system

Abstract

The system according to this embodiment aims to automatically launch the application that the user wants to open. [Solution] The system according to the embodiment comprises a behavior collection unit, a sensor collection unit, an analysis unit, and a launch unit. The behavior collection unit collects the user's behavior patterns. The sensor collection unit collects sensor information from the smartphone. The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit and estimates the application the user wanted to open. The launch unit automatically launches the application estimated 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a user to quickly find an application that the user wants to open, and there is a risk of opportunity loss.

[0005] The system according to the embodiment aims to automatically start an application that the user wants to open.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a behavior collection unit, a sensor collection unit, an analysis unit, and a launch unit. The behavior collection unit collects the user's behavior patterns. The sensor collection unit collects sensor information from the smartphone. The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate the application the user wanted to open. The launch unit automatically launches the application estimated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically launch the application that the user wants to open. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that automatically launches the app the user wanted to open as soon as they open their smartphone. This AI agent system collects the user's behavior patterns and smartphone sensor information, and based on the collected information, the AI ​​estimates the app the user wanted to open and automatically launches that app. This mechanism eliminates the need for the user to search for apps and provides an intuitive and comfortable smartphone operation experience. For example, the AI ​​agent system collects the user's behavior patterns and smartphone sensor information. In this process, it collects data such as location information, trends in the apps used, and the history of opening and closing the smartphone. For example, if the user is in a convenience store, the convenience store app can be automatically launched based on the location information and past app usage trends. Next, based on the collected information, the AI ​​estimates the app the user wanted to open. The AI ​​analyzes the collected data and learns the user's behavior patterns. For example, if the user opens their smartphone while on their way to the nearest station, the train route app can be automatically launched based on the location information and behavioral analysis. Furthermore, the app estimated by the AI ​​is automatically launched. For example, if the user opens their smartphone while traveling on a train, the SNS app can be automatically launched based on the location information, behavioral analysis, and trends in the apps used. This allows the user to use SNS smoothly. This system eliminates the need for users to search for apps, providing an intuitive and comfortable smartphone experience. It also improves user productivity and satisfaction in their daily lives and business activities. For example, payments at convenience stores become smoother, and route searches on the way to the nearest train station become more efficient. This allows the AI ​​agent system to automatically launch the app the user wanted to open, based on the user's behavior patterns and sensor information.

[0029] The AI ​​agent system according to this embodiment comprises a behavior collection unit, a sensor collection unit, an analysis unit, and an activation unit. The behavior collection unit collects the user's behavior patterns. The behavior collection unit collects data such as the user's location information, the types of apps used, and the history of opening and closing the smartphone. For example, if the user is in a convenience store, the behavior collection unit can automatically launch a convenience store app based on the location information and past app usage trends. The behavior collection unit can also automatically launch a train route app based on the location information and behavioral analysis if the user opens their smartphone while on their way to the nearest station. Furthermore, if the user opens their smartphone while traveling on a train, the behavior collection unit can automatically launch a social networking service (SNS) app based on the location information, behavioral analysis, and past app usage trends. The sensor collection unit collects sensor information from the smartphone. The sensor collection unit collects information such as GPS data, acceleration sensor data, and gyroscope sensor data. For example, the sensor collection unit collects the smartphone's location information to determine the user's current location. The sensor collection unit can also use the acceleration sensor to understand the user's movement status. Furthermore, the sensor collection unit can also detect the tilt and rotation of the smartphone using a gyroscope sensor. The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate the app the user wanted to open. For example, the analysis unit learns the user's behavior patterns based on the collected data and estimates the app the user wanted to open. For example, the analysis unit can analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. The analysis unit can also analyze the user's app usage trends and automatically launch a social networking app while the user is traveling by train. Furthermore, the analysis unit can learn the user's behavior patterns and automatically launch a convenience store app when the user is at a convenience store. The launch unit automatically launches the app estimated by the analysis unit. For example, by automatically launching the app estimated by the analysis unit, the launch unit can save the user the trouble of searching for the app. For example, the launch unit can automatically launch a train route app while the user is on their way to the nearest station.Furthermore, the activation unit can automatically launch a social networking app while the user is traveling on a train. Additionally, the activation unit can automatically launch a convenience store app when the user is at a convenience store. This allows the AI ​​agent system according to this embodiment to automatically launch the app the user wanted to open, based on the user's behavior patterns and sensor information.

[0030] The behavioral data collection unit collects user behavior patterns. For example, it collects data such as user location information, app usage trends, and smartphone opening / closing history. Specifically, the behavioral data collection unit meticulously records the places and times users visit daily, as well as the types and frequency of apps they use. For example, if a user heads to their nearest station at a specific time every morning, the unit can automatically launch a train route app based on their location information and past behavior patterns. Similarly, if a user tends to visit a specific store on certain days of the week, the unit can automatically launch the app for that store. Furthermore, the behavioral data collection unit can predict which apps a user is most likely to open next, based on the time and location where they opened their smartphone and their app usage history. For example, if a user frequently uses a specific restaurant app during lunchtime, the unit can automatically launch that app during that time. This allows the behavioral data collection unit to gain a detailed understanding of user behavior patterns and support app launches tailored to user needs. Additionally, the behavioral data collection unit stores the collected data in a cloud-based database, making it accessible to the analysis and launch units. This allows the behavioral data collection unit to collect user behavior patterns in real time, improving the overall system efficiency.

[0031] The sensor collection unit collects sensor information from the smartphone. This includes, for example, GPS data, accelerometer data, and gyroscope data. Specifically, the sensor collection unit collects smartphone location information in real time to accurately determine the user's current location. For example, if the user is moving, it can track the user's route based on GPS data and predict the time it will take to reach their destination. The sensor collection unit can also use the accelerometer to understand the user's movement. For example, it can determine whether the user is walking, in a car, or on a train, and support the launch of appropriate applications. Furthermore, the sensor collection unit can detect the smartphone's tilt and rotation using the gyroscope. This allows for a detailed understanding of how the user is using their smartphone and a more accurate analysis of their behavior patterns. For example, if a user is watching a video with their smartphone held horizontally, the system can automatically launch a video application based on this information. This allows the sensor collection unit to collect detailed sensor information from the smartphone and more accurately understand the user's behavior patterns. Finally, the sensor collection unit stores the collected data in a cloud-based database, making it accessible to the analysis and launch units. This allows the sensor data collection unit to collect user behavior patterns in real time, improving the overall efficiency of the system.

[0032] The analysis unit analyzes information collected by the behavior collection unit and sensor collection unit to estimate which app the user wanted to open. For example, the analysis unit learns the user's behavior patterns based on the collected data and estimates which app the user wanted to open. Specifically, the analysis unit uses AI to analyze the collected data and learn the user's behavior patterns and the trends in the apps they use. For example, if a user frequently uses a particular app during a specific time period, that app can be automatically launched during that time period. The analysis unit can also analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. Furthermore, the analysis unit can analyze the trends in the apps the user uses and automatically launch a social networking app while the user is traveling by train. In this way, the analysis unit can analyze the user's behavior patterns in detail and estimate which app the user wanted to open with high accuracy. In addition, the analysis unit can also use past data and statistical information to analyze long-term behavior patterns and trends. For example, based on past usage data, it can predict app usage trends at specific times and locations and optimize future app launches. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual behavioral patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to analyze long-term behavioral patterns and detect anomalies, thereby improving the reliability and safety of the entire system.

[0033] The launch unit automatically launches the app estimated by the analysis unit. For example, by automatically launching the app estimated by the analysis unit, the launch unit can save the user the trouble of searching for the app. Specifically, the launch unit receives instructions from the analysis unit and automatically launches the appropriate app on the user's smartphone. For example, it can automatically launch a train route app while the user is on their way to the nearest station. The launch unit can also automatically launch a social networking app while the user is traveling on a train. Furthermore, the launch unit can automatically launch a convenience store app when the user is at a convenience store. This allows the launch unit to quickly and accurately launch apps according to the user's behavior patterns. In addition, the launch unit can collect user feedback and continuously improve the accuracy and effectiveness of app launches. For example, if a user does not wish to launch a particular app, the launch algorithm can be adjusted based on that feedback to optimize future app launches. The launch unit can also launch multiple apps simultaneously, allowing for flexible responses to user needs. For example, if a user wants to listen to music and use social networking apps while traveling, the launch unit can launch a music app and a social networking app simultaneously. This allows the startup unit to efficiently launch applications according to the user's behavior patterns, thereby improving user convenience.

[0034] The behavioral data collection unit can collect data such as the user's location information, the types of apps used, and the smartphone's opening and closing history. For example, the behavioral data collection unit can obtain the user's location information from GPS data or Wi-Fi location information. It can also analyze the types of apps used based on their frequency and duration of use. Furthermore, the behavioral data collection unit can collect the smartphone's opening and closing history from screen on / off history and lock / unlock history. By collecting data such as the user's location information and the types of apps used, the behavioral data collection unit can perform a more accurate analysis of behavioral patterns. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's location information into AI and have the AI ​​perform the location information analysis.

[0035] The sensor collection unit can collect sensor information from a smartphone. For example, the sensor collection unit can collect user location information using GPS data. The sensor collection unit can also understand the user's movement using an accelerometer. Furthermore, the sensor collection unit can detect the tilt and rotation of the smartphone using a gyroscope. As a result, the sensor collection unit can improve the accuracy of analyzing user behavior patterns by collecting sensor information from the smartphone. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input smartphone sensor information into an AI and have the AI ​​perform the analysis of the sensor information.

[0036] The analysis unit can learn user behavior patterns based on collected data and estimate which app the user wanted to open. For example, the analysis unit can analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. The analysis unit can also analyze the user's app usage trends and automatically launch a social networking app while the user is traveling by train. Furthermore, the analysis unit can learn user behavior patterns and automatically launch a convenience store app when the user is at a convenience store. In this way, the analysis unit can accurately estimate which app the user wanted to open by learning user behavior patterns based on collected data. 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 collected data into AI and have the AI ​​learn user behavior patterns.

[0037] The launch unit can automatically launch the application estimated by the analysis unit. For example, by automatically launching the application estimated by the analysis unit, the launch unit can save the user the trouble of searching for the application. For example, the launch unit can automatically launch a train route application while the user is on their way to the nearest station. The launch unit can also automatically launch a social networking service (SNS) application while the user is traveling on a train. Furthermore, the launch unit can automatically launch a convenience store application when the user is at a convenience store. In this way, the launch unit can save the user the trouble of searching for the application by automatically launching the application estimated by the analysis unit. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the application estimated by the analysis unit into the AI ​​and have the AI ​​execute the application launch.

[0038] The behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, based on location information and past app usage trends. For example, the behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, based on location information and past app usage trends. For example, the behavioral data collection unit can automatically launch the convenience store app based on location information when a user is in a convenience store. The behavioral data collection unit can also automatically launch the convenience store app when a user is in a convenience store, based on past app usage trends. Furthermore, the behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, by combining location information and past app usage trends. This enables smooth payment by automatically launching the convenience store app when a user is in a convenience store. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's location information and past app usage trends into the AI ​​and have the AI ​​execute the launch of the convenience store app.

[0039] The behavior collection unit can automatically launch a train route app based on location information and behavioral analysis when a user opens their smartphone while on their way to the nearest station. For example, the behavior collection unit can automatically launch a train route app based on location information when a user opens their smartphone while on their way to the nearest station. For example, the behavior collection unit can automatically launch a train route app based on location information when a user opens their smartphone while on their way to the nearest station. The behavior collection unit can also automatically launch a train route app based on behavioral analysis when a user opens their smartphone while on their way to the nearest station. Furthermore, the behavior collection unit can automatically launch a train route app by combining location information and behavioral analysis when a user opens their smartphone while on their way to the nearest station. This enables smooth route searching by automatically launching the train route app when a user is on their way to the nearest station. Some or all of the above processing in the behavior collection unit may be performed using AI, for example, or without AI. For example, the behavior collection unit can input the user's location information and behavioral analysis into AI and have the AI ​​launch the train route app.

[0040] The behavior collection unit can automatically launch a social networking service (SNS) app based on location information, behavioral analysis, and usage trends when a user opens their smartphone while traveling on a train. For example, the behavior collection unit can automatically launch an SNS app based on location information, behavioral analysis, and usage trends when a user opens their smartphone while traveling on a train. Furthermore, the behavior collection unit can automatically launch an SNS app based on behavioral analysis when a user opens their smartphone while traveling on a train. In addition, the behavior collection unit can automatically launch an SNS app based on usage trends when a user opens their smartphone while traveling on a train. This allows the behavior collection unit to automatically launch SNS apps while the user is traveling on a train, enabling smooth SNS use. Some or all of the above processing in the behavior collection unit may be performed using AI, or not. For example, the behavior collection unit can input the user's location information, behavioral analysis, and usage trends into an AI and have the AI ​​launch the SNS app.

[0041] The behavioral data collection unit can analyze a user's past behavioral patterns and select the optimal data collection method. For example, the behavioral data collection unit can select a data collection method based on the user's past usage history of frequently used apps. The behavioral data collection unit can also analyze a user's past location information and optimize the data collection method for specific locations. Furthermore, the behavioral data collection unit can adjust the timing of data collection based on the user's past smartphone opening and closing history. In this way, the behavioral data collection unit can select the optimal data collection method by analyzing a user's past behavioral patterns and improve the efficiency of data collection. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's past behavioral patterns into AI and have AI select the data collection method.

[0042] The behavioral data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the behavioral data collection unit can filter behavioral data based on the user's current activities. For example, the behavioral data collection unit can collect only highly relevant data based on the user's current activities. The behavioral data collection unit can also prioritize the collection of usage data for specific apps based on the user's areas of interest. Furthermore, the behavioral data collection unit can filter relevant data by considering the user's current location information. This allows the behavioral data collection unit to efficiently collect highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's current activities and areas of interest into AI and have the AI ​​perform the data filtering.

[0043] The behavioral data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, when a user is in a specific location, the behavioral data collection unit prioritizes the collection of data related to that location. The behavioral data collection unit can also collect highly relevant data by considering the distance from the user's current location. Furthermore, the behavioral data collection unit can also prioritize the collection of relevant data by referring to the user's past location information. As a result, the behavioral data collection unit improves the accuracy of data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0044] The behavioral data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the behavioral data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the behavioral data collection unit can collect relevant data based on information shared by the user on social media. The behavioral data collection unit can also collect relevant data by referring to the activities of the user's social media followers and friends. Furthermore, the behavioral data collection unit can analyze the content of a user's social media posts and collect relevant data. In this way, the behavioral data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's social media activity into AI and have AI perform the collection of relevant data.

[0045] The sensor collection unit can analyze the user's past sensor usage history and select the optimal collection method when collecting sensor information from a smartphone. For example, the sensor collection unit can select a collection method based on the user's past sensor usage history. The sensor collection unit can also analyze the user's past sensor usage patterns and determine the optimal collection timing. Furthermore, the sensor collection unit can select the type of data to collect based on the user's past sensor usage history. In this way, the sensor collection unit can select the optimal collection method by analyzing the user's past sensor usage history and improve the efficiency of data collection. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's past sensor usage history into AI and have AI select the collection method.

[0046] The sensor collection unit can filter sensor information based on the user's current activity status and environment. For example, the sensor collection unit can collect only highly relevant sensor information based on the user's current activity status. The sensor collection unit can also prioritize the collection of specific sensor information based on the user's current environment. Furthermore, the sensor collection unit can filter relevant sensor information by considering the user's current location information. This allows the sensor collection unit to efficiently collect highly relevant data by filtering sensor information based on the user's current activity status and environment. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's current activity status and environment into the AI ​​and have the AI ​​perform the filtering of sensor information.

[0047] The sensor collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting sensor information. For example, when the user is in a specific location, the sensor collection unit prioritizes the collection of sensor information related to that location. The sensor collection unit can also collect highly relevant sensor information by considering the distance from the user's current location. Furthermore, the sensor collection unit can also prioritize the collection of relevant sensor information by referring to the user's past location information. As a result, the accuracy of data collection is improved by the sensor collection unit prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant information.

[0048] The sensor collection unit can analyze the user's device usage history and collect relevant information when collecting sensor information. For example, the sensor collection unit can collect relevant sensor information based on the user's past device usage history. The sensor collection unit can also analyze the user's device usage patterns and collect the most appropriate sensor information. Furthermore, the sensor collection unit can select the type of sensor information to collect based on the user's device usage history. This allows the sensor collection unit to efficiently collect relevant information by analyzing the user's device usage history. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's device usage history into AI and have AI collect the relevant information.

[0049] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns when analyzing the collected data. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns when analyzing the collected data. For example, the analysis unit improves the accuracy of its analysis based on the user's past behavior patterns. The analysis unit can also optimize the analysis results by referring to the user's past location information. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's past app usage history. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior patterns into AI and have AI perform the improvement of analysis accuracy.

[0050] The analysis unit can customize the analysis method based on the user's current activity status and environment during analysis. For example, the analysis unit customizes the analysis method based on the user's current activity status during analysis. For example, the analysis unit customizes the analysis method based on the user's current activity status. The analysis unit can also prioritize the use of a specific analysis method based on the user's current environment. Furthermore, the analysis unit can adjust the analysis method considering the user's current location information. This allows the analysis unit to perform more appropriate analysis by customizing the analysis method based on the user's current activity status and environment. 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 user's current activity status and environment into the AI ​​and have the AI ​​perform the customization of the analysis method.

[0051] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy based on the user's current location. The analysis unit can also optimize the analysis results by referring to the user's past location information. Furthermore, the analysis unit can adjust the analysis method by considering the user's current location information. In this way, the analysis unit can improve analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into AI and have AI perform the improvement of analysis accuracy.

[0052] The analysis unit can analyze the user's social media activity and use the relevant data for analysis during the analysis process. For example, the analysis unit can analyze the user's social media activity and use the relevant data for analysis during the analysis process. For example, the analysis unit can use the content of the user's social media posts to use the relevant data for analysis. The analysis unit can also optimize the analysis results by referring to the activities of the user's social media followers and friends. Furthermore, the analysis unit can analyze the user's social media usage history and use the relevant data for analysis. In this way, the analysis unit can efficiently use relevant data for analysis by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into AI and have AI perform the analysis of the relevant data.

[0053] The launch unit can select the optimal launch method by referring to the user's past app usage history when launching an app estimated by the analysis unit. For example, the launch unit may prioritize launching apps that the user has frequently used in the past. The launch unit can also select the optimal launch method based on the user's past app usage history. Furthermore, the launch unit can analyze the user's past app usage patterns and determine the optimal launch timing. As a result, the launch unit can improve the efficiency of app launch by selecting the optimal launch method by referring to the user's past app usage history. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's past app usage history into AI and have AI select the optimal launch method.

[0054] The launch unit can customize the launch method based on the user's current activity status and environment when launching an app. For example, the launch unit can customize the launch method based on the user's current activity status when launching an app. For example, the launch unit can customize the launch method of an app based on the user's current activity status. The launch unit can also prioritize the launch method of a specific app based on the user's current environment. Furthermore, the launch unit can adjust the launch method of an app considering the user's current location information. This allows the launch unit to enable more appropriate app launches by customizing the launch method of an app based on the user's current activity status and environment. Some or all of the above processing in the launch unit may be performed using AI, for example, or not using AI. For example, the launch unit can input the user's current activity status and environment into the AI ​​and have the AI ​​perform the customization of the launch method.

[0055] The launch unit can prioritize launching apps that are highly relevant to the user's geographical location when launching an app. For example, when launching an app, the launch unit prioritizes launching apps that are highly relevant to the user's geographical location. For example, if the user is in a specific location, the launch unit prioritizes launching apps related to that location. The launch unit can also launch apps that are highly relevant considering the user's current location. Furthermore, the launch unit can prioritize launching relevant apps by referring to the user's past location information. As a result, the launch unit improves the accuracy of app launching by prioritizing the launch of apps that are highly relevant considering the user's geographical location. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's geographical location information into AI and have the AI ​​execute the launch of highly relevant apps.

[0056] The launch unit can analyze the user's device usage history and launch relevant apps when an app is launched. For example, the launch unit can analyze the user's device usage history and launch relevant apps when an app is launched. For example, the launch unit can launch relevant apps based on the user's past device usage history. The launch unit can also analyze the user's device usage patterns and launch the most suitable app. Furthermore, the launch unit can select the type of app to launch based on the user's device usage history. In this way, the launch unit can efficiently launch relevant apps by analyzing the user's device usage history. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's device usage history into AI and have AI execute the launch of relevant apps.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The behavioral data collection unit can analyze user voice commands and launch specific applications when collecting user behavior patterns. For example, if a user says, "I want to listen to music," the unit analyzes the voice command and automatically launches a music app. Similarly, if a user says, "I want to read the news," it can launch a news app. Furthermore, if a user says, "I want to know the weather," it can launch a weather app. This allows the behavioral data collection unit to quickly launch applications that match the user's intentions by analyzing their voice commands.

[0059] The sensor collection unit can also launch apps based on the user's health data when collecting sensor information from a smartphone. For example, if the user's heart rate is high, it can launch a relaxation app. It can also launch a fitness app if the user's step count exceeds a certain number. Furthermore, it can analyze the user's sleep data and launch an alarm app. In this way, the sensor collection unit can support the user's health management by launching appropriate apps based on the user's health data.

[0060] The analysis unit can also estimate which apps to use by referring to the user's calendar information when learning the user's behavior patterns. For example, if a meeting is scheduled in the user's calendar, it can launch a meeting app. Similarly, if there is a travel plan in the user's calendar, it can launch a travel app. Furthermore, if there is an exercise plan in the user's calendar, it can launch a fitness app. In this way, the analysis unit can estimate and launch apps appropriate to the user's schedule by referring to the user's calendar information.

[0061] The startup unit can also select an app based on the battery level of the user's device when launching an app estimated by the analysis unit. For example, if the battery level is low, it will prioritize launching a lightweight app. If the battery level is sufficient, it can launch an app that consumes a lot of resources. Furthermore, if the battery level is moderate, it can launch a well-balanced app. In this way, the startup unit can achieve efficient app launching by considering the battery level of the user's device.

[0062] The analysis unit can customize the analysis results by taking into account the user's hobbies and interests when analyzing the collected data. For example, if the user is interested in music, it will prioritize estimating music-related apps. Similarly, if the user is interested in sports, it can estimate sports-related apps. Furthermore, if the user is interested in travel, it can estimate travel-related apps. In this way, the analysis unit can estimate and launch the most suitable app for the user by considering their hobbies and interests.

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

[0064] Step 1: The behavior collection unit collects user behavior patterns. For example, it collects data such as the user's location, the types of apps used, and the history of opening and closing the smartphone. This allows the system to automatically launch the appropriate app depending on the user's situation, such as when they are at a convenience store, on their way to the nearest station, or traveling on a train. Step 2: The sensor acquisition unit collects sensor information from the smartphone. For example, it collects information such as GPS data, accelerometer, and gyroscope to determine the user's current location, movement status, and the tilt and rotation of the smartphone. Step 3: The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate which app the user wanted to open. For example, it learns the user's behavior patterns based on the collected data and automatically launches the appropriate app by analyzing the user's location information and behavior patterns. Step 4: The launch unit automatically launches the app estimated by the analysis unit. This saves the user the trouble of searching for the app. For example, it can automatically launch a train route app while the user is on their way to the nearest station, a social media app while traveling on a train, or a convenience store app when the user is at a convenience store.

[0065] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that automatically launches the app the user wanted to open as soon as they open their smartphone. This AI agent system collects the user's behavior patterns and smartphone sensor information, and based on the collected information, the AI ​​estimates the app the user wanted to open and automatically launches that app. This mechanism eliminates the need for the user to search for apps and provides an intuitive and comfortable smartphone operation experience. For example, the AI ​​agent system collects the user's behavior patterns and smartphone sensor information. In this process, it collects data such as location information, trends in the apps used, and the history of opening and closing the smartphone. For example, if the user is in a convenience store, the convenience store app can be automatically launched based on the location information and past app usage trends. Next, based on the collected information, the AI ​​estimates the app the user wanted to open. The AI ​​analyzes the collected data and learns the user's behavior patterns. For example, if the user opens their smartphone while on their way to the nearest station, the train route app can be automatically launched based on the location information and behavioral analysis. Furthermore, the app estimated by the AI ​​is automatically launched. For example, if the user opens their smartphone while traveling on a train, the SNS app can be automatically launched based on the location information, behavioral analysis, and trends in the apps used. This allows the user to use SNS smoothly. This system eliminates the need for users to search for apps, providing an intuitive and comfortable smartphone experience. It also improves user productivity and satisfaction in their daily lives and business activities. For example, payments at convenience stores become smoother, and route searches on the way to the nearest train station become more efficient. This allows the AI ​​agent system to automatically launch the app the user wanted to open, based on the user's behavior patterns and sensor information.

[0066] The AI ​​agent system according to this embodiment comprises a behavior collection unit, a sensor collection unit, an analysis unit, and an activation unit. The behavior collection unit collects the user's behavior patterns. The behavior collection unit collects data such as the user's location information, the types of apps used, and the history of opening and closing the smartphone. For example, if the user is in a convenience store, the behavior collection unit can automatically launch a convenience store app based on the location information and past app usage trends. The behavior collection unit can also automatically launch a train route app based on the location information and behavioral analysis if the user opens their smartphone while on their way to the nearest station. Furthermore, if the user opens their smartphone while traveling on a train, the behavior collection unit can automatically launch a social networking service (SNS) app based on the location information, behavioral analysis, and past app usage trends. The sensor collection unit collects sensor information from the smartphone. The sensor collection unit collects information such as GPS data, acceleration sensor data, and gyroscope sensor data. For example, the sensor collection unit collects the smartphone's location information to determine the user's current location. The sensor collection unit can also use the acceleration sensor to understand the user's movement status. Furthermore, the sensor collection unit can also detect the tilt and rotation of the smartphone using a gyroscope sensor. The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate the app the user wanted to open. For example, the analysis unit learns the user's behavior patterns based on the collected data and estimates the app the user wanted to open. For example, the analysis unit can analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. The analysis unit can also analyze the user's app usage trends and automatically launch a social networking app while the user is traveling by train. Furthermore, the analysis unit can learn the user's behavior patterns and automatically launch a convenience store app when the user is at a convenience store. The launch unit automatically launches the app estimated by the analysis unit. For example, by automatically launching the app estimated by the analysis unit, the launch unit can save the user the trouble of searching for the app. For example, the launch unit can automatically launch a train route app while the user is on their way to the nearest station.Furthermore, the activation unit can automatically launch a social networking app while the user is traveling on a train. Additionally, the activation unit can automatically launch a convenience store app when the user is at a convenience store. This allows the AI ​​agent system according to this embodiment to automatically launch the app the user wanted to open, based on the user's behavior patterns and sensor information.

[0067] The behavioral data collection unit collects user behavior patterns. For example, it collects data such as user location information, app usage trends, and smartphone opening / closing history. Specifically, the behavioral data collection unit meticulously records the places and times users visit daily, as well as the types and frequency of apps they use. For example, if a user heads to their nearest station at a specific time every morning, the unit can automatically launch a train route app based on their location information and past behavior patterns. Similarly, if a user tends to visit a specific store on certain days of the week, the unit can automatically launch the app for that store. Furthermore, the behavioral data collection unit can predict which apps a user is most likely to open next, based on the time and location where they opened their smartphone and their app usage history. For example, if a user frequently uses a specific restaurant app during lunchtime, the unit can automatically launch that app during that time. This allows the behavioral data collection unit to gain a detailed understanding of user behavior patterns and support app launches tailored to user needs. Additionally, the behavioral data collection unit stores the collected data in a cloud-based database, making it accessible to the analysis and launch units. This allows the behavioral data collection unit to collect user behavior patterns in real time, improving the overall system efficiency.

[0068] The sensor collection unit collects sensor information from the smartphone. This includes, for example, GPS data, accelerometer data, and gyroscope data. Specifically, the sensor collection unit collects smartphone location information in real time to accurately determine the user's current location. For example, if the user is moving, it can track the user's route based on GPS data and predict the time it will take to reach their destination. The sensor collection unit can also use the accelerometer to understand the user's movement. For example, it can determine whether the user is walking, in a car, or on a train, and support the launch of appropriate applications. Furthermore, the sensor collection unit can detect the smartphone's tilt and rotation using the gyroscope. This allows for a detailed understanding of how the user is using their smartphone and a more accurate analysis of their behavior patterns. For example, if a user is watching a video with their smartphone held horizontally, the system can automatically launch a video application based on this information. This allows the sensor collection unit to collect detailed sensor information from the smartphone and more accurately understand the user's behavior patterns. Finally, the sensor collection unit stores the collected data in a cloud-based database, making it accessible to the analysis and launch units. This allows the sensor data collection unit to collect user behavior patterns in real time, improving the overall efficiency of the system.

[0069] The analysis unit analyzes information collected by the behavior collection unit and sensor collection unit to estimate which app the user wanted to open. For example, the analysis unit learns the user's behavior patterns based on the collected data and estimates which app the user wanted to open. Specifically, the analysis unit uses AI to analyze the collected data and learn the user's behavior patterns and the trends in the apps they use. For example, if a user frequently uses a particular app during a specific time period, that app can be automatically launched during that time period. The analysis unit can also analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. Furthermore, the analysis unit can analyze the trends in the apps the user uses and automatically launch a social networking app while the user is traveling by train. In this way, the analysis unit can analyze the user's behavior patterns in detail and estimate which app the user wanted to open with high accuracy. In addition, the analysis unit can also use past data and statistical information to analyze long-term behavior patterns and trends. For example, based on past usage data, it can predict app usage trends at specific times and locations and optimize future app launches. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual behavioral patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to analyze long-term behavioral patterns and detect anomalies, thereby improving the reliability and safety of the entire system.

[0070] The launch unit automatically launches the app estimated by the analysis unit. For example, by automatically launching the app estimated by the analysis unit, the launch unit can save the user the trouble of searching for the app. Specifically, the launch unit receives instructions from the analysis unit and automatically launches the appropriate app on the user's smartphone. For example, it can automatically launch a train route app while the user is on their way to the nearest station. The launch unit can also automatically launch a social networking app while the user is traveling on a train. Furthermore, the launch unit can automatically launch a convenience store app when the user is at a convenience store. This allows the launch unit to quickly and accurately launch apps according to the user's behavior patterns. In addition, the launch unit can collect user feedback and continuously improve the accuracy and effectiveness of app launches. For example, if a user does not wish to launch a particular app, the launch algorithm can be adjusted based on that feedback to optimize future app launches. The launch unit can also launch multiple apps simultaneously, allowing for flexible responses to user needs. For example, if a user wants to listen to music and use social networking apps while traveling, the launch unit can launch a music app and a social networking app simultaneously. This allows the startup unit to efficiently launch applications according to the user's behavior patterns, thereby improving user convenience.

[0071] The behavioral data collection unit can collect data such as the user's location information, the types of apps used, and the smartphone's opening and closing history. For example, the behavioral data collection unit can obtain the user's location information from GPS data or Wi-Fi location information. It can also analyze the types of apps used based on their frequency and duration of use. Furthermore, the behavioral data collection unit can collect the smartphone's opening and closing history from screen on / off history and lock / unlock history. By collecting data such as the user's location information and the types of apps used, the behavioral data collection unit can perform a more accurate analysis of behavioral patterns. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's location information into AI and have the AI ​​perform the location information analysis.

[0072] The sensor collection unit can collect sensor information from a smartphone. For example, the sensor collection unit can collect user location information using GPS data. The sensor collection unit can also understand the user's movement using an accelerometer. Furthermore, the sensor collection unit can detect the tilt and rotation of the smartphone using a gyroscope. As a result, the sensor collection unit can improve the accuracy of analyzing user behavior patterns by collecting sensor information from the smartphone. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input smartphone sensor information into an AI and have the AI ​​perform the analysis of the sensor information.

[0073] The analysis unit can learn user behavior patterns based on collected data and estimate which app the user wanted to open. For example, the analysis unit can analyze the user's location information and behavior patterns and automatically launch a train route app while the user is on their way to the nearest station. The analysis unit can also analyze the user's app usage trends and automatically launch a social networking app while the user is traveling by train. Furthermore, the analysis unit can learn user behavior patterns and automatically launch a convenience store app when the user is at a convenience store. In this way, the analysis unit can accurately estimate which app the user wanted to open by learning user behavior patterns based on collected data. 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 collected data into AI and have the AI ​​learn user behavior patterns.

[0074] The launch unit can automatically launch the application estimated by the analysis unit. For example, by automatically launching the application estimated by the analysis unit, the launch unit can save the user the trouble of searching for the application. For example, the launch unit can automatically launch a train route application while the user is on their way to the nearest station. The launch unit can also automatically launch a social networking service (SNS) application while the user is traveling on a train. Furthermore, the launch unit can automatically launch a convenience store application when the user is at a convenience store. In this way, the launch unit can save the user the trouble of searching for the application by automatically launching the application estimated by the analysis unit. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the application estimated by the analysis unit into the AI ​​and have the AI ​​execute the application launch.

[0075] The behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, based on location information and past app usage trends. For example, the behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, based on location information and past app usage trends. For example, the behavioral data collection unit can automatically launch the convenience store app based on location information when a user is in a convenience store. The behavioral data collection unit can also automatically launch the convenience store app when a user is in a convenience store, based on past app usage trends. Furthermore, the behavioral data collection unit can automatically launch the convenience store app when a user is in a convenience store, by combining location information and past app usage trends. This enables smooth payment by automatically launching the convenience store app when a user is in a convenience store. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's location information and past app usage trends into the AI ​​and have the AI ​​execute the launch of the convenience store app.

[0076] The behavior collection unit can automatically launch a train route app based on location information and behavioral analysis when a user opens their smartphone while on their way to the nearest station. For example, the behavior collection unit can automatically launch a train route app based on location information when a user opens their smartphone while on their way to the nearest station. For example, the behavior collection unit can automatically launch a train route app based on location information when a user opens their smartphone while on their way to the nearest station. The behavior collection unit can also automatically launch a train route app based on behavioral analysis when a user opens their smartphone while on their way to the nearest station. Furthermore, the behavior collection unit can automatically launch a train route app by combining location information and behavioral analysis when a user opens their smartphone while on their way to the nearest station. This enables smooth route searching by automatically launching the train route app when a user is on their way to the nearest station. Some or all of the above processing in the behavior collection unit may be performed using AI, for example, or without AI. For example, the behavior collection unit can input the user's location information and behavioral analysis into AI and have the AI ​​launch the train route app.

[0077] The behavior collection unit can automatically launch a social networking service (SNS) app based on location information, behavioral analysis, and usage trends when a user opens their smartphone while traveling on a train. For example, the behavior collection unit can automatically launch an SNS app based on location information, behavioral analysis, and usage trends when a user opens their smartphone while traveling on a train. Furthermore, the behavior collection unit can automatically launch an SNS app based on behavioral analysis when a user opens their smartphone while traveling on a train. In addition, the behavior collection unit can automatically launch an SNS app based on usage trends when a user opens their smartphone while traveling on a train. This allows the behavior collection unit to automatically launch SNS apps while the user is traveling on a train, enabling smooth SNS use. Some or all of the above processing in the behavior collection unit may be performed using AI, or not. For example, the behavior collection unit can input the user's location information, behavioral analysis, and usage trends into an AI and have the AI ​​launch the SNS app.

[0078] The behavioral data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the behavioral data collection unit can delay the data collection timing to reduce the user's burden. Conversely, if the user is relaxed, the behavioral data collection unit can speed up the data collection timing to collect more data. Furthermore, if the user is in a hurry, the behavioral data collection unit can optimize the data collection timing to quickly collect only the necessary data. In this way, by adjusting the timing of behavioral data collection based on the user's emotions, the behavioral data collection unit can reduce the user's burden and enable more appropriate data collection. 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 behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0079] The behavioral data collection unit can analyze a user's past behavioral patterns and select the optimal data collection method. For example, the behavioral data collection unit can select a data collection method based on the user's past usage history of frequently used apps. The behavioral data collection unit can also analyze a user's past location information and optimize the data collection method for specific locations. Furthermore, the behavioral data collection unit can adjust the timing of data collection based on the user's past smartphone opening and closing history. In this way, the behavioral data collection unit can select the optimal data collection method by analyzing a user's past behavioral patterns and improve the efficiency of data collection. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's past behavioral patterns into AI and have AI select the data collection method.

[0080] The behavioral data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the behavioral data collection unit can filter behavioral data based on the user's current activities. For example, the behavioral data collection unit can collect only highly relevant data based on the user's current activities. The behavioral data collection unit can also prioritize the collection of usage data for specific apps based on the user's areas of interest. Furthermore, the behavioral data collection unit can filter relevant data by considering the user's current location information. This allows the behavioral data collection unit to efficiently collect highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's current activities and areas of interest into AI and have the AI ​​perform the data filtering.

[0081] The behavioral data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated emotions. For example, if the user is stressed, the behavioral data collection unit will prioritize collecting high-priority data. It can also prioritize collecting detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the behavioral data collection unit will prioritize collecting only the essential data. This allows the behavioral data collection unit to prioritize collecting important data by determining the priority of behavioral data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the behavioral data collection unit may be performed using AI or not. For example, the behavioral data collection unit can input user emotion data into an AI and have the AI ​​perform the priority determination.

[0082] The behavioral data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, when a user is in a specific location, the behavioral data collection unit prioritizes the collection of data related to that location. The behavioral data collection unit can also collect highly relevant data by considering the distance from the user's current location. Furthermore, the behavioral data collection unit can also prioritize the collection of relevant data by referring to the user's past location information. As a result, the behavioral data collection unit improves the accuracy of data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0083] The behavioral data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the behavioral data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the behavioral data collection unit can collect relevant data based on information shared by the user on social media. The behavioral data collection unit can also collect relevant data by referring to the activities of the user's social media followers and friends. Furthermore, the behavioral data collection unit can analyze the content of a user's social media posts and collect relevant data. In this way, the behavioral data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the behavioral data collection unit may be performed using AI, for example, or without AI. For example, the behavioral data collection unit can input the user's social media activity into AI and have AI perform the collection of relevant data.

[0084] The sensor data collection unit can estimate the user's emotions and adjust the timing of sensor data collection based on the estimated emotions. For example, if the user is stressed, the sensor data collection unit can delay the data collection timing to reduce the user's burden. Conversely, if the user is relaxed, the sensor data collection unit can advance the data collection timing to collect more data. Furthermore, if the user is in a hurry, the sensor data collection unit can optimize the data collection timing to quickly collect only the necessary data. In this way, by adjusting the timing of sensor data collection based on the user's emotions, the sensor data collection unit can reduce the user's burden and enable more appropriate data collection. 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 sensor data collection unit may be performed using AI, for example, or without AI. For example, the sensor data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0085] The sensor collection unit can analyze the user's past sensor usage history and select the optimal collection method when collecting sensor information from a smartphone. For example, the sensor collection unit can select a collection method based on the user's past sensor usage history. The sensor collection unit can also analyze the user's past sensor usage patterns and determine the optimal collection timing. Furthermore, the sensor collection unit can select the type of data to collect based on the user's past sensor usage history. In this way, the sensor collection unit can select the optimal collection method by analyzing the user's past sensor usage history and improve the efficiency of data collection. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's past sensor usage history into AI and have AI select the collection method.

[0086] The sensor collection unit can filter sensor information based on the user's current activity status and environment. For example, the sensor collection unit can collect only highly relevant sensor information based on the user's current activity status. The sensor collection unit can also prioritize the collection of specific sensor information based on the user's current environment. Furthermore, the sensor collection unit can filter relevant sensor information by considering the user's current location information. This allows the sensor collection unit to efficiently collect highly relevant data by filtering sensor information based on the user's current activity status and environment. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's current activity status and environment into the AI ​​and have the AI ​​perform the filtering of sensor information.

[0087] The sensor collection unit can estimate the user's emotions and determine the priority of sensor information to collect based on the estimated user emotions. For example, if the user is stressed, the sensor collection unit will prioritize collecting high-priority sensor information. If the user is relaxed, the sensor collection unit can also prioritize collecting detailed sensor information. Furthermore, if the user is in a hurry, the sensor collection unit can prioritize collecting only the minimum necessary sensor information. This allows the sensor collection unit to prioritize the collection of important data by prioritizing sensor information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor collection unit may be performed using AI, or not. For example, the sensor collection unit can input user emotion data into an AI and have the AI ​​perform the priority determination.

[0088] The sensor collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting sensor information. For example, when the user is in a specific location, the sensor collection unit prioritizes the collection of sensor information related to that location. The sensor collection unit can also collect highly relevant sensor information by considering the distance from the user's current location. Furthermore, the sensor collection unit can also prioritize the collection of relevant sensor information by referring to the user's past location information. As a result, the accuracy of data collection is improved by the sensor collection unit prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant information.

[0089] The sensor collection unit can analyze the user's device usage history and collect relevant information when collecting sensor information. For example, the sensor collection unit can collect relevant sensor information based on the user's past device usage history. The sensor collection unit can also analyze the user's device usage patterns and collect the most appropriate sensor information. Furthermore, the sensor collection unit can select the type of sensor information to collect based on the user's device usage history. This allows the sensor collection unit to efficiently collect relevant information by analyzing the user's device usage history. Some or all of the above processing in the sensor collection unit may be performed using AI, for example, or without AI. For example, the sensor collection unit can input the user's device usage history into AI and have AI collect the relevant information.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm for faster analysis. If the user is relaxed, the analysis unit can adjust the algorithm for more detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can optimize the algorithm to analyze only the minimum necessary data. This enables the analysis unit to perform fast and appropriate analysis by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the analysis algorithm.

[0091] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns when analyzing the collected data. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns when analyzing the collected data. For example, the analysis unit improves the accuracy of its analysis based on the user's past behavior patterns. The analysis unit can also optimize the analysis results by referring to the user's past location information. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's past app usage history. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior patterns into AI and have AI perform the improvement of analysis accuracy.

[0092] The analysis unit can customize the analysis method based on the user's current activity status and environment during analysis. For example, the analysis unit customizes the analysis method based on the user's current activity status during analysis. For example, the analysis unit customizes the analysis method based on the user's current activity status. The analysis unit can also prioritize the use of a specific analysis method based on the user's current environment. Furthermore, the analysis unit can adjust the analysis method considering the user's current location information. This allows the analysis unit to perform more appropriate analysis by customizing the analysis method based on the user's current activity status and environment. 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 user's current activity status and environment into the AI ​​and have the AI ​​perform the customization of the analysis method.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-read display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can enable a highly visible display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0094] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy based on the user's current location. The analysis unit can also optimize the analysis results by referring to the user's past location information. Furthermore, the analysis unit can adjust the analysis method by considering the user's current location information. In this way, the analysis unit can improve analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into AI and have AI perform the improvement of analysis accuracy.

[0095] The analysis unit can analyze the user's social media activity and use the relevant data for analysis during the analysis process. For example, the analysis unit can analyze the user's social media activity and use the relevant data for analysis during the analysis process. For example, the analysis unit can use the content of the user's social media posts to use the relevant data for analysis. The analysis unit can also optimize the analysis results by referring to the activities of the user's social media followers and friends. Furthermore, the analysis unit can analyze the user's social media usage history and use the relevant data for analysis. In this way, the analysis unit can efficiently use relevant data for analysis by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into AI and have AI perform the analysis of the relevant data.

[0096] The launch unit can estimate the user's emotions and adjust the timing of app launches based on those emotions. For example, if the user is stressed, the launch unit can delay the app launch to reduce the user's burden. Conversely, if the user is relaxed, the launch unit can speed up the app launch to allow access to more apps. Furthermore, if the user is in a hurry, the launch unit can quickly launch only the necessary apps. In this way, by adjusting the app launch timing based on the user's emotions, the launch unit reduces the user's burden and enables more appropriate app launches. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the activation unit can input user emotion data into the AI ​​and have the AI ​​adjust the activation timing.

[0097] The launch unit can select the optimal launch method by referring to the user's past app usage history when launching an app estimated by the analysis unit. For example, the launch unit may prioritize launching apps that the user has frequently used in the past. The launch unit can also select the optimal launch method based on the user's past app usage history. Furthermore, the launch unit can analyze the user's past app usage patterns and determine the optimal launch timing. As a result, the launch unit can improve the efficiency of app launch by selecting the optimal launch method by referring to the user's past app usage history. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's past app usage history into AI and have AI select the optimal launch method.

[0098] The launch unit can customize the launch method based on the user's current activity status and environment when launching an app. For example, the launch unit can customize the launch method based on the user's current activity status when launching an app. For example, the launch unit can customize the launch method of an app based on the user's current activity status. The launch unit can also prioritize the launch method of a specific app based on the user's current environment. Furthermore, the launch unit can adjust the launch method of an app considering the user's current location information. This allows the launch unit to enable more appropriate app launches by customizing the launch method of an app based on the user's current activity status and environment. Some or all of the above processing in the launch unit may be performed using AI, for example, or not using AI. For example, the launch unit can input the user's current activity status and environment into the AI ​​and have the AI ​​perform the customization of the launch method.

[0099] The launch unit can estimate the user's emotions and determine the priority of apps to launch based on the estimated emotions. For example, if the user is stressed, the launch unit may prioritize launching high-priority apps. If the user is relaxed, the launch unit may also prioritize launching apps containing detailed information. Furthermore, if the user is in a hurry, the launch unit may prioritize launching only the essential apps. This allows the launch unit to prioritize launching important apps by determining the priority of apps based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the launch unit may be performed using AI or not. For example, the launch unit can input user emotion data into an AI and have the AI ​​determine the app priorities.

[0100] The launch unit can prioritize launching apps that are highly relevant to the user's geographical location when launching an app. For example, when launching an app, the launch unit prioritizes launching apps that are highly relevant to the user's geographical location. For example, if the user is in a specific location, the launch unit prioritizes launching apps related to that location. The launch unit can also launch apps that are highly relevant considering the user's current location. Furthermore, the launch unit can prioritize launching relevant apps by referring to the user's past location information. As a result, the launch unit improves the accuracy of app launching by prioritizing the launch of apps that are highly relevant considering the user's geographical location. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's geographical location information into AI and have the AI ​​execute the launch of highly relevant apps.

[0101] The launch unit can analyze the user's device usage history and launch relevant apps when an app is launched. For example, the launch unit can analyze the user's device usage history and launch relevant apps when an app is launched. For example, the launch unit can launch relevant apps based on the user's past device usage history. The launch unit can also analyze the user's device usage patterns and launch the most suitable app. Furthermore, the launch unit can select the type of app to launch based on the user's device usage history. In this way, the launch unit can efficiently launch relevant apps by analyzing the user's device usage history. Some or all of the above processing in the launch unit may be performed using AI, for example, or without AI. For example, the launch unit can input the user's device usage history into AI and have AI execute the launch of relevant apps.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The behavioral data collection unit can analyze user voice commands and launch specific applications when collecting user behavior patterns. For example, if a user says, "I want to listen to music," the unit analyzes the voice command and automatically launches a music app. Similarly, if a user says, "I want to read the news," it can launch a news app. Furthermore, if a user says, "I want to know the weather," it can launch a weather app. This allows the behavioral data collection unit to quickly launch applications that match the user's intentions by analyzing their voice commands.

[0104] The sensor collection unit can also launch apps based on the user's health data when collecting sensor information from a smartphone. For example, if the user's heart rate is high, it can launch a relaxation app. It can also launch a fitness app if the user's step count exceeds a certain number. Furthermore, it can analyze the user's sleep data and launch an alarm app. In this way, the sensor collection unit can support the user's health management by launching appropriate apps based on the user's health data.

[0105] The analysis unit can also estimate which apps to use by referring to the user's calendar information when learning the user's behavior patterns. For example, if a meeting is scheduled in the user's calendar, it can launch a meeting app. Similarly, if there is a travel plan in the user's calendar, it can launch a travel app. Furthermore, if there is an exercise plan in the user's calendar, it can launch a fitness app. In this way, the analysis unit can estimate and launch apps appropriate to the user's schedule by referring to the user's calendar information.

[0106] The startup unit can also select an app based on the battery level of the user's device when launching an app estimated by the analysis unit. For example, if the battery level is low, it will prioritize launching a lightweight app. If the battery level is sufficient, it can launch an app that consumes a lot of resources. Furthermore, if the battery level is moderate, it can launch a well-balanced app. In this way, the startup unit can achieve efficient app launching by considering the battery level of the user's device.

[0107] The behavioral data collection unit can estimate the user's emotions and recommend apps based on those estimates. For example, if the user is stressed, it can recommend a relaxation app. If the user is relaxed, it can recommend an entertainment app. Furthermore, if the user is in a hurry, it can recommend an efficient task management app. In this way, the behavioral data collection unit can support app usage that meets the user's needs by recommending appropriate apps based on the user's emotions.

[0108] The sensor data collection unit can estimate the user's emotions and adjust the frequency of sensor data collection based on those emotions. For example, if the user is stressed, the collection frequency can be lowered to reduce the user's burden. Conversely, if the user is relaxed, the collection frequency can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only the minimum necessary data can be quickly collected. In this way, the sensor data collection unit can reduce the user's burden and achieve appropriate data collection by adjusting the frequency of sensor data collection based on the user's emotions.

[0109] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand notification method. If the user is relaxed, it can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise notification method that gets straight to the point. In this way, the analysis unit can provide the user with the most optimal information by adjusting the notification method of the analysis results based on the user's emotions.

[0110] The startup unit can estimate the user's emotions and adjust the order in which apps are launched based on those emotions. For example, if the user is stressed, a relaxation app might be launched first. If the user is relaxed, an entertainment app might be launched first. Furthermore, if the user is in a hurry, an efficient task management app might be launched first. In this way, the startup unit can support app usage tailored to the user's needs by adjusting the order in which apps are launched based on the user's emotions.

[0111] The behavioral data collection unit can estimate the user's emotions and customize the method of collecting behavioral data based on those emotions. For example, if the user is stressed, the collection method can be simplified to reduce the user's burden. If the user is relaxed, a method for collecting detailed data can be adopted. Furthermore, if the user is in a hurry, a method for quickly collecting only the minimum necessary data can be adopted. In this way, the behavioral data collection unit can reduce the user's burden and achieve appropriate data collection by customizing the method of collecting behavioral data based on the user's emotions.

[0112] The analysis unit can customize the analysis results by taking into account the user's hobbies and interests when analyzing the collected data. For example, if the user is interested in music, it will prioritize estimating music-related apps. Similarly, if the user is interested in sports, it can estimate sports-related apps. Furthermore, if the user is interested in travel, it can estimate travel-related apps. In this way, the analysis unit can estimate and launch the most suitable app for the user by considering their hobbies and interests.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The behavior collection unit collects user behavior patterns. For example, it collects data such as the user's location, the types of apps used, and the history of opening and closing the smartphone. This allows the system to automatically launch the appropriate app depending on the user's situation, such as when they are at a convenience store, on their way to the nearest station, or traveling on a train. Step 2: The sensor acquisition unit collects sensor information from the smartphone. For example, it collects information such as GPS data, accelerometer, and gyroscope to determine the user's current location, movement status, and the tilt and rotation of the smartphone. Step 3: The analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate which app the user wanted to open. For example, it learns the user's behavior patterns based on the collected data and automatically launches the appropriate app by analyzing the user's location information and behavior patterns. Step 4: The launch unit automatically launches the app estimated by the analysis unit. This saves the user the trouble of searching for the app. For example, it can automatically launch a train route app while the user is on their way to the nearest station, a social media app while traveling on a train, or a convenience store app when the user is at a convenience store.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the behavior collection unit, sensor collection unit, analysis unit, and activation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the behavior collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's location information and usage trends of applications. The sensor collection unit collects GPS data and acceleration sensor information using the sensors of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to estimate the application the user wanted to open. The activation unit is implemented by the control unit 46A of the smart device 14 and automatically activates the estimated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the behavior collection unit, sensor collection unit, analysis unit, and activation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the behavior collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's location information and usage trends of applications. The sensor collection unit collects GPS data and acceleration sensor information using the sensors of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to estimate the application the user wanted to open. The activation unit is implemented by the control unit 46A of the smart glasses 214 and automatically activates the estimated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the behavior collection unit, sensor collection unit, analysis unit, and activation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the behavior collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's location information and usage trends of applications. The sensor collection unit collects GPS data and acceleration sensor information using the sensors of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to estimate the application the user wanted to open. The activation unit is implemented by the control unit 46A of the headset terminal 314 and automatically launches the estimated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the behavior collection unit, sensor collection unit, analysis unit, and activation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the behavior collection unit is implemented by the control unit 46A of the robot 414 and collects the user's location information and usage trends of applications. The sensor collection unit collects GPS data and acceleration sensor information using the sensors of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to estimate the application the user wanted to open. The activation unit is implemented by the control unit 46A of the robot 414 and automatically activates the estimated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A behavioral data collection unit that collects user behavior patterns, A sensor collection unit that collects sensor information from a smartphone, An analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate the app the user wanted to open, The system includes a startup unit that automatically launches the application estimated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned behavioral data collection unit is The system collects data such as the user's location, the apps they tend to use, and their smartphone's opening and closing history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sensor collection unit is Collecting sensor information from smartphones The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the collected data, the system learns user behavior patterns and estimates which app the user wanted to open. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned startup unit is The application estimated by the analysis unit will be launched automatically. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned behavioral data collection unit is If a user is in a convenience store, the convenience store app will automatically launch based on their location and past app usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned behavioral data collection unit is When a user opens their smartphone while on their way to the nearest station, the train route app will automatically launch based on location information and behavioral analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned behavioral data collection unit is When a user opens their smartphone while traveling on a train, the system automatically launches a social media app based on location information, behavioral analysis, and usage trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned behavioral data collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned behavioral data collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned behavioral data collection unit is When collecting behavioral data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned behavioral data collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned behavioral data collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned behavioral data collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned sensor 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 16) The aforementioned sensor collection unit is When collecting sensor information from smartphones, the system analyzes the user's past sensor usage history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned sensor collection unit is When collecting sensor information, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned sensor collection unit is It estimates the user's emotions and determines the priority of sensor information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned sensor collection unit is When collecting sensor data, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned sensor collection unit is When collecting sensor information, the system analyzes the user's device usage history and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing collected data, we improve analysis accuracy by referring to users' past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant data is used for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned startup unit is It estimates the user's emotions and adjusts the app launch timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned startup unit is When launching an app estimated by the analysis unit, the system selects the optimal launch method by referring to the user's past app usage history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned startup unit is When launching the app, the launch method is customized based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned startup unit is It estimates the user's emotions and determines the priority of which apps to launch based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned startup unit is When an app is launched, it prioritizes launching the most relevant app based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned startup unit is When the app is launched, it analyzes the user's device usage history and launches relevant apps. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 behavioral data collection unit that collects user behavior patterns, A sensor collection unit that collects sensor information from a smartphone, An analysis unit analyzes the information collected by the behavior collection unit and the sensor collection unit to estimate the app the user wanted to open, The system includes a startup unit that automatically launches the application estimated by the analysis unit. A system characterized by the following features.

2. The aforementioned behavioral data collection unit is The system collects data such as the user's location, the apps they tend to use, and their smartphone's opening and closing history. The system according to feature 1.

3. The aforementioned sensor collection unit is Collecting sensor information from smartphones The system according to feature 1.

4. The aforementioned analysis unit, Based on the collected data, the system learns user behavior patterns and estimates which app the user wanted to open. The system according to feature 1.

5. The aforementioned startup unit is The application estimated by the aforementioned analysis unit is automatically launched. The system according to feature 1.

6. The aforementioned behavioral data collection unit is If a user is in a convenience store, the convenience store app will automatically launch based on their location and past app usage patterns. The system according to feature 1.

7. The aforementioned behavioral data collection unit is When a user opens their smartphone while on their way to the nearest station, the train route app will automatically launch based on location information and behavioral analysis. The system according to feature 1.

8. The aforementioned behavioral data collection unit is When a user opens their smartphone while traveling on a train, the system automatically launches a social media app based on location information, behavioral analysis, and usage trends. The system according to feature 1.

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