system

Abstract

The system according to this embodiment aims to integrate task management, meeting optimization, notification filtering, music optimization, reminder provision, and VPN connection automation in a remote work environment. [Solution] The system according to the embodiment comprises a prioritization unit, a meeting optimization unit, a notification filtering unit, a music optimization unit, a reminder unit, and a VPN connection unit. The prioritization unit prioritizes tasks in cooperation with a calendar or task management tool. The meeting optimization unit sets up and optimizes meetings based on the tasks prioritized by the prioritization unit. The notification filtering unit filters notifications. The music optimization unit optimizes music. The reminder unit provides reminders for exercise and breaks. The VPN connection unit automatically establishes a VPN connection based on the user's location information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, in a remote work environment, task management, meeting optimization, notification filtering, music optimization, reminder provision, VPN connection automation, etc. are performed individually, and there is a problem that integrated management is difficult.

[0005] The system according to the embodiment aims to integrally perform task management, meeting optimization, notification filtering, music optimization, reminder provision, and VPN connection automation in a remote work environment.

Means for Solving the Problems

[0006] The system according to this embodiment includes a prioritization unit, a meeting optimization unit, a notification filtering unit, a music optimization unit, a reminder unit, and a VPN connection unit. The prioritization unit prioritizes tasks in conjunction with a calendar or task management tool. The meeting optimization unit sets up and optimizes meetings based on the tasks prioritized by the prioritization unit. The notification filtering unit filters notifications. The music optimization unit optimizes music. The reminder unit provides reminders for exercise and breaks. The VPN connection unit automatically establishes a VPN connection based on the user's location information. [Effects of the Invention]

[0007] The system according to this embodiment can integrate task management, meeting optimization, notification filtering, music optimization, reminder provision, and VPN connection automation in a remote work environment. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires 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 AI ​​agent system for optimizing remote work environments according to an embodiment of the present invention is a system that prioritizes tasks, sets up and optimizes meetings in conjunction with calendars and task management tools. The AI ​​agent system prioritizes tasks in conjunction with calendars and task management tools, analyzes the user's schedule, and presents important tasks with priority. Next, the AI ​​agent system sets up and optimizes meetings, suggesting the optimal meeting time considering the user's schedule. Furthermore, the AI ​​agent system filters notifications and optimizes music based on time to enhance user concentration. In addition, the AI ​​agent system provides reminders for exercise and breaks, monitors the user's work time, and prompts exercise and breaks at appropriate times. Moreover, the AI ​​agent system ensures security by automatically connecting to VPNs depending on the location. Thus, the AI ​​agent system supports improved user productivity and the maintenance of work-life balance. For example, the AI ​​agent system prioritizes tasks in conjunction with calendars and task management tools, analyzes the user's schedule, and presents important tasks with priority. Next, the remote work environment optimization AI agent system sets up and optimizes meetings, suggesting optimal meeting times considering the user's schedule. It also filters notifications and optimizes music based on time to enhance user concentration. Furthermore, it provides reminders for exercise and breaks, monitoring user work time to prompt appropriate exercise and rest. Finally, it ensures security by automatically connecting to VPNs based on location. In this way, the remote work environment optimization AI agent system supports improved user productivity and the maintenance of work-life balance.This allows the AI ​​agent system for optimizing remote work environments to support improved user productivity and the maintenance of work-life balance.

[0029] The AI ​​agent system for optimizing remote work environments according to this embodiment includes a prioritization unit, a meeting optimization unit, a notification filtering unit, a music optimization unit, a reminder unit, and a VPN connection unit. The prioritization unit prioritizes tasks in conjunction with a calendar or task management tool. For example, the prioritization unit prioritizes tasks in conjunction with a calendar or task management tool, analyzes the user's schedule, and presents important tasks first. The prioritization unit can prioritize tasks using AI. The meeting optimization unit sets up and optimizes meetings based on the tasks prioritized by the prioritization unit. For example, the meeting optimization unit suggests the optimal meeting time considering the user's schedule. The meeting optimization unit can set up and optimize meetings using AI. The notification filtering unit filters notifications. For example, the notification filtering unit filters notifications based on user behavior data. The notification filtering unit can filter notifications using AI. The music optimization unit optimizes music. For example, the music optimization unit optimizes music based on the user's preferences, work content, and time of day. The music optimization unit can optimize music using AI. The reminder unit provides reminders for exercise and breaks. For example, the reminder unit monitors the user's work time and prompts them to exercise or take breaks at appropriate times. The reminder unit can provide reminders for exercise and breaks using AI. The VPN connection unit automatically establishes a VPN connection based on the user's location information. For example, the VPN connection unit establishes a VPN connection based on the user's location information. The VPN connection unit can establish a VPN connection using AI. As a result, the remote work environment optimization AI agent system according to this embodiment can support improved user productivity and the maintenance of work-life balance.

[0030] The prioritization unit prioritizes tasks in conjunction with calendars and task management tools. Specifically, it retrieves data from the user's calendar and task management tools and uses AI to analyze the importance and urgency of tasks. For example, it automatically determines task priorities based on meeting and deadline information registered in the calendar. Furthermore, it considers the progress and deadlines of tasks registered in the task management tool to present the most important tasks to the user first. The AI ​​learns from past task completion status and user behavior patterns to perform more accurate prioritization. For example, it analyzes how long it took the user to complete important tasks in the past and adjusts the priority of current tasks. It can also dynamically change task priorities according to the user's work content and project progress. In this way, the prioritization unit supports users in efficiently managing tasks and focusing on important work.

[0031] The meeting optimization unit sets up and optimizes meetings based on tasks prioritized by the prioritization unit. Specifically, the meeting optimization unit analyzes the user's schedule and proposes the optimal meeting time. For example, it adjusts meeting times considering the priority of other appointments and tasks registered in the user's calendar. AI can analyze the user's past meeting attendance and meeting effectiveness to propose the optimal meeting time and participants. Furthermore, the meeting optimization unit can also suggest the appropriate meeting format (online meeting, in-person meeting, etc.) depending on the content and purpose of the meeting. For example, it recommends an online meeting for a meeting aimed at quick information sharing, and an in-person meeting for a meeting requiring important decision-making. The meeting optimization unit also supports the preparation of meeting agendas and materials, improving meeting efficiency. In this way, the meeting optimization unit supports users in efficiently setting up meetings and focusing on important tasks.

[0032] The notification filtering unit filters notifications. Specifically, it analyzes user behavior data and past notification responses to present only important notifications to the user. The AI ​​learns the user's behavior patterns and work content, and can automatically filter out unnecessary notifications. For example, during times when the user is concentrating on a task, it temporarily suppresses less important notifications to avoid disrupting their work. Also, if the user is focusing on a specific project, it prioritizes displaying only notifications related to that project. Furthermore, the notification filtering unit can continuously improve the accuracy of notification filtering based on user feedback. For example, if a user determines a particular notification is important, the system learns that information and incorporates it into future notification filtering. In this way, the notification filtering unit supports users in not missing important information and efficiently carrying out their work.

[0033] The music optimization unit optimizes music playback. Specifically, it selects and plays the most suitable music based on the user's preferences, work content, and time of day. The AI ​​learns the user's past music playback history and ratings, and can suggest music that matches the user's preferences. For example, during times when the user needs to concentrate on work, it plays instrumental music to enhance concentration, and during times when the user wants to relax, it plays music with a relaxing effect. The music optimization unit can also select music according to the user's work content. For example, when performing creative work, it plays music that stimulates creativity, and when performing simple tasks, it plays music with a rhythmic feel. Furthermore, the music optimization unit can continuously improve the accuracy of its music selection based on user feedback. In this way, the music optimization unit supports users in working efficiently in a comfortable work environment.

[0034] The reminder function provides reminders for exercise and breaks. Specifically, it monitors the user's work time and prompts them to exercise or take breaks at appropriate times. The AI ​​analyzes the user's work patterns and health data to provide reminders at the optimal time. For example, if the user has been working for a long time, it will display a reminder to stretch or do light exercise and encourage them to take regular breaks. The reminder function can also adjust the content and timing of reminders based on the user's health status and feedback. For example, if the user prefers a particular exercise, it will prioritize suggesting that exercise and adjust the frequency of reminders according to the user's health status. In this way, the reminder function supports users in maintaining healthy work habits and working efficiently.

[0035] The VPN connection unit automatically establishes a VPN connection based on the user's location information. Specifically, it obtains location information from the user's device and automatically configures a VPN connection to ensure the user can work in a secure network environment. The AI ​​learns the user's past connection history and network environment to select the optimal VPN server. For example, if the user is using public Wi-Fi, it automatically establishes a VPN connection to ensure data security. Also, if the user is working in a different location, it selects the optimal VPN server for that location and establishes a connection. Furthermore, the VPN connection unit can monitor the stability and speed of the connection and change the connection destination as needed. In this way, the VPN connection unit supports users in working remotely safely and comfortably.

[0036] The schedule display unit presents schedules in an easy-to-understand manner. For example, it uses visual display methods and filtering functions to present schedules clearly. The schedule display unit can also use generating AI to present schedules clearly. This allows users to easily understand their schedules.

[0037] The behavioral analysis unit analyzes user behavior data. For example, it analyzes behavioral data such as location information, activity logs, and applications used. The behavioral analysis unit can also analyze user behavior data using AI. This allows for more appropriate task management by analyzing user behavior data.

[0038] The location information analysis unit analyzes the user's location information. For example, it analyzes location information such as GPS data and Wi-Fi location information. The location information analysis unit can use AI to analyze the user's location information. This allows for more appropriate VPN connections by analyzing the user's location information.

[0039] The notification filtering unit filters notifications based on user behavior data. For example, the notification filtering unit can filter notifications based on user behavior data. The notification filtering unit can also filter notifications using AI. This allows important notifications to be prioritized by filtering them based on user behavior data.

[0040] The VPN connection unit establishes a VPN connection based on the user's location information. For example, the VPN connection unit can establish a VPN connection based on the user's location information. The VPN connection unit can also use AI to establish a VPN connection. This ensures security by establishing a VPN connection based on the user's location information.

[0041] The prioritization unit analyzes the user's past task history and selects the optimal prioritization method. For example, the prioritization unit will prioritize tasks that the user has frequently completed in the past. The prioritization unit will postpone tasks that the user has found difficult in the past. The prioritization unit sets efficient priorities based on the user's past task completion times. This enables efficient task management by analyzing the user's past task history.

[0042] The prioritization section filters tasks based on the user's current projects and areas of interest when prioritizing tasks. For example, it prioritizes tasks related to the user's current projects, tasks related to the user's areas of interest, and tasks related to the areas the user is currently focusing on. This allows for efficient task management by filtering tasks based on the user's current projects and areas of interest.

[0043] The prioritization section prioritizes tasks by considering the user's geographical location and presenting the most relevant tasks first. For example, if the user is in a specific location, the prioritization section will prioritize tasks that can be completed at that location. If the user is on the move, the prioritization section will prioritize tasks that can be completed while on the move. If the user is at home, the prioritization section will prioritize tasks that can be completed at home. By presenting tasks while considering the user's geographical location, efficient task management becomes possible.

[0044] The prioritization unit analyzes the user's social media activity when prioritizing tasks and presents relevant tasks first. For example, it prioritizes tasks mentioned by the user on social media. It prioritizes tasks of high interest based on the user's social media activity. It prioritizes tasks related to projects the user follows on social media. This allows for efficient management of relevant tasks by analyzing the user's social media activity.

[0045] The meeting optimization unit proposes the optimal meeting schedule by referring to past meeting data during the meeting optimization process. For example, the meeting optimization unit proposes the optimal meeting time based on data from meetings the user has previously attended. The meeting optimization unit proposes an efficient meeting schedule based on the user's past meeting participation history. The meeting optimization unit analyzes the user's past meeting data and proposes the most effective meeting time. In this way, an efficient meeting schedule can be proposed by referring to past meeting data.

[0046] The meeting optimization department optimizes meetings by considering participants' attribute information. For example, it sets the optimal meeting agenda based on participants' job titles and areas of expertise. The meeting optimization department proposes the optimal meeting duration considering participants' schedules. The meeting optimization department sets efficient meetings based on participants' past meeting attendance history. In this way, efficient meeting scheduling becomes possible by considering participants' attribute information.

[0047] The meeting optimization unit proposes the optimal meeting location when optimizing meetings, taking into account the geographical distribution of the meetings. For example, the meeting optimization unit proposes the optimal meeting location by considering the location of the participants. The meeting optimization unit proposes an efficient meeting location based on the geographical distribution of the meetings. The meeting optimization unit proposes an optimal meeting location by considering the travel time of the participants. In this way, by considering the geographical distribution of meetings, it is possible to propose an efficient meeting location.

[0048] The Meeting Optimization Department optimizes meeting content by referring to relevant literature and materials. For example, the Meeting Optimization Department proposes optimal meeting content by referring to literature related to the meeting agenda. The Meeting Optimization Department proposes efficient meeting content based on materials related to the meeting content. The Meeting Optimization Department proposes optimal meeting content by analyzing data related to the meeting agenda. In this way, by referring to relevant literature and materials, it can propose efficient meeting content.

[0049] The notification filtering unit selects the optimal filtering method by referring to past notification history when filtering notifications. For example, the notification filtering unit filters out notifications that the user has ignored in the past. The notification filtering unit prioritizes providing notifications that the user has previously considered important. The notification filtering unit selects an efficient filtering method based on the user's past notification history. This enables efficient notification filtering by referring to past notification history.

[0050] The notification filtering unit selects the optimal notification method when filtering notifications, taking into account the user's device information. For example, if the user is using a smartphone, the notification filtering unit provides a notification method that matches the screen size. If the user is using a tablet, the notification filtering unit provides a notification method optimized for a larger screen. If the user is using a smartwatch, the notification filtering unit provides a concise and highly visible notification method. In this way, the optimal notification method can be provided by taking the user's device information into consideration.

[0051] The notification filtering unit filters notifications based on user behavior data. For example, if the user is working, the notification filtering unit filters out notifications of low importance. If the user is on a break, the notification filtering unit prioritizes providing important notifications. The notification filtering unit performs efficient notification filtering based on user behavior data. This enables efficient notification management by filtering notifications based on user behavior data.

[0052] The music optimization unit makes optimal music selections by referring to the user's past music history. For example, the music optimization unit makes optimal selections based on music the user has listened to in the past. The music optimization unit selects relaxing music from the user's past music history. The music optimization unit analyzes the user's past music history and selects music that enhances concentration. In this way, efficient music selection becomes possible by referring to the user's past music history.

[0053] The music optimization unit selects music genres based on the user's current work activity during music optimization. For example, if the user is performing a task that requires concentration, the music optimization unit will select music that enhances concentration. If the user wants to relax, the music optimization unit will select relaxing music. If the user is performing a creative task, the music optimization unit will select music that stimulates creativity. This enables efficient music management by selecting music genres based on the user's current work activity.

[0054] The music optimization unit selects the most suitable music by considering the user's geographical location during the music optimization process. For example, if the user is at home, the music optimization unit selects music that promotes relaxation at home. If the user is in the office, the music optimization unit selects music that enhances concentration. If the user is on the move, the music optimization unit selects music suitable for travel. In this way, by considering the user's geographical location, the optimal music can be provided.

[0055] The music optimization unit analyzes users' social media activity to select relevant music during the music optimization process. For example, it selects music that users have mentioned on social media. It selects music of high interest based on users' social media activity. It selects music by artists that users follow on social media. This allows for efficient management of relevant music by analyzing users' social media activity.

[0056] The reminder function selects the optimal timing for delivering reminders by referring to the user's past behavior history. For example, the reminder function avoids delivering reminders at times when the user has previously ignored them. The reminder function selects the optimal timing based on the user's past behavior history. The reminder function provides efficient reminders based on when the user has previously accepted reminders. This enables efficient reminder delivery by referring to the user's past behavior history.

[0057] The reminder function adjusts the content of reminders based on the user's current work activity when providing them. For example, if the user is performing a task that requires concentration, the reminder function will provide a reminder to enhance concentration. If the user wants to relax, the reminder function will provide a reminder to promote relaxation. If the user is performing a creative task, the reminder function will provide a reminder to stimulate creativity. By adjusting the content of reminders based on the user's current work activity, efficient reminder delivery becomes possible.

[0058] The reminder function selects the optimal timing for delivering reminders by considering the user's geographical location. For example, if the user is at home, the reminder function will deliver a reminder at a time when they can relax at home. If the user is in the office, the reminder function will deliver a reminder at a time when they can concentrate. If the user is on the move, the reminder function will deliver a reminder at a time appropriate for their travel. In this way, by considering the user's geographical location, the system can deliver reminders at the optimal time.

[0059] The reminder function analyzes the user's social media activity when providing reminders and delivers relevant reminders. For example, the reminder function provides reminders related to tasks mentioned by the user on social media. The reminder function provides reminders of high interest based on the user's social media activity. The reminder function provides reminders related to projects the user follows on social media. This allows for the efficient delivery of relevant reminders by analyzing the user's social media activity.

[0060] The VPN connection unit selects the optimal connection method by referring to the user's past connection history when establishing a VPN connection. For example, the VPN connection unit selects the optimal connection method based on the VPNs the user has previously connected to. The VPN connection unit selects the most efficient connection method from the user's past connection history. The VPN connection unit selects the optimal connection method based on the timing of the user's past connections. This enables efficient VPN connections by referring to the user's past connection history.

[0061] The VPN connection unit adjusts connection priorities based on the user's current work activity when establishing a VPN connection. For example, if the user is performing a task requiring concentration, the VPN connection unit will attempt to connect at a time when concentration is heightened. If the user wants to relax, the VPN connection unit will attempt to connect at a time when relaxation is possible. If the user is performing a creative task, the VPN connection unit will attempt to connect at a time when creativity is stimulated. By adjusting connection priorities based on the user's current work activity, efficient VPN connections are possible.

[0062] The VPN connection unit selects the optimal connection method by considering the user's geographical location when establishing a VPN connection. For example, if the user is at home, the VPN connection unit will establish a connection at a time when the user can relax at home. If the user is at the office, the VPN connection unit will establish a connection at a time when the user can concentrate. If the user is on the move, the VPN connection unit will establish a connection at a time appropriate for the user's travel. In this way, the optimal VPN connection is possible by considering the user's geographical location.

[0063] The VPN connection unit analyzes the user's social media activity when a VPN connection is established and selects a relevant connection method. For example, the VPN connection unit selects a VPN connection method mentioned by the user on social media. The VPN connection unit selects a VPN connection method of high interest based on the user's social media activity. The VPN connection unit selects a VPN connection method related to projects the user follows on social media. This allows for efficient management of relevant VPN connection methods by analyzing the user's social media activity.

[0064] The schedule presentation unit selects the optimal presentation method by referring to the user's past schedule history when presenting a schedule. For example, the schedule presentation unit prioritizes providing schedule presentation methods that the user has preferred in the past. The schedule presentation unit selects an efficient presentation method from the user's past schedule history. The schedule presentation unit provides the optimal presentation method based on schedule presentation methods that the user has used in the past. This makes it possible to present schedules efficiently by referring to the user's past schedule history. It will be clearly stated in the output format that processing by the generation AI is also acceptable.

[0065] The schedule presentation unit adjusts the schedule presentation content based on the user's current work when presenting the schedule. For example, if the user is performing a task that requires concentration, the schedule presentation unit provides a schedule presentation method that enhances concentration. If the user wants to relax, the schedule presentation unit provides a schedule presentation method that promotes relaxation. If the user is performing a creative task, the schedule presentation unit provides a schedule presentation method that stimulates creativity. By adjusting the schedule presentation content based on the user's current work, efficient schedule management becomes possible. It will be clearly stated in the output format that processing by generation AI is also acceptable.

[0066] The schedule presentation unit selects the optimal presentation method when presenting a schedule, taking into account the user's geographical location. For example, if the user is at home, the schedule presentation unit provides a schedule presentation method that allows for relaxation at home. If the user is at the office, the schedule presentation unit provides a schedule presentation method that enhances concentration. If the user is on the move, the schedule presentation unit provides a schedule presentation method suitable for travel. This makes it possible to present the optimal schedule by taking into account the user's geographical location. It will be clearly stated in the output format that processing by the generation AI is also acceptable.

[0067] The schedule presentation unit analyzes the user's social media activity and presents relevant schedules when presenting them. For example, the schedule presentation unit may present schedules related to events mentioned by the user on social media. The schedule presentation unit presents schedules of high interest based on the user's social media activity. The schedule presentation unit presents schedules related to projects the user follows on social media. This allows for efficient management of relevant schedules by analyzing the user's social media activity. It will be clearly stated in the output format that processing by a generation AI is also acceptable.

[0068] The behavioral analysis unit selects the optimal analysis method by referring to the user's past behavioral history during behavioral analysis. For example, the behavioral analysis unit selects the optimal analysis method based on the user's past actions. The behavioral analysis unit selects an efficient analysis method from the user's past behavioral history. The behavioral analysis unit provides the optimal behavioral analysis method based on the user's past actions. This enables efficient behavioral analysis by referring to the user's past behavioral history.

[0069] The behavioral analysis unit adjusts the analysis content based on the user's current work during behavioral analysis. For example, if the user is performing a task that requires concentration, the behavioral analysis unit provides a behavioral analysis method that enhances concentration. If the user wants to relax, the behavioral analysis unit provides a behavioral analysis method that promotes relaxation. If the user is performing a creative task, the behavioral analysis unit provides a behavioral analysis method that stimulates creativity. By adjusting the analysis content based on the user's current work, efficient behavioral analysis becomes possible.

[0070] The behavioral analysis unit selects the optimal analysis method during behavioral analysis, taking into account the user's geographical location. For example, if the user is at home, the behavioral analysis unit provides a method that promotes relaxation at home. If the user is in the office, the behavioral analysis unit provides a method that enhances concentration. If the user is on the move, the behavioral analysis unit provides a method suitable for movement. By considering the user's geographical location, optimal behavioral analysis becomes possible.

[0071] The Behavioral Analysis Unit analyzes users' social media activity and related behaviors during behavioral analysis. For example, the Behavioral Analysis Unit analyzes behaviors that users mention on social media. The Behavioral Analysis Unit analyzes behaviors that users are highly interested in from their social media activity. The Behavioral Analysis Unit analyzes behaviors related to projects that users follow on social media. In this way, related behaviors can be efficiently analyzed by analyzing users' social media activity.

[0072] The location information analysis unit selects the optimal analysis method by referring to the user's past location history during location information analysis. For example, the location information analysis unit selects the optimal location information analysis method based on places the user has visited in the past. The location information analysis unit selects an efficient analysis method from the user's past location history. The location information analysis unit provides the optimal location information analysis method based on places the user has visited in the past. This enables efficient location information analysis by referring to the user's past location history.

[0073] The location information analysis unit adjusts the analysis content based on the user's current work activity during location information analysis. For example, if the user is performing a task that requires concentration, the location information analysis unit provides a location information analysis method that enhances concentration. If the user wants to relax, the location information analysis unit provides a location information analysis method that promotes relaxation. If the user is performing a creative task, the location information analysis unit provides a location information analysis method that stimulates creativity. By adjusting the analysis content based on the user's current work activity, efficient location information analysis becomes possible.

[0074] The location information analysis unit selects the optimal analysis method when analyzing location information, taking into account the user's geographical location. For example, if the user is at home, the location information analysis unit provides a method that allows for relaxation at home. If the user is in the office, the location information analysis unit provides a method that enhances concentration. If the user is on the move, the location information analysis unit provides a method suitable for movement. By considering the user's geographical location, optimal location information analysis becomes possible.

[0075] The location information analysis unit analyzes the user's social media activity and analyzes relevant location information during location information analysis. For example, the location information analysis unit analyzes places mentioned by the user on social media. The location information analysis unit analyzes places of high interest from the user's social media activity. The location information analysis unit analyzes places related to projects that the user follows on social media. In this way, relevant location information can be efficiently analyzed by analyzing the user's social media activity.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The AI ​​agent system for optimizing remote work environments can also include a meal management unit to manage the user's diet. This unit collects and analyzes the user's dietary data. For example, it monitors whether the user is consuming nutritionally balanced meals and suggests meals to supplement any missing nutrients. It manages the timing of the user's meals and provides reminders to ensure meals are eaten at appropriate times. Based on the user's meal history, it creates a healthy meal plan. This supports the user's dietary management and provides a healthy remote work environment.

[0078] The AI ​​agent system for optimizing remote work environments can also include a learning support unit to assist users' learning activities. This unit collects and analyzes user learning data. For example, it provides relevant learning resources based on what the user is studying. It monitors the user's learning progress and provides learning reminders at appropriate times. It creates efficient learning plans based on the user's learning history. This supports users' learning activities and provides an efficient remote work environment.

[0079] The AI ​​agent system for optimizing remote work environments can also include a communication support unit to assist user communication. This unit collects and analyzes user communication data. For example, it can suggest ways to streamline interactions with people the user frequently communicates with. Based on the user's communication history, it provides reminders to encourage communication at appropriate times. It also suggests effective communication methods based on the user's communication style. This supports user communication and provides an efficient remote work environment.

[0080] The AI ​​agent system for optimizing remote work environments can also include an entertainment support unit to assist users with their entertainment needs. This unit collects and analyzes user entertainment data. For example, it suggests relaxing entertainment content, provides entertainment at appropriate times based on the user's entertainment history, and creates customized entertainment plans based on user preferences. This allows the system to support users' entertainment needs and provide a relaxing remote work environment.

[0081] The following briefly describes the processing flow for example form 1.

[0082] Step 1: The prioritization section prioritizes tasks by integrating with calendars and task management tools. For example, it analyzes the user's schedule and presents important tasks with priority. Task prioritization can also be performed using AI. Step 2: The meeting optimization unit sets up and optimizes meetings based on tasks prioritized by the prioritization unit. For example, it suggests the optimal meeting time considering the user's schedule. AI can be used to set up and optimize meetings. Step 3: The notification filtering unit filters notifications. For example, notifications can be filtered based on user behavior data. Notifications can also be filtered using AI. Step 4: The music optimization unit optimizes the music. For example, it optimizes music based on the user's preferences, work content, and time of day. AI can be used to optimize the music. Step 5: The reminder section provides reminders for exercise and breaks. For example, it monitors the user's work time and prompts them to exercise or take breaks at appropriate times. AI can be used to provide reminders for exercise and breaks. Step 6: The VPN connection unit automatically establishes a VPN connection based on the user's location information. For example, it can establish a VPN connection based on the user's location information. It can also use AI to establish a VPN connection.

[0083] (Example of form 2) The AI ​​agent system for optimizing remote work environments according to an embodiment of the present invention is a system that prioritizes tasks, sets up and optimizes meetings in conjunction with calendars and task management tools. The AI ​​agent system prioritizes tasks in conjunction with calendars and task management tools, analyzes the user's schedule, and presents important tasks with priority. Next, the AI ​​agent system sets up and optimizes meetings, suggesting the optimal meeting time considering the user's schedule. Furthermore, the AI ​​agent system filters notifications and optimizes music based on time to enhance user concentration. In addition, the AI ​​agent system provides reminders for exercise and breaks, monitors the user's work time, and prompts exercise and breaks at appropriate times. Moreover, the AI ​​agent system ensures security by automatically connecting to VPNs depending on the location. Thus, the AI ​​agent system supports improved user productivity and the maintenance of work-life balance. For example, the AI ​​agent system prioritizes tasks in conjunction with calendars and task management tools, analyzes the user's schedule, and presents important tasks with priority. Next, the remote work environment optimization AI agent system sets up and optimizes meetings, suggesting optimal meeting times considering the user's schedule. It also filters notifications and optimizes music based on time to enhance user concentration. Furthermore, it provides reminders for exercise and breaks, monitoring user work time to prompt appropriate exercise and rest. Finally, it ensures security by automatically connecting to VPNs based on location. In this way, the remote work environment optimization AI agent system supports improved user productivity and the maintenance of work-life balance.This allows the AI ​​agent system for optimizing remote work environments to support improved user productivity and the maintenance of work-life balance.

[0084] The AI ​​agent system for optimizing remote work environments according to this embodiment includes a prioritization unit, a meeting optimization unit, a notification filtering unit, a music optimization unit, a reminder unit, and a VPN connection unit. The prioritization unit prioritizes tasks in conjunction with a calendar or task management tool. For example, the prioritization unit prioritizes tasks in conjunction with a calendar or task management tool, analyzes the user's schedule, and presents important tasks first. The prioritization unit can prioritize tasks using AI. The meeting optimization unit sets up and optimizes meetings based on the tasks prioritized by the prioritization unit. For example, the meeting optimization unit suggests the optimal meeting time considering the user's schedule. The meeting optimization unit can set up and optimize meetings using AI. The notification filtering unit filters notifications. For example, the notification filtering unit filters notifications based on user behavior data. The notification filtering unit can filter notifications using AI. The music optimization unit optimizes music. For example, the music optimization unit optimizes music based on the user's preferences, work content, and time of day. The music optimization unit can optimize music using AI. The reminder unit provides reminders for exercise and breaks. For example, the reminder unit monitors the user's work time and prompts them to exercise or take breaks at appropriate times. The reminder unit can provide reminders for exercise and breaks using AI. The VPN connection unit automatically establishes a VPN connection based on the user's location information. For example, the VPN connection unit establishes a VPN connection based on the user's location information. The VPN connection unit can establish a VPN connection using AI. As a result, the remote work environment optimization AI agent system according to this embodiment can support improved user productivity and the maintenance of work-life balance.

[0085] The prioritization unit prioritizes tasks in conjunction with calendars and task management tools. Specifically, it retrieves data from the user's calendar and task management tools and uses AI to analyze the importance and urgency of tasks. For example, it automatically determines task priorities based on meeting and deadline information registered in the calendar. Furthermore, it considers the progress and deadlines of tasks registered in the task management tool to present the most important tasks to the user first. The AI ​​learns from past task completion status and user behavior patterns to perform more accurate prioritization. For example, it analyzes how long it took the user to complete important tasks in the past and adjusts the priority of current tasks. It can also dynamically change task priorities according to the user's work content and project progress. In this way, the prioritization unit supports users in efficiently managing tasks and focusing on important work.

[0086] The meeting optimization unit sets up and optimizes meetings based on tasks prioritized by the prioritization unit. Specifically, the meeting optimization unit analyzes the user's schedule and proposes the optimal meeting time. For example, it adjusts meeting times considering the priority of other appointments and tasks registered in the user's calendar. AI can analyze the user's past meeting attendance and meeting effectiveness to propose the optimal meeting time and participants. Furthermore, the meeting optimization unit can also suggest the appropriate meeting format (online meeting, in-person meeting, etc.) depending on the content and purpose of the meeting. For example, it recommends an online meeting for a meeting aimed at quick information sharing, and an in-person meeting for a meeting requiring important decision-making. The meeting optimization unit also supports the preparation of meeting agendas and materials, improving meeting efficiency. In this way, the meeting optimization unit supports users in efficiently setting up meetings and focusing on important tasks.

[0087] The notification filtering unit filters notifications. Specifically, it analyzes user behavior data and past notification responses to present only important notifications to the user. The AI ​​learns the user's behavior patterns and work content, and can automatically filter out unnecessary notifications. For example, during times when the user is concentrating on a task, it temporarily suppresses less important notifications to avoid disrupting their work. Also, if the user is focusing on a specific project, it prioritizes displaying only notifications related to that project. Furthermore, the notification filtering unit can continuously improve the accuracy of notification filtering based on user feedback. For example, if a user determines a particular notification is important, the system learns that information and incorporates it into future notification filtering. In this way, the notification filtering unit supports users in not missing important information and efficiently carrying out their work.

[0088] The music optimization unit optimizes music playback. Specifically, it selects and plays the most suitable music based on the user's preferences, work content, and time of day. The AI ​​learns the user's past music playback history and ratings, and can suggest music that matches the user's preferences. For example, during times when the user needs to concentrate on work, it plays instrumental music to enhance concentration, and during times when the user wants to relax, it plays music with a relaxing effect. The music optimization unit can also select music according to the user's work content. For example, when performing creative work, it plays music that stimulates creativity, and when performing simple tasks, it plays music with a rhythmic feel. Furthermore, the music optimization unit can continuously improve the accuracy of its music selection based on user feedback. In this way, the music optimization unit supports users in working efficiently in a comfortable work environment.

[0089] The reminder function provides reminders for exercise and breaks. Specifically, it monitors the user's work time and prompts them to exercise or take breaks at appropriate times. The AI ​​analyzes the user's work patterns and health data to provide reminders at the optimal time. For example, if the user has been working for a long time, it will display a reminder to stretch or do light exercise and encourage them to take regular breaks. The reminder function can also adjust the content and timing of reminders based on the user's health status and feedback. For example, if the user prefers a particular exercise, it will prioritize suggesting that exercise and adjust the frequency of reminders according to the user's health status. In this way, the reminder function supports users in maintaining healthy work habits and working efficiently.

[0090] The VPN connection unit automatically establishes a VPN connection based on the user's location information. Specifically, it obtains location information from the user's device and automatically configures a VPN connection to ensure the user can work in a secure network environment. The AI ​​learns the user's past connection history and network environment to select the optimal VPN server. For example, if the user is using public Wi-Fi, it automatically establishes a VPN connection to ensure data security. Also, if the user is working in a different location, it selects the optimal VPN server for that location and establishes a connection. Furthermore, the VPN connection unit can monitor the stability and speed of the connection and change the connection destination as needed. In this way, the VPN connection unit supports users in working remotely safely and comfortably.

[0091] The schedule display unit presents schedules in an easy-to-understand manner. For example, it uses visual display methods and filtering functions to present schedules clearly. The schedule display unit can also use generating AI to present schedules clearly. This allows users to easily understand their schedules.

[0092] The behavioral analysis unit analyzes user behavior data. For example, it analyzes behavioral data such as location information, activity logs, and applications used. The behavioral analysis unit can also analyze user behavior data using AI. This allows for more appropriate task management by analyzing user behavior data.

[0093] The location information analysis unit analyzes the user's location information. For example, it analyzes location information such as GPS data and Wi-Fi location information. The location information analysis unit can use AI to analyze the user's location information. This allows for more appropriate VPN connections by analyzing the user's location information.

[0094] The notification filtering unit filters notifications based on user behavior data. For example, the notification filtering unit can filter notifications based on user behavior data. The notification filtering unit can also filter notifications using AI. This allows important notifications to be prioritized by filtering them based on user behavior data.

[0095] The VPN connection unit establishes a VPN connection based on the user's location information. For example, the VPN connection unit can establish a VPN connection based on the user's location information. The VPN connection unit can also use AI to establish a VPN connection. This ensures security by establishing a VPN connection based on the user's location information.

[0096] The prioritization unit estimates the user's emotions and adjusts task priorities based on those estimates. For example, if the user is stressed, the prioritization unit will postpone less important tasks and prioritize tasks that help the user relax. If the user is focused, the prioritization unit will present high-importance tasks first. If the user is tired, the prioritization unit will prioritize tasks that can be completed quickly. This allows for more appropriate task management by adjusting task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The prioritization unit analyzes the user's past task history and selects the optimal prioritization method. For example, the prioritization unit will prioritize tasks that the user has frequently completed in the past. The prioritization unit will postpone tasks that the user has found difficult in the past. The prioritization unit sets efficient priorities based on the user's past task completion times. This enables efficient task management by analyzing the user's past task history.

[0098] The prioritization section filters tasks based on the user's current projects and areas of interest when prioritizing tasks. For example, it prioritizes tasks related to the user's current projects, tasks related to the user's areas of interest, and tasks related to the areas the user is currently focusing on. This allows for efficient task management by filtering tasks based on the user's current projects and areas of interest.

[0099] The prioritization unit estimates the user's emotions and modifies the prioritization criteria based on the estimated emotions. For example, if the user is stressed, the prioritization unit prioritizes relaxing tasks. If the user is focused, the prioritization unit prioritizes high-priority tasks. If the user is tired, the prioritization unit prioritizes tasks that can be completed quickly. This allows for more appropriate task management by changing the prioritization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The prioritization section prioritizes tasks by considering the user's geographical location and presenting the most relevant tasks first. For example, if the user is in a specific location, the prioritization section will prioritize tasks that can be completed at that location. If the user is on the move, the prioritization section will prioritize tasks that can be completed while on the move. If the user is at home, the prioritization section will prioritize tasks that can be completed at home. By presenting tasks while considering the user's geographical location, efficient task management becomes possible.

[0101] The prioritization unit analyzes the user's social media activity when prioritizing tasks and presents relevant tasks first. For example, it prioritizes tasks mentioned by the user on social media. It prioritizes tasks of high interest based on the user's social media activity. It prioritizes tasks related to projects the user follows on social media. This allows for efficient management of relevant tasks by analyzing the user's social media activity.

[0102] The meeting optimization unit estimates the user's emotions and adjusts the meeting duration and content based on the estimated emotions. For example, if the user is stressed, the meeting optimization unit suggests a short meeting. If the user is relaxed, the meeting optimization unit suggests a meeting with a detailed agenda. If the user is focused, the meeting optimization unit suggests a meeting with important agenda items. This allows for efficient meeting management by adjusting the meeting duration and content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The meeting optimization unit proposes the optimal meeting schedule by referring to past meeting data during the meeting optimization process. For example, the meeting optimization unit proposes the optimal meeting time based on data from meetings the user has previously attended. The meeting optimization unit proposes an efficient meeting schedule based on the user's past meeting participation history. The meeting optimization unit analyzes the user's past meeting data and proposes the most effective meeting time. In this way, an efficient meeting schedule can be proposed by referring to past meeting data.

[0104] The meeting optimization department optimizes meetings by considering participants' attribute information. For example, it sets the optimal meeting agenda based on participants' job titles and areas of expertise. The meeting optimization department proposes the optimal meeting duration considering participants' schedules. The meeting optimization department sets efficient meetings based on participants' past meeting attendance history. In this way, efficient meeting scheduling becomes possible by considering participants' attribute information.

[0105] The meeting optimization unit estimates the user's emotions and adjusts the meeting notification method based on the estimated emotions. For example, if the user is stressed, the meeting optimization unit provides a simple notification method. If the user is relaxed, the meeting optimization unit provides a detailed notification method. If the user is focused, the meeting optimization unit prioritizes important notifications. This allows for efficient meeting management by adjusting the meeting notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The meeting optimization unit proposes the optimal meeting location when optimizing meetings, taking into account the geographical distribution of the meetings. For example, the meeting optimization unit proposes the optimal meeting location by considering the location of the participants. The meeting optimization unit proposes an efficient meeting location based on the geographical distribution of the meetings. The meeting optimization unit proposes an optimal meeting location by considering the travel time of the participants. In this way, by considering the geographical distribution of meetings, it is possible to propose an efficient meeting location.

[0107] The Meeting Optimization Department optimizes meeting content by referring to relevant literature and materials. For example, the Meeting Optimization Department proposes optimal meeting content by referring to literature related to the meeting agenda. The Meeting Optimization Department proposes efficient meeting content based on materials related to the meeting content. The Meeting Optimization Department proposes optimal meeting content by analyzing data related to the meeting agenda. In this way, by referring to relevant literature and materials, it can propose efficient meeting content.

[0108] The notification filtering unit estimates the user's emotions and adjusts the notification filtering criteria based on the estimated emotions. For example, if the user is stressed, the notification filtering unit filters out less important notifications. If the user is relaxed, the notification filtering unit provides detailed notifications. If the user is focused, the notification filtering unit prioritizes important notifications. This allows for the priority delivery of important notifications by adjusting the notification filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The notification filtering unit selects the optimal filtering method by referring to past notification history when filtering notifications. For example, the notification filtering unit filters out notifications that the user has ignored in the past. The notification filtering unit prioritizes providing notifications that the user has previously considered important. The notification filtering unit selects an efficient filtering method based on the user's past notification history. This enables efficient notification filtering by referring to past notification history.

[0110] The notification filtering unit estimates the user's emotions and determines notification priorities based on the estimated emotions. For example, if the user is stressed, the notification filtering unit will postpone less important notifications. If the user is relaxed, the notification filtering unit will provide detailed notifications. If the user is focused, the notification filtering unit will prioritize important notifications. In this way, important notifications can be prioritized by determining notification priorities according to 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.

[0111] The notification filtering unit selects the optimal notification method when filtering notifications, taking into account the user's device information. For example, if the user is using a smartphone, the notification filtering unit provides a notification method that matches the screen size. If the user is using a tablet, the notification filtering unit provides a notification method optimized for a larger screen. If the user is using a smartwatch, the notification filtering unit provides a concise and highly visible notification method. In this way, the optimal notification method can be provided by taking the user's device information into consideration.

[0112] The notification filtering unit filters notifications based on user behavior data. For example, if the user is working, the notification filtering unit filters out notifications of low importance. If the user is on a break, the notification filtering unit prioritizes providing important notifications. The notification filtering unit performs efficient notification filtering based on user behavior data. This enables efficient notification management by filtering notifications based on user behavior data.

[0113] The music optimization unit estimates the user's emotions and adjusts the music selection based on those emotions. For example, if the user is stressed, the music optimization unit selects relaxing music. If the user is relaxed, the music optimization unit selects music that enhances concentration. If the user is focused, the music optimization unit selects music suitable for the task. In this way, by adjusting the music selection according to the user's emotions, more appropriate music can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The music optimization unit makes optimal music selections by referring to the user's past music history. For example, the music optimization unit makes optimal selections based on music the user has listened to in the past. The music optimization unit selects relaxing music from the user's past music history. The music optimization unit analyzes the user's past music history and selects music that enhances concentration. In this way, efficient music selection becomes possible by referring to the user's past music history.

[0115] The music optimization unit selects music genres based on the user's current work activity during music optimization. For example, if the user is performing a task that requires concentration, the music optimization unit will select music that enhances concentration. If the user wants to relax, the music optimization unit will select relaxing music. If the user is performing a creative task, the music optimization unit will select music that stimulates creativity. This enables efficient music management by selecting music genres based on the user's current work activity.

[0116] The music optimization unit estimates the user's emotions and adjusts the music playback order based on the estimated emotions. For example, if the user is stressed, the music optimization unit will play relaxing music first. If the user is relaxed, the music optimization unit will play music that enhances concentration. If the user is focused, the music optimization unit will play music suitable for work. In this way, by adjusting the music playback order according to the user's emotions, more appropriate music can be provided. 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.

[0117] The music optimization unit selects the most suitable music by considering the user's geographical location during the music optimization process. For example, if the user is at home, the music optimization unit selects music that promotes relaxation at home. If the user is in the office, the music optimization unit selects music that enhances concentration. If the user is on the move, the music optimization unit selects music suitable for travel. In this way, by considering the user's geographical location, the optimal music can be provided.

[0118] The music optimization unit analyzes users' social media activity to select relevant music during the music optimization process. For example, it selects music that users have mentioned on social media. It selects music of high interest based on users' social media activity. It selects music by artists that users follow on social media. This allows for efficient management of relevant music by analyzing users' social media activity.

[0119] The reminder function estimates the user's emotions and adjusts the timing of reminders based on the estimated emotions. For example, if the user is stressed, the reminder function will provide a reminder at a time when the user can relax. If the user is relaxed, the reminder function will provide a reminder at a time when the user can concentrate. If the user is concentrating, the reminder function will provide a reminder in between tasks. By adjusting the timing of reminders according to the user's emotions, more appropriate reminders can be provided. 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.

[0120] The reminder function selects the optimal timing for delivering reminders by referring to the user's past behavior history. For example, the reminder function avoids delivering reminders at times when the user has previously ignored them. The reminder function selects the optimal timing based on the user's past behavior history. The reminder function provides efficient reminders based on when the user has previously accepted reminders. This enables efficient reminder delivery by referring to the user's past behavior history.

[0121] The reminder function adjusts the content of reminders based on the user's current work activity when providing them. For example, if the user is performing a task that requires concentration, the reminder function will provide a reminder to enhance concentration. If the user wants to relax, the reminder function will provide a reminder to promote relaxation. If the user is performing a creative task, the reminder function will provide a reminder to stimulate creativity. By adjusting the content of reminders based on the user's current work activity, efficient reminder delivery becomes possible.

[0122] The reminder function estimates the user's emotions and prioritizes reminders based on those emotions. For example, if the user is stressed, the reminder function will postpone less important reminders. If the user is relaxed, the reminder function will provide detailed reminders. If the user is focused, the reminder function will prioritize important reminders. This ensures that important reminders are delivered preferentially by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The reminder function selects the optimal timing for delivering reminders by considering the user's geographical location. For example, if the user is at home, the reminder function will deliver a reminder at a time when they can relax at home. If the user is in the office, the reminder function will deliver a reminder at a time when they can concentrate. If the user is on the move, the reminder function will deliver a reminder at a time appropriate for their travel. In this way, by considering the user's geographical location, the system can deliver reminders at the optimal time.

[0124] The reminder function analyzes the user's social media activity when providing reminders and delivers relevant reminders. For example, the reminder function provides reminders related to tasks mentioned by the user on social media. The reminder function provides reminders of high interest based on the user's social media activity. The reminder function provides reminders related to projects the user follows on social media. This allows for the efficient delivery of relevant reminders by analyzing the user's social media activity.

[0125] The VPN connection unit estimates the user's emotions and adjusts the timing of the VPN connection based on the estimated emotions. For example, if the user is feeling stressed, the VPN connection unit will connect at a time when the user can relax. If the user is relaxed, the VPN connection unit will connect at a time when the user can concentrate. If the user is concentrating, the VPN connection unit will connect in between tasks. In this way, by adjusting the timing of the VPN connection according to the user's emotions, a more appropriate VPN connection is possible. 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.

[0126] The VPN connection unit selects the optimal connection method by referring to the user's past connection history when establishing a VPN connection. For example, the VPN connection unit selects the optimal connection method based on the VPNs the user has previously connected to. The VPN connection unit selects the most efficient connection method from the user's past connection history. The VPN connection unit selects the optimal connection method based on the timing of the user's past connections. This enables efficient VPN connections by referring to the user's past connection history.

[0127] The VPN connection unit adjusts connection priorities based on the user's current work activity when establishing a VPN connection. For example, if the user is performing a task requiring concentration, the VPN connection unit will attempt to connect at a time when concentration is heightened. If the user wants to relax, the VPN connection unit will attempt to connect at a time when relaxation is possible. If the user is performing a creative task, the VPN connection unit will attempt to connect at a time when creativity is stimulated. By adjusting connection priorities based on the user's current work activity, efficient VPN connections are possible.

[0128] The VPN connection unit estimates the user's emotions and prioritizes VPN connections based on those emotions. For example, if the user is stressed, the VPN connection unit will postpone less important VPN connections. If the user is relaxed, the VPN connection unit will provide detailed VPN connections. If the user is focused, the VPN connection unit will prioritize important VPN connections. This allows for the priority of important VPN connections by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The VPN connection unit selects the optimal connection method by considering the user's geographical location when establishing a VPN connection. For example, if the user is at home, the VPN connection unit will establish a connection at a time when the user can relax at home. If the user is at the office, the VPN connection unit will establish a connection at a time when the user can concentrate. If the user is on the move, the VPN connection unit will establish a connection at a time appropriate for the user's travel. In this way, the optimal VPN connection is possible by considering the user's geographical location.

[0130] The VPN connection unit analyzes the user's social media activity when a VPN connection is established and selects a relevant connection method. For example, the VPN connection unit selects a VPN connection method mentioned by the user on social media. The VPN connection unit selects a VPN connection method of high interest based on the user's social media activity. The VPN connection unit selects a VPN connection method related to projects the user follows on social media. This allows for efficient management of relevant VPN connection methods by analyzing the user's social media activity.

[0131] The schedule presentation unit estimates the user's emotions and adjusts the schedule presentation method based on the estimated emotions. For example, if the user is stressed, the schedule presentation unit provides a simple schedule presentation method. If the user is relaxed, the schedule presentation unit provides a detailed schedule presentation method. If the user is focused, the schedule presentation unit prioritizes important schedules. This allows for more appropriate schedule management by adjusting the schedule presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The schedule presentation unit selects the optimal presentation method by referring to the user's past schedule history when presenting a schedule. For example, the schedule presentation unit prioritizes providing schedule presentation methods that the user has preferred in the past. The schedule presentation unit selects an efficient presentation method from the user's past schedule history. The schedule presentation unit provides the optimal presentation method based on schedule presentation methods that the user has used in the past. This makes it possible to present schedules efficiently by referring to the user's past schedule history. It will be clearly stated in the output format that processing by the generation AI is also acceptable.

[0133] The schedule presentation unit adjusts the schedule presentation content based on the user's current work when presenting the schedule. For example, if the user is performing a task that requires concentration, the schedule presentation unit provides a schedule presentation method that enhances concentration. If the user wants to relax, the schedule presentation unit provides a schedule presentation method that promotes relaxation. If the user is performing a creative task, the schedule presentation unit provides a schedule presentation method that stimulates creativity. By adjusting the schedule presentation content based on the user's current work, efficient schedule management becomes possible. It will be clearly stated in the output format that processing by generation AI is also acceptable.

[0134] The schedule presentation unit estimates the user's emotions and prioritizes schedules based on those emotions. For example, if the user is stressed, the schedule presentation unit will postpone less important schedules. If the user is relaxed, the schedule presentation unit will provide a detailed schedule. If the user is focused, the schedule presentation unit will prioritize important schedules. This allows for the prioritization of important schedules by determining schedule priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The schedule presentation unit selects the optimal presentation method when presenting a schedule, taking into account the user's geographical location. For example, if the user is at home, the schedule presentation unit provides a schedule presentation method that allows for relaxation at home. If the user is at the office, the schedule presentation unit provides a schedule presentation method that enhances concentration. If the user is on the move, the schedule presentation unit provides a schedule presentation method suitable for travel. This makes it possible to present the optimal schedule by taking into account the user's geographical location. It will be clearly stated in the output format that processing by the generation AI is also acceptable.

[0136] The schedule presentation unit analyzes the user's social media activity and presents relevant schedules when presenting them. For example, the schedule presentation unit may present schedules related to events mentioned by the user on social media. The schedule presentation unit presents schedules of high interest based on the user's social media activity. The schedule presentation unit presents schedules related to projects the user follows on social media. This allows for efficient management of relevant schedules by analyzing the user's social media activity. It will be clearly stated in the output format that processing by a generation AI is also acceptable.

[0137] The behavioral analysis unit estimates the user's emotions and adjusts the behavioral analysis method based on the estimated emotions. For example, if the user is stressed, the behavioral analysis unit provides a relaxing behavioral analysis method. If the user is relaxed, the behavioral analysis unit provides a detailed behavioral analysis method. If the user is focused, the behavioral analysis unit prioritizes analyzing important behaviors. This allows for more appropriate behavioral analysis by adjusting the behavioral analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0138] The behavioral analysis unit selects the optimal analysis method by referring to the user's past behavioral history during behavioral analysis. For example, the behavioral analysis unit selects the optimal analysis method based on the user's past actions. The behavioral analysis unit selects an efficient analysis method from the user's past behavioral history. The behavioral analysis unit provides the optimal behavioral analysis method based on the user's past actions. This enables efficient behavioral analysis by referring to the user's past behavioral history.

[0139] The behavioral analysis unit adjusts the analysis content based on the user's current work during behavioral analysis. For example, if the user is performing a task that requires concentration, the behavioral analysis unit provides a behavioral analysis method that enhances concentration. If the user wants to relax, the behavioral analysis unit provides a behavioral analysis method that promotes relaxation. If the user is performing a creative task, the behavioral analysis unit provides a behavioral analysis method that stimulates creativity. By adjusting the analysis content based on the user's current work, efficient behavioral analysis becomes possible.

[0140] The behavioral analysis unit estimates the user's emotions and determines the priority of behavioral analysis based on the estimated emotions. For example, if the user is stressed, the behavioral analysis unit will postpone less important behaviors. If the user is relaxed, the behavioral analysis unit will provide detailed behavioral analysis. If the user is focused, the behavioral analysis unit will prioritize analyzing important behaviors. This allows for the prioritization of important behaviors by determining the priority of behavioral analysis according to the user's emotions. Emotion estimation can maintain emotional stability using an emotion engine or generative AI. Emotion estimation is implemented using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0141] The behavioral analysis unit selects the optimal analysis method during behavioral analysis, taking into account the user's geographical location. For example, if the user is at home, the behavioral analysis unit provides a method that promotes relaxation at home. If the user is in the office, the behavioral analysis unit provides a method that enhances concentration. If the user is on the move, the behavioral analysis unit provides a method suitable for movement. By considering the user's geographical location, optimal behavioral analysis becomes possible.

[0142] The Behavioral Analysis Unit analyzes users' social media activity and related behaviors during behavioral analysis. For example, the Behavioral Analysis Unit analyzes behaviors that users mention on social media. The Behavioral Analysis Unit analyzes behaviors that users are highly interested in from their social media activity. The Behavioral Analysis Unit analyzes behaviors related to projects that users follow on social media. In this way, related behaviors can be efficiently analyzed by analyzing users' social media activity.

[0143] The location information analysis unit estimates the user's emotions and adjusts the location information analysis method based on the estimated emotions. For example, if the user is stressed, the location information analysis unit provides a relaxing location information analysis method. If the user is relaxed, the location information analysis unit provides a detailed location information analysis method. If the user is focused, the location information analysis unit prioritizes analyzing important location information. This allows for more appropriate location information analysis by adjusting the location information analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0144] The location information analysis unit selects the optimal analysis method by referring to the user's past location history during location information analysis. For example, the location information analysis unit selects the optimal location information analysis method based on places the user has visited in the past. The location information analysis unit selects an efficient analysis method from the user's past location history. The location information analysis unit provides the optimal location information analysis method based on places the user has visited in the past. This enables efficient location information analysis by referring to the user's past location history.

[0145] The location information analysis unit adjusts the analysis content based on the user's current work activity during location information analysis. For example, if the user is performing a task that requires concentration, the location information analysis unit provides a location information analysis method that enhances concentration. If the user wants to relax, the location information analysis unit provides a location information analysis method that promotes relaxation. If the user is performing a creative task, the location information analysis unit provides a location information analysis method that stimulates creativity. By adjusting the analysis content based on the user's current work activity, efficient location information analysis becomes possible.

[0146] The location information analysis unit estimates the user's emotions and determines the priority of location information analysis based on the estimated emotions. For example, if the user is stressed, the location information analysis unit will postpone less important location information. If the user is relaxed, the location information analysis unit will provide detailed location information analysis. If the user is focused, the location information analysis unit will prioritize analyzing important location information. In this way, by determining the priority of location information analysis according to the user's emotions, important location information can be analyzed preferentially. 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.

[0147] The location information analysis unit selects the optimal analysis method when analyzing location information, taking into account the user's geographical location. For example, if the user is at home, the location information analysis unit provides a method that allows for relaxation at home. If the user is in the office, the location information analysis unit provides a method that enhances concentration. If the user is on the move, the location information analysis unit provides a method suitable for movement. By considering the user's geographical location, optimal location information analysis becomes possible.

[0148] The location information analysis unit analyzes the user's social media activity and analyzes relevant location information during location information analysis. For example, the location information analysis unit analyzes places mentioned by the user on social media. The location information analysis unit analyzes places of high interest from the user's social media activity. The location information analysis unit analyzes places related to projects that the user follows on social media. In this way, relevant location information can be efficiently analyzed by analyzing the user's social media activity.

[0149] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0150] The AI ​​agent system for optimizing remote work environments can also include a health management unit that monitors the user's health status. This unit collects and analyzes health data such as the user's heart rate, blood pressure, and sleep patterns. For example, if a user's heart rate is high, it prioritizes suggesting relaxing tasks. If a user's sleep pattern is disrupted, it provides reminders to encourage breaks. If a user's blood pressure is high, it suggests exercise to reduce stress. This enables task management tailored to the user's health status, providing a healthier remote work environment.

[0151] The AI ​​agent system for optimizing remote work environments can also include a meal management unit to manage the user's diet. This unit collects and analyzes the user's dietary data. For example, it monitors whether the user is consuming nutritionally balanced meals and suggests meals to supplement any missing nutrients. It manages the timing of the user's meals and provides reminders to ensure meals are eaten at appropriate times. Based on the user's meal history, it creates a healthy meal plan. This supports the user's dietary management and provides a healthy remote work environment.

[0152] The AI ​​agent system for optimizing remote work environments can also include a learning support unit to assist users' learning activities. This unit collects and analyzes user learning data. For example, it provides relevant learning resources based on what the user is studying. It monitors the user's learning progress and provides learning reminders at appropriate times. It creates efficient learning plans based on the user's learning history. This supports users' learning activities and provides an efficient remote work environment.

[0153] The AI ​​agent system for optimizing remote work environments can also include a communication support unit to assist user communication. This unit collects and analyzes user communication data. For example, it can suggest ways to streamline interactions with people the user frequently communicates with. Based on the user's communication history, it provides reminders to encourage communication at appropriate times. It also suggests effective communication methods based on the user's communication style. This supports user communication and provides an efficient remote work environment.

[0154] The AI ​​agent system for optimizing remote work environments can also include an entertainment support unit to assist users with their entertainment needs. This unit collects and analyzes user entertainment data. For example, it suggests relaxing entertainment content, provides entertainment at appropriate times based on the user's entertainment history, and creates customized entertainment plans based on user preferences. This allows the system to support users' entertainment needs and provide a relaxing remote work environment.

[0155] The AI ​​agent system for optimizing remote work environments can also include an emotion estimation unit that estimates the user's emotions and adjusts task priorities based on those emotions. The emotion estimation unit analyzes the user's facial expressions and voice data to estimate their emotions. For example, if the user is stressed, it will prioritize tasks that help them relax. If the user is focused, it will prioritize tasks that are of high importance. If the user is tired, it will prioritize tasks that can be completed quickly. This enables task management that is tailored to the user's emotions, providing a more efficient remote work environment.

[0156] The AI ​​agent system for optimizing remote work environments can also include an emotion estimation unit that estimates the user's emotions and adjusts the meeting time and content based on those emotions. The emotion estimation unit analyzes the user's facial expressions and voice data to estimate their emotions. For example, if the user is feeling stressed, it suggests a short meeting. If the user is relaxed, it suggests a meeting with a detailed agenda. If the user is focused, it suggests a meeting with important agenda items. This allows for efficient meeting management by adjusting the meeting time and content according to the user's emotions.

[0157] The AI ​​agent system for optimizing remote work environments can also include an emotion estimation unit that estimates the user's emotions and adjusts the notification filtering criteria based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and voice data to estimate their emotions. For example, if the user is stressed, it filters out notifications of low importance. If the user is relaxed, it provides detailed notifications. If the user is focused, it prioritizes important notifications. This allows for the priority delivery of important notifications by adjusting the notification filtering criteria according to the user's emotions.

[0158] The AI ​​agent system for optimizing remote work environments can also include an emotion estimation unit that estimates the user's emotions and adjusts music selection based on those emotions. The emotion estimation unit analyzes the user's facial expressions and voice data to estimate their emotions. For example, if the user is feeling stressed, it selects relaxing music. If the user is relaxed, it selects music that enhances concentration. If the user is focused, it selects music suitable for the task. This allows the system to provide more appropriate music by adjusting the music selection according to the user's emotions.

[0159] The AI ​​agent system for optimizing remote work environments can also include an emotion estimation unit that estimates the user's emotions and adjusts the timing of reminders based on those emotions. The emotion estimation unit analyzes the user's facial expressions and voice data to estimate their emotions. For example, if the user is feeling stressed, it provides reminders at times when they can relax. If the user is relaxed, it provides reminders at times when they can concentrate better. If the user is focused, it provides reminders in between tasks. By adjusting the timing of reminders according to the user's emotions, the system can provide more appropriate reminders.

[0160] The following briefly describes the processing flow for example form 2.

[0161] Step 1: The prioritization section prioritizes tasks by integrating with calendars and task management tools. For example, it analyzes the user's schedule and presents important tasks with priority. Task prioritization can also be performed using AI. Step 2: The meeting optimization unit sets up and optimizes meetings based on tasks prioritized by the prioritization unit. For example, it suggests the optimal meeting time considering the user's schedule. AI can be used to set up and optimize meetings. Step 3: The notification filtering unit filters notifications. For example, notifications can be filtered based on user behavior data. Notifications can also be filtered using AI. Step 4: The music optimization unit optimizes the music. For example, it optimizes music based on the user's preferences, work content, and time of day. AI can be used to optimize the music. Step 5: The reminder section provides reminders for exercise and breaks. For example, it monitors the user's work time and prompts them to exercise or take breaks at appropriate times. AI can be used to provide reminders for exercise and breaks. Step 6: The VPN connection unit automatically establishes a VPN connection based on the user's location information. For example, it can establish a VPN connection based on the user's location information. It can also use AI to establish a VPN connection.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the prioritization unit, meeting optimization unit, notification filtering unit, music optimization unit, reminder unit, VPN connection unit, schedule presentation unit, behavioral analysis unit, and location information analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the prioritization unit is implemented by the control unit 46A of the smart device 14 and prioritizes tasks in cooperation with a calendar and task management tool. The meeting optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal meeting time considering the user's schedule. The notification filtering unit is implemented by the control unit 46A of the smart device 14 and filters notifications based on the user's behavioral data. The music optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes music based on the user's preferences, work content, and time of day. The reminder unit is implemented by the control unit 46A of the smart device 14 and monitors the user's work time to prompt exercise or breaks at appropriate times. The VPN connection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and establishes a VPN connection based on the user's location information. The schedule presentation unit is implemented, for example, by the control unit 46A of the smart device 14, and presents the schedule in an easy-to-understand manner using visual display methods and filtering functions. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes behavioral data such as location information, activity logs, and applications used. The location information analysis unit is implemented, for example, by the control unit 46A of the smart device 14, and analyzes location information such as GPS data and Wi-Fi location information. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0166] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the prioritization unit, meeting optimization unit, notification filtering unit, music optimization unit, reminder unit, VPN connection unit, schedule presentation unit, behavioral analysis unit, and location information analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the prioritization unit is implemented by the control unit 46A of the smart glasses 214 and prioritizes tasks in cooperation with a calendar and task management tool. The meeting optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal meeting time considering the user's schedule. The notification filtering unit is implemented by the control unit 46A of the smart glasses 214 and filters notifications based on the user's behavioral data. The music optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes music based on the user's preferences, work content, and time of day. The reminder unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's work time to prompt exercise or breaks at appropriate times. The VPN connection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and establishes a VPN connection based on the user's location information. The schedule presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and presents the schedule in an easy-to-understand manner using visual display methods and filtering functions. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes behavioral data such as location information, activity logs, and applications used. The location information analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214, and analyzes location information such as GPS data and Wi-Fi location information. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0182] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Each of the multiple elements described above, including the prioritization unit, meeting optimization unit, notification filtering unit, music optimization unit, reminder unit, VPN connection unit, schedule presentation unit, behavioral analysis unit, and location information analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the prioritization unit is implemented by the control unit 46A of the headset terminal 314 and prioritizes tasks in cooperation with a calendar and task management tool. The meeting optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal meeting time considering the user's schedule. The notification filtering unit is implemented by the control unit 46A of the headset terminal 314 and filters notifications based on the user's behavioral data. The music optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes music based on the user's preferences, work content, and time of day. The reminder unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's work time to prompt exercise or breaks at appropriate times. The VPN connection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and establishes a VPN connection based on the user's location information. The schedule presentation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and presents the schedule in an easy-to-understand manner using visual display methods and filtering functions. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes behavioral data such as location information, activity logs, and applications used. The location information analysis unit is implemented, for example, by the control unit 46A of the headset terminal 314, and analyzes location information such as GPS data and Wi-Fi location information. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0198] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] Each of the multiple elements described above, including the prioritization unit, meeting optimization unit, notification filtering unit, music optimization unit, reminder unit, VPN connection unit, schedule presentation unit, behavior analysis unit, and location information analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the prioritization unit is implemented by the control unit 46A of the robot 414 and prioritizes tasks in cooperation with a calendar and task management tool. The meeting optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal meeting time considering the user's schedule. The notification filtering unit is implemented by, for example, the control unit 46A of the robot 414 and filters notifications based on the user's behavior data. The music optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes music based on the user's preferences, work content, and time of day. The reminder unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the user's work time to prompt exercise or breaks at appropriate times. The VPN connection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and establishes a VPN connection based on the user's location information. The schedule presentation unit is implemented, for example, by the control unit 46A of the robot 414, and presents the schedule in an easy-to-understand manner using visual display methods and filtering functions. The behavior analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes behavioral data such as location information, activity logs, and applications used. The location information analysis unit is implemented, for example, by the control unit 46A of the robot 414, and analyzes location information such as GPS data and Wi-Fi location information. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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."

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] (Note 1) A prioritization unit that prioritizes tasks in conjunction with calendars and task management tools, A meeting optimization unit that sets up and optimizes meetings based on tasks prioritized by the prioritization unit, A notification filtering unit that filters notifications, A music optimization unit that optimizes music, The reminder section provides reminders for exercise and rest, It includes a VPN connection unit that automatically establishes a VPN connection based on the user's location information. A system characterized by the following features. (Note 2) It features a schedule display section that clearly presents the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a behavioral analysis unit that analyzes user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a location information analysis unit that analyzes the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The notification filtering unit, Filter notifications based on user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned VPN connection unit is VPN connection is established based on the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned prioritization unit, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned prioritization unit, Analyze the user's past task history and select the optimal prioritization method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned prioritization unit, When prioritizing tasks, filter based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned prioritization unit, It estimates the user's emotions and changes the prioritization criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned prioritization unit, When prioritizing tasks, the system should prioritize tasks that are most relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned prioritization unit, When prioritizing tasks, analyze users' social media activity and prioritize relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned meeting optimization unit, It estimates the user's emotions and adjusts the meeting time and content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned meeting optimization unit, When optimizing meetings, we refer to past meeting data to suggest the optimal meeting schedule. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned meeting optimization unit, When optimizing a meeting, consider participant attribute information to set up the optimal meeting environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned meeting optimization unit, It estimates the user's emotions and adjusts the meeting notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned meeting optimization unit, When optimizing meetings, we suggest the optimal meeting location considering the geographical distribution of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned meeting optimization unit, When optimizing a meeting, refer to relevant literature and materials to optimize the meeting content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The notification filtering unit, It estimates the user's sentiment and adjusts the notification filtering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The notification filtering unit, When filtering notifications, the system selects the optimal filtering method by referring to past notification history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The notification filtering 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 22) The notification filtering unit, When filtering notifications, the system selects the optimal notification method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The notification filtering unit, When filtering notifications, filter them based on user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned music optimization unit, It estimates the user's emotions and adjusts music selection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned music optimization unit, When optimizing music playback, the system references the user's past music history to select the most suitable tracks. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned music optimization unit, When optimizing music, select music genres based on the user's current work. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned music optimization unit, It estimates the user's emotions and adjusts the music playback order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned music optimization unit, When optimizing music playback, the system selects the most suitable music by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned music optimization unit, When optimizing music playback, the system analyzes the user's social media activity to select relevant music. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reminder unit is, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reminder unit is, When providing reminders, the system selects the optimal timing by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reminder unit is, When providing a reminder, adjust the content of the reminder based on the user's current work content The system according to appended note 1, characterized by this (Appended note 33) The reminder unit is Estimate the user's emotion and determine the priority of the reminder based on the estimated user emotion The system according to appended note 1, characterized by this (Appended note 34) The reminder unit is When providing a reminder, select the optimal timing considering the user's geographical location information The system according to appended note 1, characterized by this (Appended note 35) The reminder unit is When providing a reminder, analyze the user's social media activities and provide relevant reminders The system according to appended note 1, characterized by this (Appended note 36) The VPN connection unit is Estimate the user's emotion and adjust the timing of VPN connection based on the estimated user emotion The system according to appended note 1, characterized by this (Appended note 37) The VPN connection unit is When connecting to the VPN, select the optimal connection method by referring to the user's past connection history The system according to appended note 1, characterized by this (Appended note 38) The VPN connection unit is When connecting to the VPN, adjust the priority of the connection based on the user's current work content The system according to appended note 1, characterized by this (Appended note 39) The VPN connection unit is Estimate the user's emotion and determine the priority of VPN connection based on the estimated user emotion The system according to appended note 1, characterized by this (Note 40) The aforementioned VPN connection unit is When establishing a VPN connection, the system selects the optimal connection method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned VPN connection unit is When a VPN connection is established, the system analyzes the user's social media activity to select the most appropriate connection method. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned schedule display unit, It estimates the user's emotions and adjusts how the schedule is presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 43) The aforementioned schedule display unit, When presenting a schedule, the system will refer to the user's past schedule history to select the most suitable presentation method. The system described in Appendix 2, characterized by the features described herein. (Note 44) The aforementioned schedule display unit, When presenting a schedule, adjust the schedule based on the user's current work. The system described in Appendix 2, characterized by the features described herein. (Note 45) The aforementioned schedule display unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 46) The aforementioned schedule display unit, When presenting the schedule, the optimal presentation method is selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 47) The aforementioned schedule display unit, When presenting a schedule, analyze the user's social media activities and present relevant schedules The system according to appendix 2, characterized in that it does so (Appendix 48) The behavior analysis unit Estimate the user's emotions and adjust the method of behavior analysis based on the estimated user emotions The system according to appendix 3, characterized in that it does so (Appendix 49) The behavior analysis unit When performing behavior analysis, select the optimal analysis method by referring to the user's past behavior history The system according to appendix 3, characterized in that it does so (Appendix 50) The behavior analysis unit When performing behavior analysis, adjust the content of the analysis based on the user's current work content The system according to appendix 3, characterized in that it does so (Appendix 51) The behavior analysis unit Estimate the user's emotions and determine the priority of behavior analysis based on the estimated user emotions The system according to appendix 3, characterized in that it does so (Appendix 52) The behavior analysis unit When performing behavior analysis, select the optimal analysis method considering the user's geographical location information The system according to appendix 3, characterized in that it does so (Appendix 53) The behavior analysis unit​​​​​​​​​​​​​​​​ During location data analysis, the system selects the optimal analysis method by referring to the user's past location data history. The system described in Appendix 4, characterized by the features described herein. (Note 56) The aforementioned location information analysis unit, During location data analysis, the analysis content is adjusted based on the user's current activity. The system described in Appendix 4, characterized by the features described herein. (Note 57) The aforementioned location information analysis unit, The system estimates the user's emotions and determines the priority of location analysis based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 58) The aforementioned location information analysis unit, When analyzing location information, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 59) The aforementioned location information analysis unit, During location data analysis, the system analyzes the user's social media activity and then analyzes the relevant location data. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0234] 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 prioritization unit that prioritizes tasks in conjunction with calendars and task management tools, A meeting optimization unit that sets up and optimizes meetings based on tasks prioritized by the prioritization unit, A notification filtering unit that filters notifications, A music optimization unit that optimizes music, The reminder section provides reminders for exercise and rest, It includes a VPN connection unit that automatically establishes a VPN connection based on the user's location information. A system characterized by the following features.

2. It features a schedule display section that clearly presents the schedule. The system according to feature 1.

3. It includes a behavioral analysis unit that analyzes user behavior data. The system according to feature 1.

4. It includes a location information analysis unit that analyzes the user's location information. The system according to feature 1.

5. The notification filtering unit, Filter notifications based on user behavior data. The system according to feature 1.

6. The aforementioned VPN connection unit is VPN connection is established based on the user's location information. The system according to feature 1.

7. The aforementioned prioritization unit, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system according to feature 1.

8. The aforementioned prioritization unit, Analyze the user's past task history and select the optimal prioritization method. The system according to feature 1.

9. The aforementioned prioritization unit, When prioritizing tasks, filter based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned prioritization unit, It estimates the user's emotions and changes the prioritization criteria based on the estimated user emotions. The system according to feature 1.

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