System

Abstract

An object of the system according to the embodiment is to effectively manage a delay of a task that needs to be repeatedly performed by a user.SOLUTION: A system includes a collection part, an analysis part, a notification part, and a promotion part. The collection unit collects action data of a user. The analysis unit analyzes the data collected by the collection unit and specifies a task that needs to be repeatedly performed. The notification unit notifies the delay of the task specified by the analysis unit. The promotion part promotes the execution of the task based on the content notified by the notification part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to effectively manage delays in tasks that require users to perform repeatedly.

[0005] The system according to the embodiment aims to effectively manage delays in tasks that a user needs to perform repeatedly. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a promotion unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly. The notification unit notifies the user of delays in the tasks identified by the analysis unit. The promotion unit promotes the execution of the tasks based on the content of the notification by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively manage delays in tasks that require a user to perform repeatedly. [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 execution promotion system according to an embodiment of the present invention collects and analyzes user behavioral data, notifies the user of task delays, and promotes task execution. The task execution promotion system promotes task execution by making objects aware of the inconvenience that delays to tasks cause to others. For example, the task execution promotion system identifies tasks that a user must perform repeatedly. Examples of such tasks include everyday tasks such as taking out the trash, doing laundry, and cleaning. To identify these tasks, the task execution promotion system collects user behavioral data and analyzes it using AI. Next, the task execution promotion system makes objects aware of the identified tasks. Specifically, if task execution is delayed, the objects notify the user. For example, a trash can may notify the user, saying, "Please take out the trash," or a washing machine may notify the user, saying, "Please do the laundry." This notification is sent via the user's smartphone or smart speaker. Furthermore, the content of the notification emphasizes that delaying a task will cause inconvenience to others. For example, the notification may say, "If you don't take out the trash, you will cause inconvenience to your neighbors." This helps the user recognize the importance of the task and encourages them to complete it. In this way, the task execution promotion system will realize the promotion of a society where people and things coexist. By receiving notifications from things, users will be able to remember to perform tasks. Furthermore, by recognizing that delaying a task will inconvenience others, task execution will be promoted. For example, by remembering to take out the trash, you will no longer inconvenience your neighbors. In this way, by giving things awareness and appealing to users that delaying a task will inconvenience others, a mechanism is realized that promotes task execution.

[0029] A task execution promotion system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a promotion unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, location information, app usage history, and web browsing history. The collection unit collects the user behavioral data from, for example, a smartphone or a smart home device. The collection unit can also estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly. For example, the analysis unit can analyze the user's behavioral patterns using AI and identify tasks that need to be performed repeatedly. The analysis unit can also estimate the user's emotions and adjust the analysis method for the behavioral patterns based on the estimated user emotions. For example, if the user is feeling stressed, behavioral patterns related to stress reduction can be prioritized in the analysis. The notification unit notifies the user of delays in tasks identified by the analysis unit. The notification unit may include, for example, a generation unit that generates specific content emphasizing that task delays cause inconvenience to others. The notification unit may also include an optimization unit that optimizes the timing and frequency of notifications. For example, the notification unit may estimate a user's emotions and optimize the timing of notifications based on the estimated user emotions. The promotion unit promotes task execution based on the content notified by the notification unit. For example, the promotion unit may set a reminder based on the notified content to promote task execution. The promotion unit may also estimate a user's emotions and adjust a task execution promotion method based on the estimated user emotions. For example, if the user is feeling stressed, the promotion unit may encourage the user to perform the task using kind words. As a result, the task execution promotion system according to the embodiment collects and analyzes user behavioral data, notifies the user of task delays, and promotes task execution, thereby efficiently promoting task execution.

[0030] The task execution promotion system includes a generation unit that generates specific content emphasizing that task delays cause inconvenience to others. The generation unit generates specific content emphasizing that task delays cause inconvenience to others. For example, the generation unit generates content such as, "If you don't take out the trash, it will cause inconvenience to your neighbors." The generation unit can also estimate the user's emotions and adjust the method for generating notification content based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates notification content in kind words. This emphasizes that task delays cause inconvenience to others, thereby increasing the user's awareness of task execution.

[0031] The task execution promotion system includes an optimization unit that adjusts the timing and frequency of notifications. The optimization unit adjusts the timing and frequency of notifications. For example, the optimization unit estimates the user's emotions and optimizes the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit delays the timing of notifications and notifies the user when he or she is relaxed. The optimization unit can also adjust the frequency of notifications based on the importance of the task. For example, notifications are sent more frequently for tasks with higher importance. In this way, by optimizing the timing and frequency of notifications, notifications can be sent at the optimal timing for the user.

[0032] The collection unit may indicate a specific method for collecting user behavioral data from a smartphone or a smart home device. For example, the collection unit may collect user location information using a sensor in the smartphone. The collection unit may also collect user behavioral data from a smart home device. For example, the collection unit may collect user voice commands using a smart speaker. This allows for the collection of more diverse data by collecting behavioral data from the smartphone or the smart home device.

[0033] The analysis unit can analyze the collected data and analyze the user's behavioral patterns. For example, the analysis unit can use AI to analyze the collected data and analyze the user's behavioral patterns. For example, the analysis unit can analyze the frequency and time period of the user's behavior and identify tasks that need to be performed repeatedly. The analysis unit can also predict task delays based on the user's behavioral patterns. This improves the accuracy of identifying tasks that need to be performed repeatedly by analyzing the user's behavioral patterns.

[0034] The promotion unit can promote task execution based on the notified content. For example, the promotion unit can set a reminder based on the notified content to promote task execution. The promotion unit can also estimate the user's emotions and adjust the task execution promotion method based on the estimated user emotions. For example, if the user is feeling stressed, the promotion unit can encourage the user to perform the task using kind words. In this way, promoting task execution based on the notified content increases the user's awareness of performing the task.

[0035] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit selects the optimal collection method based on, for example, behaviors the user frequently performed in the past. The collection unit can also customize the collection method from the user's past behavioral data to efficiently collect data. Furthermore, the collection unit can analyze the user's behavioral patterns and select the most effective collection method. In this way, by analyzing the past behavioral data, the optimal collection method can be selected and data can be collected efficiently.

[0036] When collecting behavioral data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also filter and collect only necessary data according to the user's living situation. Furthermore, the collection unit can select the type of data to collect based on the user's areas of interest. This makes it possible to efficiently collect only necessary data by filtering data based on the user's living situation and areas of interest.

[0037] When collecting behavioral data, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user is using voice input, the collection unit will prioritize collecting voice data. Also, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting behavioral data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collection of data related to that location. The collection unit can also prioritize collection of data related to locations close to the user's current location. Furthermore, the collection unit can select the most relevant data based on the user's geographical location information. This makes it possible to efficiently collect highly relevant data by taking the user's geographical location information into consideration.

[0039] When collecting behavioral data, the collection unit can analyze the user's social media activities and indicate a specific method for collecting related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect the most relevant data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be efficiently collected by analyzing the user's social media activities.

[0040] When collecting behavioral data, the collection unit can customize the collection method based on the user's past feedback. For example, the collection unit customizes the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. Furthermore, the collection unit can also improve the collection method by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on behavioral data with a high level of importance. The analysis unit can also perform a simplified analysis on behavioral data with a low level of importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the behavioral data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of behavioral data. For example, if the behavioral data is related to health, the analysis unit can apply a health analysis algorithm. If the behavioral data is related to work, the analysis unit can also apply a work analysis algorithm. If the behavioral data is related to hobbies, the analysis unit can also apply a hobby analysis algorithm. By applying different analysis algorithms depending on the category of behavioral data, more appropriate analysis results can be obtained.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also select the optimal analysis method based on the user's past analysis results. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of behavioral data. For example, the analysis unit prioritizes analysis of recently submitted behavioral data. The analysis unit can also postpone analysis of behavioral data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of behavioral data, it is possible to prioritize analysis of the most recent data.

[0045] The analysis unit can indicate a specific method for adjusting the order of analysis based on the relevance of behavioral data during analysis. For example, the analysis unit prioritizes analysis of highly relevant behavioral data. The analysis unit can also postpone analysis of less relevant behavioral data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of behavioral data. In this way, by adjusting the order of analysis based on the relevance of behavioral data, highly relevant data can be analyzed preferentially.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed based on the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis based on the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the task when issuing a notification. For example, the notification unit provides a detailed notification for a task with a high level of importance. The notification unit can also provide a simplified notification for a task with a low level of importance. Furthermore, the notification unit can dynamically adjust the level of detail of the notification according to the importance of the task. In this way, by adjusting the level of detail of the notification based on the importance of the task, detailed notifications can be issued for important tasks.

[0048] The notification unit can apply different notification algorithms depending on the task category when providing a notification. For example, if the task is health-related, the notification unit can apply a health notification algorithm. If the task is work-related, the notification unit can also apply a work notification algorithm. Furthermore, if the task is hobby-related, the notification unit can also apply a hobby notification algorithm. This allows for more appropriate notification by applying different notification algorithms depending on the task category.

[0049] The notification unit can improve the accuracy of notifications based on the user's past notification results when notifying. The notification unit can improve the accuracy of current notifications, for example, based on the user's past notification results. The notification unit can also select the optimal notification method based on the user's past notification results. Furthermore, the notification unit can improve the notification algorithm by reflecting the user's past notification results. This makes it possible to improve the accuracy of notifications by referring to the user's past notification results.

[0050] The notification unit can determine the priority of notifications based on the submission time of the tasks when notifying. For example, the notification unit prioritizes notifications of recently submitted tasks. The notification unit can also postpone notifications of tasks that were submitted earlier. Furthermore, the notification unit can dynamically adjust the priority of notifications based on the submission time. In this way, by determining the priority of notifications based on the submission time of the tasks, it is possible to prioritize notifications of the most recent tasks.

[0051] The notification unit can indicate a specific method for adjusting the order of notifications based on the relevance of tasks when notifying. For example, the notification unit can prioritize notifications of highly relevant tasks. The notification unit can also postpone notifications of less relevant tasks. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of tasks. In this way, by adjusting the order of notifications based on the relevance of tasks, highly relevant tasks can be prioritized.

[0052] The notification unit can adjust the use of technical terms in the notification based on the user's level of expertise when making a notification. For example, if the user has specialized knowledge, the notification unit can use a lot of technical terms to make the notification. Alternatively, if the user does not have specialized knowledge, the notification unit can make the notification in simple language. Furthermore, the notification unit can adjust the way the notification is expressed based on the user's level of expertise. This allows the notification to be easy for the user to understand by adjusting the use of technical terms in the notification based on the user's level of expertise.

[0053] During promotion, the promotion unit can select the optimal promotion method based on the user's past task execution history. For example, the promotion unit selects the optimal promotion method based on promotion methods that the user has used successfully in the past. The promotion unit can also select the most effective promotion method from the user's past task execution history. Furthermore, the promotion unit can analyze the user's past task execution history and customize the promotion method. In this way, by analyzing the user's past task execution history, the optimal promotion method can be selected and task execution can be efficiently promoted.

[0054] The prompting unit can customize prompting means based on the user's current living situation during prompting. For example, if the user is busy, the prompting unit can provide concise and quick prompting means. Alternatively, if the user is relaxed, the prompting unit can provide prompting means including detailed explanations. Furthermore, the prompting unit can select the optimal prompting means according to the user's living situation. In this way, customizing the prompting means based on the user's current living situation can more effectively prompt the user to perform the task.

[0055] The facilitation unit can improve the facilitation method based on user feedback during facilitation. For example, the facilitation unit improves the facilitation method based on feedback provided by the user. The facilitation unit can also select an optimal facilitation method based on the user feedback. Furthermore, the facilitation unit can also customize the facilitation method by reflecting the user feedback. In this way, by reflecting the user feedback, the facilitation method can be improved and task execution can be more effectively promoted.

[0056] The promotion unit can select the optimal promotion method based on the user's geographical location information during promotion. For example, if the user is in a specific location, the promotion unit can prioritize promoting tasks related to that location. The promotion unit can also prioritize promoting tasks related to locations close to the user's current location. Furthermore, the promotion unit can select the most relevant task based on the user's geographical location information. This allows for efficient promotion of highly relevant tasks by taking the user's geographical location information into consideration.

[0057] During the promotion, the promotion unit can analyze the user's social media activity and suggest promotion means. For example, the promotion unit can promote related tasks based on information shared by the user on social media. The promotion unit can also analyze the user's social media activity and promote the most relevant task. Furthermore, the promotion unit can also promote related tasks by referring to the activity of the user's friends on social media. In this way, highly relevant tasks can be efficiently promoted by analyzing the user's social media activity.

[0058] The facilitation unit can customize the facilitation method by reflecting the user's past feedback during facilitation. For example, the facilitation unit customizes the facilitation method based on feedback provided by the user in the past. The facilitation unit can also select an optimal facilitation method from the user's past feedback. Furthermore, the facilitation unit can improve the facilitation method by reflecting the user's feedback. In this way, the facilitation method can be customized by reflecting the user's past feedback, and task execution can be efficiently promoted.

[0059] The generation unit can adjust the level of detail of the notification content based on the importance of the task when generating the notification. For example, the generation unit generates detailed notification content for a task with high importance. The generation unit can also generate simple notification content for a task with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the notification content according to the importance of the task. In this way, by adjusting the level of detail of the notification content based on the importance of the task, detailed notifications can be provided for important tasks.

[0060] The generation unit can apply different generation algorithms depending on the task category during generation. For example, if the task is health-related, the generation unit can apply a health notification algorithm. If the task is work-related, the generation unit can also apply a work notification algorithm. Furthermore, if the task is hobby-related, the generation unit can also apply a hobby notification algorithm. In this way, by applying different generation algorithms depending on the task category, more appropriate notification content can be generated.

[0061] The generation unit can improve the accuracy of generation based on the user's past notification content during generation. The generation unit can improve the accuracy of the current notification content based on the user's past notification content, for example. The generation unit can also select the optimal notification method based on the user's past notification content. Furthermore, the generation unit can improve the notification algorithm by reflecting the user's past notification content. In this way, the accuracy of generation can be improved by referring to the user's past notification content.

[0062] The generation unit can determine the priority of the notification content based on the submission time of the task at the time of generation. For example, the generation unit preferentially reflects the most recently submitted task in the notification content. The generation unit can also reflect the oldest submitted task later in the notification content. Furthermore, the generation unit can dynamically adjust the priority of the notification content based on the submission time. In this way, by determining the priority of the notification content based on the submission time of the task, the most recent task can be preferentially reflected in the notification content.

[0063] The generation unit can adjust the order of notification contents based on task relevance at the time of generation. For example, the generation unit preferentially reflects highly relevant tasks in the notification contents. The generation unit can also reflect less relevant tasks later in the notification contents. Furthermore, the generation unit can dynamically adjust the order of notification contents based on task relevance. In this way, by adjusting the order of notification contents based on task relevance, highly relevant tasks can be preferentially reflected in the notification contents.

[0064] The generation unit can adjust the use of technical terms in the notification content based on the user's level of expertise when generating the notification content. For example, if the user has technical knowledge, the generation unit generates the notification content using a lot of technical terms. Alternatively, if the user does not have technical knowledge, the generation unit can generate the notification content using simple language. Furthermore, the generation unit can adjust the way the notification content is expressed based on the user's level of expertise. In this way, by adjusting the use of technical terms in the notification content based on the user's level of expertise, it is possible to generate notification content that is easy for the user to understand.

[0065] During optimization, the optimization unit can adjust the notification frequency based on the importance of the task. For example, the optimization unit can provide more frequent notifications for tasks with higher importance. The optimization unit can also reduce the notification frequency for tasks with lower importance. Furthermore, the optimization unit can dynamically adjust the notification frequency according to the importance of the task. In this way, by adjusting the notification frequency based on the importance of the task, more frequent notifications can be provided for important tasks.

[0066] During optimization, the optimization unit can apply different optimization algorithms depending on the task category. For example, if the task is health-related, the optimization unit can apply a health notification optimization algorithm. If the task is work-related, the optimization unit can also apply a work notification optimization algorithm. Furthermore, if the task is hobby-related, the optimization unit can also apply a hobby notification optimization algorithm. In this way, by applying different optimization algorithms depending on the task category, more appropriate notifications can be provided.

[0067] During optimization, the optimization unit can improve the accuracy of optimization based on the user's past notification results. For example, the optimization unit improves the accuracy of current notifications based on the user's past notification results. The optimization unit can also select the optimal notification method based on the user's past notification results. Furthermore, the optimization unit can improve the notification optimization algorithm by reflecting the user's past notification results. This makes it possible to improve the accuracy of optimization by referring to the user's past notification results.

[0068] During optimization, the optimization unit can determine the priority of notifications based on the time of task submission. For example, the optimization unit prioritizes notifications of recently submitted tasks. The optimization unit can also prioritize notifications of older submitted tasks. Furthermore, the optimization unit can dynamically adjust the priority of notifications based on the time of submission. In this way, by determining the priority of notifications based on the time of task submission, it is possible to prioritize notifications of the most recent tasks.

[0069] During optimization, the optimization unit can adjust the order of notifications based on the relevance of the tasks. For example, the optimization unit prioritizes notifications of highly relevant tasks. The optimization unit can also postpone notifications of less relevant tasks. Furthermore, the optimization unit can dynamically adjust the order of notifications based on the relevance of the tasks. In this way, by adjusting the order of notifications based on the relevance of the tasks, it is possible to prioritize notifications of highly relevant tasks.

[0070] During optimization, the optimization unit can adjust the use of technical terms in the notification based on the user's level of expertise. For example, if the user has technical expertise, the optimization unit can use a lot of technical terms to provide the notification. Alternatively, if the user does not have technical expertise, the optimization unit can provide the notification in simple language. Furthermore, the optimization unit can adjust the way the notification is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the notification depending on the user's level of expertise, the notification can be made easier for the user to understand.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0073] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0074] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0075] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0076] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0077] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0078] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0079] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0080] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0081] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0082] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0083] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0084] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0085] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0086] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0087] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0088] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0089] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0090] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0091] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0092] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0093] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0094] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0095] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0096] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0097] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0098] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0099] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0100] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0101] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0102] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0103] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0104] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0105] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0106] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0107] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0108] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0109] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0110] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0111] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0112] The processing flow of the first embodiment will be briefly explained below.

[0113] Step 1: The collection unit collects user behavioral data. The user behavioral data includes location information, app usage history, web browsing history, etc. The collection unit collects user behavioral data from smartphones and smart home devices. The collection unit can also estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed and data can be collected when the user is relaxed. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly. The analysis unit uses AI to analyze the user's behavioral patterns and identify tasks that need to be performed repeatedly. The analysis unit can also estimate the user's emotions and adjust the analysis method for behavioral patterns based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize analyzing behavioral patterns related to stress reduction. Step 3: The notification unit notifies the user of the task delay identified by the analysis unit. The notification unit includes a generation unit that generates specific content emphasizing that the task delay is causing inconvenience to others. The notification unit may also include an optimization unit that optimizes the timing and frequency of notifications. For example, the notification unit estimates the user's emotions and optimizes the timing of notifications based on the estimated emotions. Step 4: The promotion unit promotes task execution based on the content notified by the notification unit. The promotion unit sets a reminder based on the content notified and promotes task execution. The promotion unit can also estimate the user's emotions and adjust the method of promoting task execution based on the estimated emotions. For example, if the user is feeling stressed, the promotion unit can encourage the user to complete the task using kind words.

[0114] (Example 2) A task execution promotion system according to an embodiment of the present invention collects and analyzes user behavioral data, notifies the user of task delays, and promotes task execution. The task execution promotion system promotes task execution by making objects aware of the inconvenience that delays to tasks cause to others. For example, the task execution promotion system identifies tasks that a user must perform repeatedly. Examples of such tasks include everyday tasks such as taking out the trash, doing laundry, and cleaning. To identify these tasks, the task execution promotion system collects user behavioral data and analyzes it using AI. Next, the task execution promotion system makes objects aware of the identified tasks. Specifically, if task execution is delayed, the objects notify the user. For example, a trash can may notify the user, saying, "Please take out the trash," or a washing machine may notify the user, saying, "Please do the laundry." This notification is sent via the user's smartphone or smart speaker. Furthermore, the content of the notification emphasizes that delaying a task will cause inconvenience to others. For example, the notification may say, "If you don't take out the trash, you will cause inconvenience to your neighbors." This helps the user recognize the importance of the task and encourages them to complete it. In this way, the task execution promotion system will realize the promotion of a society where people and things coexist. By receiving notifications from things, users will be able to remember to perform tasks. Furthermore, by recognizing that delaying a task will inconvenience others, task execution will be promoted. For example, by remembering to take out the trash, you will no longer inconvenience your neighbors. In this way, by giving things awareness and appealing to users that delaying a task will inconvenience others, a mechanism is realized that promotes task execution.

[0115] A task execution promotion system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a promotion unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, location information, app usage history, and web browsing history. The collection unit collects the user behavioral data from, for example, a smartphone or a smart home device. The collection unit can also estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly. For example, the analysis unit can analyze the user's behavioral patterns using AI and identify tasks that need to be performed repeatedly. The analysis unit can also estimate the user's emotions and adjust the analysis method for the behavioral patterns based on the estimated user emotions. For example, if the user is feeling stressed, behavioral patterns related to stress reduction can be prioritized in the analysis. The notification unit notifies the user of delays in tasks identified by the analysis unit. The notification unit may include, for example, a generation unit that generates specific content emphasizing that task delays cause inconvenience to others. The notification unit may also include an optimization unit that optimizes the timing and frequency of notifications. For example, the notification unit may estimate a user's emotions and optimize the timing of notifications based on the estimated user emotions. The promotion unit promotes task execution based on the content notified by the notification unit. For example, the promotion unit may set a reminder based on the notified content to promote task execution. The promotion unit may also estimate a user's emotions and adjust a task execution promotion method based on the estimated user emotions. For example, if the user is feeling stressed, the promotion unit may encourage the user to perform the task using kind words. As a result, the task execution promotion system according to the embodiment collects and analyzes user behavioral data, notifies the user of task delays, and promotes task execution, thereby efficiently promoting task execution.

[0116] The task execution promotion system includes a generation unit that generates specific content emphasizing that task delays cause inconvenience to others. The generation unit generates specific content emphasizing that task delays cause inconvenience to others. For example, the generation unit generates content such as, "If you don't take out the trash, it will cause inconvenience to your neighbors." The generation unit can also estimate the user's emotions and adjust the method for generating notification content based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates notification content in kind words. This emphasizes that task delays cause inconvenience to others, thereby increasing the user's awareness of task execution.

[0117] The task execution promotion system includes an optimization unit that adjusts the timing and frequency of notifications. The optimization unit adjusts the timing and frequency of notifications. For example, the optimization unit estimates the user's emotions and optimizes the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit delays the timing of notifications and notifies the user when he or she is relaxed. The optimization unit can also adjust the frequency of notifications based on the importance of the task. For example, notifications are sent more frequently for tasks with higher importance. In this way, by optimizing the timing and frequency of notifications, notifications can be sent at the optimal timing for the user.

[0118] The collection unit may indicate a specific method for collecting user behavioral data from a smartphone or a smart home device. For example, the collection unit may collect user location information using a sensor in the smartphone. The collection unit may also collect user behavioral data from a smart home device. For example, the collection unit may collect user voice commands using a smart speaker. This allows for the collection of more diverse data by collecting behavioral data from the smartphone or the smart home device.

[0119] The analysis unit can analyze the collected data and analyze the user's behavioral patterns. For example, the analysis unit can use AI to analyze the collected data and analyze the user's behavioral patterns. For example, the analysis unit can analyze the frequency and time period of the user's behavior and identify tasks that need to be performed repeatedly. The analysis unit can also predict task delays based on the user's behavioral patterns. This improves the accuracy of identifying tasks that need to be performed repeatedly by analyzing the user's behavioral patterns.

[0120] The promotion unit can promote task execution based on the notified content. For example, the promotion unit can set a reminder based on the notified content to promote task execution. The promotion unit can also estimate the user's emotions and adjust the task execution promotion method based on the estimated user emotions. For example, if the user is feeling stressed, the promotion unit can encourage the user to perform the task using kind words. In this way, promoting task execution based on the notified content increases the user's awareness of performing the task.

[0121] The collection unit can estimate the user's emotions and adjust the timing of collecting behavioral data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect data when the user is relaxed. Also, if the user is relaxed, the collection unit can advance the collection timing to collect more data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to collect necessary data in a short amount of time. In this way, by adjusting the collection timing according to the user's emotions, data can be collected at more appropriate timing.

[0122] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit selects the optimal collection method based on, for example, behaviors the user frequently performed in the past. The collection unit can also customize the collection method from the user's past behavioral data to efficiently collect data. Furthermore, the collection unit can analyze the user's behavioral patterns and select the most effective collection method. In this way, by analyzing the past behavioral data, the optimal collection method can be selected and data can be collected efficiently.

[0123] When collecting behavioral data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also filter and collect only necessary data according to the user's living situation. Furthermore, the collection unit can select the type of data to collect based on the user's areas of interest. This makes it possible to efficiently collect only necessary data by filtering data based on the user's living situation and areas of interest.

[0124] When collecting behavioral data, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user is using voice input, the collection unit will prioritize collecting voice data. Also, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0125] The collection unit can estimate the user's emotions and determine the priority of behavioral data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Also, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data related to urgent tasks. In this way, by determining the priority of data to be collected according to the user's emotions, more important data can be collected preferentially.

[0126] When collecting behavioral data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collection of data related to that location. The collection unit can also prioritize collection of data related to locations close to the user's current location. Furthermore, the collection unit can select the most relevant data based on the user's geographical location information. This makes it possible to efficiently collect highly relevant data by taking the user's geographical location information into consideration.

[0127] When collecting behavioral data, the collection unit can analyze the user's social media activities and indicate a specific method for collecting related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect the most relevant data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be efficiently collected by analyzing the user's social media activities.

[0128] When collecting behavioral data, the collection unit can customize the collection method based on the user's past feedback. For example, the collection unit customizes the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. Furthermore, the collection unit can also improve the collection method by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently.

[0129] The analysis unit can estimate the user's emotions and adjust the analysis method for behavioral patterns based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing behavioral patterns related to stress reduction. Also, if the user is relaxed, the analysis unit can prioritize analyzing behavioral patterns related to relaxation. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing behavioral patterns related to urgent tasks. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained.

[0130] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on behavioral data with a high level of importance. The analysis unit can also perform a simplified analysis on behavioral data with a low level of importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the behavioral data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0131] During analysis, the analysis unit can apply different analysis algorithms depending on the category of behavioral data. For example, if the behavioral data is related to health, the analysis unit can apply a health analysis algorithm. If the behavioral data is related to work, the analysis unit can also apply a work analysis algorithm. If the behavioral data is related to hobbies, the analysis unit can also apply a hobby analysis algorithm. By applying different analysis algorithms depending on the category of behavioral data, more appropriate analysis results can be obtained.

[0132] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also select the optimal analysis method based on the user's past analysis results. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0133] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis related to stress reduction. Also, if the user is relaxed, the analysis unit can prioritize analysis related to relaxation. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis related to urgent tasks. In this way, by determining the priority of analysis according to the user's emotions, more important analyses can be prioritized.

[0134] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of behavioral data. For example, the analysis unit prioritizes analysis of recently submitted behavioral data. The analysis unit can also postpone analysis of behavioral data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of behavioral data, it is possible to prioritize analysis of the most recent data.

[0135] The analysis unit can indicate a specific method for adjusting the order of analysis based on the relevance of behavioral data during analysis. For example, the analysis unit prioritizes analysis of highly relevant behavioral data. The analysis unit can also postpone analysis of less relevant behavioral data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of behavioral data. In this way, by adjusting the order of analysis based on the relevance of behavioral data, highly relevant data can be analyzed preferentially.

[0136] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed based on the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis based on the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0137] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide a notification in gentle words. If the user is relaxed, the notification unit can also provide a notification including detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise and to-the-point notification. In this way, by adjusting the notification expression method according to the user's emotions, more effective notifications can be provided.

[0138] The notification unit can adjust the level of detail of the notification based on the importance of the task when issuing a notification. For example, the notification unit provides a detailed notification for a task with a high level of importance. The notification unit can also provide a simplified notification for a task with a low level of importance. Furthermore, the notification unit can dynamically adjust the level of detail of the notification according to the importance of the task. In this way, by adjusting the level of detail of the notification based on the importance of the task, detailed notifications can be issued for important tasks.

[0139] The notification unit can apply different notification algorithms depending on the task category when providing a notification. For example, if the task is health-related, the notification unit can apply a health notification algorithm. If the task is work-related, the notification unit can also apply a work notification algorithm. Furthermore, if the task is hobby-related, the notification unit can also apply a hobby notification algorithm. This allows for more appropriate notification by applying different notification algorithms depending on the task category.

[0140] The notification unit can improve the accuracy of notifications based on the user's past notification results when notifying. The notification unit can improve the accuracy of current notifications, for example, based on the user's past notification results. The notification unit can also select the optimal notification method based on the user's past notification results. Furthermore, the notification unit can improve the notification algorithm by reflecting the user's past notification results. This makes it possible to improve the accuracy of notifications by referring to the user's past notification results.

[0141] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide a short, to-the-point notification. If the user is relaxed, the notification unit can also provide a longer notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide a quick, concise notification. This allows for more effective notification by adjusting the length of the notification according to the user's emotions.

[0142] The notification unit can determine the priority of notifications based on the submission time of the tasks when notifying. For example, the notification unit prioritizes notifications of recently submitted tasks. The notification unit can also postpone notifications of tasks that were submitted earlier. Furthermore, the notification unit can dynamically adjust the priority of notifications based on the submission time. In this way, by determining the priority of notifications based on the submission time of the tasks, it is possible to prioritize notifications of the most recent tasks.

[0143] The notification unit can indicate a specific method for adjusting the order of notifications based on the relevance of tasks when notifying. For example, the notification unit can prioritize notifications of highly relevant tasks. The notification unit can also postpone notifications of less relevant tasks. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of tasks. In this way, by adjusting the order of notifications based on the relevance of tasks, highly relevant tasks can be prioritized.

[0144] The notification unit can adjust the use of technical terms in the notification based on the user's level of expertise when making a notification. For example, if the user has specialized knowledge, the notification unit can use a lot of technical terms to make the notification. Alternatively, if the user does not have specialized knowledge, the notification unit can make the notification in simple language. Furthermore, the notification unit can adjust the way the notification is expressed based on the user's level of expertise. This allows the notification to be easy for the user to understand by adjusting the use of technical terms in the notification based on the user's level of expertise.

[0145] The prompting unit can estimate the user's emotions and adjust the task execution prompting method based on the estimated user's emotions. For example, if the user is feeling stressed, the prompting unit can encourage the user to perform the task using gentle words. If the user is relaxed, the prompting unit can also provide a prompting method that includes detailed explanations. If the user is in a hurry, the prompting unit can also provide a simple prompting method that gets to the point. In this way, by adjusting the task execution prompting method according to the user's emotions, task execution can be more effectively promoted.

[0146] During promotion, the promotion unit can select the optimal promotion method based on the user's past task execution history. For example, the promotion unit selects the optimal promotion method based on promotion methods that the user has used successfully in the past. The promotion unit can also select the most effective promotion method from the user's past task execution history. Furthermore, the promotion unit can analyze the user's past task execution history and customize the promotion method. In this way, by analyzing the user's past task execution history, the optimal promotion method can be selected and task execution can be efficiently promoted.

[0147] The prompting unit can customize prompting means based on the user's current living situation during prompting. For example, if the user is busy, the prompting unit can provide concise and quick prompting means. Alternatively, if the user is relaxed, the prompting unit can provide prompting means including detailed explanations. Furthermore, the prompting unit can select the optimal prompting means according to the user's living situation. In this way, customizing the prompting means based on the user's current living situation can more effectively prompt the user to perform the task.

[0148] The facilitation unit can improve the facilitation method based on user feedback during facilitation. For example, the facilitation unit improves the facilitation method based on feedback provided by the user. The facilitation unit can also select an optimal facilitation method based on the user feedback. Furthermore, the facilitation unit can also customize the facilitation method by reflecting the user feedback. In this way, by reflecting the user feedback, the facilitation method can be improved and task execution can be more effectively promoted.

[0149] The promotion unit can estimate the user's emotions and determine the priority of promotion based on the estimated user's emotions. For example, if the user is feeling stressed, the promotion unit can prioritize promoting tasks related to stress reduction. Also, if the user is relaxed, the promotion unit can prioritize promoting tasks related to relaxation. Furthermore, if the user is in a hurry, the promotion unit can prioritize promoting urgent tasks. In this way, by determining the priority of promotion according to the user's emotions, it is possible to prioritize promoting more important tasks.

[0150] The promotion unit can select the optimal promotion method based on the user's geographical location information during promotion. For example, if the user is in a specific location, the promotion unit can prioritize promoting tasks related to that location. The promotion unit can also prioritize promoting tasks related to locations close to the user's current location. Furthermore, the promotion unit can select the most relevant task based on the user's geographical location information. This allows for efficient promotion of highly relevant tasks by taking the user's geographical location information into consideration.

[0151] During the promotion, the promotion unit can analyze the user's social media activity and suggest promotion means. For example, the promotion unit can promote related tasks based on information shared by the user on social media. The promotion unit can also analyze the user's social media activity and promote the most relevant task. Furthermore, the promotion unit can also promote related tasks by referring to the activity of the user's friends on social media. In this way, highly relevant tasks can be efficiently promoted by analyzing the user's social media activity.

[0152] The facilitation unit can customize the facilitation method by reflecting the user's past feedback during facilitation. For example, the facilitation unit customizes the facilitation method based on feedback provided by the user in the past. The facilitation unit can also select an optimal facilitation method from the user's past feedback. Furthermore, the facilitation unit can improve the facilitation method by reflecting the user's feedback. In this way, the facilitation method can be customized by reflecting the user's past feedback, and task execution can be efficiently promoted.

[0153] The generation unit can estimate the user's emotions and adjust the notification content generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate notification content in gentle language. If the user is relaxed, the generation unit can also generate notification content including detailed information. Furthermore, if the user is in a hurry, the generation unit can also generate notification content that is concise and to the point. In this way, by adjusting the notification content generation method according to the user's emotions, more effective notifications can be provided.

[0154] The generation unit can adjust the level of detail of the notification content based on the importance of the task when generating the notification. For example, the generation unit generates detailed notification content for a task with high importance. The generation unit can also generate simple notification content for a task with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the notification content according to the importance of the task. In this way, by adjusting the level of detail of the notification content based on the importance of the task, detailed notifications can be provided for important tasks.

[0155] The generation unit can apply different generation algorithms depending on the task category during generation. For example, if the task is health-related, the generation unit can apply a health notification algorithm. If the task is work-related, the generation unit can also apply a work notification algorithm. Furthermore, if the task is hobby-related, the generation unit can also apply a hobby notification algorithm. In this way, by applying different generation algorithms depending on the task category, more appropriate notification content can be generated.

[0156] The generation unit can improve the accuracy of generation based on the user's past notification content during generation. The generation unit can improve the accuracy of the current notification content based on the user's past notification content, for example. The generation unit can also select the optimal notification method based on the user's past notification content. Furthermore, the generation unit can improve the notification algorithm by reflecting the user's past notification content. In this way, the accuracy of generation can be improved by referring to the user's past notification content.

[0157] The generation unit can estimate the user's emotions and adjust the length of the notification content based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate short and to-the-point notification content. If the user is relaxed, the generation unit can also generate longer notification content including detailed information. Furthermore, if the user is in a hurry, the generation unit can also generate quick and concise notification content. In this way, by adjusting the length of the notification content according to the user's emotions, more effective notifications can be provided.

[0158] The generation unit can determine the priority of the notification content based on the submission time of the task at the time of generation. For example, the generation unit preferentially reflects the most recently submitted task in the notification content. The generation unit can also reflect the oldest submitted task later in the notification content. Furthermore, the generation unit can dynamically adjust the priority of the notification content based on the submission time. In this way, by determining the priority of the notification content based on the submission time of the task, the most recent task can be preferentially reflected in the notification content.

[0159] The generation unit can adjust the order of notification contents based on task relevance at the time of generation. For example, the generation unit preferentially reflects highly relevant tasks in the notification contents. The generation unit can also reflect less relevant tasks later in the notification contents. Furthermore, the generation unit can dynamically adjust the order of notification contents based on task relevance. In this way, by adjusting the order of notification contents based on task relevance, highly relevant tasks can be preferentially reflected in the notification contents.

[0160] The generation unit can adjust the use of technical terms in the notification content based on the user's level of expertise when generating the notification content. For example, if the user has technical knowledge, the generation unit generates the notification content using a lot of technical terms. Alternatively, if the user does not have technical knowledge, the generation unit can generate the notification content using simple language. Furthermore, the generation unit can adjust the way the notification content is expressed based on the user's level of expertise. In this way, by adjusting the use of technical terms in the notification content based on the user's level of expertise, it is possible to generate notification content that is easy for the user to understand.

[0161] The optimization unit can estimate the user's emotions and optimize the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit delays the timing of notifications to notify the user when the user is relaxed. Also, if the user is relaxed, the optimization unit can advance the timing of notifications to provide more notifications. Furthermore, if the user is in a hurry, the optimization unit can optimize the timing of notifications to provide necessary notifications in a short amount of time. In this way, by optimizing the timing of notifications according to the user's emotions, notifications can be provided at more effective times.

[0162] During optimization, the optimization unit can adjust the notification frequency based on the importance of the task. For example, the optimization unit can provide more frequent notifications for tasks with higher importance. The optimization unit can also reduce the notification frequency for tasks with lower importance. Furthermore, the optimization unit can dynamically adjust the notification frequency according to the importance of the task. In this way, by adjusting the notification frequency based on the importance of the task, more frequent notifications can be provided for important tasks.

[0163] During optimization, the optimization unit can apply different optimization algorithms depending on the task category. For example, if the task is health-related, the optimization unit can apply a health notification optimization algorithm. If the task is work-related, the optimization unit can also apply a work notification optimization algorithm. Furthermore, if the task is hobby-related, the optimization unit can also apply a hobby notification optimization algorithm. In this way, by applying different optimization algorithms depending on the task category, more appropriate notifications can be provided.

[0164] During optimization, the optimization unit can improve the accuracy of optimization based on the user's past notification results. For example, the optimization unit improves the accuracy of current notifications based on the user's past notification results. The optimization unit can also select the optimal notification method based on the user's past notification results. Furthermore, the optimization unit can improve the notification optimization algorithm by reflecting the user's past notification results. This makes it possible to improve the accuracy of optimization by referring to the user's past notification results.

[0165] The optimization unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the optimization unit can reduce the frequency of notifications and notify the user when the user is relaxed. The optimization unit can also increase the frequency of notifications to provide more notifications when the user is relaxed. Furthermore, if the user is in a hurry, the optimization unit can optimize the frequency of notifications to provide necessary notifications in a short period of time. In this way, by adjusting the frequency of notifications according to the user's emotions, notifications can be provided at a more effective frequency.

[0166] During optimization, the optimization unit can determine the priority of notifications based on the time of task submission. For example, the optimization unit prioritizes notifications of recently submitted tasks. The optimization unit can also prioritize notifications of older submitted tasks. Furthermore, the optimization unit can dynamically adjust the priority of notifications based on the time of submission. In this way, by determining the priority of notifications based on the time of task submission, it is possible to prioritize notifications of the most recent tasks.

[0167] During optimization, the optimization unit can adjust the order of notifications based on the relevance of the tasks. For example, the optimization unit prioritizes notifications of highly relevant tasks. The optimization unit can also postpone notifications of less relevant tasks. Furthermore, the optimization unit can dynamically adjust the order of notifications based on the relevance of the tasks. In this way, by adjusting the order of notifications based on the relevance of the tasks, it is possible to prioritize notifications of highly relevant tasks.

[0168] During optimization, the optimization unit can adjust the use of technical terms in the notification based on the user's level of expertise. For example, if the user has technical expertise, the optimization unit can use a lot of technical terms to provide the notification. Alternatively, if the user does not have technical expertise, the optimization unit can provide the notification in simple language. Furthermore, the optimization unit can adjust the way the notification is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the notification depending on the user's level of expertise, the notification can be made easier for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and promotion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by either the smart device 14 or the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and identifies tasks that need to be performed repeatedly. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of delays in the identified tasks. The promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and promotes the execution of the tasks based on the notification content. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and promotion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by either the smart glasses 214 or the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and identifies tasks that need to be repeated. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of delays in the identified tasks. The promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and promotes the execution of tasks based on the notification content. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and promotion unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by either the headset type terminal 314 or the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and identifies tasks that need to be repeated. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of delays in the identified tasks. The promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and promotes the execution of the tasks based on the notification content. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and promotion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by either the robot 414 or the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and identifies tasks that need to be repeated. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of delays in the identified tasks. The promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and promotes the execution of the tasks based on the notification content.

[0169] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0170] The task execution promotion system may further include a reward unit. The reward unit provides a reward when the user completes a task. For example, if the user completes taking out the trash, the reward unit awards points. The reward unit may also estimate the user's emotions and adjust the type of reward based on the estimated emotions. For example, if the user is feeling stressed, the reward unit may provide relaxing music. This may strengthen the user's motivation to complete the task.

[0171] The task execution promotion system may further include an advice unit. The advice unit provides advice to the user to efficiently complete the task. For example, when the user cleans, the advice unit suggests an efficient cleaning method. The advice unit may also estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is tired, the advice unit may suggest a task that can be completed in a short time. This allows the user to efficiently complete the task.

[0172] The task execution promotion system may further include a sharing unit. The sharing unit allows a user to share task progress with other users. For example, users can share task progress with family and friends and encourage each other. The sharing unit may also estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user feels motivated, the sharing unit may encourage the user to actively share their progress. This allows the user to receive social support for completing the task.

[0173] The task execution promotion system may further include a learning unit. The learning unit analyzes the user's past task execution data and learns the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, notifications are sent during that time period. The learning unit may also estimate the user's emotions and adjust the learning algorithm based on the estimated emotions. For example, if the user is feeling stressed, behavioral patterns related to stress reduction are preferentially learned. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0174] The task execution promotion system may further include a feedback unit. The feedback unit provides feedback when the user completes a task. For example, when the user completes the laundry, the feedback unit displays a message such as "Good job!" The feedback unit may also estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, when the user is relaxed, detailed feedback is provided. This allows the user to feel a sense of satisfaction when completing the task.

[0175] The task execution promotion system may further include a scheduling unit. The scheduling unit schedules the user's tasks and notifies the user at the optimal time. For example, if the user takes out the trash every Monday, the scheduling unit notifies the user at that time. The scheduling unit may also estimate the user's emotions and adjust the schedule based on the estimated emotions. For example, if the user is feeling stressed, the scheduling unit may ease the task schedule. This allows the user to complete the task without straining themselves.

[0176] The task execution promotion system may further include a customization unit. The customization unit customizes the content and method of notification according to the user's preferences. For example, if the user prefers voice notification, the notification is provided by voice. The customization unit may also estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is relaxed, detailed notifications are provided. This allows the user to receive task notifications in a way that suits them.

[0177] The task execution promotion system may further include a prediction unit. The prediction unit predicts task delays based on user behavior data. For example, if a user has forgotten to take out the trash in the past, the prediction unit predicts a delay the next time the user takes out the trash and notifies the user in advance. The prediction unit may also estimate the user's emotions and adjust the prediction algorithm based on the estimated emotions. For example, if the user is feeling stressed, the prediction unit may prioritize delays in tasks related to stress reduction. This allows the user to prevent task delays in advance.

[0178] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0179] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0180] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0181] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0182] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0183] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0184] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0185] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0186] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0187] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0188] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0189] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0190] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0191] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0192] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0193] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0194] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0195] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0196] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0197] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0198] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0199] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0200] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0201] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0202] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0203] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0204] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0205] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0206] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0207] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0208] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0209] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0210] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0211] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0212] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0213] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0214] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0215] The task execution promotion system may further include a data analysis unit. The data analysis unit analyzes the collected behavioral data in detail to understand the user's behavioral patterns. For example, if the user tends to complete tasks during a specific time period, a notification may be sent during that time period. The data analysis unit may also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis may prioritize behavioral patterns related to stress reduction. This makes it possible to provide optimal notification timing based on the user's behavioral patterns.

[0216] The task execution promotion system may further include a notification history unit. The notification history unit saves past notification history and records how the user responded to each notification. For example, if the user responded quickly to a particular notification, that notification method may be used preferentially. The notification history unit may also estimate the user's emotions and analyze the notification history based on the estimated emotions. For example, if the user is relaxed, a detailed notification history may be provided. This allows the optimal notification method to be selected based on the user's reactions.

[0217] The task execution promotion system may further include a task management unit. The task management unit centrally manages the user's tasks and sets priorities. For example, it may prioritize notifications of tasks with high importance. The task management unit may also estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize notifications of tasks related to stress reduction. This allows the user to manage tasks efficiently.

[0218] The task execution promotion system may further include a reminder unit. The reminder unit periodically sends reminders to the user so that the user does not forget to complete the task. For example, if the user tends to forget to drink water every day, the reminder unit periodically notifies the user to "drink water." The reminder unit may also estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit may send a reminder in kind words. This helps the user to remember to complete the task.

[0219] The task execution promotion system may further include an incentive unit. The incentive unit provides an incentive when the user completes a task. For example, if the user completes cleaning, the incentive unit provides a coupon. The incentive unit may also estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit may provide a service that helps the user relax. This strengthens the user's motivation to complete the task.

[0220] The processing flow of the second embodiment will be briefly explained below.

[0221] Step 1: The collection unit collects user behavioral data. The user behavioral data includes location information, app usage history, web browsing history, etc. The collection unit collects user behavioral data from smartphones and smart home devices. The collection unit can also estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed and data can be collected when the user is relaxed. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly. The analysis unit uses AI to analyze the user's behavioral patterns and identify tasks that need to be performed repeatedly. The analysis unit can also estimate the user's emotions and adjust the analysis method for behavioral patterns based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize analyzing behavioral patterns related to stress reduction. Step 3: The notification unit notifies the user of the task delay identified by the analysis unit. The notification unit includes a generation unit that generates specific content emphasizing that the task delay is causing inconvenience to others. The notification unit may also include an optimization unit that optimizes the timing and frequency of notifications. For example, the notification unit estimates the user's emotions and optimizes the timing of notifications based on the estimated emotions. Step 4: The promotion unit promotes task execution based on the content notified by the notification unit. The promotion unit sets a reminder based on the content notified and promotes task execution. The promotion unit can also estimate the user's emotions and adjust the method of promoting task execution based on the estimated emotions. For example, if the user is feeling stressed, the promotion unit can encourage the user to complete the task using kind words.

[0222] 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.

[0223] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0224] 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.

[0225] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0226] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0240] 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.

[0241] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0242] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0243] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0256] 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.

[0257] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0258] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0259] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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).

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] 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.

[0272] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0273] 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.

[0274] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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).

[0279] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0280] 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."

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0288] 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.

[0289] 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.

[0290] 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.

[0291] 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.

[0292] 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.

[0293] [Explanation of symbols]

[0294] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user behavior data; an analysis unit that analyzes the data collected by the collection unit and identifies tasks that need to be performed repeatedly; a notification unit that notifies a delay in the task identified by the analysis unit; a promotion unit that promotes the execution of a task based on the content notified by the notification unit; A system characterized by:

2. The notification unit It has a generator that generates specific content that emphasizes that delaying a task causes inconvenience to others.

2. The system of claim 1.

3. The notification unit Equipped with an optimization section that adjusts the timing and frequency of notifications 2. The system of claim 1.

4. The collecting unit Demonstrate specific methods for collecting user behavior data from smartphones or smart home devices 2. The system of claim 1.

5. The analysis unit Analyze the collected data and analyze user behavior patterns 2. The system of claim 1.

6. The promotion unit Promote task execution based on notifications 2. The system of claim 1.

7. The collecting unit The system estimates user emotions and adjusts the timing of behavioral data collection based on the estimated user emotions.

2. The system of claim 1.

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

9. The collecting unit Demonstrate specific ways to filter behavioral data based on the user's current life situation or interests when collecting it.

2. The system of claim 1.

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