system

Abstract

The system according to this embodiment aims to efficiently display and support the use of applications based on the user's usage patterns. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a display unit, a timing unit, and a notification unit. The collection unit collects the user's usage patterns. The learning unit learns the usage patterns collected by the collection unit. The display unit displays the application based on the patterns learned by the learning unit. The timing unit displays the application at specific times or on specific days of the week. The notification unit notifies the user when it is not possible to display the application fluidly.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

[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, the method including 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] <​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a collection unit, a learning unit, a display unit, a timing unit, and a notification unit. The collection unit collects the user's usage patterns. The learning unit learns the usage patterns collected by the collection unit. The display unit displays the application based on the patterns learned by the learning unit. The timing unit displays the application at specific times or on specific days of the week. The notification unit notifies the user when it is not possible to display the application fluidly. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently display and support the use of applications based on the user's usage patterns. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smartphone home screen customization system according to an embodiment of the present invention is an application in which AI learns based on the user's usage patterns and provides the optimal display of apps. This system aims to improve ease of use, especially for the elderly and people of a generation unfamiliar with smartphones. First, when a user uses a smartphone, the AI ​​learns their usage patterns. For example, if a specific app is frequently used at a particular time of day, the AI ​​recognizes this pattern and automatically displays the icon of that app on the home screen at that time of day from then onward. Similarly, if a specific app is used on a particular day of the week or date, the AI ​​learns this pattern and displays the app at the appropriate time. For elderly and young users, the system provides ease of operation by displaying only the phone and apps they use most often. For example, only the phone and messaging apps can be displayed on the home screen, while other apps are hidden. For other users, if they use certain apps during their weekday commute to work or school, the system automatically displays their icons at that time. Furthermore, if it is not possible to display apps dynamically, the system also provides a function that notifies the user with "Do you want to launch the app XX?" and launches the app when tapped. This application will be provided to all users of the group's mobile business, aiming to encourage greater use of smartphones and promote the use of our services. This will allow the smartphone home screen customization system to optimize app display based on user usage patterns, improving usability.

[0029] The smartphone home screen customization system according to this embodiment comprises a data collection unit, a learning unit, a display unit, a timing unit, and a notification unit. The data collection unit collects user usage patterns. For example, the data collection unit collects data when a user uses a smartphone. The data collection unit can collect data such as the frequency of app usage, the time of day of use, and the functions used. For example, the data collection unit records the time of day when a user frequently uses a particular app. The data collection unit can also collect patterns in which a user uses a particular app on a particular day of the week. The data collection unit collects user usage patterns in detail and provides them to the learning unit. The learning unit learns the usage patterns collected by the data collection unit. For example, the learning unit analyzes the collected data using AI and learns the usage patterns. The learning unit can predict user usage patterns using an AI model. For example, the learning unit learns patterns in which a particular app is used during a particular time period. The learning unit can also learn patterns in which a particular app is used on a particular day of the week. Based on the learned patterns, the learning unit provides information to the display unit. The display unit displays apps based on the patterns learned by the learning unit. The display unit, for example, displays app icons on the home screen. The display unit can automatically display specific apps at specific times. For example, the display unit displays apps that the user frequently uses on the home screen. The display unit can also display specific apps on specific days of the week. The display unit provides optimal app display based on the user's usage patterns. The timing unit displays apps at specific times or on specific days of the week. For example, the timing unit displays specific apps during weekday commuting hours. The timing unit can display specific apps on specific days of the week. The timing unit displays apps at the optimal time based on the user's usage patterns. The notification unit notifies the user when it is not possible to display an app fluidly. For example, the notification unit notifies the user with a message such as, "Do you want to launch the app XX?". The notification unit allows the user to launch the app by tapping it. The notification unit provides notifications at the appropriate time based on the user's usage patterns.As a result, the smartphone home screen customization system according to the embodiment can optimize the display of apps based on the user's usage patterns and improve ease of use.

[0030] The data collection unit meticulously collects user usage patterns. Specifically, it collects data from multiple perspectives on how users use their smartphones. For example, the data collection unit can collect data such as app usage frequency, usage time, functions used, app launch and termination times, and in-app operation history. This allows for a detailed understanding of which apps users use, when, and to what extent. Furthermore, the data collection unit also collects environmental information when users use specific apps. For example, if a user uses different apps when at home and when out, the unit also collects location information. It can also collect patterns of how users use specific apps on specific days of the week. For example, if a user frequently uses news apps during weekday commutes and entertainment apps on weekends, the unit records these patterns in detail. The data collection unit centrally manages this data and provides it to the learning unit. This allows the data collection unit to gain a detailed understanding of user usage patterns and provides a foundation for the learning unit to perform more accurate analysis.

[0031] The learning unit analyzes usage patterns collected by the collection unit using AI to learn user usage patterns. Specifically, the learning unit trains an AI model based on the collected data to predict user behavior patterns. For example, it learns patterns of using specific apps at specific times of day or on specific days of the week. Based on these patterns, the learning unit can predict which app the user is most likely to use next. Furthermore, the learning unit also responds to changes in user usage patterns. For example, if a user installs a new app or their usage frequency changes, it quickly learns these changes and updates the prediction model. This allows the learning unit to always make predictions based on the latest usage patterns. The learning unit can also use anomaly detection algorithms to detect unusual usage patterns and notify the user as needed. As a result, the learning unit can learn user usage patterns with high accuracy and provide optimal information to the display unit.

[0032] The display unit shows apps based on patterns learned by the learning unit. Specifically, the display unit dynamically arranges app icons on the home screen, prioritizing the apps the user is most likely to use. For example, it can automatically display specific apps at certain times of the day. It places frequently used apps in a prominent position on the home screen for easy access. It can also display specific apps on certain days of the week. For example, it can display entertainment apps on weekends and work-related apps on weekdays. Because the display unit provides the optimal app display based on the user's usage patterns, the user can quickly access the apps they need. Furthermore, the display unit can adjust its display content based on user feedback. For example, if a user no longer uses a particular app frequently, it can reduce the frequency of that app's display. The display unit also provides customizable settings according to the user's preferences, allowing the user to adjust the home screen to their liking. In this way, the display unit can improve user convenience and optimize the smartphone user experience.

[0033] The Timing Unit displays apps at specific times of day or on specific days of the week. Specifically, it displays apps at the optimal time based on the user's usage patterns. For example, it might display news apps or traffic information apps during weekday commutes, and social media apps or game apps during lunch breaks. It can also display specific apps on specific days of the week. For example, it might display entertainment apps or shopping apps on weekends. The Timing Unit analyzes user usage patterns in detail and displays apps at the optimal time, enabling users to quickly access the information and services they need. Furthermore, the Timing Unit can utilize the user's schedule and calendar information to display apps tailored to specific events or appointments. For example, it might display memo apps or calendar apps before a meeting, or travel-related apps before a trip. In this way, the Timing Unit can provide flexible app display tailored to the user's lifestyle, improving the convenience of smartphones.

[0034] The notification unit provides notifications to the user when it is not possible to display information dynamically. Specifically, the notification unit provides appropriate notifications when the user misses an opportunity to use a particular app or when a particular app is not displayed. For example, it may notify the user with "Do you want to launch the app XX?", and the user can launch the app by tapping it. Because the notification unit provides notifications at the appropriate time based on the user's usage patterns, the user can quickly access the apps they need. Furthermore, the notification unit can adjust the content of notifications based on user feedback. For example, if a user ignores a particular notification, the frequency of that notification can be reduced. In addition, the notification unit can reliably convey information using multiple notification methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with quick and reliable instructions, improving the smartphone user experience.

[0035] The data collection unit can analyze the user's past usage history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on apps that the user has frequently used in the past. For example, the data collection unit can focus on collecting data on apps used during specific time periods based on the user's past usage history. For example, the data collection unit can analyze the user's past usage history and collect detailed data on frequently used apps. This enables efficient data collection by selecting the optimal data collection method based on past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting data from work-related apps. For example, if the user is on vacation, the data collection unit can prioritize collecting data from leisure-related apps. For example, the data collection unit can filter and collect data from relevant apps based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data from apps related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data from apps related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data from apps that are frequently used at home. This enables more accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data on apps that the user frequently uses on social media. For example, the data collection unit can collect data on apps related to topics of interest from the user's social media activity. For example, the data collection unit can consider the user's social media friendships and collect data on apps used by their friends. This enables more accurate data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective learning algorithm from past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and identify areas for improvement in the learning algorithm. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0040] The learning unit can update the learning model during training, taking into account changes in user usage patterns. For example, if the user's usage pattern changes, the learning unit retrains and updates the learning model. For example, the learning unit can detect changes in user usage patterns in real time and adapt the learning model. For example, the learning unit can periodically analyze changes in user usage patterns and update the learning model. This improves the accuracy of the learning model by taking changes in user usage patterns into account. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user usage pattern data into a generating AI and have the generating AI perform the update of the learning model.

[0041] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign higher weights to recently collected data and reflect this in the learning algorithm. For example, the learning unit can assign lower weights to past data to improve the accuracy of the learning algorithm. For example, the learning unit can weight data collected during specific time periods to optimize the learning algorithm. This improves the accuracy of the learning algorithm by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data collection timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0042] The learning unit can customize the learning model during training, taking into account the user's device usage. For example, if the user frequently uses a smartphone, the learning unit can customize the learning model based on that usage. For example, if the user uses a tablet, the learning unit can customize the learning model based on that usage. For example, if the user uses a smartwatch, the learning unit can customize the learning model based on that usage. This allows for more appropriate learning by customizing the learning model based on the user's device usage. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user device usage data into a generating AI and have the generating AI perform the customization of the learning model.

[0043] The display unit can determine the display priority based on usage frequency when displaying apps. For example, the display unit can display frequently used apps at the top of the home screen. For example, the display unit can display less frequently used apps at the bottom of the home screen. For example, the display unit can adjust the icon size of apps based on usage frequency. This makes the display more user-friendly by determining the display priority based on usage frequency. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input app usage frequency data into a generating AI and have the generating AI perform the determination of display priority.

[0044] The display unit can apply different display algorithms depending on the user's usage pattern when displaying apps. For example, the display unit can prioritize displaying apps that the user frequently uses during specific time periods. For example, the display unit can dynamically change the display order of apps based on the user's usage pattern. For example, the display unit can analyze the user's usage pattern and apply the optimal display algorithm. This allows for more appropriate display by applying different display algorithms depending on the user's usage pattern. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user usage pattern data into a generating AI and have the generating AI execute the application of the display algorithm.

[0045] The display unit can customize the display based on the user's device settings when displaying an app. For example, if the user is using dark mode, the display unit can make the app display compatible with dark mode. For example, if the user has set a large font size, the display unit can make the app display compatible with a large font size. For example, if the user has turned off notifications, the display unit can make the app display without notifications. By customizing the display based on the user's device settings, a more user-friendly display is possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device setting data into a generating AI and have the generating AI perform the display customization.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying an app. For example, the display unit can prioritize displaying apps that the user has frequently used in the past. For example, the display unit can prioritize displaying apps used during specific time periods based on the user's past operation history. For example, the display unit can analyze the user's past operation history and select the optimal display method. This makes it possible to display apps in a more user-friendly way by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0047] The timing unit can determine the priority of app display based on specific time periods and days of the week. For example, the timing unit can prioritize displaying apps related to commuting during weekday commuting hours. For example, the timing unit can prioritize displaying leisure-related apps on weekends. For example, the timing unit can prioritize displaying apps that users frequently use on specific days of the week. This allows for more appropriate app display by determining the priority of app display based on specific time periods and days of the week. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input data on specific time periods and days of the week into a generating AI and have the generating AI perform the determination of the priority of app display.

[0048] The timing unit can customize the timing of app display based on the user's daily rhythm. For example, the timing unit can display an alarm app at the time the user wakes up in the morning. For example, the timing unit can display relaxation-related apps before the user goes to bed at night. For example, the timing unit can display apps at the optimal time based on the user's daily rhythm. By customizing the timing of app display based on the user's daily rhythm, apps can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the customization of the app display timing.

[0049] The timing unit can optimize the timing of app display based on the user's calendar information. For example, the timing unit can display relevant apps based on appointments registered in the user's calendar. For example, the timing unit can display apps related to a specific event based on the user's calendar information. For example, the timing unit can display apps at the optimal timing to match appointments based on the user's calendar information. By optimizing the timing of app display based on the user's calendar information, apps can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user calendar information data into a generating AI and have the generating AI perform the optimization of the timing of app display.

[0050] The timing unit can adjust the timing of app display based on the user's device usage. For example, the timing unit can display the app during times when the user frequently uses their smartphone. For example, the timing unit can display the app during times when the user uses their tablet. For example, the timing unit can display the app during times when the user uses their smartwatch. By adjusting the timing of app display based on the user's device usage, the app can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user device usage data into a generating AI and have the generating AI perform the adjustment of the app display timing.

[0051] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit may prioritize using notification methods that the user has previously preferred to receive. For example, the notification unit may select the optimal notification timing from the user's past response history. For example, the notification unit may analyze the user's past response history and select the most effective notification method. This makes it possible to send more effective notifications by selecting the optimal notification method based on the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past response history data into a generating AI and have the generating AI select the optimal notification method.

[0052] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is in a meeting, the notification unit can delay the notification. For example, if the user is on vacation, the notification unit can advance the notification. For example, the notification unit can deliver notifications at the optimal time based on the user's current situation. By adjusting the timing of notifications based on the user's current situation, it becomes possible to deliver notifications at a more appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0053] The notification unit can customize notifications based on the user's device settings when a notification is sent. For example, if the user has turned off notification sounds, the notification unit can notify via vibration. For example, if the user has set up notification pop-ups, the notification unit can notify via pop-ups. For example, the notification unit can select the optimal notification method based on the user's device settings. This allows for more appropriate notifications by customizing notifications based on the user's device settings. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device setting data into a generating AI and have the generating AI perform the notification customization.

[0054] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can prioritize notifications related to apps that the user frequently uses on social media. For example, the notification unit can send notifications related to topics of interest based on the user's social media activity. For example, the notification unit can consider the user's social media friendships and send notifications related to apps used by their friends. This makes it possible to send more appropriate notifications based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute relevant notifications.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect not only user usage patterns but also user health data. For example, it can collect data such as the user's steps and heart rate, and optimize app display based on this data. For instance, if the user is exercising, the data collection unit can prioritize displaying fitness-related apps. Conversely, if the user is resting, it can display relaxation-related apps. This enables app display tailored to the user's health status.

[0057] The display unit can dynamically change the display position of apps based on the user's usage patterns. For example, if a user frequently uses a particular app, that app will be displayed in the center of the home screen. Conversely, less frequently used apps can be displayed at the edge of the home screen. This allows for optimization of app display positions according to the user's usage patterns.

[0058] The notification section can customize notification content based on the user's usage patterns. For example, if a user frequently uses a particular app, notifications related to that app will be prioritized. Also, if a user uses a particular app during a specific time period, notifications related to that time period can be displayed. This allows for the customization of notifications according to the user's usage patterns.

[0059] The learning unit can prioritize training data based on the user's usage patterns. For example, if a user frequently uses a particular app, it will prioritize learning data related to that app. Similarly, if a user uses a particular app during a specific time period, it can prioritize learning data related to that time period. This allows for the prioritization of training data according to the user's usage patterns.

[0060] The timing section can dynamically change the timing of app display based on user usage patterns. For example, if a user frequently uses a particular app during a specific time period, the app can be displayed during that time. Similarly, if a user uses a particular app on a specific day of the week, the app can be displayed on that day. This enables dynamic changes to app display timing according to user usage patterns.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user usage patterns. For example, the data collection unit collects data on how users use their smartphones, such as app usage frequency, usage time, and features used. The data collection unit records patterns such as the times of day when users frequently use specific apps and the days of the week when they use specific apps. Step 2: The learning unit learns the usage patterns collected by the collection unit. The learning unit analyzes the collected data using AI and learns usage patterns. The learning unit learns patterns of using specific apps at specific times of day or on specific days of the week, and provides information to the display unit based on the learned patterns. Step 3: The display unit displays apps based on patterns learned by the learning unit. The display unit can display app icons on the home screen and automatically display specific apps at specific times or on specific days of the week. The display unit provides the optimal app display based on the user's usage patterns. Step 4: The timing unit displays apps at specific times and days of the week. The timing unit displays specific apps during weekday commuting hours or on specific days of the week, displaying apps at the optimal time based on the user's usage patterns. Step 5: The notification section notifies the user when it is not possible to display the information dynamically. The notification section notifies the user with a message such as "Do you want to launch the app XX?", and the user can launch the app by tapping it. The notification section provides notifications at the appropriate time based on the user's usage patterns.

[0063] (Example of form 2) The smartphone home screen customization system according to an embodiment of the present invention is an application in which AI learns based on the user's usage patterns and provides the optimal display of apps. This system aims to improve ease of use, especially for the elderly and people of a generation unfamiliar with smartphones. First, when a user uses a smartphone, the AI ​​learns their usage patterns. For example, if a specific app is frequently used at a particular time of day, the AI ​​recognizes this pattern and automatically displays the icon of that app on the home screen at that time of day from then onward. Similarly, if a specific app is used on a particular day of the week or date, the AI ​​learns this pattern and displays the app at the appropriate time. For elderly and young users, the system provides ease of operation by displaying only the phone and apps they use most often. For example, only the phone and messaging apps can be displayed on the home screen, while other apps are hidden. For other users, if they use certain apps during their weekday commute to work or school, the system automatically displays their icons at that time. Furthermore, if it is not possible to display apps dynamically, the system also provides a function that notifies the user with "Do you want to launch the app XX?" and launches the app when tapped. This application will be provided to all users of the group's mobile business, aiming to encourage greater use of smartphones and promote the use of our services. This will allow the smartphone home screen customization system to optimize app display based on user usage patterns, improving usability.

[0064] The smartphone home screen customization system according to this embodiment comprises a data collection unit, a learning unit, a display unit, a timing unit, and a notification unit. The data collection unit collects user usage patterns. For example, the data collection unit collects data when a user uses a smartphone. The data collection unit can collect data such as the frequency of app usage, the time of day of use, and the functions used. For example, the data collection unit records the time of day when a user frequently uses a particular app. The data collection unit can also collect patterns in which a user uses a particular app on a particular day of the week. The data collection unit collects user usage patterns in detail and provides them to the learning unit. The learning unit learns the usage patterns collected by the data collection unit. For example, the learning unit analyzes the collected data using AI and learns the usage patterns. The learning unit can predict user usage patterns using an AI model. For example, the learning unit learns patterns in which a particular app is used during a particular time period. The learning unit can also learn patterns in which a particular app is used on a particular day of the week. Based on the learned patterns, the learning unit provides information to the display unit. The display unit displays apps based on the patterns learned by the learning unit. The display unit, for example, displays app icons on the home screen. The display unit can automatically display specific apps at specific times. For example, the display unit displays apps that the user frequently uses on the home screen. The display unit can also display specific apps on specific days of the week. The display unit provides optimal app display based on the user's usage patterns. The timing unit displays apps at specific times or on specific days of the week. For example, the timing unit displays specific apps during weekday commuting hours. The timing unit can display specific apps on specific days of the week. The timing unit displays apps at the optimal time based on the user's usage patterns. The notification unit notifies the user when it is not possible to display an app fluidly. For example, the notification unit notifies the user with a message such as, "Do you want to launch the app XX?". The notification unit allows the user to launch the app by tapping it. The notification unit provides notifications at the appropriate time based on the user's usage patterns.As a result, the smartphone home screen customization system according to the embodiment can optimize the display of apps based on the user's usage patterns and improve ease of use.

[0065] The data collection unit meticulously collects user usage patterns. Specifically, it collects data from multiple perspectives on how users use their smartphones. For example, the data collection unit can collect data such as app usage frequency, usage time, functions used, app launch and termination times, and in-app operation history. This allows for a detailed understanding of which apps users use, when, and to what extent. Furthermore, the data collection unit also collects environmental information when users use specific apps. For example, if a user uses different apps when at home and when out, the unit also collects location information. It can also collect patterns of how users use specific apps on specific days of the week. For example, if a user frequently uses news apps during weekday commutes and entertainment apps on weekends, the unit records these patterns in detail. The data collection unit centrally manages this data and provides it to the learning unit. This allows the data collection unit to gain a detailed understanding of user usage patterns and provides a foundation for the learning unit to perform more accurate analysis.

[0066] The learning unit analyzes usage patterns collected by the collection unit using AI to learn user usage patterns. Specifically, the learning unit trains an AI model based on the collected data to predict user behavior patterns. For example, it learns patterns of using specific apps at specific times of day or on specific days of the week. Based on these patterns, the learning unit can predict which app the user is most likely to use next. Furthermore, the learning unit also responds to changes in user usage patterns. For example, if a user installs a new app or their usage frequency changes, it quickly learns these changes and updates the prediction model. This allows the learning unit to always make predictions based on the latest usage patterns. The learning unit can also use anomaly detection algorithms to detect unusual usage patterns and notify the user as needed. As a result, the learning unit can learn user usage patterns with high accuracy and provide optimal information to the display unit.

[0067] The display unit shows apps based on patterns learned by the learning unit. Specifically, the display unit dynamically arranges app icons on the home screen, prioritizing the apps the user is most likely to use. For example, it can automatically display specific apps at certain times of the day. It places frequently used apps in a prominent position on the home screen for easy access. It can also display specific apps on certain days of the week. For example, it can display entertainment apps on weekends and work-related apps on weekdays. Because the display unit provides the optimal app display based on the user's usage patterns, the user can quickly access the apps they need. Furthermore, the display unit can adjust its display content based on user feedback. For example, if a user no longer uses a particular app frequently, it can reduce the frequency of that app's display. The display unit also provides customizable settings according to the user's preferences, allowing the user to adjust the home screen to their liking. In this way, the display unit can improve user convenience and optimize the smartphone user experience.

[0068] The Timing Unit displays apps at specific times of day or on specific days of the week. Specifically, it displays apps at the optimal time based on the user's usage patterns. For example, it might display news apps or traffic information apps during weekday commutes, and social media apps or game apps during lunch breaks. It can also display specific apps on specific days of the week. For example, it might display entertainment apps or shopping apps on weekends. The Timing Unit analyzes user usage patterns in detail and displays apps at the optimal time, enabling users to quickly access the information and services they need. Furthermore, the Timing Unit can utilize the user's schedule and calendar information to display apps tailored to specific events or appointments. For example, it might display memo apps or calendar apps before a meeting, or travel-related apps before a trip. In this way, the Timing Unit can provide flexible app display tailored to the user's lifestyle, improving the convenience of smartphones.

[0069] The notification unit provides notifications to the user when it is not possible to display information dynamically. Specifically, the notification unit provides appropriate notifications when the user misses an opportunity to use a particular app or when a particular app is not displayed. For example, it may notify the user with "Do you want to launch the app XX?", and the user can launch the app by tapping it. Because the notification unit provides notifications at the appropriate time based on the user's usage patterns, the user can quickly access the apps they need. Furthermore, the notification unit can adjust the content of notifications based on user feedback. For example, if a user ignores a particular notification, the frequency of that notification can be reduced. In addition, the notification unit can reliably convey information using multiple notification methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with quick and reliable instructions, improving the smartphone user experience.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed usage patterns. For example, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it later. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze the user's past usage history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on apps that the user has frequently used in the past. For example, the data collection unit can focus on collecting data on apps used during specific time periods based on the user's past usage history. For example, the data collection unit can analyze the user's past usage history and collect detailed data on frequently used apps. This enables efficient data collection by selecting the optimal data collection method based on past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history data into a generating AI and have the generating AI select the optimal data collection method.

[0072] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting data from work-related apps. For example, if the user is on vacation, the data collection unit can prioritize collecting data from leisure-related apps. For example, the data collection unit can filter and collect data from relevant apps based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle data into a generating AI and have the generating AI perform data filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data from apps that help reduce stress. For example, if the user is relaxed, the data collection unit can prioritize collecting data from apps related to relaxation. For example, if the user is in a hurry, the data collection unit can prioritize collecting data from apps that can be accessed quickly. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data from apps related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data from apps related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data from apps that are frequently used at home. This enables more accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0075] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data on apps that the user frequently uses on social media. For example, the data collection unit can collect data on apps related to topics of interest from the user's social media activity. For example, the data collection unit can consider the user's social media friendships and collect data on apps used by their friends. This enables more accurate data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0076] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit can learn data on apps that help reduce stress. For example, if the user is relaxed, the learning unit can learn data on apps related to relaxation. For example, if the user is in a hurry, the learning unit can learn data on apps that can be accessed quickly. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective learning algorithm from past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and identify areas for improvement in the learning algorithm. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0078] The learning unit can update the learning model during training, taking into account changes in user usage patterns. For example, if the user's usage pattern changes, the learning unit retrains and updates the learning model. For example, the learning unit can detect changes in user usage patterns in real time and adapt the learning model. For example, the learning unit can periodically analyze changes in user usage patterns and update the learning model. This improves the accuracy of the learning model by taking changes in user usage patterns into account. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user usage pattern data into a generating AI and have the generating AI perform the update of the learning model.

[0079] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the user's burden. For example, if the user is relaxed, the learning unit can increase the learning frequency to learn more detailed usage patterns. For example, if the user is in a hurry, the learning unit can temporarily stop the learning frequency and resume it later. This reduces the user's burden by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign higher weights to recently collected data and reflect this in the learning algorithm. For example, the learning unit can assign lower weights to past data to improve the accuracy of the learning algorithm. For example, the learning unit can weight data collected during specific time periods to optimize the learning algorithm. This improves the accuracy of the learning algorithm by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data collection timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0081] The learning unit can customize the learning model during training, taking into account the user's device usage. For example, if the user frequently uses a smartphone, the learning unit can customize the learning model based on that usage. For example, if the user uses a tablet, the learning unit can customize the learning model based on that usage. For example, if the user uses a smartwatch, the learning unit can customize the learning model based on that usage. This allows for more appropriate learning by customizing the learning model based on the user's device usage. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user device usage data into a generating AI and have the generating AI perform the customization of the learning model.

[0082] The display unit can estimate the user's emotions and adjust the application's display method based on the estimated emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. This allows for a more appropriate display by adjusting the application's display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The display unit can determine the display priority based on usage frequency when displaying apps. For example, the display unit can display frequently used apps at the top of the home screen. For example, the display unit can display less frequently used apps at the bottom of the home screen. For example, the display unit can adjust the icon size of apps based on usage frequency. This makes the display more user-friendly by determining the display priority based on usage frequency. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input app usage frequency data into a generating AI and have the generating AI perform the determination of display priority.

[0084] The display unit can apply different display algorithms depending on the user's usage pattern when displaying apps. For example, the display unit can prioritize displaying apps that the user frequently uses during specific time periods. For example, the display unit can dynamically change the display order of apps based on the user's usage pattern. For example, the display unit can analyze the user's usage pattern and apply the optimal display algorithm. This allows for more appropriate display by applying different display algorithms depending on the user's usage pattern. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user usage pattern data into a generating AI and have the generating AI execute the application of the display algorithm.

[0085] The display unit can estimate the user's emotions and adjust the timing of the app's display based on the estimated emotions. For example, if the user is stressed, the display unit can delay the app's display. For example, if the user is relaxed, the display unit can speed up the app's display. For example, if the user is in a hurry, the display unit can make the app's display instantaneous. By adjusting the app's display timing according to the user's emotions, the app can be displayed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The display unit can customize the display based on the user's device settings when displaying an app. For example, if the user is using dark mode, the display unit can make the app display compatible with dark mode. For example, if the user has set a large font size, the display unit can make the app display compatible with a large font size. For example, if the user has turned off notifications, the display unit can make the app display without notifications. By customizing the display based on the user's device settings, a more user-friendly display is possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device setting data into a generating AI and have the generating AI perform the display customization.

[0087] The display unit can select the optimal display method by referring to the user's past operation history when displaying an app. For example, the display unit can prioritize displaying apps that the user has frequently used in the past. For example, the display unit can prioritize displaying apps used during specific time periods based on the user's past operation history. For example, the display unit can analyze the user's past operation history and select the optimal display method. This makes it possible to display apps in a more user-friendly way by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0088] The timing unit can estimate the user's emotions and adjust the timing of app display based on the estimated emotions. For example, if the user is stressed, the timing unit can delay the app display timing. For example, if the user is relaxed, the timing unit can speed up the app display timing. For example, if the user is in a hurry, the timing unit can make the app display timing immediate. By adjusting the timing of app display according to the user's emotions, the app can be displayed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The timing unit can determine the priority of app display based on specific time periods and days of the week. For example, the timing unit can prioritize displaying apps related to commuting during weekday commuting hours. For example, the timing unit can prioritize displaying leisure-related apps on weekends. For example, the timing unit can prioritize displaying apps that users frequently use on specific days of the week. This allows for more appropriate app display by determining the priority of app display based on specific time periods and days of the week. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input data on specific time periods and days of the week into a generating AI and have the generating AI perform the determination of the priority of app display.

[0090] The timing unit can customize the timing of app display based on the user's daily rhythm. For example, the timing unit can display an alarm app at the time the user wakes up in the morning. For example, the timing unit can display relaxation-related apps before the user goes to bed at night. For example, the timing unit can display apps at the optimal time based on the user's daily rhythm. By customizing the timing of app display based on the user's daily rhythm, apps can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the customization of the app display timing.

[0091] The timing unit can estimate the user's emotions and adjust the frequency of app display based on the estimated emotions. For example, if the user is stressed, the timing unit can reduce the frequency of app display. For example, if the user is relaxed, the timing unit can increase the frequency of app display. For example, if the user is in a hurry, the timing unit can temporarily stop the frequency of app display. This allows the app to be displayed at a more appropriate frequency by adjusting the frequency of app display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The timing unit can optimize the timing of app display based on the user's calendar information. For example, the timing unit can display relevant apps based on appointments registered in the user's calendar. For example, the timing unit can display apps related to a specific event based on the user's calendar information. For example, the timing unit can display apps at the optimal timing to match appointments based on the user's calendar information. By optimizing the timing of app display based on the user's calendar information, apps can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user calendar information data into a generating AI and have the generating AI perform the optimization of the timing of app display.

[0093] The timing unit can adjust the timing of app display based on the user's device usage. For example, the timing unit can display the app during times when the user frequently uses their smartphone. For example, the timing unit can display the app during times when the user uses their tablet. For example, the timing unit can display the app during times when the user uses their smartwatch. By adjusting the timing of app display based on the user's device usage, the app can be displayed at a more appropriate time. Some or all of the above processing in the timing unit may be performed using AI, for example, or without AI. For example, the timing unit can input user device usage data into a generating AI and have the generating AI perform the adjustment of the app display timing.

[0094] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. For example, if the user is relaxed, the notification unit can provide a notification containing detailed information. For example, if the user is in a hurry, the notification unit can provide a notification that gets straight to the point. By adjusting the content of the notification according to the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit may prioritize using notification methods that the user has previously preferred to receive. For example, the notification unit may select the optimal notification timing from the user's past response history. For example, the notification unit may analyze the user's past response history and select the most effective notification method. This makes it possible to send more effective notifications by selecting the optimal notification method based on the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past response history data into a generating AI and have the generating AI select the optimal notification method.

[0096] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is in a meeting, the notification unit can delay the notification. For example, if the user is on vacation, the notification unit can advance the notification. For example, the notification unit can deliver notifications at the optimal time based on the user's current situation. By adjusting the timing of notifications based on the user's current situation, it becomes possible to deliver notifications at a more appropriate time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0097] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can prioritize displaying only important notifications. For example, if the user is relaxed, the notification unit can prioritize displaying detailed notifications. For example, if the user is in a hurry, the notification unit can prioritize displaying urgent notifications. This allows for more appropriate notifications by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The notification unit can customize notifications based on the user's device settings when a notification is sent. For example, if the user has turned off notification sounds, the notification unit can notify via vibration. For example, if the user has set up notification pop-ups, the notification unit can notify via pop-ups. For example, the notification unit can select the optimal notification method based on the user's device settings. This allows for more appropriate notifications by customizing notifications based on the user's device settings. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device setting data into a generating AI and have the generating AI perform the notification customization.

[0099] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can prioritize notifications related to apps that the user frequently uses on social media. For example, the notification unit can send notifications related to topics of interest based on the user's social media activity. For example, the notification unit can consider the user's social media friendships and send notifications related to apps used by their friends. This makes it possible to send more appropriate notifications based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute relevant notifications.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect not only user usage patterns but also user health data. For example, it can collect data such as the user's steps and heart rate, and optimize app display based on this data. For instance, if the user is exercising, the data collection unit can prioritize displaying fitness-related apps. Conversely, if the user is resting, it can display relaxation-related apps. This enables app display tailored to the user's health status.

[0102] The learning unit can estimate the user's emotions and adjust the learning algorithm based on those emotions. For example, if the user is stressed, the learning algorithm can be made more relaxed to reduce the user's burden. Conversely, if the user is relaxed, the learning algorithm can be strengthened to learn more detailed data. This makes it possible to adjust the learning algorithm in accordance with the user's emotions.

[0103] The display unit can dynamically change the display position of apps based on the user's usage patterns. For example, if a user frequently uses a particular app, that app will be displayed in the center of the home screen. Conversely, less frequently used apps can be displayed at the edge of the home screen. This allows for optimization of app display positions according to the user's usage patterns.

[0104] The timing unit can estimate the user's emotions and adjust the app's display frequency based on those emotions. For example, if the user is stressed, the app's display frequency can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the app's display frequency can be increased to show more apps. This makes it possible to adjust the app's display frequency according to the user's emotions.

[0105] The notification section can customize notification content based on the user's usage patterns. For example, if a user frequently uses a particular app, notifications related to that app will be prioritized. Also, if a user uses a particular app during a specific time period, notifications related to that time period can be displayed. This allows for the customization of notifications according to the user's usage patterns.

[0106] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. This makes it possible to adjust the data collection method according to the user's emotions.

[0107] The learning unit can prioritize training data based on the user's usage patterns. For example, if a user frequently uses a particular app, it will prioritize learning data related to that app. Similarly, if a user uses a particular app during a specific time period, it can prioritize learning data related to that time period. This allows for the prioritization of training data according to the user's usage patterns.

[0108] The display unit can estimate the user's emotions and adjust the app's display method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. Conversely, if the user is relaxed, it can provide a display method that includes detailed information. This makes it possible to adjust the app's display method according to the user's emotions.

[0109] The timing section can dynamically change the timing of app display based on user usage patterns. For example, if a user frequently uses a particular app during a specific time period, the app can be displayed during that time. Similarly, if a user uses a particular app on a specific day of the week, the app can be displayed on that day. This enables dynamic changes to app display timing according to user usage patterns.

[0110] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, only important notifications will be displayed preferentially. Conversely, if the user is relaxed, detailed notifications can be displayed preferentially. This makes it possible to prioritize notifications according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects user usage patterns. For example, the data collection unit collects data on how users use their smartphones, such as app usage frequency, usage time, and features used. The data collection unit records patterns such as the times of day when users frequently use specific apps and the days of the week when they use specific apps. Step 2: The learning unit learns the usage patterns collected by the collection unit. The learning unit analyzes the collected data using AI and learns usage patterns. The learning unit learns patterns of using specific apps at specific times of day or on specific days of the week, and provides information to the display unit based on the learned patterns. Step 3: The display unit displays apps based on patterns learned by the learning unit. The display unit can display app icons on the home screen and automatically display specific apps at specific times or on specific days of the week. The display unit provides the optimal app display based on the user's usage patterns. Step 4: The timing unit displays apps at specific times and days of the week. The timing unit displays specific apps during weekday commuting hours or on specific days of the week, displaying apps at the optimal time based on the user's usage patterns. Step 5: The notification section notifies the user when it is not possible to display the information dynamically. The notification section notifies the user with a message such as "Do you want to launch the app XX?", and the user can launch the app by tapping it. The notification section provides notifications at the appropriate time based on the user's usage patterns.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, learning unit, display unit, timing unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user usage patterns using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the usage patterns. The display unit is implemented, for example, by the display 40A of the smart device 14, which displays the app icon on the home screen based on the learned pattern. The timing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which displays the app during specific time periods or on specific days of the week. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, which notifies the user when display is not possible. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, learning unit, display unit, timing unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's usage patterns using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the usage patterns. The display unit is implemented, for example, by the display of the smart glasses 214, which displays app icons based on the learned patterns. The timing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which displays apps during specific time periods or on specific days of the week. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, which notifies the user when display is not possible. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, learning unit, display unit, timing unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user usage patterns using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the usage patterns. The display unit is implemented, for example, by the display 343 of the headset terminal 314, which displays the app icon based on the learned pattern. The timing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which displays the app during specific time periods or on specific days of the week. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314, which notifies the user when display is not feasible. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, learning unit, display unit, timing unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user usage patterns using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the usage patterns. The display unit is implemented, for example, by the display of the robot 414, which displays app icons based on the learned patterns. The timing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which displays apps during specific time periods or on specific days of the week. The notification unit is implemented, for example, by the control unit 46A of the robot 414, which notifies the user when it is not possible to display the app fluidly. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects user usage patterns, A learning unit that learns the usage patterns collected by the aforementioned collection unit, A display unit that displays an application based on a pattern learned by the learning unit, The timing section for displaying the app at specific times or days of the week, It includes a notification unit that notifies the user when it is not possible to display the information dynamically. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, the learning model is updated to take into account variations in user usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the training model is customized to take into account the user's device usage. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is The app estimates the user's emotions and adjusts how the app is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is When displaying apps, the display priority is determined based on frequency of use. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying an app, different display algorithms are applied depending on the user's usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is The app estimates the user's emotions and adjusts the timing of app displays based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying the app, the display is customized based on the user's device settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying the app, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned timing unit, The system estimates the user's emotions and adjusts the timing of app displays based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned timing unit, Prioritize app display based on specific time slots and days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned timing unit, Customize the timing of app display based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned timing unit, The app estimates the user's emotions and adjusts the frequency of app displays based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned timing unit, Optimize the timing of app display based on the user's calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned timing unit, Adjust the timing of app display based on the user's device usage. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to the user's past response history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When a notification is sent, it can be customized based on the user's device settings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to provide relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user usage patterns, A learning unit that learns the usage patterns collected by the aforementioned collection unit, A display unit that displays an application based on a pattern learned by the learning unit, The timing section for displaying the app at specific times or days of the week, It includes a notification unit that notifies the user when it is not possible to display the information dynamically. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

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

4. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

Images