, and the generation AI can analyze the data and dynamically change the schedule.

[0101] The schedule creation unit can estimate the user's emotions and adjust the schedule display method based on the estimated user emotions. For example, if the user is feeling stressed, the schedule creation unit provides a simple, highly visible display method. For example, if the user is relaxed, the schedule creation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the schedule creation unit can provide a display method that focuses on the main points. This makes it possible to adjust the schedule display method based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the schedule creation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the schedule creation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the schedule display method.

[0102] The schedule creation unit can propose an optimal schedule by taking into consideration the user's geographical location information when creating a schedule. For example, when the user is in the office, the schedule creation unit prioritizes office-related tasks in the schedule. For example, when the user is at home, the schedule creation unit can prioritize tasks to be performed at home in the schedule. Furthermore, when the user is out, the schedule creation unit can also prioritize tasks to be performed while away from home in the schedule. This makes it possible to propose an optimal schedule based on the user's geographical location information. Some or all of the above-described processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input the user's geographical location information into the generation AI, which can then analyze the data and propose an optimal schedule.

[0103] The schedule creation unit can analyze the user's social media activity and automatically adjust the schedule when creating the schedule. For example, if the user indicates on social media that they will be attending an event, the schedule creation unit can incorporate tasks related to the event into the schedule. For example, if the user mentions a specific project on social media, the schedule creation unit can incorporate tasks related to the project into the schedule. Furthermore, if the user indicates on social media that they will be going to a specific place, the schedule creation unit can also incorporate tasks related to the place into the schedule. This makes it possible to automatically adjust the schedule based on the user's social media activity. Some or all of the above-mentioned processing in the schedule creation unit may be performed using or without the generation AI. For example, the schedule creation unit can input the user's social media data into the generation AI, which can then analyze the data and automatically adjust the schedule.

[0104] The reminder unit can estimate the user's emotions and adjust the timing of sending a reminder based on the estimated user's emotions. For example, if the user is feeling stressed, the reminder unit can delay the timing of sending a reminder. For example, if the user is relaxed, the reminder unit can advance the timing of sending a reminder. Furthermore, if the user is in a hurry, the reminder unit can also expedite the timing of sending a reminder. This allows the timing of sending a reminder to be adjusted based on the user's emotions. 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 reminder unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reminder unit can input user emotion data into the generation AI, which can then analyze the data and adjust the timing of sending a reminder.

[0105] When sending a reminder, the reminder unit can select the optimal sending method by referring to the user's past reminder history. For example, the reminder unit preferentially selects a reminder sending method that the user has used favorably in the past. For example, the reminder unit can select the optimal sending method from the user's past reminder history. The reminder unit can also analyze the user's past reminder history and select the optimal sending method. This makes it possible to select the optimal sending method based on the user's past reminder history. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's past reminder history data into the generation AI, which can then analyze the data and select the optimal sending method.

[0106] When sending a reminder, the reminder unit can customize the content of the reminder by taking into account the importance and deadline of the task. For example, if the importance of the task is high, the reminder unit can make the content of the reminder more detailed. For example, if the deadline of the task is approaching, the reminder unit can emphasize the content of the reminder. The reminder unit can also customize the content of the reminder by taking into account both the importance and deadline of the task. This makes it possible to customize the content of the reminder based on the importance and deadline of the task. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input task importance and deadline data into the generation AI, which can then analyze the data and customize the content of the reminder.

[0107] The reminder unit can estimate the user's emotions and adjust the reminder display method based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can provide a simple, highly visible display method. For example, if the user is relaxed, the reminder unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reminder unit can provide a display method that focuses on the main points. This allows the reminder display method to be adjusted based on the user's emotions. 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-mentioned processing in the reminder unit can be performed using or without the generation AI. For example, the reminder unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the reminder display method.

[0108] When sending a reminder, the reminder unit can send the most appropriate reminder by taking into account the user's geographical location information. For example, when the user is in the office, the reminder unit can prioritize sending office-related reminders. For example, when the user is at home, the reminder unit can prioritize sending reminders for tasks to be done at home. Furthermore, when the user is out, the reminder unit can also prioritize sending reminders for tasks to be done away from home. This makes it possible to send the most appropriate reminder based on the user's geographical location information. Some or all of the above-described processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's geographical location information into the generation AI, which can analyze the data and send the most appropriate reminder.

[0109] When sending a reminder, the reminder unit can analyze the user's social media activity and automatically adjust the content of the reminder. For example, if a user indicates on social media that they will be attending an event, the reminder unit can send a reminder related to the event. For example, if a user mentions a specific project on social media, the reminder unit can send a reminder related to the project. Also, if a user indicates on social media that they will be going to a specific place, the reminder unit can send a reminder related to the place. This makes it possible to automatically adjust the content of the reminder based on the user's social media activity. Some or all of the above-mentioned processing in the reminder unit may be performed using or without the generation AI. For example, the reminder unit can input the user's social media data into the generation AI, which can analyze the data and automatically adjust the content of the reminder.

[0110] The input support unit can estimate the user's emotions and adjust the method of extracting tasks from voice input or photos based on the estimated user emotions. For example, if the user is feeling stressed, the input support unit can provide a simple voice input interface. For example, if the user is relaxed, the input support unit can provide detailed voice input options. Furthermore, if the user is in a hurry, the input support unit can provide a voice input method that can quickly extract tasks. This allows the method of extracting tasks from voice input or photos to be adjusted based on the user's emotions. 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-mentioned processing in the input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the method of extracting tasks from voice input or photos.

[0111] When extracting tasks from voice input or photos, the input support unit can select the optimal extraction method by referring to the user's past input history. For example, the input support unit can preferentially provide a voice input method that the user has frequently used in the past. For example, the input support unit can select the optimal voice input method from the user's past input history. The input support unit can also analyze the user's past input history and provide the optimal voice input method. This makes it possible to select the optimal extraction method based on the user's past input history. Some or all of the above-mentioned processing in the input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's past input history data into the generation AI, which can then analyze the data and select the optimal extraction method.

[0112] The input support unit can estimate the user's emotions and determine the priority of task extraction from voice input or photos based on the estimated user emotions. For example, if the user is feeling stressed, the input support unit can prioritize extraction of tasks with high importance. For example, if the user is relaxed, the input support unit can prioritize extraction of long-term tasks. Furthermore, if the user is in a hurry, the input support unit can prioritize extraction of tasks that can be completed in a short time. This allows the priority of task extraction from voice input or photos to be determined based on the user's emotions. 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 input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of task extraction from voice input or photos.

[0113] When extracting tasks from voice input or photos, the input support unit can extract highly relevant tasks by taking into account the user's geographical location information. For example, when the user is in the office, the input support unit prioritizes extracting office-related tasks. For example, when the user is at home, the input support unit can prioritize extracting tasks to be performed at home. Furthermore, when the user is out, the input support unit can also prioritize extracting tasks to be performed while away from home. This makes it possible to extract highly relevant tasks based on the user's geographical location information. Some or all of the above-described processing in the input support unit may be performed using or without the generation AI. For example, the input support unit can input the user's geographical location information to the generation AI, which can then analyze the data to extract highly relevant tasks.

[0114] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can prioritize selecting simple training data. For example, if the user is relaxed, the learning unit can prioritize selecting detailed training data. Furthermore, if the user is in a hurry, the learning unit can prioritize selecting data that can be quickly learned. This allows the selection of training data based on the user's emotions. 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 such examples. Some or all of the above-mentioned processing in the learning unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the learning unit can input the user's emotion data into the generation AI, which can then analyze the data and select training data.

[0115] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and improve the accuracy of the learning algorithm. The learning unit can also select an optimal learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into a generation AI, which then analyzes the data to optimize the learning algorithm.

[0116] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can reduce the frequency of learning when the user is stressed. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also adjust the frequency of learning when the user is in a hurry. This makes it possible to adjust the frequency of learning based on the user's emotions. 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 such examples. Some or all of the above-mentioned processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the frequency of learning.

[0117] During learning, the learning unit can weight the learning data based on the submission time of the task. For example, the learning unit weights the learning data of a task with an upcoming submission deadline. For example, the learning unit can lighten the weight of the learning data of a task with a distant submission deadline. The learning unit can also dynamically adjust the weighting of the learning data based on the submission time. This allows the learning data to be weighted based on the submission time of the task. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input task submission time data into the generation AI, and the generation AI can analyze the data and weight the learning data.

[0118] The monitoring unit can estimate the user's emotions and adjust the task progress monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple progress monitoring method. For example, if the user is relaxed, the monitoring unit can provide a detailed progress monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can provide a quick progress monitoring method. This allows the task progress monitoring method to be adjusted based on the user's emotions. 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 such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the monitoring unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the progress monitoring method.

[0119] When monitoring the progress of a task, the monitoring unit can select the optimal monitoring method by referring to the user's past progress history. For example, the monitoring unit preferentially selects a progress monitoring method that the user has used favorably in the past. For example, the monitoring unit can select the optimal monitoring method from the user's past progress history. The monitoring unit can also analyze the user's past progress history and select the optimal monitoring method. This makes it possible to select the optimal monitoring method based on the user's past progress history. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's past progress history data into the generation AI, which can then analyze the data and select the optimal monitoring method.

[0120] The monitoring unit can estimate the user's emotions and adjust the progress status display method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple, highly visible display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. This makes it possible to adjust the progress status display method based on the user's emotions. 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-mentioned processing in the monitoring unit can be performed using or without the generation AI. For example, the monitoring unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the progress status display method.

[0121] When monitoring the progress of a task, the monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. For example, when the user is in the office, the monitoring unit can prioritize monitoring the progress of office-related tasks. For example, when the user is at home, the monitoring unit can prioritize monitoring the progress of tasks performed at home. Furthermore, when the user is out, the monitoring unit can also prioritize monitoring the progress of tasks performed away from home. This makes it possible to select an optimal monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the user's geographical location information into the generation AI, which can then analyze the data to select an optimal monitoring method.

[0122] The readjustment unit can estimate the user's emotions and adjust the schedule readjustment method based on the estimated user emotions. For example, if the user is feeling stressed, the readjustment unit can provide a simple and highly visible readjustment method. For example, if the user is relaxed, the readjustment unit can provide a detailed readjustment method. Furthermore, if the user is in a hurry, the readjustment unit can also provide a method for quickly readjusting. This makes it possible to adjust the schedule readjustment method based on the user's emotions. 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-mentioned processing in the readjustment unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the readjustment unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the readjustment method.

[0123] When readjusting a schedule, the readjustment unit can select an optimal readjustment method by referring to the user's past schedule history. For example, the readjustment unit preferentially selects a readjustment method that the user has used favorably in the past. For example, the readjustment unit can select an optimal readjustment method from the user's past schedule history. The readjustment unit can also analyze the user's past schedule history and select an optimal readjustment method. This makes it possible to select an optimal readjustment method based on the user's past schedule history. Some or all of the above-described processing in the readjustment unit may be performed using or without the generation AI. For example, the readjustment unit can input the user's past schedule history data into the generation AI, and the generation AI can analyze the data to select an optimal readjustment method.

[0124] The readjustment unit can estimate the user's emotions and determine the priority of readjustment based on the estimated user's emotions. For example, if the user is feeling stressed, the readjustment unit can prioritize readjustment of tasks with high importance. For example, if the user is relaxed, the readjustment unit can prioritize readjustment of long-term tasks. Furthermore, if the user is in a hurry, the readjustment unit can prioritize readjustment of tasks that can be completed in a short time. This makes it possible to determine the priority of readjustment based on the user's emotions. 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 readjustment unit can be performed using or without the generation AI. For example, the readjustment unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of readjustment.

[0125] When readjusting a schedule, the readjustment unit can select an optimal readjustment method taking into account the user's geographical location information. For example, when the user is in the office, the readjustment unit prioritizes readjustment of office-related tasks. For example, when the user is at home, the readjustment unit can prioritize readjustment of tasks to be performed at home. Furthermore, when the user is out, the readjustment unit can also prioritize readjustment of tasks to be performed while away from home. This makes it possible to select an optimal readjustment method based on the user's geographical location information. Some or all of the above-described processing in the readjustment unit may be performed using or without the generation AI. For example, the readjustment unit can input the user's geographical location information into the generation AI, which can then analyze the data to select an optimal readjustment method.

[0126] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide simple, positive feedback. For example, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide quick feedback. This allows the content of the feedback to be adjusted based on the user's emotions. 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 feedback unit can be performed using or without the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the content of the feedback.

[0127] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, the feedback unit can preferentially provide a form of feedback that the user has preferred in the past. For example, the feedback unit can provide optimal feedback from the user's past feedback history. The feedback unit can also analyze the user's past feedback history and provide optimal feedback. This makes it possible to provide optimal feedback based on the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI, which can then analyze the data and provide optimal feedback.

[0128] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit can provide a simple, highly visible display method. For example, if the user is relaxed, the feedback unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the feedback unit can provide a display method that focuses on the main points. This makes it possible to adjust the feedback display method based on the user's emotions. 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-mentioned processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the feedback display method.

[0129] When providing feedback, the feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, when the user is in the office, the feedback unit can prioritize providing office-related feedback. For example, when the user is at home, the feedback unit can prioritize providing feedback related to tasks to be performed at home. Furthermore, when the user is out, the feedback unit can also prioritize providing feedback related to tasks to be performed while out. This makes it possible to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI, which can then analyze the data to provide optimal feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, priority setting unit, estimation unit, schedule creation unit, reminder unit, input support unit, learning unit, monitoring unit, readjustment unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and extracts tasks from the user's voice input or photos. The priority setting unit is implemented by the specific processing unit 290 of the data processing device 12 and determines priorities based on the importance and deadlines of tasks. The estimation unit is implemented by the specific processing unit 290 of the data processing device 12 and estimates the time required for each task. The schedule creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a schedule based on the user's free time and priorities. The reminder unit is implemented by the control unit 46A of the smart device 14 and sends reminders at appropriate times. The input support unit is implemented by the control unit 46A of the smart device 14 and extracts tasks using voice recognition technology or image recognition technology. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past behavioral patterns. The monitoring unit is realized by the control unit 46A of the smart device 14 and monitors the progress of the task in real time. The readjustment unit is realized by the specific processing unit 290 of the data processing device 12 and readjusts the schedule. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback for completed tasks. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, priority setting unit, estimation unit, schedule creation unit, reminder unit, input support unit, learning unit, monitoring unit, readjustment unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and extracts tasks from the user's voice input or photos. The priority setting unit is realized by the specific processing unit 290 of the data processing device 12 and determines priorities taking into account the importance and deadlines of tasks. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the time required for each task. The schedule creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a schedule taking into account the user's free time and priorities. The reminder unit is realized by the control unit 46A of the smart glasses 214 and sends reminders at appropriate times. The input support unit is realized by the control unit 46A of the smart glasses 214 and extracts tasks using voice recognition technology or image recognition technology. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past behavioral patterns. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and monitors the progress of the task in real time. The readjustment unit is realized by the specific processing unit 290 of the data processing device 12 and readjusts the schedule. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback for completed tasks. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, priority setting unit, estimation unit, schedule creation unit, reminder unit, input support unit, learning unit, monitoring unit, readjustment unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and extracts tasks from the user's voice input or photos. The priority setting unit is realized by the specific processing unit 290 of the data processing device 12 and determines priorities taking into account the importance and deadlines of tasks. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the time required for each task. The schedule creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a schedule taking into account the user's free time and priorities. The reminder unit is realized by the control unit 46A of the headset-type terminal 314 and sends reminders at appropriate times. The input support unit is realized by the control unit 46A of the headset-type terminal 314 and extracts tasks using voice recognition technology or image recognition technology. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past behavioral patterns. The monitoring unit is realized by the control unit 46A of the headset terminal 314 and monitors the progress of the task in real time. The readjustment unit is realized by the specific processing unit 290 of the data processing device 12 and readjusts the schedule. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback for completed tasks. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, priority setting unit, estimation unit, schedule creation unit, reminder unit, input support unit, learning unit, monitoring unit, readjustment unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and extracts tasks from the user's voice input or photos. The priority setting unit is implemented by the specific processing unit 290 of the data processing device 12 and determines priorities taking into account the importance and deadlines of tasks. The estimation unit is implemented by the specific processing unit 290 of the data processing device 12 and estimates the time required for each task. The schedule creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a schedule taking into account the user's free time and priorities. The reminder unit is implemented by the control unit 46A of the robot 414 and sends reminders at appropriate times. The input support unit is implemented by the control unit 46A of the robot 414 and extracts tasks using voice recognition technology or image recognition technology. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past behavioral patterns. The monitoring unit is realized by the control unit 46A of the robot 414 and monitors the progress of the task in real time. The readjustment unit is realized by the specific processing unit 290 of the data processing device 12 and readjusts the schedule. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback for completed tasks.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The time management support system can acquire the user's health data and adjust task priorities and schedules. For example, if the user's heart rate and sleep data indicate a high level of fatigue, the system can reduce the task load. Also, if the user's health condition is good, the system can assign more tasks. Furthermore, the system can suggest appropriate rest times based on the health data. This allows for flexible time management according to the user's health condition.

[0132] The time management support system can suggest tasks based on the user's hobbies and interests. For example, if the user likes reading, it will preferentially suggest tasks related to reading. If the user likes sports, it can suggest exercise-related tasks. It can also suggest tasks for refreshing yourself based on the user's hobbies and interests. This allows for effective time management while increasing the user's motivation.

[0133] The time management support system can analyze a user's social network data and suggest collaborative tasks with other users. For example, it can synchronize the tasks of users participating in the same project. It can also suggest collaborative work by referring to tasks being performed by the user's friends and colleagues. Furthermore, it can suggest tasks that are effective for the user to do together with other users based on the social network data. This enables collaborative time management.

[0134] The time management support system can dynamically adjust the estimated time for a task based on the user's past task completion times. For example, if a similar task was completed in a short time in the past, the estimated time can be shortened. Also, for tasks that took a long time in the past, the estimated time can be extended. Furthermore, if the user's work efficiency improves, the estimated time can be adjusted appropriately. This allows for more accurate time estimation.

[0135] The time management support system can suggest the optimal task order based on the user's geographic location information. For example, if the user is out, nearby tasks can be suggested with priority. Also, if the user is at home, tasks to be done at home can be suggested with priority. Furthermore, the system can suggest an efficient task order taking into account the user's travel route. This enables effective time management with minimal travel time.

[0136] The time management support system can estimate the user's emotions and adjust the difficulty of tasks based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize and suggest easy tasks. On the other hand, if the user is relaxed, it can suggest more difficult tasks. Furthermore, it can also adjust the order of tasks according to the user's emotions. This enables flexible time management that takes the user's emotions into consideration.

[0137] The time management support system can estimate the user's emotions and customize the content of reminders based on the estimated emotions. For example, if the user is feeling stressed, a reminder containing an encouraging message can be sent. Alternatively, if the user is feeling relaxed, a reminder containing detailed task information can be sent. Furthermore, the system can adjust the timing of sending reminders according to the user's emotions. This allows the system to provide reminders that take the user's emotions into consideration.

[0138] The time management support system can estimate the user's emotions and adjust the flexibility of the schedule based on the estimated emotions. For example, if the user is feeling stressed, the system can provide some leeway in the schedule. On the other hand, if the user is feeling relaxed, the system can suggest a tighter schedule. Furthermore, the system can also facilitate schedule changes according to the user's emotions. This enables flexible schedule management that takes the user's emotions into consideration.

[0139] The time management support system can estimate the user's emotions and customize task feedback based on the estimated emotions. For example, if the user is feeling stressed, positive feedback can be given preferentially. On the other hand, if the user is relaxed, detailed feedback can be given. Furthermore, the frequency of feedback can be adjusted according to the user's emotions. This allows the system to provide feedback that takes the user's emotions into consideration.

[0140] The time management support system can estimate the user's emotions and adjust the way task progress is displayed based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. On the other hand, if the user is relaxed, detailed progress can be displayed. Furthermore, the update frequency of progress can be adjusted according to the user's emotions. This makes it possible to display progress that takes the user's emotions into consideration.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The reception unit is a section where the user inputs tasks. When the user inputs a task, it is possible to extract tasks from voice input or photos, for example. The reception unit can convert the user's voice into text using voice recognition technology and extract tasks from photos using image recognition technology. For example, it can extract tasks from photos taken by the user with a smartphone camera using OCR technology. Step 2: The priority setting unit sets the priority of each task based on the task information received by the receiving unit. The priority setting unit determines the priority taking into consideration the importance and deadline of the task, and the user's past behavioral patterns. For example, it sets priority to tasks with high importance or upcoming deadlines, and sets optimal priorities by learning the user's past behavioral patterns. Step 3: The estimation unit estimates the time required for each task based on the priorities set by the priority setting unit. The estimation unit estimates the time required for each task based on past data and the content of the task. For example, the estimation is based on the time required for similar tasks in the past, and different estimation methods are applied depending on the content and complexity of the task. Step 4: The schedule creation unit creates a schedule based on the time estimated by the estimation unit. The schedule creation unit proposes an optimal schedule taking into account the user's free time and priorities. For example, it references the user's calendar information, assigns tasks to free time, and dynamically changes the schedule taking into account the importance and deadlines of tasks. Step 5: The reminder unit provides reminders based on the schedule created by the schedule creation unit. The reminder unit sends reminders at appropriate times to ensure the user does not forget to complete tasks. For example, the reminder unit can send notifications when a task deadline is approaching and can estimate the user's emotions and adjust the timing of reminder sending based on the emotions.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Explanation of symbols]

[0215] 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 reception unit that receives task input; a priority setting unit that sets a priority of each task based on the task information received by the receiving unit; an estimation unit that estimates the time required for each task based on the priority set by the priority setting unit; a schedule creation unit that creates a schedule based on the time estimated by the estimation unit; a reminder unit that provides reminders based on the schedule created by the schedule creation unit. A system characterized by:

2. Equipped with an input support unit that extracts tasks from voice input or photos 2. The system of claim 1.

3. Equipped with a learning unit that learns the user's past behavioral patterns 2. The system of claim 1.

4. Equipped with a monitoring unit that monitors the progress of tasks in real time 2. The system of claim 1.

5. Equipped with a readjustment unit that readjusts the schedule 2. The system of claim 1.

6. Equipped with a feedback section that provides feedback on completed tasks 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the task input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past task input history and provides the optimal input interface 2. The system of claim 1.

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