System

Abstract

An object of the system according to the embodiment is to automatically generate a task based on a plan input to a calendar, optimally schedule the task, and track the progress.SOLUTION: A system according to an embodiment includes a task generation unit, a scheduling unit, a task association unit, a progress tracking unit, and a performance analysis unit. The task generation unit generates a task based on a schedule input to a calendar of a user. The scheduling unit optimally schedules the tasks generated by the task generation unit. The task handling unit automatically handles the task scheduled by the scheduling unit. The progress tracking unit tracks the progress of the task associated by the task association unit. The performance analyzer analyzes the progress data tracked by the progress tracker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately automate the generation of tasks based on schedules entered into a calendar, optimally schedule them, and track their progress, leaving room for improvement.

[0005] The system according to the embodiment aims to automatically generate tasks based on schedules entered in a calendar, optimally schedule them, and track their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a task generation unit, a scheduling unit, a task response unit, a progress tracking unit, and a performance analysis unit. The task generation unit generates tasks based on plans entered in a user's calendar. The scheduling unit optimally schedules the tasks generated by the task generation unit. The task response unit automatically responds to the tasks scheduled by the scheduling unit. The progress tracking unit tracks the progress of the tasks responded to by the task response unit. The performance analysis unit analyzes the progress data tracked by the progress tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate tasks based on schedules entered in a calendar, optimally schedule them, and track their progress. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The calendar service according to an embodiment of the present invention is a system that automatically generates necessary tasks based on the schedule entered by the user in the calendar, performs optimal scheduling, and improves productivity through automatic task handling, progress tracking, and performance analysis. As a result, the calendar service can smoothly start tasks for the user and significantly improve productivity.

[0029] A calendar service according to an embodiment includes a task generation unit, a scheduling unit, a task response unit, a progress tracking unit, and a performance analysis unit. The task generation unit generates tasks based on appointments entered in a user's calendar. For example, when a meeting appointment is entered, the task generation unit automatically generates tasks such as preparing for the meeting and preparing materials. The task generation unit can also analyze the user's past behavioral history and predict and automatically generate future tasks. The scheduling unit optimally schedules the tasks generated by the task generation unit. For example, optimal scheduling is performed based on the user's abilities, lifestyle habits, and personal data. The scheduling unit can also monitor the user's physiological data in real time to optimally schedule tasks. The task response unit automatically handles tasks scheduled by the scheduling unit. For example, it automatically handles tasks such as collecting, investigating, and analyzing required data. The task response unit can also link with an external API to automatically acquire and integrate required data. The progress tracking unit tracks the progress of tasks handled by the task response unit. For example, it records the progress status each time the user completes a task. The progress tracking unit can also track a user's productivity data over the long term and analyze performance trends. The performance analysis unit analyzes the progress data tracked by the progress tracking unit. For example, the generation AI analyzes performance based on the data and provides feedback to the user. This allows the calendar service according to the embodiment to facilitate the user's task initiation and significantly improve productivity. For example, tasks such as meeting preparation, document creation, and research are automatically generated and handled, allowing the user to focus on important work. Furthermore, efficient task management is possible by understanding progress in real time and analyzing performance.

[0030] The task generation unit can analyze the user's past behavioral history and predict and automatically generate future tasks. For example, the task generation unit uses a generation AI to analyze the user's past calendar entries and task completion history to find patterns. For example, if there is a meeting every Monday, it will automatically generate preparation tasks for that meeting. The task generation unit also uses an algorithm where the generation AI predicts future tasks based on the user's past behavioral history. For example, it predicts and generates preparation tasks for the next meeting based on past data. This enables efficient task management by analyzing the user's past behavioral history and predicting and automatically generating future tasks.

[0031] The scheduling unit can monitor the user's physiological data in real time and perform optimal task scheduling. For example, the generation AI in the scheduling unit monitors the user's heart rate data in real time and schedules important tasks for times when stress is low. For example, it schedules meetings in the morning when heart rates are stable. The scheduling unit also monitors the user's sleep patterns and performs optimal task scheduling. For example, it schedules important tasks after the user has had enough sleep. The scheduling unit also performs optimal task scheduling based on the user's activity data. For example, it schedules tasks that allow the user to relax after exercising. In this way, by monitoring the user's physiological data in real time and performing optimal task scheduling, the user's productivity is improved.

[0032] The task response unit can analyze the user's past task completion data and automatically select the optimal task response method. In the task response unit, for example, the generation AI analyzes the user's past task completion data and automatically selects the most efficient task response method. For example, the task is handled based on a method that has been successful in the past. In addition, the task response unit uses an algorithm that optimizes the task response method based on the user's past task completion data. For example, the optimal task response method is selected based on past data. In addition, the task response unit automatically adjusts the task response method based on the user's past task completion data. For example, the task priority is adjusted based on past data. In this way, efficient task response is possible by analyzing the user's past task completion data and automatically selecting the optimal task response method.

[0033] The task response unit can work with external APIs to automatically acquire and integrate required data. For example, the generation AI in the task response unit works with external APIs to automatically acquire required data and integrate it into tasks. For example, a weather information API is used to adjust an event schedule. The generation AI in the task response unit also works with external APIs to automatically acquire required data and reflect it in tasks. For example, a traffic information API is used to create a schedule that takes travel time into consideration. The generation AI in the task response unit also works with external APIs to automatically acquire required data and support the progress of tasks. For example, a news API is used to collect the latest information and reflect it in research tasks. This enables efficient task response by working with external APIs and automatically acquiring and integrating required data.

[0034] The task response unit can automatically analyze the user's emails and messages, and extract and respond to necessary tasks. For example, the generation AI in the task response unit analyzes the user's emails and automatically extracts and responds to important tasks. For example, it analyzes meeting invitation emails and adds them to the calendar. The generation AI in the task response unit also analyzes the user's messages and automatically extracts and responds to necessary tasks. For example, it analyzes messages checking the progress of a project and generates tasks. The generation AI in the task response unit also analyzes the user's emails and messages and automatically adjusts task priorities. For example, it prioritizes important emails. This enables efficient task management by automatically analyzing the user's emails and messages and extracting and responding to necessary tasks.

[0035] The task response unit can automatically send reminders and notifications based on the user's schedule. In the task response unit, for example, the generation AI analyzes the user's schedule and automatically sends reminders for important tasks. For example, sending a reminder the day before a meeting. In addition, the task response unit automatically sends notifications based on the user's schedule. For example, sending a notification before a task deadline. In addition, the task response unit adjusts the timing of reminders and notifications based on the user's schedule. For example, sending notifications to avoid times when the user is busy. This enables efficient task management by automatically sending reminders and notifications based on the user's schedule.

[0036] The progress tracking unit can analyze the user's task completion time and make an optimal task completion prediction. In the progress tracking unit, for example, the generation AI analyzes the user's past task completion times and makes an optimal task completion prediction. For example, the task completion time is predicted based on past data. The progress tracking unit also adjusts task priorities based on the user's task completion times. For example, the task order is changed based on the predicted completion times. The progress tracking unit also tracks the task progress in real time based on the user's task completion times. For example, the generation AI manages progress by comparing the predicted completion time with the actual completion time. This enables efficient task management by analyzing the user's task completion time and making an optimal task completion prediction.

[0037] The progress tracking unit can track a user's productivity data over the long term and analyze performance trends. In the progress tracking unit, for example, the generation AI tracks a user's productivity data over the long term and analyzes performance trends. For example, it analyzes the number of tasks completed per month and the time taken to complete them, and identifies improvements or declines in performance. In addition, the generation AI predicts performance trends based on the user's productivity data. For example, it predicts future fluctuations in performance based on past data. In addition, the progress tracking unit identifies areas for improvement in performance based on the user's productivity data. For example, it analyzes the data and suggests efficient task management methods. In this way, efficient task management is possible by tracking a user's productivity data over the long term and analyzing performance trends.

[0038] The progress tracking unit can analyze the relationship between physical health status and productivity in conjunction with the user's fitness data. In the progress tracking unit, for example, the generation AI analyzes the user's fitness data and analyzes the relationship between physical health status and productivity. For example, it analyzes the correlation between the amount of exercise and the number of tasks completed. In addition, the generation AI predicts the relationship between health status and productivity based on the user's fitness data. For example, it identifies a trend where productivity improves as the amount of exercise increases based on past data. In addition, the progress tracking unit identifies areas for improvement in health status based on the user's fitness data. For example, it analyzes the data and suggests efficient exercise methods. In this way, efficient task management is possible by linking with the user's fitness data and analyzing the relationship between physical health status and productivity.

[0039] The progress tracking unit can analyze a user's social media activity and clarify the correlation between social interaction and productivity. In the progress tracking unit, for example, the generation AI analyzes a user's social media activity and clarifies the correlation between social media interaction and productivity. For example, it analyzes the correlation between social media usage time and the number of tasks completed. In addition, the generation AI predicts the correlation between social media interaction and productivity based on the user's social media activity. For example, it identifies a trend of decreased productivity as social media use increases based on past data. In addition, the progress tracking unit identifies areas for improvement in social interaction based on the user's social media activity. For example, it analyzes data and suggests efficient ways to use social media. In this way, efficient task management is possible by analyzing a user's social media activity and clarifying the correlation between social media interaction and productivity.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The calendar service can also generate refreshment tasks based on the user's hobbies and interests. For example, if the user likes music, time for listening to music can be scheduled. If the user likes reading, time for reading can be added to the schedule. Furthermore, if the user likes outdoor activities, time for walking or hiking can be scheduled. In this way, generating refreshment tasks based on the user's hobbies and interests can reduce the user's stress and improve productivity.

[0042] The task generation unit can also generate tasks based on the user's health data. For example, it can analyze the user's fitness data and generate an exercise task if the user is not getting enough exercise. It can also generate a task for eating a balanced diet based on the user's diet data. It can also generate a task for getting enough sleep based on the user's sleep data. In this way, by generating tasks based on the user's health data, it is possible to maintain the user's health and improve productivity.

[0043] The task response unit can also optimize the task response method based on the user's past task completion data. For example, it can respond to tasks based on methods that have been successful in the past. It can also select the optimal task response method based on past data. It can also adjust task priorities based on past data. This makes it possible to efficiently respond to tasks by optimizing the task response method based on the user's past task completion data.

[0044] The task response section can also automatically analyze users' emails and messages to extract and respond to necessary tasks. For example, it can analyze meeting invitation emails and add them to a calendar. It can also analyze messages checking project progress and generate tasks. It can also prioritize important emails. This allows for efficient task management by automatically analyzing users' emails and messages and extracting and responding to necessary tasks.

[0045] The task response unit can also automatically send reminders and notifications based on the user's schedule. For example, it can send a reminder the day before a meeting. It can also send notifications before a task deadline. It can also send notifications when the user is not busy. This allows for efficient task management by automatically sending reminders and notifications based on the user's schedule.

[0046] The progress tracking unit can also link with the user's fitness data to analyze the relationship between physical health and productivity. For example, it can analyze the correlation between the amount of exercise and the number of tasks completed. It can also predict the relationship between health and productivity. It can also identify areas for improvement in health. This allows for efficient task management by linking with the user's fitness data and analyzing the relationship between physical health and productivity.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The task generation unit generates tasks based on the schedule entered in the user's calendar. For example, if a meeting schedule is entered, the task generation unit automatically generates tasks such as preparing for the meeting and creating materials. The task generation unit can also analyze the user's past behavioral history and predict and automatically generate future tasks. Step 2: The scheduling unit optimally schedules the tasks generated by the task generation unit. For example, optimal scheduling is performed based on the user's abilities, lifestyle habits, and personal data. The scheduling unit can also monitor the user's physiological data in real time to perform optimal task scheduling. Step 3: The task response unit automatically handles tasks scheduled by the scheduling unit. For example, it automatically handles tasks such as collecting, investigating, and analyzing the necessary data. The task response unit can also link with external APIs to automatically obtain and integrate the necessary data. Step 4: The progress tracking unit tracks the progress of the tasks handled by the task handling unit. For example, it records the progress status each time the user completes a task. The progress tracking unit can also track user productivity data over time and analyze performance trends. Step 5: The performance analysis unit analyzes the progress data tracked by the progress tracking unit. For example, the generation AI analyzes performance based on the data and provides feedback to the user. This allows the calendar service according to the embodiment to smoothly start tasks and significantly improve productivity.

[0049] (Example 2) The calendar service according to an embodiment of the present invention is a system that automatically generates necessary tasks based on the schedule entered by the user in the calendar, performs optimal scheduling, and improves productivity through automatic task handling, progress tracking, and performance analysis. As a result, the calendar service can smoothly start tasks for the user and significantly improve productivity.

[0050] A calendar service according to an embodiment includes a task generation unit, a scheduling unit, a task response unit, a progress tracking unit, and a performance analysis unit. The task generation unit generates tasks based on appointments entered in a user's calendar. For example, when a meeting appointment is entered, the task generation unit automatically generates tasks such as preparing for the meeting and preparing materials. The task generation unit can also analyze the user's past behavioral history and predict and automatically generate future tasks. The scheduling unit optimally schedules the tasks generated by the task generation unit. For example, optimal scheduling is performed based on the user's abilities, lifestyle habits, and personal data. The scheduling unit can also monitor the user's physiological data in real time to optimally schedule tasks. The task response unit automatically handles tasks scheduled by the scheduling unit. For example, it automatically handles tasks such as collecting, investigating, and analyzing required data. The task response unit can also link with an external API to automatically acquire and integrate required data. The progress tracking unit tracks the progress of tasks handled by the task response unit. For example, it records the progress status each time the user completes a task. The progress tracking unit can also track a user's productivity data over the long term and analyze performance trends. The performance analysis unit analyzes the progress data tracked by the progress tracking unit. For example, the generation AI analyzes performance based on the data and provides feedback to the user. This allows the calendar service according to the embodiment to facilitate the user's task initiation and significantly improve productivity. For example, tasks such as meeting preparation, document creation, and research are automatically generated and handled, allowing the user to focus on important work. Furthermore, efficient task management is possible by understanding progress in real time and analyzing performance.

[0051] The task generation unit can analyze the user's past behavioral history and predict and automatically generate future tasks. For example, the task generation unit uses a generation AI to analyze the user's past calendar entries and task completion history to find patterns. For example, if there is a meeting every Monday, it will automatically generate preparation tasks for that meeting. The task generation unit also uses an algorithm where the generation AI predicts future tasks based on the user's past behavioral history. For example, it predicts and generates preparation tasks for the next meeting based on past data. This enables efficient task management by analyzing the user's past behavioral history and predicting and automatically generating future tasks.

[0052] The scheduling unit can monitor the user's physiological data in real time and perform optimal task scheduling. For example, the generation AI in the scheduling unit monitors the user's heart rate data in real time and schedules important tasks for times when stress is low. For example, it schedules meetings in the morning when heart rates are stable. The scheduling unit also monitors the user's sleep patterns and performs optimal task scheduling. For example, it schedules important tasks after the user has had enough sleep. The scheduling unit also performs optimal task scheduling based on the user's activity data. For example, it schedules tasks that allow the user to relax after exercising. In this way, by monitoring the user's physiological data in real time and performing optimal task scheduling, the user's productivity is improved.

[0053] The scheduling unit uses the emotion estimation function to generate tasks according to the user's emotional state and perform scheduling that reduces stress. For example, the scheduling unit uses the emotion estimation function to schedule tasks that will help the user relax when the user is feeling stressed. For example, it sets a time for meditation or light exercise. The scheduling unit also uses the emotion estimation function to schedule important tasks when the user is feeling positive. For example, it schedules project progress when the user is motivated. The scheduling unit also uses the emotion estimation function to adjust task priorities based on the user's emotional state. For example, if the user is tired, it schedules easier tasks first. In this way, the scheduling unit generates tasks according to the user's emotional state and performs scheduling that reduces stress, thereby improving user productivity.

[0054] The task response unit can analyze the user's past task completion data and automatically select the optimal task response method. In the task response unit, for example, the generation AI analyzes the user's past task completion data and automatically selects the most efficient task response method. For example, the task is handled based on a method that has been successful in the past. In addition, the task response unit uses an algorithm that optimizes the task response method based on the user's past task completion data. For example, the optimal task response method is selected based on past data. In addition, the task response unit automatically adjusts the task response method based on the user's past task completion data. For example, the task priority is adjusted based on past data. In this way, efficient task response is possible by analyzing the user's past task completion data and automatically selecting the optimal task response method.

[0055] The task response unit can work with external APIs to automatically acquire and integrate required data. For example, the generation AI in the task response unit works with external APIs to automatically acquire required data and integrate it into tasks. For example, a weather information API is used to adjust an event schedule. The generation AI in the task response unit also works with external APIs to automatically acquire required data and reflect it in tasks. For example, a traffic information API is used to create a schedule that takes travel time into consideration. The generation AI in the task response unit also works with external APIs to automatically acquire required data and support the progress of tasks. For example, a news API is used to collect the latest information and reflect it in research tasks. This enables efficient task response by working with external APIs and automatically acquiring and integrating required data.

[0056] The task response unit uses the emotion estimation function to select a task response method according to the user's emotional state, thereby reducing stress. For example, the task response unit uses the emotion estimation function to select a task response method that will help the user relax when they are feeling stressed. For example, it may suggest meditation or light exercise. The task response unit also uses the emotion estimation function to select an important task response method when the user is feeling positive. For example, it may suggest progressing with a project when the user is motivated. The task response unit also uses the emotion estimation function to adjust the task response method based on the user's emotional state. For example, if the user is tired, it may select an easy task response method. This allows for efficient task response by selecting a task response method according to the user's emotional state and reducing stress.

[0057] The task response unit can automatically analyze the user's emails and messages, and extract and respond to necessary tasks. For example, the generation AI in the task response unit analyzes the user's emails and automatically extracts and responds to important tasks. For example, it analyzes meeting invitation emails and adds them to the calendar. The generation AI in the task response unit also analyzes the user's messages and automatically extracts and responds to necessary tasks. For example, it analyzes messages checking the progress of a project and generates tasks. The generation AI in the task response unit also analyzes the user's emails and messages and automatically adjusts task priorities. For example, it prioritizes important emails. This enables efficient task management by automatically analyzing the user's emails and messages and extracting and responding to necessary tasks.

[0058] The task response unit can automatically send reminders and notifications based on the user's schedule. In the task response unit, for example, the generation AI analyzes the user's schedule and automatically sends reminders for important tasks. For example, sending a reminder the day before a meeting. In addition, the task response unit automatically sends notifications based on the user's schedule. For example, sending a notification before a task deadline. In addition, the task response unit adjusts the timing of reminders and notifications based on the user's schedule. For example, sending notifications to avoid times when the user is busy. This enables efficient task management by automatically sending reminders and notifications based on the user's schedule.

[0059] The progress tracking unit can analyze the user's task completion time and make an optimal task completion prediction. In the progress tracking unit, for example, the generation AI analyzes the user's past task completion times and makes an optimal task completion prediction. For example, the task completion time is predicted based on past data. The progress tracking unit also adjusts task priorities based on the user's task completion times. For example, the task order is changed based on the predicted completion times. The progress tracking unit also tracks the task progress in real time based on the user's task completion times. For example, the generation AI manages progress by comparing the predicted completion time with the actual completion time. This enables efficient task management by analyzing the user's task completion time and making an optimal task completion prediction.

[0060] The progress tracking unit can track a user's productivity data over the long term and analyze performance trends. In the progress tracking unit, for example, the generation AI tracks a user's productivity data over the long term and analyzes performance trends. For example, it analyzes the number of tasks completed per month and the time taken to complete them, and identifies improvements or declines in performance. In addition, the generation AI predicts performance trends based on the user's productivity data. For example, it predicts future fluctuations in performance based on past data. In addition, the progress tracking unit identifies areas for improvement in performance based on the user's productivity data. For example, it analyzes the data and suggests efficient task management methods. In this way, efficient task management is possible by tracking a user's productivity data over the long term and analyzing performance trends.

[0061] The progress tracking unit can use the emotion estimation function to analyze performance based on the user's emotional state and provide feedback that takes emotional factors into consideration. The progress tracking unit, for example, uses the emotion estimation function to analyze performance based on the user's emotional state. For example, it can identify declines in productivity during periods of high stress and suggest relaxation methods. The progress tracking unit also uses the emotion estimation function to provide feedback based on the user's emotional state. For example, it can provide praising feedback during periods of strong positive emotions. The progress tracking unit also uses the emotion estimation function to adjust task priorities based on the user's emotional state. For example, it can schedule easier tasks preferentially during periods of high stress. This enables efficient task management by analyzing performance based on the user's emotional state and providing feedback that takes emotional factors into consideration.

[0062] The progress tracking unit can analyze the relationship between physical health status and productivity in conjunction with the user's fitness data. In the progress tracking unit, for example, the generation AI analyzes the user's fitness data and analyzes the relationship between physical health status and productivity. For example, it analyzes the correlation between the amount of exercise and the number of tasks completed. In addition, the generation AI predicts the relationship between health status and productivity based on the user's fitness data. For example, it identifies a trend where productivity improves as the amount of exercise increases based on past data. In addition, the progress tracking unit identifies areas for improvement in health status based on the user's fitness data. For example, it analyzes the data and suggests efficient exercise methods. In this way, efficient task management is possible by linking with the user's fitness data and analyzing the relationship between physical health status and productivity.

[0063] The progress tracking unit can analyze a user's social media activity and clarify the correlation between social interaction and productivity. In the progress tracking unit, for example, the generation AI analyzes a user's social media activity and clarifies the correlation between social media interaction and productivity. For example, it analyzes the correlation between social media usage time and the number of tasks completed. In addition, the generation AI predicts the correlation between social media interaction and productivity based on the user's social media activity. For example, it identifies a trend of decreased productivity as social media use increases based on past data. In addition, the progress tracking unit identifies areas for improvement in social interaction based on the user's social media activity. For example, it analyzes data and suggests efficient ways to use social media. In this way, efficient task management is possible by analyzing a user's social media activity and clarifying the correlation between social media interaction and productivity.

[0064] The progress tracking unit can use the emotion estimation function to identify tasks that the user finds most motivating and prioritize progress tracking of those tasks. For example, the progress tracking unit uses the emotion estimation function to identify tasks that the user finds most motivating and prioritize progress tracking of those tasks. For example, it prioritizes tracking of tasks that evoke strong positive emotions. The progress tracking unit also uses the emotion estimation function to identify tasks that highly motivate the user and prioritize scheduling those tasks. For example, it schedules important tasks when the user is highly motivated. The progress tracking unit also uses the emotion estimation function to identify tasks that low motivate the user and postpones those tasks. For example, it schedules easy tasks when the user is tired. In this way, efficient task management is possible by identifying tasks that motivate the user most and prioritize tracking progress of those tasks.

[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 calendar service can also generate refreshment tasks based on the user's hobbies and interests. For example, if the user likes music, time for listening to music can be scheduled. If the user likes reading, time for reading can be added to the schedule. Furthermore, if the user likes outdoor activities, time for walking or hiking can be scheduled. In this way, generating refreshment tasks based on the user's hobbies and interests can reduce the user's stress and improve productivity.

[0067] The task generation unit can also generate tasks based on the user's health data. For example, it can analyze the user's fitness data and generate an exercise task if the user is not getting enough exercise. It can also generate a task for eating a balanced diet based on the user's diet data. It can also generate a task for getting enough sleep based on the user's sleep data. In this way, by generating tasks based on the user's health data, it is possible to maintain the user's health and improve productivity.

[0068] The scheduling unit can also adjust task priorities based on the user's emotional state. For example, if the user is feeling stressed, it can prioritize relaxing tasks in scheduling. Also, if the user is feeling positive, it can prioritize important tasks in scheduling. Furthermore, if the user is tired, it can prioritize easy tasks in scheduling. In this way, adjusting task priorities based on the user's emotional state can reduce the user's stress and improve productivity.

[0069] The scheduling unit can also adjust the task schedule based on the user's emotional state. For example, if the user is feeling stressed, it can schedule relaxing tasks. If the user is feeling positive, it can schedule important tasks. Furthermore, if the user is tired, it can schedule easy tasks. In this way, adjusting the task schedule based on the user's emotional state can reduce the user's stress and improve productivity.

[0070] The task response unit can also select a task response method based on the user's emotional state. For example, if the user is feeling stressed, a task response method that will help them relax can be selected. Also, if the user is feeling positive, an important task response method can be selected. Furthermore, if the user is tired, an easy task response method can be selected. In this way, by selecting a task response method based on the user's emotional state, it is possible to reduce the user's stress and improve productivity.

[0071] The task response unit can also optimize the task response method based on the user's past task completion data. For example, it can respond to tasks based on methods that have been successful in the past. It can also select the optimal task response method based on past data. It can also adjust task priorities based on past data. This makes it possible to efficiently respond to tasks by optimizing the task response method based on the user's past task completion data.

[0072] The task response section can also automatically analyze users' emails and messages to extract and respond to necessary tasks. For example, it can analyze meeting invitation emails and add them to a calendar. It can also analyze messages checking project progress and generate tasks. It can also prioritize important emails. This allows for efficient task management by automatically analyzing users' emails and messages and extracting and responding to necessary tasks.

[0073] The task response unit can also automatically send reminders and notifications based on the user's schedule. For example, it can send a reminder the day before a meeting. It can also send notifications before a task deadline. It can also send notifications when the user is not busy. This allows for efficient task management by automatically sending reminders and notifications based on the user's schedule.

[0074] The progress tracking unit can also link with the user's fitness data to analyze the relationship between physical health and productivity. For example, it can analyze the correlation between the amount of exercise and the number of tasks completed. It can also predict the relationship between health and productivity. It can also identify areas for improvement in health. This allows for efficient task management by linking with the user's fitness data and analyzing the relationship between physical health and productivity.

[0075] The progress tracking unit can further use the emotion estimation function to identify tasks that the user finds most motivating and prioritize tracking the progress of those tasks. For example, tasks that evoke strong positive emotions can be prioritized. The progress tracking unit can also identify tasks that motivate the user highly and schedule those tasks with priority. Furthermore, it can identify tasks that motivate the user less and postpone those tasks. This allows for efficient task management by identifying tasks that motivate the user most and tracking the progress of those tasks with priority.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The task generation unit generates tasks based on the schedule entered in the user's calendar. For example, if a meeting schedule is entered, the task generation unit automatically generates tasks such as preparing for the meeting and creating materials. The task generation unit can also analyze the user's past behavioral history and predict and automatically generate future tasks. Step 2: The scheduling unit optimally schedules the tasks generated by the task generation unit. For example, optimal scheduling is performed based on the user's abilities, lifestyle habits, and personal data. The scheduling unit can also monitor the user's physiological data in real time to perform optimal task scheduling. Step 3: The task response unit automatically handles tasks scheduled by the scheduling unit. For example, it automatically handles tasks such as collecting, investigating, and analyzing the necessary data. The task response unit can also link with external APIs to automatically obtain and integrate the necessary data. Step 4: The progress tracking unit tracks the progress of the tasks handled by the task handling unit. For example, it records the progress status each time the user completes a task. The progress tracking unit can also track user productivity data over time and analyze performance trends. Step 5: The performance analysis unit analyzes the progress data tracked by the progress tracking unit. For example, the generation AI analyzes performance based on the data and provides feedback to the user. This allows the calendar service according to the embodiment to smoothly start tasks and significantly improve productivity.

[0078] 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.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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. [Explanation of symbols]

[0145] 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 task generation unit that generates a task based on a schedule entered in a user's calendar; a scheduling unit that optimally schedules the tasks generated by the task generation unit; a task handling unit that automatically handles the tasks scheduled by the scheduling unit; a progress tracking unit that tracks the progress of the task handled by the task handling unit; a performance analysis unit that analyzes the progress data tracked by the progress tracking unit. A system characterized by:

2. The scheduling unit The physiological data of the user is monitored in real time, and optimal task scheduling is performed.

2. The system of claim 1.

3. The task correspondence unit Analyze the user's past task completion data and automatically select the optimal task response method.

2. The system of claim 1.

4. The progress tracking unit Analyze the task completion time of the user and make an optimal task completion prediction.

2. The system of claim 1.

5. The scheduling unit Using an emotion estimation function, a task is generated according to the user's emotional state, and scheduling is performed to reduce stress.

2. The system of claim 1.

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