system

Abstract

The system according to the embodiment aims to efficiently manage housework and childcare tasks and provide an optimal schedule. [Solution] A system according to an embodiment includes a receiving unit, a setting unit, a prediction unit, and an adjustment unit. The receiving unit receives input of tasks. The setting unit sets priorities based on the tasks received by the receiving unit. The prediction unit predicts tasks based on the priorities set by the setting unit. The adjustment unit adjusts a schedule based on the tasks predicted by the prediction unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult to efficiently manage housework and childcare tasks and create optimal schedules.

[0005] The system according to the embodiment aims to efficiently manage housework and childcare tasks and provide an optimal schedule. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, a setting unit, a prediction unit, and an adjustment unit. The receiving unit receives input of tasks. The setting unit sets priorities based on the tasks received by the receiving unit. The prediction unit predicts tasks based on the priorities set by the setting unit. The adjustment unit adjusts a schedule based on the tasks predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage housework and childcare tasks and provide an optimal schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A task management system according to an embodiment of the present invention is a platform for centrally managing housework and childcare tasks. This task management system allows users to create task lists and set priorities through an app or web app. Furthermore, it utilizes AI to predict tasks, optimize schedules, and propose efficient plans tailored to the user's lifestyle. For example, a user logs in to the app or web app and adds housework and childcare tasks to a list. Then, the user sets priorities for each task. Next, AI analyzes the task list entered by the user and predicts tasks and optimizes schedules. The AI ​​optimizes the user's schedule and proposes efficient plans. This platform allows users to efficiently manage housework and childcare tasks. This allows the task management system to provide efficient task management tailored to the user's lifestyle.

[0029] A task management system according to an embodiment includes a reception unit, a setting unit, a prediction unit, and an adjustment unit. The reception unit provides an interface for a user to input tasks. For example, the reception unit accepts tasks input by the user using text input or voice input. Text input is performed using a keyboard or a touchscreen. Voice input is performed using a microphone and converted into text using voice recognition technology. The setting unit sets priorities based on the tasks accepted by the reception unit. For example, the setting unit accepts task priorities set by the user using a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touchscreen. The prediction unit predicts tasks based on the priorities set by the setting unit. For example, the prediction unit analyzes past task history and predicts future tasks. The prediction unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. The adjustment unit adjusts a schedule based on the tasks predicted by the prediction unit. For example, the adjustment unit collects lifestyle data about the user and proposes an optimal schedule. The adjustment unit collects lifestyle data about the user, such as eating habits, exercise frequency, and sleep patterns, and optimizes the schedule. As a result, the task management system according to the embodiment can provide efficient task management that matches the user's lifestyle.

[0030] The task management system includes a collection unit that collects lifestyle data of a user. The collection unit provides an interface for collecting the lifestyle data of the user. For example, the collection unit collects data such as the user's eating habits, exercise frequency, and sleep patterns. The collection unit accepts data manually entered by the user. The collection unit can also automatically collect data using sensors in a wearable device or smartphone. For example, the collection unit collects heart rate and step count data from a smartwatch to determine the frequency of the user's exercise. This enables data collection based on the user's lifestyle.

[0031] The task management system includes an analysis unit that analyzes past task history. The analysis unit provides an interface for analyzing the past task history. For example, the analysis unit collects and analyzes data such as the completion status, required time, and frequency of a user's past tasks. The analysis unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. For example, the analysis unit analyzes what tasks a user has completed in the past and how long it took them to complete them, and predicts the required time for future tasks. This enables more accurate predictions by analyzing past task history.

[0032] The task management system includes a providing unit that provides an optimized schedule to the user. The providing unit provides an interface for providing the optimized schedule to the user. For example, the providing unit suggests an optimal schedule based on the user's lifestyle data and past task history. The providing unit accepts manual schedule adjustments by the user. The providing unit can also automatically optimize the schedule using AI. For example, the providing unit suggests an optimal schedule based on data such as the user's eating habits, exercise frequency, and sleep patterns. This enables efficient task management by providing the user with an optimized schedule.

[0033] The reception unit can accept that the user inputs a task using text input or voice input. The reception unit provides an interface for the user to input a task. For example, the reception unit accepts that the user inputs a task using text input or voice input. Text input is performed using a keyboard or a touch screen. Voice input is performed using a microphone and is converted into text using voice recognition technology. This allows the user to input a task using text input or voice input.

[0034] The setting unit can accept that the user sets the priority of the tasks by a drag-and-drop operation. The setting unit provides an interface for the user to set the priority of the tasks. For example, the setting unit accepts that the user sets the priority of the tasks by a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touch screen. This allows the user to set the priority of the tasks by drag-and-drop.

[0035] The reception unit can analyze the user's past task input history and suggest the optimal input method. For example, the reception unit can automatically display tasks that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past task input history.

[0036] The reception unit can customize the input method based on the user's current situation and environment when inputting a task. For example, when the user is out, the reception unit prioritizes voice input to allow the user to easily input a task. In addition, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is busy, the reception unit can provide a simple template to allow the user to quickly input a task. This allows more appropriate input by customizing the input method based on the user's current situation and environment.

[0037] When inputting tasks, the reception unit can prioritize inputting highly relevant tasks in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize inputting tasks related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize inputting tasks related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting tasks to be performed at home. This enables efficient task management by prioritized input of highly relevant tasks in consideration of the user's geographical location information.

[0038] When a task is input, the reception unit can analyze the user's social media activity and suggest related tasks. For example, the reception unit can suggest tasks related to events shared by the user on social media. The reception unit can also suggest tasks related to places mentioned by the user on social media. Furthermore, the reception unit can also suggest tasks related to accounts the user follows on social media. In this way, related tasks can be suggested by analyzing the user's social media activity.

[0039] The setting unit can adjust the level of detail of the settings based on the importance of the tasks when setting priorities. For example, the setting unit can provide detailed setting options for tasks with high importance. The setting unit can also provide simple setting options for tasks with low importance. Furthermore, the setting unit can automatically adjust the level of detail of the settings according to the importance of the tasks. This allows for efficient task management by adjusting the level of detail of the settings based on the importance of the tasks.

[0040] When setting priorities, the setting unit can apply different setting algorithms depending on the task category. For example, the setting unit applies a dedicated setting algorithm to housework tasks. The setting unit can also apply a dedicated setting algorithm to childcare tasks. Furthermore, the setting unit can also apply a general-purpose setting algorithm to other tasks. This allows for efficient task management by applying different setting algorithms depending on the task category.

[0041] When setting priorities, the setting unit can determine the priorities based on the submission dates of the tasks. For example, the setting unit can prioritize tasks with upcoming deadlines. The setting unit can also postpone tasks with more distant submission dates. Furthermore, the setting unit can automatically adjust the priorities based on the submission dates. This enables efficient task management by determining priorities based on the submission dates of the tasks.

[0042] The setting unit can adjust the order of setting based on the relevance of tasks when setting priorities. For example, the setting unit prioritizes highly relevant tasks. The setting unit can also postpone less relevant tasks. Furthermore, the setting unit can automatically adjust the priorities based on the relevance of tasks. This allows for efficient task management by adjusting the order of setting based on the relevance of tasks.

[0043] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships between tasks during prediction. For example, the prediction unit can group related tasks to improve the accuracy of the prediction. The prediction unit can also provide prediction results by taking into account the dependency relationships between tasks. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the interrelationships between tasks. In this way, the accuracy of the prediction is improved by taking into account the interrelationships between tasks.

[0044] The prediction unit can make predictions taking into account attribute information of the task submitter. The prediction unit provides prediction results based on, for example, the submitter's past task history. The prediction unit can also provide prediction results taking into account attribute information (age, gender, etc.) of the submitter. Furthermore, the prediction unit can automatically adjust the accuracy of predictions based on the attribute information of the submitter. In this way, taking into account the attribute information of the task submitter improves the accuracy of predictions.

[0045] The prediction unit can make predictions taking into account the geographic distribution of tasks. For example, the prediction unit predicts the optimal task based on the user's current location. The prediction unit can also provide prediction results based on the user's destination. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the geographic distribution of tasks. This improves the accuracy of the prediction by taking the geographic distribution of tasks into account.

[0046] The prediction unit can improve the accuracy of the prediction by referring to the related literature of the task during prediction. The prediction unit provides a prediction result based on, for example, the related literature. The prediction unit can also improve the accuracy of the prediction by taking into account information from the related literature. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the related literature. As a result, the accuracy of the prediction is improved by referring to the related literature of the task.

[0047] During optimization, the optimization unit can optimize the current schedule by referring to past optimization data. The optimization unit optimizes the current schedule based on, for example, past optimization data. The optimization unit can also analyze past optimization data and provide an optimal schedule. Furthermore, the optimization unit can automatically adjust the accuracy of optimization based on past optimization data. This makes it possible to optimize the current schedule by referring to past optimization data.

[0048] During optimization, the optimization unit can apply different optimization methods to each task category. For example, the optimization unit applies a dedicated optimization method to housework tasks. The optimization unit can also apply a dedicated optimization method to childcare tasks. Furthermore, the optimization unit can also apply a general-purpose optimization method to other tasks. This allows for efficient task management by applying different optimization methods to each task category.

[0049] During optimization, the optimization unit can determine the priority of optimization based on the submission time of the task. For example, the optimization unit prioritizes optimization of tasks with upcoming deadlines. The optimization unit can also postpone tasks with distant submission times. Furthermore, the optimization unit can automatically adjust the priority of optimization based on the submission time. This enables efficient task management by determining the priority of optimization based on the submission time of the task.

[0050] The optimization unit can perform optimization by referring to market data related to the tasks. For example, the optimization unit can propose an optimal task order based on the market data. The optimization unit can also provide optimization results taking into account trends in the market data. Furthermore, the optimization unit can automatically adjust the accuracy of optimization based on the market data. As a result, by referring to market data related to the tasks, the accuracy of optimization is improved.

[0051] The collection unit can select the optimal collection method by referring to the user's past lifestyle data when collecting data. The collection unit can, for example, suggest the optimal collection method based on the user's past data collection history. The collection unit can also analyze the user's past lifestyle data and provide the optimal collection method. Furthermore, the collection unit can automatically adjust the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by referring to the user's past lifestyle data.

[0052] The collection unit can customize the collection method based on the user's current situation and environment at the time of collection. For example, the collection unit can provide a simple collection method when the user is out. The collection unit can also provide detailed collection options when the user is at home. Furthermore, the collection unit can also provide a method for quickly collecting data when the user is busy. This allows for efficient data collection by customizing the collection method based on the user's current situation and environment.

[0053] During collection, the collection unit can prioritize collecting highly relevant data by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. This enables efficient data collection by prioritized collection of highly relevant data by taking into consideration the user's geographical location information.

[0054] During collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to information shared by the user on social media. The collection unit can also collect data related to topics mentioned by the user on social media. Furthermore, the collection unit can collect data related to accounts the user follows on social media. This allows related data to be collected by analyzing the user's social media activities.

[0055] During analysis, the analysis unit can optimize the analysis algorithm by referring to past task history data. The analysis unit, for example, optimizes the analysis algorithm based on past task history data. The analysis unit can also analyze past task history data and provide optimal analysis results. Furthermore, the analysis unit can automatically adjust the analysis algorithm based on past task history data. This allows the analysis algorithm to be optimized by referring to past task history data.

[0056] The analysis unit can apply different analysis methods to each task category during analysis. For example, the analysis unit applies a dedicated analysis method to housework tasks. The analysis unit can also apply a dedicated analysis method to childcare tasks. Furthermore, the analysis unit can apply a general-purpose analysis method to other tasks. This allows for efficient analysis by applying different analysis methods to each task category.

[0057] During analysis, the analysis unit can weight the analysis data based on the submission time of the task. For example, the analysis unit may prioritize the data of tasks with upcoming deadlines. The analysis unit can also analyze the data of tasks with more distant submission times. Furthermore, the analysis unit can automatically adjust the weighting of the analysis data based on the submission time. This allows for efficient analysis by weighting the analysis data based on the submission time of the task.

[0058] The analysis unit can improve the accuracy of the analysis by referring to related literature of the task during the analysis. The analysis unit provides an analysis result based on, for example, related literature. The analysis unit can also improve the accuracy of the analysis by taking into account information in the related literature. Furthermore, the analysis unit can automatically adjust the accuracy of the analysis based on the related literature. In this way, the accuracy of the analysis is improved by referring to related literature of the task.

[0059] When providing the information, the providing unit can select the optimal providing method by referring to the user's past schedule history. The providing unit, for example, proposes the optimal providing method based on the user's past schedule history. The providing unit can also analyze the user's past schedule history and provide the optimal providing method. Furthermore, the providing unit can automatically adjust the providing method based on the user's past schedule history. In this way, the optimal providing method can be selected by referring to the user's past schedule history.

[0060] The providing unit can customize the delivery method based on the user's current situation and environment at the time of delivery. For example, when the user is out, the providing unit provides a simple delivery method. When the user is at home, the providing unit can also provide detailed delivery options. Furthermore, when the user is busy, the providing unit can also provide a method that allows for quick delivery. This enables efficient task management by customizing the delivery method based on the user's current situation and environment.

[0061] The providing unit can select the optimal delivery method by taking into consideration the user's device information when providing the task. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is concise and highly visible. This enables efficient task management by selecting the optimal delivery method by taking into consideration the user's device information.

[0062] When providing the schedule, the providing unit can make suggestions based on the schedule by referring to the user's calendar information. The providing unit, for example, references the schedules registered in the user's calendar and automatically sets the schedule. The providing unit can also propose a schedule related to a specific event from the user's calendar information. Furthermore, the providing unit can also propose an optimal schedule that matches the schedule based on the user's calendar information. This makes it possible to make suggestions based on the schedule by referring to the user's calendar information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The task management system may further include a notification unit. The notification unit can send a reminder when the deadline for a task set by the user approaches. For example, the notification unit can send a push notification to the user's smartphone to notify the user of the task deadline. The notification unit can also link with the user's calendar app to automatically add the task deadline to the calendar. The notification unit can also send a reminder email to the user's email address. This allows the user to efficiently manage tasks without forgetting the task deadlines.

[0065] The task management system may further include a sharing unit. The sharing unit provides an interface for users to share tasks with other users. For example, the sharing unit allows users to share tasks with family members or team members and manage the tasks collaboratively. The sharing unit may also update the progress of the task in real time and notify the shared users. Furthermore, the sharing unit may provide a comment function for the task, allowing users to communicate with each other. This allows multiple users to cooperate and efficiently manage tasks.

[0066] The task management system may further include a reporting unit. The reporting unit provides an interface for users to report task progress. For example, the reporting unit allows users to input task completion status and generate progress reports. The reporting unit may also visually display task progress using graphs or charts. Furthermore, the reporting unit may share task progress with other users and receive feedback. This allows users to grasp task progress and efficiently manage tasks.

[0067] The task management system may further include an analysis unit. The analysis unit provides an interface for analyzing a user's task management performance. For example, the analysis unit may measure a user's task completion rate and average time required and generate a performance report. The analysis unit may also analyze a user's task management trends and suggest areas for improvement. Furthermore, the analysis unit may predict future task management based on the user's task management history. This allows a user to understand their own task management performance and manage tasks efficiently.

[0068] The task management system may further include a customization unit. The customization unit provides a function for the user to customize the interface of the task management system. For example, the customization unit allows the user to change the way tasks are displayed and the layout. The customization unit also allows the user to add task categories and tags to organize tasks. The customization unit also allows the user to change notification settings and adjust the frequency and method of reminders. This allows the user to customize the task management system to suit their needs and efficiently manage tasks.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit provides an interface for the user to input a task. For example, the reception unit accepts the user's task input using text input or voice input. Text input is performed using a keyboard or touch screen. Voice input is performed using a microphone and is converted into text using voice recognition technology. Step 2: The setting unit sets the priority based on the tasks accepted by the accepting unit. For example, the setting unit accepts that the user sets the priority of the tasks by a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touch screen. Step 3: The prediction unit predicts tasks based on the priorities set by the setting unit. For example, the prediction unit analyzes past task history and predicts future tasks. The prediction unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. Step 4: The adjustment unit adjusts the schedule based on the tasks predicted by the prediction unit. For example, the adjustment unit collects lifestyle data about the user and proposes an optimal schedule. The adjustment unit collects lifestyle data such as the user's eating habits, exercise frequency, and sleep patterns, and optimizes the schedule.

[0071] (Example 2) A task management system according to an embodiment of the present invention is a platform for centrally managing housework and childcare tasks. This task management system allows users to create task lists and set priorities through an app or web app. Furthermore, it utilizes AI to predict tasks, optimize schedules, and propose efficient plans tailored to the user's lifestyle. For example, a user logs in to the app or web app and adds housework and childcare tasks to a list. Then, the user sets priorities for each task. Next, AI analyzes the task list entered by the user and predicts tasks and optimizes schedules. The AI ​​optimizes the user's schedule and proposes efficient plans. This platform allows users to efficiently manage housework and childcare tasks. This allows the task management system to provide efficient task management tailored to the user's lifestyle.

[0072] A task management system according to an embodiment includes a reception unit, a setting unit, a prediction unit, and an adjustment unit. The reception unit provides an interface for a user to input tasks. For example, the reception unit accepts tasks input by the user using text input or voice input. Text input is performed using a keyboard or a touchscreen. Voice input is performed using a microphone and converted into text using voice recognition technology. The setting unit sets priorities based on the tasks accepted by the reception unit. For example, the setting unit accepts task priorities set by the user using a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touchscreen. The prediction unit predicts tasks based on the priorities set by the setting unit. For example, the prediction unit analyzes past task history and predicts future tasks. The prediction unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. The adjustment unit adjusts a schedule based on the tasks predicted by the prediction unit. For example, the adjustment unit collects lifestyle data about the user and proposes an optimal schedule. The adjustment unit collects lifestyle data about the user, such as eating habits, exercise frequency, and sleep patterns, and optimizes the schedule. As a result, the task management system according to the embodiment can provide efficient task management that matches the user's lifestyle.

[0073] The task management system includes a collection unit that collects lifestyle data of a user. The collection unit provides an interface for collecting the lifestyle data of the user. For example, the collection unit collects data such as the user's eating habits, exercise frequency, and sleep patterns. The collection unit accepts data manually entered by the user. The collection unit can also automatically collect data using sensors in a wearable device or smartphone. For example, the collection unit collects heart rate and step count data from a smartwatch to determine the frequency of the user's exercise. This enables data collection based on the user's lifestyle.

[0074] The task management system includes an analysis unit that analyzes past task history. The analysis unit provides an interface for analyzing the past task history. For example, the analysis unit collects and analyzes data such as the completion status, required time, and frequency of a user's past tasks. The analysis unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. For example, the analysis unit analyzes what tasks a user has completed in the past and how long it took them to complete them, and predicts the required time for future tasks. This enables more accurate predictions by analyzing past task history.

[0075] The task management system includes a providing unit that provides an optimized schedule to the user. The providing unit provides an interface for providing the optimized schedule to the user. For example, the providing unit suggests an optimal schedule based on the user's lifestyle data and past task history. The providing unit accepts manual schedule adjustments by the user. The providing unit can also automatically optimize the schedule using AI. For example, the providing unit suggests an optimal schedule based on data such as the user's eating habits, exercise frequency, and sleep patterns. This enables efficient task management by providing the user with an optimized schedule.

[0076] The reception unit can accept that the user inputs a task using text input or voice input. The reception unit provides an interface for the user to input a task. For example, the reception unit accepts that the user inputs a task using text input or voice input. Text input is performed using a keyboard or a touch screen. Voice input is performed using a microphone and is converted into text using voice recognition technology. This allows the user to input a task using text input or voice input.

[0077] The setting unit can accept that the user sets the priority of the tasks by a drag-and-drop operation. The setting unit provides an interface for the user to set the priority of the tasks. For example, the setting unit accepts that the user sets the priority of the tasks by a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touch screen. This allows the user to set the priority of the tasks by drag-and-drop.

[0078] 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. Furthermore, 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 for more appropriate input by adjusting the task input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can analyze the user's past task input history and suggest the optimal input method. For example, the reception unit can automatically display tasks that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past task input history.

[0080] The reception unit can customize the input method based on the user's current situation and environment when inputting a task. For example, when the user is out, the reception unit prioritizes voice input to allow the user to easily input a task. In addition, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is busy, the reception unit can provide a simple template to allow the user to quickly input a task. This allows more appropriate input by customizing the input method based on the user's current situation and environment.

[0081] 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. The reception unit can also flexibly prioritize tasks when the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also prioritize tasks with high urgency. This enables efficient task management by automatically prioritizing tasks 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When inputting tasks, the reception unit can prioritize inputting highly relevant tasks in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize inputting tasks related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize inputting tasks related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting tasks to be performed at home. This enables efficient task management by prioritized input of highly relevant tasks in consideration of the user's geographical location information.

[0083] When a task is input, the reception unit can analyze the user's social media activity and suggest related tasks. For example, the reception unit can suggest tasks related to events shared by the user on social media. The reception unit can also suggest tasks related to places mentioned by the user on social media. Furthermore, the reception unit can also suggest tasks related to accounts the user follows on social media. In this way, related tasks can be suggested by analyzing the user's social media activity.

[0084] The setting unit can estimate the user's emotions and adjust the priority setting method based on the estimated user emotions. For example, the setting unit can provide a simple priority setting method when the user is stressed. The setting unit can also provide detailed priority setting options when the user is relaxed. Furthermore, the setting unit can also provide a method for quickly setting priorities when the user is in a hurry. This enables efficient task management by adjusting the priority setting method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0085] The setting unit can adjust the level of detail of the settings based on the importance of the tasks when setting priorities. For example, the setting unit can provide detailed setting options for tasks with high importance. The setting unit can also provide simple setting options for tasks with low importance. Furthermore, the setting unit can automatically adjust the level of detail of the settings according to the importance of the tasks. This allows for efficient task management by adjusting the level of detail of the settings based on the importance of the tasks.

[0086] When setting priorities, the setting unit can apply different setting algorithms depending on the task category. For example, the setting unit applies a dedicated setting algorithm to housework tasks. The setting unit can also apply a dedicated setting algorithm to childcare tasks. Furthermore, the setting unit can also apply a general-purpose setting algorithm to other tasks. This allows for efficient task management by applying different setting algorithms depending on the task category.

[0087] The setting unit can estimate the user's emotions and adjust the length of the priority list based on the estimated user emotions. For example, the setting unit can shorten the priority list when the user is stressed. The setting unit can also lengthen the priority list when the user is relaxed. Furthermore, the setting unit can display only important tasks in the priority list when the user is in a hurry. This enables efficient task management by adjusting the length of the priority list based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When setting priorities, the setting unit can determine the priorities based on the submission dates of the tasks. For example, the setting unit can prioritize tasks with upcoming deadlines. The setting unit can also postpone tasks with more distant submission dates. Furthermore, the setting unit can automatically adjust the priorities based on the submission dates. This enables efficient task management by determining priorities based on the submission dates of the tasks.

[0089] The setting unit can adjust the order of setting based on the relevance of tasks when setting priorities. For example, the setting unit prioritizes highly relevant tasks. The setting unit can also postpone less relevant tasks. Furthermore, the setting unit can automatically adjust the priorities based on the relevance of tasks. This allows for efficient task management by adjusting the order of setting based on the relevance of tasks.

[0090] The prediction unit can estimate the user's emotions and adjust the task prediction method based on the estimated user emotions. For example, the prediction unit can provide a simple prediction method when the user is stressed. The prediction unit can also provide detailed prediction options when the user is relaxed. Furthermore, the prediction unit can quickly provide prediction results when the user is in a hurry. This enables efficient task management by adjusting the task prediction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships between tasks during prediction. For example, the prediction unit can group related tasks to improve the accuracy of the prediction. The prediction unit can also provide prediction results by taking into account the dependency relationships between tasks. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the interrelationships between tasks. In this way, the accuracy of the prediction is improved by taking into account the interrelationships between tasks.

[0092] The prediction unit can make predictions taking into account attribute information of the task submitter. The prediction unit provides prediction results based on, for example, the submitter's past task history. The prediction unit can also provide prediction results taking into account attribute information (age, gender, etc.) of the submitter. Furthermore, the prediction unit can automatically adjust the accuracy of predictions based on the attribute information of the submitter. In this way, taking into account the attribute information of the task submitter improves the accuracy of predictions.

[0093] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. This enables efficient task management by adjusting the display method of the prediction results based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The prediction unit can make predictions taking into account the geographic distribution of tasks. For example, the prediction unit predicts the optimal task based on the user's current location. The prediction unit can also provide prediction results based on the user's destination. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the geographic distribution of tasks. This improves the accuracy of the prediction by taking the geographic distribution of tasks into account.

[0095] The prediction unit can improve the accuracy of the prediction by referring to the related literature of the task during prediction. The prediction unit provides a prediction result based on, for example, the related literature. The prediction unit can also improve the accuracy of the prediction by taking into account information from the related literature. Furthermore, the prediction unit can automatically adjust the accuracy of the prediction based on the related literature. As a result, the accuracy of the prediction is improved by referring to the related literature of the task.

[0096] The optimization unit can estimate the user's emotions and adjust the schedule optimization method based on the estimated user emotions. For example, the optimization unit can provide a simple optimization method when the user is stressed. The optimization unit can also provide detailed optimization options when the user is relaxed. Furthermore, the optimization unit can quickly provide optimization results when the user is in a hurry. This enables efficient task management by adjusting the schedule optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] During optimization, the optimization unit can optimize the current schedule by referring to past optimization data. The optimization unit optimizes the current schedule based on, for example, past optimization data. The optimization unit can also analyze past optimization data and provide an optimal schedule. Furthermore, the optimization unit can automatically adjust the accuracy of optimization based on past optimization data. This makes it possible to optimize the current schedule by referring to past optimization data.

[0098] During optimization, the optimization unit can apply different optimization methods to each task category. For example, the optimization unit applies a dedicated optimization method to housework tasks. The optimization unit can also apply a dedicated optimization method to childcare tasks. Furthermore, the optimization unit can also apply a general-purpose optimization method to other tasks. This allows for efficient task management by applying different optimization methods to each task category.

[0099] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit can provide a simple, highly visible display method. If the user is relaxed, the optimization unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the optimization unit can also provide a display method that focuses on the main points. This enables efficient task management by adjusting the display method of the optimization results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] During optimization, the optimization unit can determine the priority of optimization based on the submission time of the task. For example, the optimization unit prioritizes optimization of tasks with upcoming deadlines. The optimization unit can also postpone tasks with distant submission times. Furthermore, the optimization unit can automatically adjust the priority of optimization based on the submission time. This enables efficient task management by determining the priority of optimization based on the submission time of the task.

[0101] The optimization unit can perform optimization by referring to market data related to the tasks. For example, the optimization unit can propose an optimal task order based on the market data. The optimization unit can also provide optimization results taking into account trends in the market data. Furthermore, the optimization unit can automatically adjust the accuracy of optimization based on the market data. As a result, by referring to market data related to the tasks, the accuracy of optimization is improved.

[0102] The collection unit can estimate the user's emotions and adjust the lifestyle data collection method based on the estimated user emotions. For example, the collection unit can provide a simple collection method when the user is stressed. The collection unit can also provide detailed collection options when the user is relaxed. Furthermore, the collection unit can also provide a method for quickly collecting data when the user is in a hurry. This enables efficient data collection by adjusting the lifestyle data collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0103] The collection unit can select the optimal collection method by referring to the user's past lifestyle data when collecting data. The collection unit can, for example, suggest the optimal collection method based on the user's past data collection history. The collection unit can also analyze the user's past lifestyle data and provide the optimal collection method. Furthermore, the collection unit can automatically adjust the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by referring to the user's past lifestyle data.

[0104] The collection unit can customize the collection method based on the user's current situation and environment at the time of collection. For example, the collection unit can provide a simple collection method when the user is out. The collection unit can also provide detailed collection options when the user is at home. Furthermore, the collection unit can also provide a method for quickly collecting data when the user is busy. This allows for efficient data collection by customizing the collection method based on the user's current situation and environment.

[0105] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0106] During collection, the collection unit can prioritize collecting highly relevant data by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. This enables efficient data collection by prioritized collection of highly relevant data by taking into consideration the user's geographical location information.

[0107] During collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to information shared by the user on social media. The collection unit can also collect data related to topics mentioned by the user on social media. Furthermore, the collection unit can collect data related to accounts the user follows on social media. This allows related data to be collected by analyzing the user's social media activities.

[0108] The analysis unit can estimate the user's emotions and adjust the analysis method of the task history based on the estimated user emotions. For example, the analysis unit can provide a simple analysis method when the user is stressed. The analysis unit can also provide detailed analysis options when the user is relaxed. Furthermore, the analysis unit can quickly provide analysis results when the user is in a hurry. This enables efficient analysis by adjusting the analysis method of the task history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] During analysis, the analysis unit can optimize the analysis algorithm by referring to past task history data. The analysis unit, for example, optimizes the analysis algorithm based on past task history data. The analysis unit can also analyze past task history data and provide optimal analysis results. Furthermore, the analysis unit can automatically adjust the analysis algorithm based on past task history data. This allows the analysis algorithm to be optimized by referring to past task history data.

[0110] The analysis unit can apply different analysis methods to each task category during analysis. For example, the analysis unit applies a dedicated analysis method to housework tasks. The analysis unit can also apply a dedicated analysis method to childcare tasks. Furthermore, the analysis unit can apply a general-purpose analysis method to other tasks. This allows for efficient analysis by applying different analysis methods to each task category.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This enables efficient analysis by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] During analysis, the analysis unit can weight the analysis data based on the submission time of the task. For example, the analysis unit may prioritize the data of tasks with upcoming deadlines. The analysis unit can also analyze the data of tasks with more distant submission times. Furthermore, the analysis unit can automatically adjust the weighting of the analysis data based on the submission time. This allows for efficient analysis by weighting the analysis data based on the submission time of the task.

[0113] The analysis unit can improve the accuracy of the analysis by referring to related literature of the task during the analysis. The analysis unit provides an analysis result based on, for example, related literature. The analysis unit can also improve the accuracy of the analysis by taking into account information in the related literature. Furthermore, the analysis unit can automatically adjust the accuracy of the analysis based on the related literature. In this way, the accuracy of the analysis is improved by referring to related literature of the task.

[0114] The providing unit can estimate the user's emotions and adjust the method of providing the optimized schedule based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple method of providing the schedule. Furthermore, if the user is relaxed, the providing unit can also provide detailed options for providing the schedule. Furthermore, if the user is in a hurry, the providing unit can also quickly provide the results. This enables efficient task management by adjusting the method of providing the optimized schedule based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When providing the information, the providing unit can select the optimal providing method by referring to the user's past schedule history. The providing unit, for example, proposes the optimal providing method based on the user's past schedule history. The providing unit can also analyze the user's past schedule history and provide the optimal providing method. Furthermore, the providing unit can automatically adjust the providing method based on the user's past schedule history. In this way, the optimal providing method can be selected by referring to the user's past schedule history.

[0116] The providing unit can customize the delivery method based on the user's current situation and environment at the time of delivery. For example, when the user is out, the providing unit provides a simple delivery method. When the user is at home, the providing unit can also provide detailed delivery options. Furthermore, when the user is busy, the providing unit can also provide a method that allows for quick delivery. This enables efficient task management by customizing the delivery method based on the user's current situation and environment.

[0117] The providing unit can estimate the user's emotions and determine the priority of schedules to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important schedules. Furthermore, when the user is relaxed, the providing unit can also prioritize providing detailed schedules. Furthermore, when the user is in a hurry, the providing unit can also prioritize providing schedules that can be provided quickly. This enables efficient task management by determining the priority of schedules to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The providing unit can select the optimal delivery method by taking into consideration the user's device information when providing the task. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is concise and highly visible. This enables efficient task management by selecting the optimal delivery method by taking into consideration the user's device information.

[0119] When providing the schedule, the providing unit can make suggestions based on the schedule by referring to the user's calendar information. The providing unit, for example, references the schedules registered in the user's calendar and automatically sets the schedule. The providing unit can also propose a schedule related to a specific event from the user's calendar information. Furthermore, the providing unit can also propose an optimal schedule that matches the schedule based on the user's calendar information. This makes it possible to make suggestions based on the schedule by referring to the user's calendar information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, setting unit, prediction unit, adjustment unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts task input from the user using a touch screen or microphone of the smart device 14. The setting unit sets task priorities using the control unit 46A of the smart device 14. The prediction unit predicts tasks using the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the schedule using the specific processing unit 290 of the data processing device 12. The collection unit collects lifestyle data using sensors and wearable devices of the smart device 14. The analysis unit analyzes past task history using the specific processing unit 290 of the data processing device 12. The provision unit provides the user with an optimized schedule using the display and speaker of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, setting unit, prediction unit, adjustment unit, collection unit, analysis unit, and provision 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 receives task input from the user using a microphone of the smart glasses 214. The setting unit sets task priorities by the control unit 46A of the smart glasses 214. The prediction unit predicts tasks by the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the schedule by the specific processing unit 290 of the data processing device 12. The collection unit collects lifestyle data using sensors and wearable devices of the smart glasses 214. The analysis unit analyzes past task history by the specific processing unit 290 of the data processing device 12. The provision unit provides the optimized schedule to the user using the display and speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, setting unit, prediction unit, adjustment unit, collection unit, analysis unit, and provision 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 receives task input from the user using a microphone of the headset type terminal 314. The setting unit sets task priorities by the control unit 46A of the headset type terminal 314. The prediction unit predicts tasks by the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the schedule by the specific processing unit 290 of the data processing device 12. The collection unit collects lifestyle data using sensors and wearable devices of the headset type terminal 314. The analysis unit analyzes past task history by the specific processing unit 290 of the data processing device 12. The provision unit provides the optimized schedule to the user using the display and speaker of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, setting unit, prediction unit, adjustment unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives task input from the user using a microphone of the robot 414. The setting unit sets task priorities by a control unit 46A of the robot 414. The prediction unit predicts tasks by a specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the schedule by a specific processing unit 290 of the data processing device 12. The collection unit collects lifestyle data using sensors and wearable devices of the robot 414. The analysis unit analyzes past task history by a specific processing unit 290 of the data processing device 12. The provision unit provides the optimized schedule to the user by a display and speaker of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The task management system may further include a notification unit. The notification unit can send a reminder when the deadline for a task set by the user approaches. For example, the notification unit can send a push notification to the user's smartphone to notify the user of the task deadline. The notification unit can also link with the user's calendar app to automatically add the task deadline to the calendar. The notification unit can also send a reminder email to the user's email address. This allows the user to efficiently manage tasks without forgetting the task deadlines.

[0122] The task management system may further include a sharing unit. The sharing unit provides an interface for users to share tasks with other users. For example, the sharing unit allows users to share tasks with family members or team members and manage the tasks collaboratively. The sharing unit may also update the progress of the task in real time and notify the shared users. Furthermore, the sharing unit may provide a comment function for the task, allowing users to communicate with each other. This allows multiple users to cooperate and efficiently manage tasks.

[0123] The task management system may further include a reporting unit. The reporting unit provides an interface for users to report task progress. For example, the reporting unit allows users to input task completion status and generate progress reports. The reporting unit may also visually display task progress using graphs or charts. Furthermore, the reporting unit may share task progress with other users and receive feedback. This allows users to grasp task progress and efficiently manage tasks.

[0124] The task management system may further include an analysis unit. The analysis unit provides an interface for analyzing a user's task management performance. For example, the analysis unit may measure a user's task completion rate and average time required and generate a performance report. The analysis unit may also analyze a user's task management trends and suggest areas for improvement. Furthermore, the analysis unit may predict future task management based on the user's task management history. This allows a user to understand their own task management performance and manage tasks efficiently.

[0125] The task management system may further include a customization unit. The customization unit provides a function for the user to customize the interface of the task management system. For example, the customization unit allows the user to change the way tasks are displayed and the layout. The customization unit also allows the user to add task categories and tags to organize tasks. The customization unit also allows the user to change notification settings and adjust the frequency and method of reminders. This allows the user to customize the task management system to suit their needs and efficiently manage tasks.

[0126] The task management system may further include an emotion feedback unit. The emotion feedback unit provides an interface for the user to input emotions after completing a task. For example, the emotion feedback unit allows the user to select emotions such as "satisfaction," "dissatisfaction," or "stress" after completing a task. The emotion feedback unit may also collect emotion data from the user and suggest improvements to task management. Furthermore, the emotion feedback unit may adjust task priorities and schedules based on the user's emotion data. This allows the user to optimize task management based on their own emotions and manage tasks efficiently.

[0127] The task management system may further include a motivation unit. The motivation unit estimates the user's emotions and provides feedback to increase motivation. For example, the motivation unit may display an encouraging message when the user completes a task. The motivation unit may also provide rewards or badges according to the progress of the task based on the user's emotional data. Furthermore, the motivation unit may analyze the user's emotional data and provide advice to maintain motivation. This allows the user to increase motivation based on their own emotions and efficiently manage tasks.

[0128] The task management system may further include a stress management unit. The stress management unit estimates the user's emotions and makes suggestions to reduce stress. For example, if the user is feeling stressed, the stress management unit may suggest a task to help the user relax. The stress management unit may also incorporate break times to reduce stress into the schedule based on the user's emotional data. Furthermore, the stress management unit may analyze the user's emotional data, identify the cause of stress, and suggest remedial measures. This allows the user to manage stress based on their own emotions and efficiently manage tasks.

[0129] The task management system may further include an emotion history unit. The emotion history unit provides an interface for recording and analyzing the user's emotion data over the long term. For example, the emotion history unit may display the user's past emotions in chronological order. The emotion history unit may also analyze the user's emotion fluctuation patterns based on the user's emotion data and suggest improvements to task management. The emotion history unit may also compare the user's emotion data with other users and provide a benchmark. This allows the user to understand their own emotion history and manage tasks efficiently.

[0130] The task management system may further include an emotion prediction unit. The emotion prediction unit provides an interface for predicting future emotions based on the user's emotion data. For example, the emotion prediction unit may analyze the user's past emotion data and predict emotional fluctuations regarding a specific task. The emotion prediction unit may also predict future stress levels and suggest preventative measures based on the user's emotion data. Furthermore, the emotion prediction unit may combine the user's emotion data with other data (e.g., weather or season) to predict emotional fluctuations. This allows the user to predict future emotions and efficiently manage tasks.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit provides an interface for the user to input a task. For example, the reception unit accepts the user's task input using text input or voice input. Text input is performed using a keyboard or touch screen. Voice input is performed using a microphone and is converted into text using voice recognition technology. Step 2: The setting unit sets the priority based on the tasks accepted by the accepting unit. For example, the setting unit accepts that the user sets the priority of the tasks by a drag-and-drop operation. The drag-and-drop operation is performed using a mouse or a touch screen. Step 3: The prediction unit predicts tasks based on the priorities set by the setting unit. For example, the prediction unit analyzes past task history and predicts future tasks. The prediction unit uses a machine learning algorithm to learn the user's past behavioral patterns and predict future tasks. Step 4: The adjustment unit adjusts the schedule based on the tasks predicted by the prediction unit. For example, the adjustment unit collects lifestyle data about the user and proposes an optimal schedule. The adjustment unit collects lifestyle data such as the user's eating habits, exercise frequency, and sleep patterns, and optimizes the schedule.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The hardware resource that executes the specific processing 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 processing may be a single processor.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 setting unit that sets a priority order based on the tasks accepted by the accepting unit; a prediction unit that predicts tasks based on the priority order set by the setting unit; an adjustment unit that adjusts the schedule based on the tasks predicted by the prediction unit; A system characterized by:

2. A collection unit is provided for collecting lifestyle data of users. The system of claim 1 .

3. Equipped with an analysis section that analyzes past task history The system of claim 1 .

4. A provision unit is provided that provides an optimized schedule to a user. The system of claim 1 .

5. The reception unit Accepts user input of tasks using text or voice input The system of claim 1 .

6. The setting unit Allows users to set task priorities using drag-and-drop operations 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. The system of claim 1 .

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

9. The reception unit Customize your input experience based on your current situation and environment when completing tasks The system of claim 1 .

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