Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user's normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.

Embodiments are described for a pattern-based control system that learns and applies device usage patterns for identifying and disabling devices exhibiting abnormal usage patterns. The system can learn a user's normal usage pattern or can learn abnormal usage patterns, such as a typical usage pattern for a stolen device. This learning can include human or algorithmic identification of particular sets of usage conditions (e.g., locations, changes in settings, personal data access events, application events, IMU data, etc.) or training a machine learning model to identify usage condition combinations or sequences. Constraints (e.g., particular times or locations) can specify circumstances where abnormal pattern matching is enabled or disabled. Upon identifying an abnormal usage pattern, the system can disable the device, e.g., by permanently destroying a physical component, semi-permanently disabling a component, or through a software lock or data encryption.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing hotword recognition and passive assistance are disclosed. In one aspect, a method includes the actions of receiving, by a computing device that is operating in a low-power mode and that includes a display that displays a graphical interface while the computing device is in the low-power mode and that is configured to exit the low-power mode in response to detecting a first hotword, audio data corresponding to an utterance. The method further includes determining that the audio data includes a second, different hotword. The method further includes obtaining a transcription of the utterance by performing speech recognition on the audio data. The method further includes generating an additional user interface. The method further includes providing, for output on the display, the additional graphical interface.

Devices, systems, and methods for monitoring livestock can include a mobile platform arranged to present the user with an optimal breeding period based on mount activity of the livestock assets. Monitoring optimal breeding periods can increase likelihood of healthy breeding, including through real-time information.

Devices, systems, and methods for monitoring livestock can include a mobile platform arranged to present the user with an optimal breeding period based on mount activity of the livestock assets. Monitoring optimal breeding periods can increase likelihood of healthy breeding, including through real-time information.

Reduced-size user interfaces for providing weather information are disclosed. At an electronic device with a touch-sensitive display, indications of a location and a temperature at the location may be displayed. In some examples, a user may provide input through a touch on the touch-sensitive display and/or through a rotation of a rotatable input mechanism to display additional weather information, such as weather information for another location, another temperature, another time, and so forth. In some examples, the device may obtain data representing an upcoming activity, determine whether the activity is to begin within a threshold amount of time, and display weather information based on the upcoming activity. In some examples, the device may display an affordance at a position to indicate the time of day for which a weather condition is provided.

Disclosed are an apparatus and control method for recommending an application based on a recognized situation of a user of an electronic device by executing an artificial intelligence (AI) algorithm and/or machine learning algorithm in a 5G environment connected for the Internet of Things and a driving method thereof. The apparatus control method according to an embodiment of the present disclosure includes applying context information including at least one of environmental information collected through a sensor of the electronic device or a network, or usage information generated by the use of the electronic device to a machine learning based first learning model in response to a user input, and displaying, on a display, a first shortcut related to an application determined on the basis of a result of applying the context information to the first learning model and displaying, on the display, a second shortcut related to a preset application.

Disclosed are an apparatus and control method for recommending an application based on a recognized situation of a user of an electronic device by executing an artificial intelligence (AI) algorithm and/or machine learning algorithm in a 5G environment connected for the Internet of Things and a driving method thereof. The apparatus control method according to an embodiment of the present disclosure includes applying context information including at least one of environmental information collected through a sensor of the electronic device or a network, or usage information generated by the use of the electronic device to a machine learning based first learning model in response to a user input, and displaying, on a display, a first shortcut related to an application determined on the basis of a result of applying the context information to the first learning model and displaying, on the display, a second shortcut related to a preset application.

A method for enabling a task management interface includes receiving an instruction for enabling the task management interface, displaying the task management interface in response to the instruction for enabling the task management interface, where the task management interface includes a preview interface of at least one application program and an icon corresponding to at least one function of the application program, receiving an operation instruction for the icon, and switching the application program corresponding to the icon to a foreground and executing the function in response to the operation instruction.

Methods and apparatuses for arranging applications for a mobile device are described, in which the mobile device includes a set of applications. The mobile device may identify certain applications that a user of the mobile device is likely to activate, based on a particular environmental setting. The environmental setting may include a present location determined by a GPS, a present date and time, a present day, or the like. The mobile device may display graphical elements (e.g., icons) corresponding to the identified applications on a screen of the mobile device such that the user may conveniently activate the identified applications. In some embodiments, the mobile device may sort individual applications of the set based on quantities and/or lengths of activations to identify such applications. The mobile device may enlarge the icons by a predetermined factor before displaying them on the screen.