system

Abstract

The system according to this embodiment aims to automatically select and switch applications based on sensor data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a selection unit. The collection unit collects sensor data. The analysis unit analyzes the sensor data collected by the collection unit. The selection unit selects and switches applications based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the automatic selection and switching of applications based on sensor data have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically select and switch applications based on sensor data.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a selection unit. The collection unit collects sensor data. The analysis unit analyzes the sensor data collected by the collection unit. The selection unit selects and switches applications based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can automatically select and switch applications based on sensor data. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 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 app selection agent system according to an embodiment of the present invention considers AR glasses as a next-generation mobile phone and is a system that automatically selects and switches apps using an AI agent. The app selection agent system uses a group of sensors built into the AR glasses to detect the user's actions and surrounding environment and transmits this data to the AI ​​agent. The AI ​​agent analyzes the received sensor data and automatically selects and switches to the app that is best suited to the user's current situation and needs. For example, if the user is walking, a navigation app will be launched, and if they are sitting, a movie or game app will be launched, selecting apps according to the situation. This eliminates the need for the user to manually select apps, allowing them to use the AR glasses more intuitively and efficiently. Furthermore, by learning the user's behavior patterns, the AI ​​agent can make more accurate app selections. For example, the app selection agent system collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. Next, the app selection agent system analyzes the sensor data using a machine learning algorithm to analyze the user's behavior patterns. Furthermore, based on the analyzed data, the app selection agent system automatically selects and switches to the app that is best suited to the user's current situation and needs. This eliminates the need for users to manually select apps, allowing them to use AR glasses more intuitively and efficiently. As a result, the app selection agent system learns user behavior patterns, enabling more accurate app selection.

[0029] The application selection agent system according to this embodiment comprises a data collection unit, an analysis unit, and a selection unit. The data collection unit collects sensor data. The data collection unit collects sensor data using sensors such as an accelerometer, a gyroscope, a camera, and a microphone. The data collection unit can, for example, detect user movement using an accelerometer. The data collection unit can, for example, detect user posture using a gyroscope. The data collection unit can, for example, capture the user's field of view using a camera. The data collection unit can, for example, collect ambient sound using a microphone. The analysis unit analyzes the sensor data collected by the data collection unit. The analysis unit analyzes the sensor data using machine learning algorithms to analyze user behavior patterns. The analysis unit can, for example, analyze sensor data using deep learning. The analysis unit can, for example, analyze sensor data using support vector machines. The analysis unit can, for example, analyze sensor data using clustering algorithms. The selection unit selects and switches applications based on the data analyzed by the analysis unit. The selection unit automatically selects and switches to the app best suited to the user's current situation and needs. For example, the selection unit can launch a navigation app when the user is walking. For example, the selection unit can launch a movie or game app when the user is sitting. For example, the selection unit can launch a fitness app when the user is exercising. As a result, the app selection agent system according to this embodiment can collect and analyze sensor data and automatically select and switch apps.

[0030] The data collection unit collects sensor data. The data collection unit uses sensors such as accelerometers, gyroscopes, cameras, and microphones to collect sensor data. Specifically, the accelerometer detects the user's movements in three dimensions, allowing for real-time tracking of actions such as walking, running, and sitting. The gyroscope detects the user's posture and rotational movement, accurately determining whether the user is standing, sitting, or lying down. The camera captures the user's field of view, tracking the direction the user is looking and their gaze. This allows for a detailed understanding of the user's environment and what they are seeing. The microphone collects ambient sounds, allowing for an understanding of the user's sound environment. For example, it can detect whether the user is in a quiet room, a noisy place, or if specific sounds (music, conversation, traffic noise, etc.) are audible. The data collected from these sensors is transmitted in real-time to a central database for processing in the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and selection units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the sensor data collected by the data collection unit. For example, the analysis unit uses machine learning algorithms to analyze the sensor data and analyze user behavior patterns. Specifically, it can analyze sensor data using deep learning. Deep learning can automatically extract data features using multi-layered neural networks and learn complex patterns. This allows for high-precision detection of subtle movements and changes in user behavior. Support vector machines (SVMs) are used for data classification and regression analysis, and can classify user behavior based on sensor data. For example, they can accurately classify whether a user is walking, running, or sitting. Clustering algorithms can group data and combine data with similar behavior patterns. This allows for grouping user behavior patterns and understanding trends for specific behaviors. By using a combination of these algorithms, the analysis unit can analyze the collected sensor data from multiple angles and gain a detailed understanding of user behavior patterns. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term changes and trends in behavior patterns. For example, by using past data, it is possible to predict user behavior patterns during specific time periods or situations, and to predict future behavior. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term behavior prediction and trend analysis, thereby improving the reliability and accuracy of the entire system.

[0032] The selection unit selects and switches apps based on data analyzed by the analysis unit. Specifically, it automatically selects and switches to the app best suited to the user's current situation and needs. For example, if the user is walking, a navigation app can be launched. The navigation app calculates the optimal route based on the user's current location and guides them to their destination. If the user is sitting, a movie or game app can be launched. The movie app displays recommended movies based on the user's preferences and starts streaming playback. The game app suggests recommended games based on the user's play history and preferences, allowing them to start playing immediately. If the user is exercising, a fitness app can be launched. The fitness app monitors the user's exercise data in real time and displays exercise progress and calorie consumption. It can also suggest an optimal training menu based on the user's exercise pattern. By automatically selecting and switching these apps, the selection unit can improve user convenience. Furthermore, the selection unit can collect user feedback and continuously improve the accuracy of app selection and switching timing. For example, if a user prefers to use a particular app, the unit learns to prioritize selecting that app. Furthermore, the selection unit can launch multiple apps simultaneously and switch to the optimal app according to the user's situation. This enables flexible app selection and switching according to the user's needs, improving the overall system convenience and user experience.

[0033] The data collection unit collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. For example, the data collection unit can detect the user's movement using the accelerometer. For example, the data collection unit can detect the user's posture using the gyroscope. For example, the data collection unit can capture the user's field of view using the camera. For example, the data collection unit can collect ambient sounds using the microphone. By using multiple sensors, detailed sensor data can be collected. 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 data acquired from sensors such as the accelerometer, gyroscope, camera, and microphone into a generating AI, which can then analyze the data.

[0034] The analysis unit analyzes sensor data using machine learning algorithms to analyze user behavior patterns. The analysis unit can, for example, analyze sensor data using deep learning. The analysis unit can, for example, analyze sensor data using support vector machines. The analysis unit can, for example, analyze sensor data using clustering algorithms. This allows for highly accurate analysis of user behavior patterns using machine learning algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI, and the generating AI can perform the analysis of user behavior patterns.

[0035] The selection unit automatically selects and switches to the most suitable app based on the user's current situation and needs, using the analyzed data. For example, the selection unit can launch a navigation app if the user is walking. For example, it can launch a movie or game app if the user is sitting. For example, it can launch a fitness app if the user is exercising. This allows the system to automatically select and switch to the most suitable app according to the user's situation and needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the analyzed data into a generating AI, which can then select and switch to the most suitable app.

[0036] The learning unit learns user behavior patterns. The learning unit can learn user behavior patterns using, for example, machine learning algorithms. The learning unit can learn user behavior patterns using, for example, deep learning. The learning unit can learn user behavior patterns using, for example, support vector machines. By learning user behavior patterns, it becomes possible to select apps with greater accuracy. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input user behavior patterns into a generative AI, and the generative AI can learn the behavior patterns.

[0037] The service provider provides the results of the app selection. The service provider can, for example, notify the user of the app selection results. The service provider can, for example, display the app selection results on the user's AR glasses. The service provider can, for example, notify the user of the app selection results on their smartphone. By providing the user with the app selection results, the user can confirm the selection results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the app selection results into a generating AI, and the generating AI can provide the selection results.

[0038] The data collection unit analyzes the user's past behavior history and selects the optimal combination of sensors. For example, the data collection unit can prioritize the selection of sensors that the user has frequently used in the past. For example, the data collection unit can automatically select the sensors needed in a specific situation based on the user's past behavior patterns. For example, the data collection unit can predict and select the sensors to be used during a specific time period based on the user's past behavior history. This enables efficient data collection by selecting the optimal combination of sensors based on the user's past behavior history. 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 past behavior history into a generating AI, which can then select the optimal combination of sensors.

[0039] The data collection unit filters sensor data based on the user's current activity and environment. For example, if the user is outdoors, the data collection unit can filter out ambient noise before collecting the data. For example, if the user is exercising, the data collection unit can prioritize the collection of accelerometer data. For example, if the user is in a quiet place, the data collection unit can filter out ambient noise before collecting the data. This allows for efficient collection of necessary data by filtering the data according to the user's activity and environment. 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 current activity and environment data into a generating AI, which can then filter the data.

[0040] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information, when collecting sensor data. For example, if the user is in a specific location, the data collection unit can prioritize collecting sensor data related to that location. For example, if the user is on the move, the data collection unit can prioritize collecting sensor data related to the travel route. For example, if the user is in a specific region, the data collection unit can prioritize collecting environmental data for that region. This enables efficient data collection by prioritizing the collection of 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 into a generating AI, which can then select highly relevant data.

[0041] The data collection unit analyzes the user's social media activity when collecting sensor data and collects relevant data. For example, if the user participates in a specific event on social media, the data collection unit can collect sensor data related to that event. For example, if the user checks in to a specific location on social media, the data collection unit can collect sensor data related to that location. For example, if the user shares a specific activity on social media, the data collection unit can collect sensor data related to that activity. This allows for more appropriate 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 the generating AI can collect relevant data.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the sensor data during the analysis. For example, the analysis unit can perform a detailed analysis on sensor data with high importance. For example, the analysis unit can perform a simpler analysis on sensor data with low importance. For example, the analysis unit can perform a moderate analysis on sensor data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the sensor data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the sensor data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the type of sensor data during analysis. For example, the analysis unit can apply a motion analysis algorithm to acceleration sensor data. For example, the analysis unit can apply a speech analysis algorithm to ambient sound sensor data. For example, the analysis unit can apply a biosignal analysis algorithm to heart rate sensor data. By applying an appropriate analysis algorithm according to the type of sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of sensor data into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0044] The analysis unit adjusts the order of analysis based on the timing of sensor data collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent sensor data. For example, the analysis unit can perform analysis while referring to past sensor data. For example, the analysis unit can prioritize the analysis of sensor data collected during a specific time period. This allows for efficient analysis by adjusting the order of analysis based on the timing of sensor data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of sensor data collection into a generating AI, and the generating AI can adjust the order of analysis.

[0045] The analysis unit adjusts its analysis method based on the relationships between sensor data during analysis. For example, the analysis unit can perform a detailed analysis on highly relevant sensor data. For example, the analysis unit can perform a simple analysis on less relevant sensor data. For example, the analysis unit can perform a moderate analysis on sensor data with a moderate relationship. By adjusting the analysis method based on the relationships between sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between sensor data into a generating AI, and the generating AI can adjust the analysis method.

[0046] The selection unit analyzes the user's past app usage history to select the most suitable app. For example, the selection unit can prioritize apps that the user has frequently used in the past. For example, the selection unit can predict and select apps to be used in specific situations based on the user's past app usage history. For example, the selection unit can analyze the user's past app usage history and select the most efficient app. This enables efficient app selection by selecting the most suitable app based on the user's past app usage history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past app usage history into a generating AI, which can then select the most suitable app.

[0047] The selection unit customizes the app selection based on the user's current activity and environment when selecting an app. For example, if the user is exercising, the selection unit can prioritize fitness apps. For example, if the user is in a quiet place, the selection unit can prioritize reading apps. For example, if the user is on the move, the selection unit can prioritize navigation apps. This allows for the selection of more appropriate apps by customizing the app selection according to the user's current activity and environment. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current activity and environment data into a generating AI, which can then customize the app selection.

[0048] The selection unit selects the most suitable app when the user is selecting an app, taking into account the user's geographical location. For example, if the user is in a specific location, the selection unit can prioritize selecting apps related to that location. For example, if the user is on the move, the selection unit can prioritize selecting apps related to the user's travel route. For example, if the user is in a specific region, the selection unit can prioritize selecting apps that provide information about that region. This enables efficient app selection by selecting the most suitable app based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can then select the most suitable app.

[0049] The selection unit analyzes the user's social media activity to select an app. For example, if the user is participating in a specific event on social media, the selection unit can select an app related to that event. For example, if the user is checking in to a specific location on social media, the selection unit can select an app related to that location. For example, if the user is sharing a specific activity on social media, the selection unit can select an app related to that activity. This allows for the selection of a more appropriate app based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI, and the generating AI can then select an app.

[0050] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can extract areas for improvement in the learning algorithm from past learning data and optimize it. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by optimizing 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 using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI optimize the learning algorithm.

[0051] The learning unit weights the training data based on the timing of sensor data collection during training. For example, the learning unit can assign a high weight to the most recent sensor data. For example, the learning unit can assign a low weight to past sensor data. For example, the learning unit can assign an appropriate weight to sensor data collected during a specific time period. This enables efficient training by weighting the training data based on the timing of sensor data collection. 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 the timing of sensor data collection into a generating AI, and the generating AI can weight the training data.

[0052] The service provider selects the optimal display method by referring to the user's past operation history when providing the app selection results. For example, the service provider can prioritize providing display methods that the user has used in the past. For example, the service provider can predict and provide the display method to be used in a specific situation based on the user's past operation history. For example, the service provider can analyze the user's past operation history and provide the most efficient display method. This enables efficient display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history into a generating AI, and the generating AI can select the optimal display method.

[0053] The service provider selects the optimal display method when providing the app selection results, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This enables efficient display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI, and the generating AI can select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can also analyze the user's past behavior history and select the optimal combination of sensors. For example, it can prioritize the selection of sensors that the user has frequently used in the past. Based on the user's past behavior patterns, it can automatically select the sensors needed in specific situations. Based on the user's past behavior history, it can predict and select the sensors to be used during specific time periods. This enables efficient data collection by selecting the optimal combination of sensors based on the user's past behavior history.

[0056] The service provider can also select the optimal display method by referring to the user's past operation history when providing the app selection results. For example, it can prioritize providing the display method the user has used in the past. It can predict and provide the display method used in a specific situation based on the user's past operation history. It can analyze the user's past operation history and provide the most efficient display method. As a result, by selecting the optimal display method based on the user's past operation history, efficient display becomes possible.

[0057] The analysis unit can also adjust the order of analysis based on the timing of sensor data collection. For example, it can prioritize the analysis of the most recent sensor data. It can also perform analysis while referring to past sensor data. It can prioritize the analysis of sensor data collected during a specific time period. By adjusting the order of analysis based on the timing of sensor data collection, efficient analysis becomes possible.

[0058] The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location when collecting sensor data. For example, if the user is in a specific location, sensor data related to that location can be prioritized for collection. If the user is on the move, sensor data related to their travel route can be prioritized for collection. If the user is in a specific region, environmental data for that region can be prioritized for collection. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0059] The selection function can also analyze the user's social media activity when selecting an app. For example, if a user participates in a specific event on social media, it can select an app related to that event. If a user checks in to a specific location on social media, it can select an app related to that location. If a user shares a specific activity on social media, it can select an app related to that activity. This allows for more appropriate app selection based on the user's social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects sensor data. The data collection unit collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. The data collection unit can, for example, detect user movement using an accelerometer. The data collection unit can, for example, detect user posture using a gyroscope. The data collection unit can, for example, capture the user's field of view using a camera. The data collection unit can, for example, collect ambient sound using a microphone. Step 2: The analysis unit analyzes the sensor data collected by the collection unit. The analysis unit analyzes the sensor data using, for example, a machine learning algorithm to analyze user behavior patterns. The analysis unit can analyze the sensor data using, for example, deep learning. The analysis unit can analyze the sensor data using, for example, a support vector machine. The analysis unit can analyze the sensor data using, for example, a clustering algorithm. Step 3: The selection unit selects and switches apps based on the data analyzed by the analysis unit. The selection unit automatically selects and switches to the app that is best suited to the user's current situation and needs. For example, the selection unit can launch a navigation app if the user is walking. For example, the selection unit can launch a movie or game app if the user is sitting. For example, the selection unit can launch a fitness app if the user is exercising.

[0062] (Example of form 2) The app selection agent system according to an embodiment of the present invention considers AR glasses as a next-generation mobile phone and is a system that automatically selects and switches apps using an AI agent. The app selection agent system uses a group of sensors built into the AR glasses to detect the user's actions and surrounding environment and transmits this data to the AI ​​agent. The AI ​​agent analyzes the received sensor data and automatically selects and switches to the app that is best suited to the user's current situation and needs. For example, if the user is walking, a navigation app will be launched, and if they are sitting, a movie or game app will be launched, selecting apps according to the situation. This eliminates the need for the user to manually select apps, allowing them to use the AR glasses more intuitively and efficiently. Furthermore, by learning the user's behavior patterns, the AI ​​agent can make more accurate app selections. For example, the app selection agent system collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. Next, the app selection agent system analyzes the sensor data using a machine learning algorithm to analyze the user's behavior patterns. Furthermore, based on the analyzed data, the app selection agent system automatically selects and switches to the app that is best suited to the user's current situation and needs. This eliminates the need for users to manually select apps, allowing them to use AR glasses more intuitively and efficiently. As a result, the app selection agent system learns user behavior patterns, enabling more accurate app selection.

[0063] The application selection agent system according to this embodiment comprises a data collection unit, an analysis unit, and a selection unit. The data collection unit collects sensor data. The data collection unit collects sensor data using sensors such as an accelerometer, a gyroscope, a camera, and a microphone. The data collection unit can, for example, detect user movement using an accelerometer. The data collection unit can, for example, detect user posture using a gyroscope. The data collection unit can, for example, capture the user's field of view using a camera. The data collection unit can, for example, collect ambient sound using a microphone. The analysis unit analyzes the sensor data collected by the data collection unit. The analysis unit analyzes the sensor data using machine learning algorithms to analyze user behavior patterns. The analysis unit can, for example, analyze sensor data using deep learning. The analysis unit can, for example, analyze sensor data using support vector machines. The analysis unit can, for example, analyze sensor data using clustering algorithms. The selection unit selects and switches applications based on the data analyzed by the analysis unit. The selection unit automatically selects and switches to the app best suited to the user's current situation and needs. For example, the selection unit can launch a navigation app when the user is walking. For example, the selection unit can launch a movie or game app when the user is sitting. For example, the selection unit can launch a fitness app when the user is exercising. As a result, the app selection agent system according to this embodiment can collect and analyze sensor data and automatically select and switch apps.

[0064] The data collection unit collects sensor data. The data collection unit uses sensors such as accelerometers, gyroscopes, cameras, and microphones to collect sensor data. Specifically, the accelerometer detects the user's movements in three dimensions, allowing for real-time tracking of actions such as walking, running, and sitting. The gyroscope detects the user's posture and rotational movement, accurately determining whether the user is standing, sitting, or lying down. The camera captures the user's field of view, tracking the direction the user is looking and their gaze. This allows for a detailed understanding of the user's environment and what they are seeing. The microphone collects ambient sounds, allowing for an understanding of the user's sound environment. For example, it can detect whether the user is in a quiet room, a noisy place, or if specific sounds (music, conversation, traffic noise, etc.) are audible. The data collected from these sensors is transmitted in real-time to a central database for processing in the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and selection units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the sensor data collected by the data collection unit. For example, the analysis unit uses machine learning algorithms to analyze the sensor data and analyze user behavior patterns. Specifically, it can analyze sensor data using deep learning. Deep learning can automatically extract data features using multi-layered neural networks and learn complex patterns. This allows for high-precision detection of subtle movements and changes in user behavior. Support vector machines (SVMs) are used for data classification and regression analysis, and can classify user behavior based on sensor data. For example, they can accurately classify whether a user is walking, running, or sitting. Clustering algorithms can group data and combine data with similar behavior patterns. This allows for grouping user behavior patterns and understanding trends for specific behaviors. By using a combination of these algorithms, the analysis unit can analyze the collected sensor data from multiple angles and gain a detailed understanding of user behavior patterns. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term changes and trends in behavior patterns. For example, by using past data, it is possible to predict user behavior patterns during specific time periods or situations, and to predict future behavior. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term behavior prediction and trend analysis, thereby improving the reliability and accuracy of the entire system.

[0066] The selection unit selects and switches apps based on data analyzed by the analysis unit. Specifically, it automatically selects and switches to the app best suited to the user's current situation and needs. For example, if the user is walking, a navigation app can be launched. The navigation app calculates the optimal route based on the user's current location and guides them to their destination. If the user is sitting, a movie or game app can be launched. The movie app displays recommended movies based on the user's preferences and starts streaming playback. The game app suggests recommended games based on the user's play history and preferences, allowing them to start playing immediately. If the user is exercising, a fitness app can be launched. The fitness app monitors the user's exercise data in real time and displays exercise progress and calorie consumption. It can also suggest an optimal training menu based on the user's exercise pattern. By automatically selecting and switching these apps, the selection unit can improve user convenience. Furthermore, the selection unit can collect user feedback and continuously improve the accuracy of app selection and switching timing. For example, if a user prefers to use a particular app, the unit learns to prioritize selecting that app. Furthermore, the selection unit can launch multiple apps simultaneously and switch to the optimal app according to the user's situation. This enables flexible app selection and switching according to the user's needs, improving the overall system convenience and user experience.

[0067] The data collection unit collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. For example, the data collection unit can detect the user's movement using the accelerometer. For example, the data collection unit can detect the user's posture using the gyroscope. For example, the data collection unit can capture the user's field of view using the camera. For example, the data collection unit can collect ambient sounds using the microphone. By using multiple sensors, detailed sensor data can be collected. 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 data acquired from sensors such as the accelerometer, gyroscope, camera, and microphone into a generating AI, which can then analyze the data.

[0068] The analysis unit analyzes sensor data using machine learning algorithms to analyze user behavior patterns. The analysis unit can, for example, analyze sensor data using deep learning. The analysis unit can, for example, analyze sensor data using support vector machines. The analysis unit can, for example, analyze sensor data using clustering algorithms. This allows for highly accurate analysis of user behavior patterns using machine learning algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI, and the generating AI can perform the analysis of user behavior patterns.

[0069] The selection unit automatically selects and switches to the most suitable app based on the user's current situation and needs, using the analyzed data. For example, the selection unit can launch a navigation app if the user is walking. For example, it can launch a movie or game app if the user is sitting. For example, it can launch a fitness app if the user is exercising. This allows the system to automatically select and switch to the most suitable app according to the user's situation and needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the analyzed data into a generating AI, which can then select and switch to the most suitable app.

[0070] The learning unit learns user behavior patterns. The learning unit can learn user behavior patterns using, for example, machine learning algorithms. The learning unit can learn user behavior patterns using, for example, deep learning. The learning unit can learn user behavior patterns using, for example, support vector machines. By learning user behavior patterns, it becomes possible to select apps with greater accuracy. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input user behavior patterns into a generative AI, and the generative AI can learn the behavior patterns.

[0071] The service provider provides the results of the app selection. The service provider can, for example, notify the user of the app selection results. The service provider can, for example, display the app selection results on the user's AR glasses. The service provider can, for example, notify the user of the app selection results on their smartphone. By providing the user with the app selection results, the user can confirm the selection results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the app selection results into a generating AI, and the generating AI can provide the selection results.

[0072] The data collection unit estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of sensor data collection to collect more detailed data. For example, if the user is relaxed, the data collection unit can decrease the frequency of sensor data collection to collect only the minimum necessary data. For example, if the user is excited, the data collection unit can prioritize data collection from a specific sensor (e.g., heart rate sensor). This allows for the collection of more appropriate data by adjusting the timing of sensor data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 emotion data into a generative AI, which can then perform emotion estimation.

[0073] The data collection unit analyzes the user's past behavior history and selects the optimal combination of sensors. For example, the data collection unit can prioritize the selection of sensors that the user has frequently used in the past. For example, the data collection unit can automatically select the sensors needed in a specific situation based on the user's past behavior patterns. For example, the data collection unit can predict and select the sensors to be used during a specific time period based on the user's past behavior history. This enables efficient data collection by selecting the optimal combination of sensors based on the user's past behavior history. 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 past behavior history into a generating AI, which can then select the optimal combination of sensors.

[0074] The data collection unit filters sensor data based on the user's current activity and environment. For example, if the user is outdoors, the data collection unit can filter out ambient noise before collecting the data. For example, if the user is exercising, the data collection unit can prioritize the collection of accelerometer data. For example, if the user is in a quiet place, the data collection unit can filter out ambient noise before collecting the data. This allows for efficient collection of necessary data by filtering the data according to the user's activity and environment. 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 current activity and environment data into a generating AI, which can then filter the data.

[0075] The data collection unit estimates the user's emotions and determines the priority of sensor 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 the heart rate sensor. For example, if the user is relaxed, the data collection unit can prioritize collecting data from the ambient sound sensor. For example, if the user is excited, the data collection unit can prioritize collecting data from the acceleration sensor. In this way, by prioritizing sensor data according to the user's emotions, important data can be collected preferentially. 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, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0076] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information, when collecting sensor data. For example, if the user is in a specific location, the data collection unit can prioritize collecting sensor data related to that location. For example, if the user is on the move, the data collection unit can prioritize collecting sensor data related to the travel route. For example, if the user is in a specific region, the data collection unit can prioritize collecting environmental data for that region. This enables efficient data collection by prioritizing the collection of 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 into a generating AI, which can then select highly relevant data.

[0077] The data collection unit analyzes the user's social media activity when collecting sensor data and collects relevant data. For example, if the user participates in a specific event on social media, the data collection unit can collect sensor data related to that event. For example, if the user checks in to a specific location on social media, the data collection unit can collect sensor data related to that location. For example, if the user shares a specific activity on social media, the data collection unit can collect sensor data related to that activity. This allows for more appropriate 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 the generating AI can collect relevant data.

[0078] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a detailed analysis to identify the cause of the stress. For example, if the user is relaxed, the analysis unit can perform a simpler analysis to identify the factors contributing to the relaxation. For example, if the user is excited, the analysis unit can focus on specific sensor data. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0079] The analysis unit adjusts the level of detail of the analysis based on the importance of the sensor data during the analysis. For example, the analysis unit can perform a detailed analysis on sensor data with high importance. For example, the analysis unit can perform a simpler analysis on sensor data with low importance. For example, the analysis unit can perform a moderate analysis on sensor data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the sensor data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the sensor data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0080] The analysis unit applies different analysis algorithms depending on the type of sensor data during analysis. For example, the analysis unit can apply a motion analysis algorithm to acceleration sensor data. For example, the analysis unit can apply a speech analysis algorithm to ambient sound sensor data. For example, the analysis unit can apply a biosignal analysis algorithm to heart rate sensor data. By applying an appropriate analysis algorithm according to the type of sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of sensor data into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0081] The analysis unit estimates the user's emotions and determines the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit can prioritize the analysis of relaxation-related data. For example, if the user is excited, the analysis unit can prioritize the analysis of excitement-related data. This allows important data to be analyzed preferentially by determining the priority of analysis 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0082] The analysis unit adjusts the order of analysis based on the timing of sensor data collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent sensor data. For example, the analysis unit can perform analysis while referring to past sensor data. For example, the analysis unit can prioritize the analysis of sensor data collected during a specific time period. This allows for efficient analysis by adjusting the order of analysis based on the timing of sensor data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of sensor data collection into a generating AI, and the generating AI can adjust the order of analysis.

[0083] The analysis unit adjusts its analysis method based on the relationships between sensor data during analysis. For example, the analysis unit can perform a detailed analysis on highly relevant sensor data. For example, the analysis unit can perform a simple analysis on less relevant sensor data. For example, the analysis unit can perform a moderate analysis on sensor data with a moderate relationship. By adjusting the analysis method based on the relationships between sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between sensor data into a generating AI, and the generating AI can adjust the analysis method.

[0084] The selection unit estimates the user's emotions and adjusts the app selection method based on the estimated user emotions. For example, if the user is stressed, the selection unit can prioritize selecting relaxing apps. For example, if the user is relaxed, the selection unit can prioritize selecting entertainment apps. For example, if the user is excited, the selection unit can prioritize selecting active apps. In this way, by adjusting the app selection method according to the user's emotions, a more appropriate app can be selected. 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 selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0085] The selection unit analyzes the user's past app usage history to select the most suitable app. For example, the selection unit can prioritize apps that the user has frequently used in the past. For example, the selection unit can predict and select apps to be used in specific situations based on the user's past app usage history. For example, the selection unit can analyze the user's past app usage history and select the most efficient app. This enables efficient app selection by selecting the most suitable app based on the user's past app usage history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past app usage history into a generating AI, which can then select the most suitable app.

[0086] The selection unit customizes the app selection based on the user's current activity and environment when selecting an app. For example, if the user is exercising, the selection unit can prioritize fitness apps. For example, if the user is in a quiet place, the selection unit can prioritize reading apps. For example, if the user is on the move, the selection unit can prioritize navigation apps. This allows for the selection of more appropriate apps by customizing the app selection according to the user's current activity and environment. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current activity and environment data into a generating AI, which can then customize the app selection.

[0087] The selection unit estimates the user's emotions and determines the priority of app selection based on the estimated user emotions. For example, if the user is stressed, the selection unit can prioritize apps that promote relaxation. For example, if the user is relaxed, the selection unit can prioritize entertainment apps. For example, if the user is excited, the selection unit can prioritize active apps. In this way, by determining the priority of app selection according to the user's emotions, important apps can be selected preferentially. 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 selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0088] The selection unit selects the most suitable app when the user is selecting an app, taking into account the user's geographical location. For example, if the user is in a specific location, the selection unit can prioritize selecting apps related to that location. For example, if the user is on the move, the selection unit can prioritize selecting apps related to the user's travel route. For example, if the user is in a specific region, the selection unit can prioritize selecting apps that provide information about that region. This enables efficient app selection by selecting the most suitable app based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can then select the most suitable app.

[0089] The selection unit analyzes the user's social media activity to select an app. For example, if the user is participating in a specific event on social media, the selection unit can select an app related to that event. For example, if the user is checking in to a specific location on social media, the selection unit can select an app related to that location. For example, if the user is sharing a specific activity on social media, the selection unit can select an app related to that activity. This allows for the selection of a more appropriate app based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI, and the generating AI can then select an app.

[0090] The learning unit estimates the user's emotions and selects training data based on the estimated user emotions. For example, if the user is stressed, the learning unit can prioritize learning data related to stress. For example, if the user is relaxed, the learning unit can prioritize learning data related to relaxation. For example, if the user is excited, the learning unit can prioritize learning data related to excitement. This allows for the learning of more appropriate data 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 emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0091] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can extract areas for improvement in the learning algorithm from past learning data and optimize it. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by optimizing 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 using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI optimize the learning algorithm.

[0092] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can increase the learning frequency and collect more detailed data. For example, if the user is relaxed, the learning unit can decrease the learning frequency and collect only the minimum necessary data. For example, if the user is excited, the learning unit can prioritize data collection from specific sensors (e.g., heart rate sensors). This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0093] The learning unit weights the training data based on the timing of sensor data collection during training. For example, the learning unit can assign a high weight to the most recent sensor data. For example, the learning unit can assign a low weight to past sensor data. For example, the learning unit can assign an appropriate weight to sensor data collected during a specific time period. This enables efficient training by weighting the training data based on the timing of sensor data collection. 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 the timing of sensor data collection into a generating AI, and the generating AI can weight the training data.

[0094] The service provider estimates the user's emotions and adjusts the display method of the app selection results based on the estimated user emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is excited, the service provider can provide a visually stimulating display method. By adjusting the display method of the app selection results according to the user's emotions, a more appropriate display becomes 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0095] The service provider selects the optimal display method by referring to the user's past operation history when providing the app selection results. For example, the service provider can prioritize providing display methods that the user has used in the past. For example, the service provider can predict and provide the display method to be used in a specific situation based on the user's past operation history. For example, the service provider can analyze the user's past operation history and provide the most efficient display method. This enables efficient display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history into a generating AI, and the generating AI can select the optimal display method.

[0096] The service provider estimates the user's emotions and adjusts the display order of app selection results based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize displaying important information. For example, if the user is relaxed, the service provider can prioritize displaying detailed information. For example, if the user is excited, the service provider can prioritize displaying visually stimulating information. This allows for prioritizing the display order of app selection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions.

[0097] The service provider selects the optimal display method when providing the app selection results, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This enables efficient display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI, and the generating AI can select the optimal display method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, a detailed analysis can be performed to identify the cause of the stress. If the user is relaxed, a simpler analysis can be performed to identify the factors contributing to the relaxation. If the user is excited, the analysis can focus on specific sensor data. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible.

[0100] The data collection unit can also analyze the user's past behavior history and select the optimal combination of sensors. For example, it can prioritize the selection of sensors that the user has frequently used in the past. Based on the user's past behavior patterns, it can automatically select the sensors needed in specific situations. Based on the user's past behavior history, it can predict and select the sensors to be used during specific time periods. This enables efficient data collection by selecting the optimal combination of sensors based on the user's past behavior history.

[0101] The selection function can also estimate the user's emotions and adjust the app selection method based on those estimates. For example, if the user is stressed, it can prioritize apps that promote relaxation. If the user is relaxed, it can prioritize entertainment apps. If the user is excited, it can prioritize active apps. By adjusting the app selection method according to the user's emotions, it can select more appropriate apps.

[0102] The service provider can also select the optimal display method by referring to the user's past operation history when providing the app selection results. For example, it can prioritize providing the display method the user has used in the past. It can predict and provide the display method used in a specific situation based on the user's past operation history. It can analyze the user's past operation history and provide the most efficient display method. As a result, by selecting the optimal display method based on the user's past operation history, efficient display becomes possible.

[0103] The learning unit can also estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is stressed, it can prioritize learning data related to stress. If the user is relaxed, it can prioritize learning data related to relaxation. If the user is excited, it can prioritize learning data related to excitement. By selecting training data according to the user's emotions, the system can learn more appropriate data.

[0104] The analysis unit can also adjust the order of analysis based on the timing of sensor data collection. For example, it can prioritize the analysis of the most recent sensor data. It can also perform analysis while referring to past sensor data. It can prioritize the analysis of sensor data collected during a specific time period. By adjusting the order of analysis based on the timing of sensor data collection, efficient analysis becomes possible.

[0105] The service provider can also estimate the user's emotions and adjust how the app selection results are displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is excited, a visually stimulating display method can be provided. By adjusting how the app selection results are displayed according to the user's emotions, a more appropriate display becomes possible.

[0106] The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location when collecting sensor data. For example, if the user is in a specific location, sensor data related to that location can be prioritized for collection. If the user is on the move, sensor data related to their travel route can be prioritized for collection. If the user is in a specific region, environmental data for that region can be prioritized for collection. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0107] The selection function can also analyze the user's social media activity when selecting an app. For example, if a user participates in a specific event on social media, it can select an app related to that event. If a user checks in to a specific location on social media, it can select an app related to that location. If a user shares a specific activity on social media, it can select an app related to that activity. This allows for more appropriate app selection based on the user's social media activity.

[0108] The learning unit can also estimate the user's emotions and adjust the learning frequency based on that estimation. For example, if the user is stressed, the learning frequency can be increased to collect more detailed data. If the user is relaxed, the learning frequency can be decreased to collect only the minimum necessary data. If the user is excited, data collection from specific sensors (e.g., heart rate sensor) can be prioritized. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects sensor data. The data collection unit collects sensor data using sensors such as an accelerometer, gyroscope, camera, and microphone. The data collection unit can, for example, detect user movement using an accelerometer. The data collection unit can, for example, detect user posture using a gyroscope. The data collection unit can, for example, capture the user's field of view using a camera. The data collection unit can, for example, collect ambient sound using a microphone. Step 2: The analysis unit analyzes the sensor data collected by the collection unit. The analysis unit analyzes the sensor data using, for example, a machine learning algorithm to analyze user behavior patterns. The analysis unit can analyze the sensor data using, for example, deep learning. The analysis unit can analyze the sensor data using, for example, a support vector machine. The analysis unit can analyze the sensor data using, for example, a clustering algorithm. Step 3: The selection unit selects and switches apps based on the data analyzed by the analysis unit. The selection unit automatically selects and switches to the app that is best suited to the user's current situation and needs. For example, the selection unit can launch a navigation app if the user is walking. For example, the selection unit can launch a movie or game app if the user is sitting. For example, the selection unit can launch a fitness app if the user is exercising.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, learning unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects sensor data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected sensor data. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12 and selects and switches to the optimal application based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and notifies the user of the application selection result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, learning unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects sensor data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected sensor data. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects and switches the optimal application based on the analysis results. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the application selection result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, learning unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects sensor data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected sensor data. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects and switches the optimal application based on the analysis results. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies the user of the application selection result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, learning unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects sensor data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected sensor data. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects and switches to the optimal application based on the analysis results. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the user of the application selection result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects sensor data, An analysis unit analyzes the sensor data collected by the aforementioned collection unit, The system includes a selection unit that selects and switches applications based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensor data is collected using sensors such as accelerometers, gyroscopes, cameras, and microphones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze sensor data using machine learning algorithms to analyze user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Based on the analyzed data, the system automatically selects and switches to the app best suited to the user's current situation and needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a learning unit that learns user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a section that provides the selection results for the app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal combination of sensors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting sensor data, filtering is performed based on the user's current activities and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of sensor data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sensor data, 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 12) The aforementioned collection unit is When collecting sensor data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type of sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the timing of sensor data collection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis method is adjusted based on the relationships between the sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is It estimates the user's emotions and adjusts how apps are selected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When selecting an app, the system analyzes the user's past app usage history to select the most suitable app. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When selecting an app, customize the app selection based on the user's current activity and environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and determines the priority of app selection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When selecting an app, the system will consider the user's geographical location to select the most suitable app. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When selecting an app, the system analyzes the user's social media activity to make app recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 26) 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 27) 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 28) The aforementioned learning unit, During training, the training data is weighted based on the timing of sensor data collection. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how app selection results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing app selection results, 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 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of app selection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing app selection results, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection unit that collects sensor data, An analysis unit analyzes the sensor data collected by the aforementioned collection unit, The system includes a selection unit that selects and switches applications based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Sensor data is collected using sensors such as accelerometers, gyroscopes, cameras, and microphones. The system according to feature 1.

3. The aforementioned analysis unit, We analyze sensor data using machine learning algorithms to analyze user behavior patterns. The system according to feature 1.

4. The aforementioned selection unit is Based on the analyzed data, the system automatically selects and switches to the app best suited to the user's current situation and needs. The system according to feature 1.

5. It includes a learning unit that learns user behavior patterns. The system according to feature 1.

6. It includes a section that provides the selection results for the app. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal combination of sensors. The system according to feature 1.

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