system

Abstract

The system according to this embodiment aims to analyze the user's schedule and task information and provide advice on the optimal task progress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an advice unit, and a notification unit. The collection unit collects information on the user's schedule and tasks. The analysis unit analyzes the information collected by the collection unit. The advice unit provides advice on the optimal task progress based on the information analyzed by the analysis unit. The notification unit provides reminders based on the task progress advised by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the management of the user's schedule and tasks is not efficiently performed, and there are problems in improving productivity.

[0005] The system according to the embodiment aims to analyze the information of the user's schedule and tasks and advise on the optimal task progress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an advice unit, and a notification unit. The collection unit collects information on the user's schedule and tasks. The analysis unit analyzes the information collected by the collection unit. The advice unit provides advice on the optimal task progress based on the information analyzed by the analysis unit. The notification unit sends reminders based on the task progress advised by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's schedule and task information and provide advice on the optimal task progress. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The personal assistant system according to an embodiment of the present invention is a system that analyzes the user's schedule, task priorities, and interactions and requests in communication tools to advise on the optimal task progress for the day. The personal assistant system analyzes the user's schedule and task priorities, and analyzes interactions and requests in communication tools to advise on the optimal task progress for the day. For example, the personal assistant system collects appointments entered by the user in their calendar and tasks registered in task management tools, and the AI ​​determines their urgency and importance. Next, the personal assistant system analyzes interactions and requests in communication tools. The AI ​​analyzes the content of messages and extracts important requests and messages. Based on this information, the AI ​​advises on the optimal task progress for the day. For example, it may suggest prioritizing high-urgency tasks or set reminders to ensure important requests are not missed. The user can maximize their productivity simply by following the AI's advice and completing tasks in order from top to bottom. This personal assistant system is provided not only to individual users but also to companies and teams. For example, it can help project managers efficiently manage the tasks of their team members. This improves the productivity of the entire team and ensures smooth project progress. It also features a reminder notification function, informing users of important task deadlines and changes. This allows users to efficiently manage tasks without being rushed by deadlines. This personal assistant system addresses the rapidly growing need for efficient task management and communication tools due to the spread of remote work, and advancements in AI technology enable sophisticated task management tailored to individual needs. It helps users efficiently manage tasks and maximize productivity, thereby increasing leisure time and self-investment time, and leading a more fulfilling life. This allows the personal assistant system to efficiently collect, analyze, advise on, and notify users about their schedules and tasks.

[0029] The personal assistant system according to this embodiment comprises a data collection unit, an analysis unit, an advice unit, and a notification unit. The data collection unit collects information on the user's schedule and tasks. For example, the data collection unit collects appointments entered by the user in a calendar and tasks registered in a task management tool. The data collection unit can collect information using an API. The data collection unit can also accept manual input. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using machine learning algorithms. The analysis unit determines the urgency and importance of the collected information. The advice unit advises on the optimal task progress based on the information analyzed by the analysis unit. For example, the advice unit suggests prioritizing tasks with high urgency. The advice unit can also set reminders to ensure that important requests are not missed. The notification unit notifies the user of reminders based on the task progress advised by the advice unit. For example, the notification unit notifies the user of reminders using email or push notifications. The notification unit can also send reminders via SMS. This enables the personal assistant system according to the embodiment to efficiently collect, analyze, advise on, and notify users about their schedules and tasks.

[0030] The data collection unit collects user schedule and task information. For example, it collects appointments entered by users in their calendars and tasks registered in task management tools. Specifically, the unit obtains appointment information from online calendar services via APIs. It can also collect task information from task management tools. By using these APIs, the data collection unit can automatically obtain the latest schedule and task information, saving users time and effort. Furthermore, the data collection unit can accept manual input. For example, users can directly input appointments and tasks into the system using their smartphones or computers. This manual input function allows users to flexibly add and modify information. The data collection unit also supports voice input, allowing users to add appointments and tasks through a voice assistant. This enables the data collection unit to collect user schedule and task information in diverse ways, maintaining the accuracy and timeliness of information throughout the system. Additionally, the data collection unit can centrally manage the collected information and efficiently share it with other departments. For example, collected information can be stored on a cloud server and made accessible to the analysis and advisory departments. This allows the data collection unit to efficiently and effectively collect data and improve the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it analyzes user behavior patterns and priorities based on collected schedule and task information. The analysis unit can also analyze information using machine learning algorithms. For example, it can learn from past data to predict which tasks users tend to prioritize. This allows the analysis unit to understand user behavior patterns and build a foundation for providing more appropriate advice. The analysis unit determines urgency and importance based on the collected information. For example, it calculates the priority of each task based on task deadlines and importance tags. Furthermore, the analysis unit can predict how long a particular task will take based on the user's past behavior data, thereby optimizing the schedule. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. For example, if a user has more tasks than usual or is missing important tasks, the analysis unit can detect the anomaly and notify the advice unit. This allows the analysis unit to quickly and accurately analyze the collected data, optimizing the user's schedule and task management.

[0032] The advisory department provides optimal task management advice based on information analyzed by the analysis department. Specifically, it suggests prioritizing tasks with high urgency. For example, the advisory department checks the user's schedule and advises prioritizing tasks with approaching deadlines or high importance. The advisory department can also set reminders to ensure important requests are not missed. For example, it sets reminders in advance to ensure users don't forget important meetings or presentations, and notifies them at the appropriate time. Furthermore, the advisory department can suggest efficient task management methods based on the user's past behavioral data. For example, it analyzes the most efficient task management order in the user's past and suggests the optimal task management method based on the results. The advisory department can also provide advice tailored to the user's current situation and environment. For example, if the user is on the go, it suggests tasks that can be completed quickly; if the user is in the office, it suggests tasks that require focused work. In this way, the advisory department can provide flexible advice tailored to the user's situation and support efficient task management. In addition, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the advice unit to advise users on the optimal task progression, thereby improving the efficiency of schedule management.

[0033] The notification unit sends reminders based on task progress advised by the advice unit. Specifically, it sends reminders via email or push notifications. For example, if a user has a smartphone, it sends task reminders via push notifications to draw their attention. It can also send detailed task information and progress updates via email. Furthermore, the notification unit can also send reminders via SMS. For example, even if a user does not have an internet connection, they can receive important task reminders via SMS. This ensures that the notification unit can reliably receive reminders regardless of the user's circumstances. In addition, the notification unit can customize notification methods according to user preferences. For example, if a user does not want to receive notifications during certain times, it can be set to avoid sending notifications during those times. The notification unit can also provide the optimal notification experience for the user by adjusting the content and frequency of reminders. For example, it can send frequent reminders for high-priority tasks and notify users at a moderate frequency for low-priority tasks. This allows the notification system to provide users with timely reminders and support task progress. Furthermore, the notification system can collect user feedback and continuously improve notification content and methods. This enables the notification system to provide users with optimal reminder notifications and improve the efficiency of task management.

[0034] The data collection unit can collect appointments entered into the user's calendar and tasks registered in task management tools. For example, the data collection unit can collect appointments such as meetings, events, and deadlines entered into the user's calendar. The data collection unit can also collect project tasks, personal tasks, and team tasks registered in task management tools. This streamlines schedule and task management by collecting information from the user's calendar and task management tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using APIs for calendars and task management tools, and then input that information into AI for analysis.

[0035] The analysis unit can determine urgency and importance based on the collected information. For example, the analysis unit can determine urgency based on the proximity of deadlines and the impact of tasks based on the collected information. The analysis unit can also determine priority based on the importance of tasks. In this way, task priorities can be appropriately determined by determining urgency and importance based on the collected information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into AI, and the AI ​​can determine urgency and importance.

[0036] The advisory unit can suggest prioritizing high-priority tasks. For example, the advisory unit might suggest to the user that they prioritize high-priority tasks. The advisory unit can also set reminders to ensure important requests are not missed. This streamlines the user's task management by suggesting that high-priority tasks be prioritized. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit could use AI to suggest that high-priority tasks be prioritized.

[0037] The notification unit can set reminders to ensure that important requests and messages are not missed. For example, the notification unit can set reminders for users to ensure that important requests and messages are not missed. The notification unit can send reminders via email, push notifications, or SMS. This streamlines the user's task management by allowing them to set reminders to ensure that important requests and messages are not missed. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can set reminders so that AI does not miss important requests and messages.

[0038] The data collection unit can collect interactions and requests from communication tools. For example, the data collection unit collects interactions and requests from communication tools. The data collection unit can collect information using APIs. The data collection unit can also accept manual input. This streamlines task management by collecting interactions and requests from communication tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using the API of a communication tool and input that information into AI for analysis.

[0039] The analysis unit can analyze the content of messages in communication tools and extract important requests and messages. For example, the analysis unit can analyze the content of messages in communication tools and extract important requests and messages. The analysis unit can analyze the content of messages using natural language processing technology. This allows for more efficient task management by analyzing the content of messages in communication tools and extracting important requests and messages. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can use AI to analyze the content of messages in communication tools and extract important requests and messages.

[0040] The data collection unit can analyze the user's past schedule history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. The data collection unit can also suggest the optimal data collection method for a specific time period based on the user's past schedule history. The data collection unit can also analyze the user's past schedule history and suggest an efficient data collection method. This allows for efficient task management by selecting the optimal data collection method through analysis of the user's past schedule history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past schedule history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter schedules and tasks based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting tasks related to projects the user is currently working on. The data collection unit can also filter relevant schedules and tasks based on the user's areas of interest. The data collection unit can also adjust the tasks it collects according to the progress of the user's current projects. This allows for the efficient collection of highly relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting schedules and tasks. For example, if the user is in a specific location, the data collection unit will prioritize collecting tasks related to that location. The data collection unit can also prioritize collecting tasks that can be performed in locations close to the user's current location. The data collection unit can also perform efficient task collection based on the user's geographical location. This allows for the efficient collection of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information when collecting schedules and tasks. For example, the data collection unit can prioritize collecting tasks mentioned by the user on social media. The data collection unit can also collect relevant schedules and tasks from the user's social media activity. The data collection unit can also analyze the user's social media activity to efficiently collect tasks. This allows for the efficient collection of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0044] The analysis unit can improve the accuracy of determining urgency and importance by referring to past analysis data during the analysis process. For example, the analysis unit can adjust the criteria for determining urgency and importance based on past analysis data. The analysis unit can also improve the accuracy of determining urgency and importance by referring to past analysis data. The analysis unit can also analyze past analysis data and optimize the criteria for determining urgency and importance. As a result, the accuracy of determining urgency and importance is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0045] The analysis unit can apply different analysis algorithms to each task category during analysis. For example, the analysis unit can select the optimal analysis algorithm for each task category. The analysis unit can also apply different analysis algorithms depending on the task category. The analysis unit can also adjust the analysis algorithm for each task category. By applying different analysis algorithms to each task category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the urgency and importance of tasks based on their submission dates during the analysis. For example, the analysis unit may determine a task to be highly urgent if its submission date is approaching. It may also determine a task to be highly important if its submission date is far in the future. The analysis unit can also adjust the criteria for determining urgency and importance based on the task's submission date. This improves the accuracy of determining urgency and importance by basing the determination on the task's submission date. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input task submission date data into a generating AI and have the generating AI perform the determination.

[0047] The analysis unit can improve the accuracy of determining urgency and importance by referring to relevant literature for the task during the analysis. For example, the analysis unit can adjust the criteria for determining urgency and importance by referring to relevant literature for the task. The analysis unit can also improve the accuracy of determining urgency and importance based on relevant literature for the task. The analysis unit can also analyze relevant literature for the task and optimize the criteria for determining urgency and importance. As a result, the accuracy of determining urgency and importance is improved by referring to relevant literature for the task. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the determination accuracy.

[0048] The advice unit can adjust the level of detail of its advice based on the importance of the task. For example, it can provide detailed advice for high-importance tasks, and concise advice for low-importance tasks. The advice unit can also adjust the level of detail of its advice according to the importance of the task. This allows the advice unit to provide the most suitable advice for the user by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0049] The advice unit can apply different advice algorithms depending on the task category when providing advice. For example, the advice unit can select the optimal advice algorithm for each task category. The advice unit can also apply different advice algorithms depending on the task category. The advice unit can also adjust the advice algorithm for each task category. This allows the advice unit to provide the user with the best possible advice by applying different advice algorithms depending on the task category. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task category data into a generating AI and have the generating AI perform the application of the advice algorithm.

[0050] The advice unit can determine the priority of advice based on the task submission deadline. For example, the advice unit will provide priority advice if the task submission deadline is approaching. If the task submission deadline is far away, the advice unit may postpone providing advice. The advice unit can also adjust the priority of advice based on the task submission deadline. This allows the advice unit to provide the best possible advice to the user by determining the priority of advice based on the task submission deadline. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task submission deadline data into a generating AI and have the generating AI perform the priority determination.

[0051] The advice unit can adjust the order of advice based on the relevance of the tasks. For example, the advice unit may prioritize advice for highly relevant tasks. The advice unit may also postpone advice for less relevant tasks. The advice unit can also adjust the order of advice based on the relevance of the tasks. This allows the advice unit to provide the user with the most appropriate advice by adjusting the order of advice based on the relevance of the tasks. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task relevance data into a generating AI and have the generating AI perform the order adjustment.

[0052] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can also select the optimal notification method from the user's past notification history. The notification unit can also analyze the user's past notification history and suggest an efficient notification method. This allows for efficient notifications by selecting the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0053] The notification unit can adjust the timing of notifications based on the urgency and importance of the task. For example, the notification unit can immediately notify users of high-urgency tasks. It can also notify users of high-importance tasks at an appropriate time. The notification unit can adjust the timing of notifications based on the urgency and importance of the task. This allows the system to provide users with the most optimal notifications by adjusting the timing of notifications based on the urgency and importance of the task. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input task urgency and importance data into a generating AI and have the generating AI adjust the notification timing.

[0054] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit may prioritize push notifications. If the user is using a personal computer, the notification unit may also prioritize desktop notifications. The notification unit can also select the optimal notification method based on the user's device information. This allows for efficient notifications by selecting the optimal notification method while considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input device information into a generating AI and have the generating AI select the notification method.

[0055] The notification unit can analyze the user's social media activity and customize the content of notifications when sending them. For example, the notification unit can prioritize notifications related to tasks mentioned by the user on social media. The notification unit can also customize relevant notification content based on the user's social media activity. The notification unit can also analyze the user's social media activity and suggest efficient notification content. This allows for efficient notifications by customizing relevant notification content through analysis of the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input social media activity data into a generating AI and have the generating AI customize the notification content.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The personal assistant system can also include a health management unit that collects and analyzes the user's health data. This unit can collect health data such as heart rate, sleep data, and exercise levels from the user's smartwatch or fitness tracker, for example. The health management unit then analyzes the collected data to assess the user's health status. For instance, if the user has a high heart rate or is experiencing persistent sleep deprivation, it can advise them to rest. If insufficient exercise is detected, it can suggest appropriate exercises. This enables task management that takes the user's health status into account, thereby improving their overall quality of life.

[0058] A personal assistant system can also include a geographic information unit that prioritizes the collection of highly relevant information by considering the user's geographic location. For example, if the user is in a specific location, the geographic information unit will prioritize collecting tasks related to that location. It can also prioritize collecting tasks that can be performed in locations close to the user's current location. This allows for the efficient collection of highly relevant tasks by considering the user's geographic location.

[0059] The personal assistant system may also include a social media analysis unit that analyzes the user's social media activity and collects relevant information. For example, the social media analysis unit prioritizes collecting tasks mentioned by the user on social media. It can also collect relevant schedules and tasks from the user's social media activity. This allows for the efficient collection of relevant tasks by analyzing the user's social media activity.

[0060] The personal assistant system can also include a history analysis unit that analyzes the user's past schedule history and selects the optimal data collection method. For example, the history analysis unit might prioritize suggesting data collection methods the user has frequently used in the past. It can also suggest the optimal data collection method for specific time periods based on the user's past schedule history. This allows for efficient task management by selecting the optimal data collection method through analysis of the user's past schedule history.

[0061] The personal assistant system may also include a device information unit that selects the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the device information unit might prioritize push notifications. If the user is using a personal computer, it might prioritize desktop notifications. This allows for efficient notifications by selecting the most suitable notification method based on the user's device information.

[0062] The personal assistant system may also include a notification history analysis unit that selects the optimal notification method by referring to the user's past notification history. For example, the notification history analysis unit might prioritize suggesting notification methods the user has previously preferred. It can also select the optimal notification method from the user's past notification history. This allows for efficient notifications by selecting the most suitable method based on the user's past notification history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects user schedule and task information. For example, it collects appointments entered by the user in their calendar and tasks registered in task management tools. The collection unit can collect information using an API, or it can accept manual input. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the information using data mining techniques and machine learning algorithms, and determines the urgency and importance based on the collected information. Step 3: The advice unit provides optimal task management advice based on the information analyzed by the analysis unit. For example, it might suggest prioritizing high-priority tasks and set reminders to ensure important requests are not overlooked. Step 4: The notification unit sends reminders based on the task progress advised by the advice unit. For example, reminders are sent via email, push notifications, or SMS.

[0065] (Example of form 2) The personal assistant system according to an embodiment of the present invention is a system that analyzes the user's schedule, task priorities, and interactions and requests in communication tools to advise on the optimal task progress for the day. The personal assistant system analyzes the user's schedule and task priorities, and analyzes interactions and requests in communication tools to advise on the optimal task progress for the day. For example, the personal assistant system collects appointments entered by the user in their calendar and tasks registered in task management tools, and the AI ​​determines their urgency and importance. Next, the personal assistant system analyzes interactions and requests in communication tools. The AI ​​analyzes the content of messages and extracts important requests and messages. Based on this information, the AI ​​advises on the optimal task progress for the day. For example, it may suggest prioritizing high-urgency tasks or set reminders to ensure important requests are not missed. The user can maximize their productivity simply by following the AI's advice and completing tasks in order from top to bottom. This personal assistant system is provided not only to individual users but also to companies and teams. For example, it can help project managers efficiently manage the tasks of their team members. This improves the productivity of the entire team and ensures smooth project progress. It also features a reminder notification function, informing users of important task deadlines and changes. This allows users to efficiently manage tasks without being rushed by deadlines. This personal assistant system addresses the rapidly growing need for efficient task management and communication tools due to the spread of remote work, and advancements in AI technology enable sophisticated task management tailored to individual needs. It helps users efficiently manage tasks and maximize productivity, thereby increasing leisure time and self-investment time, and leading a more fulfilling life. This allows the personal assistant system to efficiently collect, analyze, advise on, and notify users about their schedules and tasks.

[0066] The personal assistant system according to this embodiment comprises a data collection unit, an analysis unit, an advice unit, and a notification unit. The data collection unit collects information on the user's schedule and tasks. For example, the data collection unit collects appointments entered by the user in a calendar and tasks registered in a task management tool. The data collection unit can collect information using an API. The data collection unit can also accept manual input. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using machine learning algorithms. The analysis unit determines the urgency and importance of the collected information. The advice unit advises on the optimal task progress based on the information analyzed by the analysis unit. For example, the advice unit suggests prioritizing tasks with high urgency. The advice unit can also set reminders to ensure that important requests are not missed. The notification unit notifies the user of reminders based on the task progress advised by the advice unit. For example, the notification unit notifies the user of reminders using email or push notifications. The notification unit can also send reminders via SMS. This enables the personal assistant system according to the embodiment to efficiently collect, analyze, advise on, and notify users about their schedules and tasks.

[0067] The data collection unit collects user schedule and task information. For example, it collects appointments entered by users in their calendars and tasks registered in task management tools. Specifically, the data collection unit obtains appointment information from online calendar services via APIs. By using these APIs, the data collection unit can automatically obtain the latest schedule and task information, saving users time and effort. Furthermore, the data collection unit can also accept manual input. For example, users can directly input appointments and tasks into the system using their smartphones or computers. This manual input function allows users to flexibly add and modify information. The data collection unit also supports voice input, allowing users to add appointments and tasks through a voice assistant. As a result, the data collection unit can collect user schedule and task information in a variety of ways, maintaining the accuracy and timeliness of information throughout the entire system. In addition, the data collection unit can centrally manage the collected information and efficiently share it in cooperation with other departments. For example, the collected information can be stored on a cloud server and made accessible to the analysis and advisory departments. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it analyzes user behavior patterns and priorities based on collected schedule and task information. The analysis unit can also analyze information using machine learning algorithms. For example, it can learn from past data to predict which tasks users tend to prioritize. This allows the analysis unit to understand user behavior patterns and build a foundation for providing more appropriate advice. The analysis unit determines urgency and importance based on the collected information. For example, it calculates the priority of each task based on task deadlines and importance tags. Furthermore, the analysis unit can predict how long a particular task will take based on the user's past behavior data, thereby optimizing the schedule. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. For example, if a user has more tasks than usual or is missing important tasks, the analysis unit can detect the anomaly and notify the advice unit. This allows the analysis unit to quickly and accurately analyze the collected data, optimizing the user's schedule and task management.

[0069] The advisory department provides optimal task management advice based on information analyzed by the analysis department. Specifically, it suggests prioritizing tasks with high urgency. For example, the advisory department checks the user's schedule and advises prioritizing tasks with approaching deadlines or high importance. The advisory department can also set reminders to ensure important requests are not missed. For example, it sets reminders in advance to ensure users don't forget important meetings or presentations, and notifies them at the appropriate time. Furthermore, the advisory department can suggest efficient task management methods based on the user's past behavioral data. For example, it analyzes the most efficient task management order in the user's past and suggests the optimal task management method based on the results. The advisory department can also provide advice tailored to the user's current situation and environment. For example, if the user is on the go, it suggests tasks that can be completed quickly; if the user is in the office, it suggests tasks that require focused work. In this way, the advisory department can provide flexible advice tailored to the user's situation and support efficient task management. In addition, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the advice unit to advise users on the optimal task progression, thereby improving the efficiency of schedule management.

[0070] The notification unit sends reminders based on task progress advised by the advice unit. Specifically, it sends reminders via email or push notifications. For example, if a user has a smartphone, it sends task reminders via push notifications to draw their attention. It can also send detailed task information and progress updates via email. Furthermore, the notification unit can also send reminders via SMS. For example, even if a user does not have an internet connection, they can receive important task reminders via SMS. This ensures that the notification unit can reliably receive reminders regardless of the user's circumstances. In addition, the notification unit can customize notification methods according to user preferences. For example, if a user does not want to receive notifications during certain times, it can be set to avoid sending notifications during those times. The notification unit can also provide the optimal notification experience for the user by adjusting the content and frequency of reminders. For example, it can send frequent reminders for high-priority tasks and notify users at a moderate frequency for low-priority tasks. This allows the notification system to provide users with timely reminders and support task progress. Furthermore, the notification system can collect user feedback and continuously improve notification content and methods. This enables the notification system to provide users with optimal reminder notifications and improve the efficiency of task management.

[0071] The data collection unit can collect appointments entered into the user's calendar and tasks registered in task management tools. For example, the data collection unit can collect appointments such as meetings, events, and deadlines entered into the user's calendar. The data collection unit can also collect project tasks, personal tasks, and team tasks registered in task management tools. This streamlines schedule and task management by collecting information from the user's calendar and task management tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using APIs for calendars and task management tools, and then input that information into AI for analysis.

[0072] The analysis unit can determine urgency and importance based on the collected information. For example, the analysis unit can determine urgency based on the proximity of deadlines and the impact of tasks based on the collected information. The analysis unit can also determine priority based on the importance of tasks. In this way, task priorities can be appropriately determined by determining urgency and importance based on the collected information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into AI, and the AI ​​can determine urgency and importance.

[0073] The advisory unit can suggest prioritizing high-priority tasks. For example, the advisory unit might suggest to the user that they prioritize high-priority tasks. The advisory unit can also set reminders to ensure important requests are not missed. This streamlines the user's task management by suggesting that high-priority tasks be prioritized. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit could use AI to suggest that high-priority tasks be prioritized.

[0074] The notification unit can set reminders to ensure that important requests and messages are not missed. For example, the notification unit can set reminders for users to ensure that important requests and messages are not missed. The notification unit can send reminders via email, push notifications, or SMS. This streamlines the user's task management by allowing them to set reminders to ensure that important requests and messages are not missed. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can set reminders so that AI does not miss important requests and messages.

[0075] The data collection unit can collect interactions and requests from communication tools. For example, the data collection unit collects interactions and requests from communication tools. The data collection unit can collect information using APIs. The data collection unit can also accept manual input. This streamlines task management by collecting interactions and requests from communication tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using the API of a communication tool and input that information into AI for analysis.

[0076] The analysis unit can analyze the content of messages in communication tools and extract important requests and messages. For example, the analysis unit can analyze the content of messages in communication tools and extract important requests and messages. The analysis unit can analyze the content of messages using natural language processing technology. This allows for more efficient task management by analyzing the content of messages in communication tools and extracting important requests and messages. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can use AI to analyze the content of messages in communication tools and extract important requests and messages.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of schedule and task collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to allow the user time to relax. If the user is focused, the data collection unit can also advance the collection timing to efficiently collect tasks. If the user is tired, the data collection unit can adjust the collection timing to allow for breaks. By adjusting the collection timing based on the user's emotions, the burden on the user is reduced, and efficient task management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0078] The data collection unit can analyze the user's past schedule history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. The data collection unit can also suggest the optimal data collection method for a specific time period based on the user's past schedule history. The data collection unit can also analyze the user's past schedule history and suggest an efficient data collection method. This allows for efficient task management by selecting the optimal data collection method through analysis of the user's past schedule history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past schedule history data into a generating AI and have the generating AI select the optimal data collection method.

[0079] The data collection unit can filter schedules and tasks based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting tasks related to projects the user is currently working on. The data collection unit can also filter relevant schedules and tasks based on the user's areas of interest. The data collection unit can also adjust the tasks it collects according to the progress of the user's current projects. This allows for the efficient collection of highly relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of schedules and tasks to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone less important tasks. If the user is relaxed, the data collection unit may prioritize collecting more important tasks. If the user is in a hurry, the data collection unit may prioritize collecting more urgent tasks. This reduces the user's burden and enables efficient task management by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting schedules and tasks. For example, if the user is in a specific location, the data collection unit will prioritize collecting tasks related to that location. The data collection unit can also prioritize collecting tasks that can be performed in locations close to the user's current location. The data collection unit can also perform efficient task collection based on the user's geographical location. This allows for the efficient collection of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0082] The data collection unit can analyze the user's social media activity and collect relevant information when collecting schedules and tasks. For example, the data collection unit can prioritize collecting tasks mentioned by the user on social media. The data collection unit can also collect relevant schedules and tasks from the user's social media activity. The data collection unit can also analyze the user's social media activity to efficiently collect tasks. This allows for the efficient collection of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0083] The analysis unit can estimate the user's emotions and adjust the criteria for determining urgency and importance based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize tasks of high urgency. If the user is relaxed, the analysis unit can also prioritize tasks of high importance. If the user is in a hurry, the analysis unit can also prioritize tasks of high urgency. By adjusting the criteria based on the user's emotions, the burden on the user is reduced, and efficient task management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the criteria.

[0084] The analysis unit can improve the accuracy of determining urgency and importance by referring to past analysis data during the analysis process. For example, the analysis unit can adjust the criteria for determining urgency and importance based on past analysis data. The analysis unit can also improve the accuracy of determining urgency and importance by referring to past analysis data. The analysis unit can also analyze past analysis data and optimize the criteria for determining urgency and importance. As a result, the accuracy of determining urgency and importance is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0085] The analysis unit can apply different analysis algorithms to each task category during analysis. For example, the analysis unit can select the optimal analysis algorithm for each task category. The analysis unit can also apply different analysis algorithms depending on the task category. The analysis unit can also adjust the analysis algorithm for each task category. By applying different analysis algorithms to each task category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the display order of urgency and importance rankings based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying high-urgency tasks. If the user is relaxed, the analysis unit can also prioritize displaying high-importance tasks. If the user is in a hurry, the analysis unit can also prioritize displaying high-urgency tasks. By adjusting the display order based on the user's emotions, the user's burden is reduced, and efficient task management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0087] The analysis unit can determine the urgency and importance of tasks based on their submission dates during the analysis. For example, the analysis unit may determine a task to be highly urgent if its submission date is approaching. It may also determine a task to be highly important if its submission date is far in the future. The analysis unit can also adjust the criteria for determining urgency and importance based on the task's submission date. This improves the accuracy of determining urgency and importance by basing the determination on the task's submission date. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input task submission date data into a generating AI and have the generating AI perform the determination.

[0088] The analysis unit can improve the accuracy of determining urgency and importance by referring to relevant literature for the task during the analysis. For example, the analysis unit can adjust the criteria for determining urgency and importance by referring to relevant literature for the task. The analysis unit can also improve the accuracy of determining urgency and importance based on relevant literature for the task. The analysis unit can also analyze relevant literature for the task and optimize the criteria for determining urgency and importance. As a result, the accuracy of determining urgency and importance is improved by referring to relevant literature for the task. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the determination accuracy.

[0089] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple and easy-to-understand 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 provide quick and concise advice. By adjusting the way it expresses advice based on the user's emotions, it can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses the advice.

[0090] The advice unit can adjust the level of detail of its advice based on the importance of the task. For example, it can provide detailed advice for high-importance tasks, and concise advice for low-importance tasks. The advice unit can also adjust the level of detail of its advice according to the importance of the task. This allows the advice unit to provide the most suitable advice for the user by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0091] The advice unit can apply different advice algorithms depending on the task category when providing advice. For example, the advice unit can select the optimal advice algorithm for each task category. The advice unit can also apply different advice algorithms depending on the task category. The advice unit can also adjust the advice algorithm for each task category. This allows the advice unit to provide the user with the best possible advice by applying different advice algorithms depending on the task category. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task category data into a generating AI and have the generating AI perform the application of the advice algorithm.

[0092] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise 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 provide quick and concise advice. By adjusting the length of the advice based on the user's emotions, the advice unit can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0093] The advice unit can determine the priority of advice based on the task submission deadline. For example, the advice unit will provide priority advice if the task submission deadline is approaching. If the task submission deadline is far away, the advice unit may postpone providing advice. The advice unit can also adjust the priority of advice based on the task submission deadline. This allows the advice unit to provide the best possible advice to the user by determining the priority of advice based on the task submission deadline. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task submission deadline data into a generating AI and have the generating AI perform the priority determination.

[0094] The advice unit can adjust the order of advice based on the relevance of the tasks. For example, the advice unit may prioritize advice for highly relevant tasks. The advice unit may also postpone advice for less relevant tasks. The advice unit can also adjust the order of advice based on the relevance of the tasks. This allows the advice unit to provide the user with the most appropriate advice by adjusting the order of advice based on the relevance of the tasks. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input task relevance data into a generating AI and have the generating AI perform the order adjustment.

[0095] The notification unit can estimate the user's emotions and adjust the way reminders are delivered based on those emotions. For example, if the user is stressed, the notification unit can set a reminder with a calm notification sound. If the user is relaxed, the notification unit can also set a reminder with a bright notification sound. If the user is in a hurry, the notification unit can provide a quick and concise notification. This allows the system to provide the most suitable notifications for the user by adjusting the reminder notification method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0096] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can also select the optimal notification method from the user's past notification history. The notification unit can also analyze the user's past notification history and suggest an efficient notification method. This allows for efficient notifications by selecting the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0097] The notification unit can adjust the timing of notifications based on the urgency and importance of the task. For example, the notification unit can immediately notify users of high-urgency tasks. It can also notify users of high-importance tasks at an appropriate time. The notification unit can adjust the timing of notifications based on the urgency and importance of the task. This allows the system to provide users with the most optimal notifications by adjusting the timing of notifications based on the urgency and importance of the task. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input task urgency and importance data into a generating AI and have the generating AI adjust the notification timing.

[0098] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may postpone less important notifications. If the user is relaxed, the notification unit may also prioritize more important notifications. If the user is in a hurry, the notification unit may also prioritize more urgent notifications. In this way, by determining the priority of notifications based on the user's emotions, the system can provide the user with the most appropriate notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0099] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit may prioritize push notifications. If the user is using a personal computer, the notification unit may also prioritize desktop notifications. The notification unit can also select the optimal notification method based on the user's device information. This allows for efficient notifications by selecting the optimal notification method while considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input device information into a generating AI and have the generating AI select the notification method.

[0100] The notification unit can analyze the user's social media activity and customize the content of notifications when sending them. For example, the notification unit can prioritize notifications related to tasks mentioned by the user on social media. The notification unit can also customize relevant notification content based on the user's social media activity. The notification unit can also analyze the user's social media activity and suggest efficient notification content. This allows for efficient notifications by customizing relevant notification content through analysis of the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input social media activity data into a generating AI and have the generating AI customize the notification content.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The personal assistant system can also include a health management unit that collects and analyzes the user's health data. This unit can collect health data such as heart rate, sleep data, and exercise levels from the user's smartwatch or fitness tracker, for example. The health management unit then analyzes the collected data to assess the user's health status. For instance, if the user has a high heart rate or is experiencing persistent sleep deprivation, it can advise them to rest. If insufficient exercise is detected, it can suggest appropriate exercises. This enables task management that takes the user's health status into account, thereby improving their overall quality of life.

[0103] The personal assistant system may also include an emotion analysis unit that estimates the user's emotions and adjusts task priorities based on those emotions. For example, if the user is stressed, the emotion analysis unit might postpone less important tasks. If the user is relaxed, it might prioritize collecting more important tasks. If the user is in a hurry, it might prioritize collecting more urgent tasks. This reduces the user's burden and enables efficient task management by determining priorities based on the user's emotions.

[0104] A personal assistant system can also include a geographic information unit that prioritizes the collection of highly relevant information by considering the user's geographic location. For example, if the user is in a specific location, the geographic information unit will prioritize collecting tasks related to that location. It can also prioritize collecting tasks that can be performed in locations close to the user's current location. This allows for the efficient collection of highly relevant tasks by considering the user's geographic location.

[0105] The personal assistant system may also include a social media analysis unit that analyzes the user's social media activity and collects relevant information. For example, the social media analysis unit prioritizes collecting tasks mentioned by the user on social media. It can also collect relevant schedules and tasks from the user's social media activity. This allows for the efficient collection of relevant tasks by analyzing the user's social media activity.

[0106] The personal assistant system can also include a history analysis unit that analyzes the user's past schedule history and selects the optimal data collection method. For example, the history analysis unit might prioritize suggesting data collection methods the user has frequently used in the past. It can also suggest the optimal data collection method for specific time periods based on the user's past schedule history. This allows for efficient task management by selecting the optimal data collection method through analysis of the user's past schedule history.

[0107] The personal assistant system may also include an emotional advice unit that estimates the user's emotions and adjusts the way advice is presented based on those emotions. For example, if the user is stressed, the emotional advice unit can provide simple and easy-to-understand advice. If the user is relaxed, it can provide more detailed advice. If the user is in a hurry, it can provide quick and concise advice. This allows the system to provide the most appropriate advice for the user by adjusting the way advice is presented based on their emotions.

[0108] The personal assistant system may also include an emotion notification unit that estimates the user's emotions and adjusts the way reminders are notified based on those emotions. For example, if the user is feeling stressed, the emotion notification unit might set a reminder with a calm notification sound. If the user is relaxed, it might set a reminder with a bright notification sound. If the user is in a hurry, it might provide a quick and concise notification. This allows the system to provide the most appropriate notifications for the user by adjusting the way reminders are notified based on their emotions.

[0109] The personal assistant system may also include a device information unit that selects the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the device information unit might prioritize push notifications. If the user is using a personal computer, it might prioritize desktop notifications. This allows for efficient notifications by selecting the most suitable notification method based on the user's device information.

[0110] The personal assistant system may also include an emotion notification prioritization unit that estimates the user's emotions and determines notification priorities based on those emotions. For example, if the user is stressed, the emotion notification prioritization unit might postpone less important notifications. If the user is relaxed, it might prioritize more important notifications. If the user is in a hurry, it might prioritize more urgent notifications. This allows the system to provide the most relevant notifications to the user by prioritizing them based on their emotions.

[0111] The personal assistant system may also include a notification history analysis unit that selects the optimal notification method by referring to the user's past notification history. For example, the notification history analysis unit might prioritize suggesting notification methods the user has previously preferred. It can also select the optimal notification method from the user's past notification history. This allows for efficient notifications by selecting the most suitable method based on the user's past notification history.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects user schedule and task information. For example, it collects appointments entered by the user in their calendar and tasks registered in task management tools. The collection unit can collect information using an API, or it can accept manual input. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the information using data mining techniques and machine learning algorithms, and determines the urgency and importance based on the collected information. Step 3: The advice unit provides optimal task management advice based on the information analyzed by the analysis unit. For example, it might suggest prioritizing high-priority tasks and set reminders to ensure important requests are not overlooked. Step 4: The notification unit sends reminders based on the task progress advised by the advice unit. For example, reminders are sent via email, push notifications, or SMS.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information from the user's calendar and task management tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine its urgency and importance. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal task progress based on the analysis results. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of a reminder. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from the user's calendar and task management tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine its urgency and importance. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal task progress based on the analysis results. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of a reminder. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from the user's calendar and task management tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine its urgency and importance. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal task progress based on the analysis results. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of reminders. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects information from the user's calendar or task management tool. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine its urgency and importance. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal task progression based on the analysis results. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the user of a reminder. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that collects user schedule and task information, An analysis unit analyzes the information collected by the aforementioned collection unit, An advice unit provides advice on the optimal task progression based on the information analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies a reminder based on the task progress advised by the aforementioned advice unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects appointments entered into the user's calendar and tasks registered in task management tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, we determine the urgency and importance of the situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, I suggest prioritizing tasks that are of high urgency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Set reminders so you don't miss important requests or messages. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect communications and requests from communication tools. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Analyze the content of messages in communication tools to extract important requests and messages. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of schedules and task collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past schedule history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting schedules and tasks, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and determines the priority of collection schedules and tasks based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting schedules and tasks, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting schedules and tasks, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the criteria for determining urgency and importance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past analysis data is referenced to improve the accuracy of determining urgency and importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each task category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the order in which the urgency and importance ratings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the urgency and importance of tasks are determined based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we improve the accuracy of determining urgency and importance by referring to relevant literature for the task. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When giving advice, prioritize the advice based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When giving advice, adjust the order of advice based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts how reminders are notified based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted based on the urgency and importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to customize the content of the notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user schedule and task information, An analysis unit analyzes the information collected by the aforementioned collection unit, An advice unit provides advice on the optimal task progression based on the information analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies a reminder based on the task progress advised by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects appointments entered into the user's calendar and tasks registered in task management tools. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, we determine the urgency and importance of the situation. The system according to feature 1.

4. The aforementioned advice section, I suggest prioritizing tasks that are of high urgency. The system according to feature 1.

5. The aforementioned notification unit, Set reminders so you don't miss important requests or messages. The system according to feature 1.

6. The aforementioned collection unit is Collect communications and requests from communication tools. The system according to feature 1.

7. The aforementioned analysis unit, Analyze the content of messages in communication tools to extract important requests and messages. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of schedules and task collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past schedule history and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting schedules and tasks, filter them based on the user's current projects and areas of interest. The system according to feature 1.

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