system

Abstract

An object of a system according to an embodiment is to enable an ADHD user to efficiently perform task management.SOLUTION: A system includes a collection unit, a proposal unit, a monitoring unit, an advice unit, and a reminding unit. The collection unit collects characteristics or past task management of a user. The proposal unit proposes a scheduling method based on the data collected by the collection unit. The monitoring unit monitors task progress of a user in real time. The advice unit provides advice based on the data obtained by the monitoring unit. The reminding unit transmits an alert at an appropriate timing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult for users with ADHD to efficiently manage tasks, and there is room for improvement.

[0005] The system according to the embodiment aims to enable users with ADHD to efficiently manage tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a proposal unit, a monitoring unit, an advice unit, and a reminder unit. The collection unit collects data on user characteristics or past task management. The proposal unit proposes a scheduling method based on the data collected by the collection unit. The monitoring unit monitors the user's task progress in real time. The advice unit provides advice based on the data obtained by the monitoring unit. The reminder unit sends alerts at appropriate times. [Effects of the Invention]

[0007] The system according to the embodiment allows a user with ADHD to efficiently manage tasks. [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) An AI task coaching system according to an embodiment of the present invention is a system that collects user characteristics and past task management data, proposes an efficient scheduling method, monitors task progress in real time, provides appropriate advice, and sends alerts at appropriate times. For example, the AI ​​task coaching system collects user characteristics and past task management data and proposes an efficient scheduling method. Next, the AI ​​task coaching system monitors the user's task progress in real time and provides appropriate advice. Furthermore, the AI ​​task coaching system sends alerts at appropriate times. This enables people with ADHD characteristics to efficiently manage tasks in business situations, contributing to the resolution of social issues. This enables people with ADHD characteristics to efficiently manage tasks in business situations, contributing to the resolution of social issues. For example, by collecting user characteristics and past task management data and proposing an efficient scheduling method, the user can find the task management method that is best for them. Furthermore, by monitoring task progress in real time and providing appropriate advice, the user can easily understand the progress of their tasks. Furthermore, by sending alerts at appropriate times, the user can remember to complete tasks.

[0029] An AI task coaching system according to an embodiment includes a collection unit, a suggestion unit, a monitoring unit, an advice unit, and a reminder unit. The collection unit collects data on user characteristics or past task management. The collection unit, for example, collects past task management data or user input information to understand the user's characteristics. The collection unit may also include AI processing. The suggestion unit proposes a scheduling method based on the data collected by the collection unit. The suggestion unit may, for example, include AI processing to propose an efficient scheduling method. The monitoring unit monitors the user's task progress in real time. The monitoring unit may, for example, include AI processing to monitor the user's task progress in real time. The advice unit provides advice based on the data obtained by the monitoring unit. The advice unit may, for example, include AI processing to provide appropriate advice based on the user's task progress. The reminder unit sends alerts at appropriate times. The reminder unit may, for example, include AI processing to send alerts at appropriate times so that the user does not forget to complete tasks. As a result, the AI ​​task coaching system according to the embodiment enables efficient scheduling, real-time monitoring, advice, and reminders based on the user's characteristics and past task management data.

[0030] The collection unit can analyze the user's past task management data and select a data collection method. The collection unit analyzes the user's past task management data and selects a data collection method. For example, the collection unit analyzes data from task management tools used by the user in the past and selects the most effective collection method. The collection unit can also adjust the frequency and timing of data collection based on the user's past task completion rate. The collection unit can also analyze the user's past task management patterns and select the optimal data collection method (voice, text, etc.). In this way, the optimal data collection method can be selected by analyzing the past task management data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past task management data into a generation AI and have the generation AI select the optimal data collection method.

[0031] The collection unit may filter data based on the user's current project or area of ​​interest when collecting data. The collection unit may filter data based on the user's current project or area of ​​interest when collecting data. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may also prioritize collecting highly relevant data based on the user's area of ​​interest. The collection unit may also limit the scope of data collection based on topics in which the user has previously shown interest. This allows highly relevant data to be collected by filtering data based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's project or area of ​​interest to a generation AI and have the generation AI perform the filtering.

[0032] The collection unit can select a collection means according to the user's input method when collecting data. The collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Also, if the user prefers text input, the collection unit can preferentially collect text data. Also, if the user prefers image input, the collection unit can preferentially collect image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is moving, the collection unit can also collect highly relevant data based on the user's current location. Furthermore, when the user is staying in a specific place, the collection unit can also prioritize collecting data related to that place. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0034] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can collect related data, for example, based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect highly relevant data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method based on the user's past feedback when collecting data. The collection unit customizes the collection method based on the user's past feedback when collecting data. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select an optimal collection means from the user's past feedback. The collection unit can also adjust the frequency and timing of data collection by reflecting the user's feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. For example, the suggestion unit makes a detailed proposal for a task with high importance. The suggestion unit can also make a concise proposal for a task with low importance. The suggestion unit can also adjust the priority of the proposal according to the importance of the task. This enables more effective proposals by adjusting the level of detail of the proposal according to the importance of the task. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0037] The suggestion unit can apply different suggestion algorithms depending on the task category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the task category when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for project management to a project management task. The suggestion unit can also apply a suggestion algorithm specialized for daily work to a daily work task. The suggestion unit can also apply a suggestion algorithm that allows for a quick response to an urgent task. In this way, applying different suggestion algorithms depending on the task category enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input task category data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0038] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can make a similar suggestion based on a suggestion that the user previously accepted. The suggestion unit can also make an improved suggestion based on a suggestion that the user previously rejected. The suggestion unit can also analyze the user's past suggestion results and make an optimal suggestion. This improves the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0039] The suggestion unit can determine the priority of the proposals based on the submission times of the tasks when making the proposals. The suggestion unit can determine the priority of the proposals based on the submission times of the tasks when making the proposals. For example, the suggestion unit prioritizes proposals for tasks with an approaching deadline. The suggestion unit can also postpone proposals for tasks with a more distant submission time. The suggestion unit can also adjust the priority of the proposals according to the submission times. This enables more effective proposals by determining the priority of the proposals based on the submission times of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task submission time data into a generation AI and cause the generation AI to determine the priority of the proposals.

[0040] The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant tasks. The suggestion unit can also postpone proposing less relevant tasks. The suggestion unit can also adjust the order of proposals according to the relevance of the tasks. This enables more effective proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0041] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal using easy-to-understand language. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This enables more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0042] The monitoring unit can improve the accuracy of monitoring based on the interrelationships between tasks during monitoring. The monitoring unit improves the accuracy of monitoring based on the interrelationships between tasks during monitoring. The monitoring unit improves the accuracy of monitoring, for example, by taking into account dependencies between tasks. The monitoring unit can also monitor related tasks based on the progress of the tasks. The monitoring unit can also analyze the interrelationships between tasks and select an optimal monitoring method. In this way, the accuracy of monitoring is improved by taking the interrelationships between tasks into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task interrelationship data into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0043] The monitoring unit can perform monitoring based on attribute information of the task submitter during monitoring. The monitoring unit can perform monitoring based on attribute information of the task submitter during monitoring. The monitoring unit can improve the accuracy of monitoring by taking into account, for example, the job title and job content of the task submitter. The monitoring unit can also adjust the monitoring criteria based on the task submitter's past performance. The monitoring unit can also analyze the attribute information of the task submitter and select the optimal monitoring method. This enables more appropriate monitoring by taking into account the attribute information of the task submitter. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input attribute information data of the task submitter into a generation AI and have the generation AI perform monitoring.

[0044] The monitoring unit can weight the monitoring based on the frequency of task submission during monitoring. The monitoring unit weights the monitoring based on the frequency of task submission during monitoring. For example, the monitoring unit performs detailed monitoring for tasks that are submitted frequently. The monitoring unit can also perform simplified monitoring for tasks that are submitted infrequently. The monitoring unit can also adjust the monitoring weight according to the submission frequency. Thus, weighting the monitoring based on the frequency of task submission enables more effective monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task submission frequency data to a generation AI and cause the generation AI to perform monitoring weighting.

[0045] The monitoring unit can perform monitoring based on the geographic distribution of tasks during monitoring. The monitoring unit performs monitoring based on the geographic distribution of tasks during monitoring. The monitoring unit adjusts the accuracy of monitoring based on, for example, the location where the task is performed. The monitoring unit can also monitor geographically dispersed tasks by region. The monitoring unit can also analyze the geographic distribution of tasks and select an optimal monitoring method. This enables more appropriate monitoring by taking the geographic distribution of tasks into consideration. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographic distribution data of tasks to a generation AI and have the generation AI perform monitoring.

[0046] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the task during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the task during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the task. The monitoring unit can also perform monitoring by referring to literature related to the task based on the progress of the task. The monitoring unit can also analyze literature related to the task and select an optimal monitoring method. In this way, the accuracy of monitoring is improved by referring to literature related to the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input literature data related to the task into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0047] The monitoring unit can perform monitoring based on the market value of the task during monitoring. The monitoring unit can perform monitoring based on the market value of the task during monitoring. For example, the monitoring unit can perform detailed monitoring for tasks with high market value. The monitoring unit can also perform simplified monitoring for tasks with low market value. The monitoring unit can also analyze the market value of the task and select the optimal monitoring method. This enables more effective monitoring by taking the market value of the task into consideration. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input market value data of the task into the generation AI and have the generation AI perform monitoring.

[0048] The advice unit can adjust the level of detail of the advice based on the importance of the task when giving advice. The advice unit can adjust the level of detail of the advice based on the importance of the task when giving advice. For example, the advice unit provides detailed advice for a task with high importance. The advice unit can also provide concise advice for a task with low importance. The advice unit can also adjust the priority of the advice according to the importance of the task. This allows for more effective advice by adjusting the level of detail of the advice according to the importance of the task. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI, for example. For example, the advice unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0049] The advice unit can apply different advice algorithms depending on the task category when providing advice. The advice unit can apply different advice algorithms depending on the task category when providing advice. For example, the advice unit can apply an advice algorithm specialized for project management to a project management task. The advice unit can also apply an advice algorithm specialized for daily work to a daily work task. The advice unit can also apply an advice algorithm that allows for quick response to an urgent task. In this way, applying different advice algorithms depending on the task category enables more appropriate advice. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can input task category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0050] When providing advice, the advice unit can improve the accuracy of the advice based on the user's past advice results. When providing advice, the advice unit improves the accuracy of the advice based on the user's past advice results. The advice unit, for example, provides similar advice based on advice that the user has previously accepted. The advice unit can also provide improved advice based on advice that the user has previously rejected. The advice unit can also analyze the user's past advice results and provide optimal advice. This improves the accuracy of the advice by referring to the user's past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0051] The advice unit can determine the priority of advice based on the submission time of the task when giving advice. The advice unit determines the priority of advice based on the submission time of the task when giving advice. For example, the advice unit prioritizes advice for tasks with an approaching deadline. The advice unit can also postpone providing advice for tasks with a distant submission time. The advice unit can also adjust the priority of advice according to the submission time. In this way, more effective advice can be provided by determining the priority of advice based on the submission time of the task. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input task submission time data to the generation AI and cause the generation AI to determine the priority of advice.

[0052] The advice unit can adjust the order of advice based on the relevance of tasks when giving advice. The advice unit adjusts the order of advice based on the relevance of tasks when giving advice. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can also give advice on less relevant tasks later. The advice unit can also adjust the order of advice according to the relevance of tasks. In this way, adjusting the order of advice based on the relevance of tasks enables more effective advice. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of advice.

[0053] The advice unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. The advice unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, if the user has technical expertise, the advice unit provides advice using technical terms. Furthermore, if the user does not have technical expertise, the advice unit can provide advice in easy-to-understand language. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. This enables more appropriate advice to be provided by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms.

[0054] The reminding unit can adjust the frequency of reminders based on the importance of the task when reminding. The reminding unit can adjust the frequency of reminders based on the importance of the task when reminding. For example, the reminding unit frequently reminds tasks with high importance. The reminding unit can also reduce the frequency of reminders for tasks with low importance. The reminding unit can also adjust the frequency of reminders according to the importance of the task. This allows for more effective reminders by adjusting the frequency of reminders based on the importance of the task. Some or all of the above-mentioned processing in the reminding unit may be performed using AI, for example, or may be performed without using AI. For example, the reminding unit can input task importance data into the generation AI and have the generation AI adjust the frequency of reminders.

[0055] The reminding unit can apply different reminding methods depending on the task category when reminding. The reminding unit can apply different reminding methods depending on the task category when reminding. For example, the reminding unit applies a reminding method specialized for project management to project management tasks. The reminding unit can also apply a reminding method specialized for daily work to daily work tasks. The reminding unit can also apply a reminding method that allows for a quick response to urgent tasks. In this way, applying different reminding methods depending on the task category enables more appropriate reminding. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input task category data into the generation AI and cause the generation AI to apply the reminding method.

[0056] The reminding unit can improve the accuracy of reminders based on the user's past reminder results when reminding. The reminding unit improves the accuracy of reminders based on the user's past reminder results when reminding. For example, the reminding unit performs a similar reminder based on reminders that the user previously accepted. The reminding unit can also perform an improved reminder based on reminders that the user previously rejected. The reminding unit can also analyze the user's past reminder results and perform an optimal reminder. This improves the accuracy of reminders by referring to the user's past reminder results. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's past reminder result data into the generation AI and cause the generation AI to improve the accuracy of reminders.

[0057] The reminding unit can determine the priority of reminders based on the submission time of the tasks when a reminder is given. The reminding unit determines the priority of reminders based on the submission time of the tasks when a reminder is given. For example, the reminding unit prioritizes reminders for tasks with an approaching deadline. The reminding unit can also postpone reminders for tasks with a distant submission time. The reminding unit can also adjust the priority of reminders according to the submission time. This enables more effective reminders by determining the priority of reminders based on the submission time of the tasks. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input task submission time data into the generation AI and have the generation AI determine the priority of reminders.

[0058] The reminding unit can adjust the order of reminders based on the relevance of tasks when reminding. The reminding unit adjusts the order of reminders based on the relevance of tasks when reminding. For example, the reminding unit prioritizes reminders for highly relevant tasks. The reminding unit can also postpone reminders for less relevant tasks. The reminding unit can also adjust the order of reminders according to the relevance of tasks. This allows for more effective reminders by adjusting the order of reminders based on the relevance of tasks. Some or all of the above-described processing in the reminding unit may be performed using, or without, AI, for example. For example, the reminding unit can input task relevance data into a generation AI and have the generation AI adjust the order of reminders.

[0059] The reminding unit can adjust the use of technical terms in the reminder according to the user's level of expertise when reminding. The reminding unit can adjust the use of technical terms in the reminder according to the user's level of expertise when reminding. For example, if the user has technical knowledge, the reminding unit can remind the user using technical terms. Furthermore, if the user does not have technical knowledge, the reminding unit can remind the user using easy-to-understand language. The reminding unit can also adjust the use of technical terms in the reminder according to the user's level of expertise. This enables more appropriate reminders by adjusting the use of technical terms in the reminder according to the user's level of expertise. Some or all of the above-described processing in the reminding unit may be performed using AI, for example, or may be performed without using AI. For example, the reminding unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past task management data and select a data collection method. For example, it can analyze data from task management tools used by the user in the past and select the most effective collection method. It can also adjust the frequency and timing of data collection based on the user's past task completion rate. It can also analyze the user's past task management patterns and select the optimal data collection method (audio, text, etc.). In this way, the optimal data collection method can be selected by analyzing past task management data.

[0062] When collecting data, the collection unit can filter the data based on the user's current project or area of ​​interest. For example, only data related to the project the user is currently working on can be collected. Also, the collection unit can prioritize collection of highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can limit the scope of data collection based on topics in which the user has shown interest in the past. In this way, highly relevant data can be collected by filtering data based on the user's current project or area of ​​interest.

[0063] When collecting 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 prefers voice input, it can prioritize collection of voice data. Also, if the user prefers text input, it can prioritize collection of text data. Furthermore, if the user prefers image input, it can prioritize collection of image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0064] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to that area can be collected preferentially. Also, if the user is moving, highly relevant data can be collected based on the user's current location. Furthermore, if the user is staying in a specific location, data related to that location can be collected preferentially. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information.

[0065] When collecting data, the collection unit can analyze the user's social media activities and collect relevant data. For example, the collection unit can collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect highly relevant data. Furthermore, the collection unit can also collect relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data on user characteristics or past task management. For example, the collection unit collects past task management data or user input information in order to understand the user's characteristics. The collection unit may also include AI processing. Step 2: The suggestion unit proposes a scheduling method based on the data collected by the collection unit. The suggestion unit may include, for example, AI processing to propose an efficient scheduling method. Step 3: The monitoring unit monitors the user's task progress in real time. The monitoring unit may include, for example, AI processing to monitor the user's task progress in real time. Step 4: The advice unit provides advice based on the data obtained by the monitoring unit. The advice unit may include, for example, AI processing to provide appropriate advice based on the user's task progress. Step 5: The reminder unit sends an alert at an appropriate time. The reminder unit may include, for example, AI processing to send an alert at an appropriate time so that the user does not forget the task.

[0068] (Example 2) An AI task coaching system according to an embodiment of the present invention is a system that collects user characteristics and past task management data, proposes an efficient scheduling method, monitors task progress in real time, provides appropriate advice, and sends alerts at appropriate times. For example, the AI ​​task coaching system collects user characteristics and past task management data and proposes an efficient scheduling method. Next, the AI ​​task coaching system monitors the user's task progress in real time and provides appropriate advice. Furthermore, the AI ​​task coaching system sends alerts at appropriate times. This enables people with ADHD characteristics to efficiently manage tasks in business situations, contributing to the resolution of social issues. This enables people with ADHD characteristics to efficiently manage tasks in business situations, contributing to the resolution of social issues. For example, by collecting user characteristics and past task management data and proposing an efficient scheduling method, the user can find the task management method that is best for them. Furthermore, by monitoring task progress in real time and providing appropriate advice, the user can easily understand the progress of their tasks. Furthermore, by sending alerts at appropriate times, the user can remember to complete tasks.

[0069] An AI task coaching system according to an embodiment includes a collection unit, a suggestion unit, a monitoring unit, an advice unit, and a reminder unit. The collection unit collects data on user characteristics or past task management. The collection unit, for example, collects past task management data or user input information to understand the user's characteristics. The collection unit may also include AI processing. The suggestion unit proposes a scheduling method based on the data collected by the collection unit. The suggestion unit may, for example, include AI processing to propose an efficient scheduling method. The monitoring unit monitors the user's task progress in real time. The monitoring unit may, for example, include AI processing to monitor the user's task progress in real time. The advice unit provides advice based on the data obtained by the monitoring unit. The advice unit may, for example, include AI processing to provide appropriate advice based on the user's task progress. The reminder unit sends alerts at appropriate times. The reminder unit may, for example, include AI processing to send alerts at appropriate times so that the user does not forget to complete tasks. As a result, the AI ​​task coaching system according to the embodiment enables efficient scheduling, real-time monitoring, advice, and reminders based on the user's characteristics and past task management data.

[0070] The AI ​​task coaching system further includes a collection unit that estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. The collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection and collects data when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can collect detailed data at that time, which can be useful for efficient scheduling. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0071] The collection unit can analyze the user's past task management data and select a data collection method. The collection unit analyzes the user's past task management data and selects a data collection method. For example, the collection unit analyzes data from task management tools used by the user in the past and selects the most effective collection method. The collection unit can also adjust the frequency and timing of data collection based on the user's past task completion rate. The collection unit can also analyze the user's past task management patterns and select the optimal data collection method (voice, text, etc.). In this way, the optimal data collection method can be selected by analyzing the past task management data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past task management data into a generation AI and have the generation AI select the optimal data collection method.

[0072] The collection unit may filter data based on the user's current project or area of ​​interest when collecting data. The collection unit may filter data based on the user's current project or area of ​​interest when collecting data. For example, the collection unit may collect only data related to the project the user is currently working on. The collection unit may also prioritize collecting highly relevant data based on the user's area of ​​interest. The collection unit may also limit the scope of data collection based on topics in which the user has previously shown interest. This allows highly relevant data to be collected by filtering data based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's project or area of ​​interest to a generation AI and have the generation AI perform the filtering.

[0073] The collection unit can select a collection means according to the user's input method when collecting data. The collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Also, if the user prefers text input, the collection unit can preferentially collect text data. Also, if the user prefers image input, the collection unit can preferentially collect image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important data and prioritizes collecting more important data. The collection unit can also prioritize collecting detailed data when the user is relaxed. The collection unit can also prioritize collecting data that can be collected quickly when the user is in a hurry. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is moving, the collection unit can also collect highly relevant data based on the user's current location. Furthermore, when the user is staying in a specific place, the collection unit can also prioritize collecting data related to that place. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit can collect related data, for example, based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect highly relevant data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0077] The collection unit can customize the collection method based on the user's past feedback when collecting data. The collection unit customizes the collection method based on the user's past feedback when collecting data. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select an optimal collection means from the user's past feedback. The collection unit can also adjust the frequency and timing of data collection by reflecting the user's feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0078] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide quick and understandable suggestions. This enables more appropriate suggestions by adjusting the way the suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0079] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of the task when making the proposal. For example, the suggestion unit makes a detailed proposal for a task with high importance. The suggestion unit can also make a concise proposal for a task with low importance. The suggestion unit can also adjust the priority of the proposal according to the importance of the task. This enables more effective proposals by adjusting the level of detail of the proposal according to the importance of the task. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0080] The suggestion unit can apply different suggestion algorithms depending on the task category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the task category when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for project management to a project management task. The suggestion unit can also apply a suggestion algorithm specialized for daily work to a daily work task. The suggestion unit can also apply a suggestion algorithm that allows for a quick response to an urgent task. In this way, applying different suggestion algorithms depending on the task category enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input task category data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0081] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can make a similar suggestion based on a suggestion that the user previously accepted. The suggestion unit can also make an improved suggestion based on a suggestion that the user previously rejected. The suggestion unit can also analyze the user's past suggestion results and make an optimal suggestion. This improves the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0082] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide short, quickly understandable suggestions. This enables more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0083] The suggestion unit can determine the priority of the proposals based on the submission times of the tasks when making the proposals. The suggestion unit can determine the priority of the proposals based on the submission times of the tasks when making the proposals. For example, the suggestion unit prioritizes proposals for tasks with an approaching deadline. The suggestion unit can also postpone proposals for tasks with a more distant submission time. The suggestion unit can also adjust the priority of the proposals according to the submission times. This enables more effective proposals by determining the priority of the proposals based on the submission times of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task submission time data into a generation AI and cause the generation AI to determine the priority of the proposals.

[0084] The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant tasks. The suggestion unit can also postpone proposing less relevant tasks. The suggestion unit can also adjust the order of proposals according to the relevance of the tasks. This enables more effective proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0085] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal using easy-to-understand language. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This enables more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0086] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the frequency of monitoring and monitor when the user is relaxed. The monitoring unit can also perform detailed monitoring when the user is concentrating. The monitoring unit can also temporarily stop monitoring when the user is tired and resume monitoring after the user has rested. This enables more appropriate monitoring by adjusting the monitoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring criteria.

[0087] The monitoring unit can improve the accuracy of monitoring based on the interrelationships between tasks during monitoring. The monitoring unit improves the accuracy of monitoring based on the interrelationships between tasks during monitoring. The monitoring unit improves the accuracy of monitoring, for example, by taking into account dependencies between tasks. The monitoring unit can also monitor related tasks based on the progress of the tasks. The monitoring unit can also analyze the interrelationships between tasks and select an optimal monitoring method. In this way, the accuracy of monitoring is improved by taking the interrelationships between tasks into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task interrelationship data into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0088] The monitoring unit can perform monitoring based on attribute information of the task submitter during monitoring. The monitoring unit can perform monitoring based on attribute information of the task submitter during monitoring. The monitoring unit can improve the accuracy of monitoring by taking into account, for example, the job title and job content of the task submitter. The monitoring unit can also adjust the monitoring criteria based on the task submitter's past performance. The monitoring unit can also analyze the attribute information of the task submitter and select the optimal monitoring method. This enables more appropriate monitoring by taking into account the attribute information of the task submitter. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input attribute information data of the task submitter into a generation AI and have the generation AI perform monitoring.

[0089] The monitoring unit can weight the monitoring based on the frequency of task submission during monitoring. The monitoring unit weights the monitoring based on the frequency of task submission during monitoring. For example, the monitoring unit performs detailed monitoring for tasks that are submitted frequently. The monitoring unit can also perform simplified monitoring for tasks that are submitted infrequently. The monitoring unit can also adjust the monitoring weight according to the submission frequency. Thus, weighting the monitoring based on the frequency of task submission enables more effective monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input task submission frequency data to a generation AI and cause the generation AI to perform monitoring weighting.

[0090] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user emotions. For example, when the user is feeling stressed, the monitoring unit can prioritize displaying monitoring results with high importance. The monitoring unit can also display detailed monitoring results when the user is relaxed. The monitoring unit can also prioritize displaying monitoring results that can be quickly understood when the user is in a hurry. This enables more appropriate information to be provided by adjusting the order in which the monitoring results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the monitoring results.

[0091] The monitoring unit can perform monitoring based on the geographic distribution of tasks during monitoring. The monitoring unit performs monitoring based on the geographic distribution of tasks during monitoring. The monitoring unit adjusts the accuracy of monitoring based on, for example, the location where the task is performed. The monitoring unit can also monitor geographically dispersed tasks by region. The monitoring unit can also analyze the geographic distribution of tasks and select an optimal monitoring method. This enables more appropriate monitoring by taking the geographic distribution of tasks into consideration. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographic distribution data of tasks to a generation AI and have the generation AI perform monitoring.

[0092] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the task during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the task during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the task. The monitoring unit can also perform monitoring by referring to literature related to the task based on the progress of the task. The monitoring unit can also analyze literature related to the task and select an optimal monitoring method. In this way, the accuracy of monitoring is improved by referring to literature related to the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input literature data related to the task into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0093] The monitoring unit can perform monitoring based on the market value of the task during monitoring. The monitoring unit can perform monitoring based on the market value of the task during monitoring. For example, the monitoring unit can perform detailed monitoring for tasks with high market value. The monitoring unit can also perform simplified monitoring for tasks with low market value. The monitoring unit can also analyze the market value of the task and select the optimal monitoring method. This enables more effective monitoring by taking the market value of the task into consideration. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input market value data of the task into the generation AI and have the generation AI perform monitoring.

[0094] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-understand advice. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide advice that can be quickly understood. This enables more appropriate advice to be provided by adjusting the way the advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0095] The advice unit can adjust the level of detail of the advice based on the importance of the task when giving advice. The advice unit can adjust the level of detail of the advice based on the importance of the task when giving advice. For example, the advice unit provides detailed advice for a task with high importance. The advice unit can also provide concise advice for a task with low importance. The advice unit can also adjust the priority of the advice according to the importance of the task. This allows for more effective advice by adjusting the level of detail of the advice according to the importance of the task. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI, for example. For example, the advice unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0096] The advice unit can apply different advice algorithms depending on the task category when providing advice. The advice unit can apply different advice algorithms depending on the task category when providing advice. For example, the advice unit can apply an advice algorithm specialized for project management to a project management task. The advice unit can also apply an advice algorithm specialized for daily work to a daily work task. The advice unit can also apply an advice algorithm that allows for quick response to an urgent task. In this way, applying different advice algorithms depending on the task category enables more appropriate advice. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can input task category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0097] When providing advice, the advice unit can improve the accuracy of the advice based on the user's past advice results. When providing advice, the advice unit improves the accuracy of the advice based on the user's past advice results. The advice unit, for example, provides similar advice based on advice that the user has previously accepted. The advice unit can also provide improved advice based on advice that the user has previously rejected. The advice unit can also analyze the user's past advice results and provide optimal advice. This improves the accuracy of the advice by referring to the user's past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0098] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, if the user is feeling stressed, the advice unit can provide short, to-the-point advice. If the user is relaxed, the advice unit can also provide detailed advice. If the user is in a hurry, the advice unit can also provide short, quickly understandable advice. This enables more appropriate advice to be provided by adjusting the length of the advice according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit can be performed using an AI, for example, or without an AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0099] The advice unit can determine the priority of advice based on the submission time of the task when giving advice. The advice unit determines the priority of advice based on the submission time of the task when giving advice. For example, the advice unit prioritizes advice for tasks with an approaching deadline. The advice unit can also postpone providing advice for tasks with a distant submission time. The advice unit can also adjust the priority of advice according to the submission time. In this way, more effective advice can be provided by determining the priority of advice based on the submission time of the task. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input task submission time data to the generation AI and cause the generation AI to determine the priority of advice.

[0100] The advice unit can adjust the order of advice based on the relevance of tasks when giving advice. The advice unit adjusts the order of advice based on the relevance of tasks when giving advice. For example, the advice unit gives priority to advice on highly relevant tasks. The advice unit can also give advice on less relevant tasks later. The advice unit can also adjust the order of advice according to the relevance of tasks. In this way, adjusting the order of advice based on the relevance of tasks enables more effective advice. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of advice.

[0101] The advice unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. The advice unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, if the user has technical expertise, the advice unit provides advice using technical terms. Furthermore, if the user does not have technical expertise, the advice unit can provide advice in easy-to-understand language. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. This enables more appropriate advice to be provided by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms.

[0102] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. The reminder unit can estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can reduce the frequency of reminders and remind the user when the user is relaxed. The reminder unit can also remind the user when the user is concentrating. The reminder unit can also temporarily stop reminders when the user is tired and resume them after the user has rested. This allows for more appropriate reminders by adjusting the timing of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder unit can be performed using AI, for example, or without AI. For example, the reminder unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of reminders.

[0103] The reminding unit can adjust the frequency of reminders based on the importance of the task when reminding. The reminding unit can adjust the frequency of reminders based on the importance of the task when reminding. For example, the reminding unit frequently reminds tasks with high importance. The reminding unit can also reduce the frequency of reminders for tasks with low importance. The reminding unit can also adjust the frequency of reminders according to the importance of the task. This allows for more effective reminders by adjusting the frequency of reminders based on the importance of the task. Some or all of the above-mentioned processing in the reminding unit may be performed using AI, for example, or may be performed without using AI. For example, the reminding unit can input task importance data into the generation AI and have the generation AI adjust the frequency of reminders.

[0104] The reminding unit can apply different reminding methods depending on the task category when reminding. The reminding unit can apply different reminding methods depending on the task category when reminding. For example, the reminding unit applies a reminding method specialized for project management to project management tasks. The reminding unit can also apply a reminding method specialized for daily work to daily work tasks. The reminding unit can also apply a reminding method that allows for a quick response to urgent tasks. In this way, applying different reminding methods depending on the task category enables more appropriate reminding. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input task category data into the generation AI and cause the generation AI to apply the reminding method.

[0105] The reminding unit can improve the accuracy of reminders based on the user's past reminder results when reminding. The reminding unit improves the accuracy of reminders based on the user's past reminder results when reminding. For example, the reminding unit performs a similar reminder based on reminders that the user previously accepted. The reminding unit can also perform an improved reminder based on reminders that the user previously rejected. The reminding unit can also analyze the user's past reminder results and perform an optimal reminder. This improves the accuracy of reminders by referring to the user's past reminder results. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's past reminder result data into the generation AI and cause the generation AI to improve the accuracy of reminders.

[0106] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user emotions. The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can provide a simple and easy-to-understand reminder. If the user is relaxed, the reminder unit can also provide a detailed reminder. If the user is in a hurry, the reminder unit can also provide a quickly understandable reminder. This allows for more appropriate reminders by adjusting the content of the reminder according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reminder unit can be performed using, for example, AI, or without AI. For example, the reminder unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the reminder.

[0107] The reminding unit can determine the priority of reminders based on the submission time of the tasks when a reminder is given. The reminding unit determines the priority of reminders based on the submission time of the tasks when a reminder is given. For example, the reminding unit prioritizes reminders for tasks with an approaching deadline. The reminding unit can also postpone reminders for tasks with a distant submission time. The reminding unit can also adjust the priority of reminders according to the submission time. This enables more effective reminders by determining the priority of reminders based on the submission time of the tasks. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input task submission time data into the generation AI and have the generation AI determine the priority of reminders.

[0108] The reminding unit can adjust the order of reminders based on the relevance of tasks when reminding. The reminding unit adjusts the order of reminders based on the relevance of tasks when reminding. For example, the reminding unit prioritizes reminders for highly relevant tasks. The reminding unit can also postpone reminders for less relevant tasks. The reminding unit can also adjust the order of reminders according to the relevance of tasks. This allows for more effective reminders by adjusting the order of reminders based on the relevance of tasks. Some or all of the above-described processing in the reminding unit may be performed using, or without, AI, for example. For example, the reminding unit can input task relevance data into a generation AI and have the generation AI adjust the order of reminders.

[0109] The reminding unit can adjust the use of technical terms in the reminder according to the user's level of expertise when reminding. The reminding unit can adjust the use of technical terms in the reminder according to the user's level of expertise when reminding. For example, if the user has technical knowledge, the reminding unit can remind the user using technical terms. Furthermore, if the user does not have technical knowledge, the reminding unit can remind the user using easy-to-understand language. The reminding unit can also adjust the use of technical terms in the reminder according to the user's level of expertise. This enables more appropriate reminders by adjusting the use of technical terms in the reminder according to the user's level of expertise. Some or all of the above-described processing in the reminding unit may be performed using AI, for example, or may be performed without using AI. For example, the reminding unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, advice unit, and reminder unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect user characteristics and past task management data using the camera 42 and microphone 38B of the smart device 14. The suggestion unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes an efficient scheduling method based on the collected data. The monitoring unit, implemented, for example, by the control unit 46A of the smart device 14, monitors the user's task progress in real time. The advice unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides appropriate advice based on data obtained from the monitoring unit. The reminder unit, implemented, for example, by the control unit 46A of the smart device 14, sends alerts at appropriate times. The collection unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, advice unit, and reminder unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect user characteristics and past task management data using the camera 42 and microphone 238 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an efficient scheduling method based on the collected data. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the user's task progress in real time. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on data obtained from the monitoring unit. The reminder unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends alerts at appropriate times. The collection unit, for example, estimates the user's emotions using the specific processing unit 290 of the data processing device 12 and adjusts the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, advice unit, and reminder unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect user characteristics and past task management data using the camera 42 and microphone 238 of the headset-type terminal 314. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes an efficient scheduling method based on the collected data. The monitoring unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and monitors the user's task progress in real time. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on data obtained from the monitoring unit. The reminder unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and sends alerts at appropriate times. The collection unit, for example, estimates the user's emotions using the specific processing unit 290 of the data processing device 12 and adjusts the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, advice unit, and reminder unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect user characteristics and past task management data using the camera 42 and microphone 238 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an efficient scheduling method based on the collected data. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the user's task progress in real time. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on data obtained from the monitoring unit. The reminder unit is realized, for example, by the control unit 46A of the robot 414 and sends alerts at appropriate times. The collection unit, for example, estimates the user's emotions using the specific processing unit 290 of the data processing device 12 and adjusts the timing of data collection.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of suggestions can be reduced and suggestions can be made when the user is relaxed. Also, if the user is concentrating, detailed suggestions can be made at that time. Furthermore, if the user is tired, suggestions can be temporarily stopped and resumed after the user has rested. In this way, more appropriate suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0112] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced and monitoring can be performed when the user is relaxed. Also, if the user is concentrating, detailed monitoring can be performed at that time. Furthermore, if the user is tired, monitoring can be temporarily stopped and resumed after a rest. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions.

[0113] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, advice that can be quickly understood can be provided. In this way, more appropriate advice can be provided by adjusting the content of the advice according to the user's emotions.

[0114] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand reminder can be provided. If the user is relaxed, a detailed reminder can be provided. Furthermore, if the user is in a hurry, a quickly understandable reminder can be provided. This allows for more appropriate reminders by adjusting the content of the reminder according to the user's emotions.

[0115] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy-to-understand suggestion can be made. If the user is relaxed, a detailed suggestion can be made. Furthermore, if the user is in a hurry, a suggestion that can be quickly understood can be made. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be made.

[0116] The collection unit can analyze the user's past task management data and select a data collection method. For example, it can analyze data from task management tools used by the user in the past and select the most effective collection method. It can also adjust the frequency and timing of data collection based on the user's past task completion rate. It can also analyze the user's past task management patterns and select the optimal data collection method (audio, text, etc.). In this way, the optimal data collection method can be selected by analyzing past task management data.

[0117] When collecting data, the collection unit can filter the data based on the user's current project or area of ​​interest. For example, only data related to the project the user is currently working on can be collected. Also, the collection unit can prioritize collection of highly relevant data based on the user's area of ​​interest. Furthermore, the collection unit can limit the scope of data collection based on topics in which the user has shown interest in the past. In this way, highly relevant data can be collected by filtering data based on the user's current project or area of ​​interest.

[0118] When collecting 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 prefers voice input, it can prioritize collection of voice data. Also, if the user prefers text input, it can prioritize collection of text data. Furthermore, if the user prefers image input, it can prioritize collection of image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0119] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to that area can be collected preferentially. Also, if the user is moving, highly relevant data can be collected based on the user's current location. Furthermore, if the user is staying in a specific location, data related to that location can be collected preferentially. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information.

[0120] When collecting data, the collection unit can analyze the user's social media activities and collect relevant data. For example, the collection unit can collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect highly relevant data. Furthermore, the collection unit can also collect relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects data on user characteristics or past task management. For example, the collection unit collects past task management data or user input information in order to understand the user's characteristics. The collection unit may also include AI processing. Step 2: The suggestion unit proposes a scheduling method based on the data collected by the collection unit. The suggestion unit may include, for example, AI processing to propose an efficient scheduling method. Step 3: The monitoring unit monitors the user's task progress in real time. The monitoring unit may include, for example, AI processing to monitor the user's task progress in real time. Step 4: The advice unit provides advice based on the data obtained by the monitoring unit. The advice unit may include, for example, AI processing to provide appropriate advice based on the user's task progress. Step 5: The reminder unit sends an alert at an appropriate time. The reminder unit may include, for example, AI processing to send an alert at an appropriate time so that the user does not forget the task.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on user characteristics or past task management; a proposal unit that proposes a scheduling method based on the data collected by the collection unit; a monitoring unit that monitors the user's task progress in real time; an advice unit that provides advice based on the data obtained by the monitoring unit; A reminder unit that sends an alert at an appropriate time. A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

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

4. The collecting unit Filter data collection based on the user's current project or area of ​​interest 2. The system of claim 1.

5. The collecting unit When collecting data, select the collection method according to the user's input method 2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, prioritize the collection of relevant data based on the user's geographic location 2. The system of claim 1.

8. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

Images