Abstract: Systems and methods for configuring hardware resources of an electronic device based on operating characteristics of the electronic device are provided. In one embodiment, a processor may configure the hardware resources of the electronic device that equally weighs decisions made by a machine learning (ML) engine and a finite state machine (FSM) engine. In another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the ML engine compared to decisions made by the FSM engine. In yet another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the FSM engine compared to decisions made by the ML engine.

Abstract: The localization of a vehicle is determined using less expensive and computationally robust equipment compared to conventional methods. Localization is determined by estimating the position of a vehicle relative to a map of the environment, and the process thereof includes using a map of the surrounding environment of the vehicle, a model of the motion of the frame of reference of the vehicle (e.g., ego-motion), sensor data from the surrounding environment, and a process to match sensory data to the map. Localization also includes a process to estimate the position based on the sensor data, the motion of the frame of reference of the vehicle, and/or the map. Such methods and systems enable the use of less expensive components while achieving useful results for a variety of applications, such as autonomous vehicles.

Abstract: A network controller, a user equipment (UE) device, and associated methods for conducting intelligent enhanced multimedia broadcast multicast services (eMBMS). A network controller receives user preference information and location information from a plurality of UEs. The network controller selects one or more data packages to transmit using eMBMS in one or more broadcast regions based on the user preference information and the location information. The one or more data packages may comprise an updated machine learning model in a distributed learning application. The network controller provides an indication to a remote device to broadcast the one or more data packages in the respective one or more broadcast regions using eMBMS.

Abstract: In some implementations, a user device (or resident device) can notify the user of anomalous accessory states. For example, the user device can determine which accessory states and contexts represent normal accessory states in the respective contexts. Similarly to scene suggestions, the user device can analyze historical accessory state data and context data to determine an accessory state pattern that indicates a normal state of an accessory for a given context. The user device can compare the current state and/or context of an accessory to historical accessory state data to determine when the current state of the accessory is abnormal for the current context. If the current accessory state is abnormal for the current context, the user device can present a notification to the user informing the user of the anomalous accessory state.

Abstract: A network controller, a user equipment (UE) device, and associated methods for conducting intelligent enhanced multimedia broadcast multicast services (eMBMS). A network controller receives user preference information and location information from a plurality of UEs. The network controller selects one or more data packages to transmit using eMBMS in one or more broadcast regions based on the user preference information and the location information. The one or more data packages may comprise an updated machine learning model in a distributed learning application. The network controller provides an indication to a remote device to broadcast the one or more data packages in the respective one or more broadcast regions using eMBMS.

Abstract: In some implementations, a user device can automatically generate scene suggestions and/or trigger suggestions. For example, a scene can be a collection of accessories and corresponding accessory states. When the scene is invoked, the user device can cause the accessories associated with the scene to assume the corresponding accessory states. The user device can automatically determine the accessories and/or corresponding accessory states for a scene based on historical patterns of use represented in historical accessory state data. The user device can automatically determine triggers for automatically invoking scenes based on historical patterns of use. For example, the user device can analyze historical context (e.g., location, time, accessory state, etc.) data associated with accessory state transitions to determine triggers for automatically invoking a scene. The user device can present suggest scenes and/or triggers to the user of the user device.

Abstract: A network controller, a user equipment (UE) device, and associated methods for conducting intelligent enhanced multimedia broadcast multicast services (eMBMS). A network controller receives user preference information and location information from a plurality of UEs. The network controller selects one or more data packages to transmit using eMBMS in one or more broadcast regions based on the user preference information and the location information. The one or more data packages may comprise an updated machine learning model in a distributed learning application. The network controller provides an indication to a remote device to broadcast the one or more data packages in the respective one or more broadcast regions using eMBMS.

Abstract: In some implementations, a user device can automatically generate scene suggestions and/or trigger suggestions. For example, a scene can be a collection of accessories and corresponding accessory states. When the scene is invoked, the user device can cause the accessories associated with the scene to assume the corresponding accessory states. The user device can automatically determine the accessories and/or corresponding accessory states for a scene based on historical patterns of use represented in historical accessory state data. The user device can automatically determine triggers for automatically invoking scenes based on historical patterns of use. For example, the user device can analyze historical context (e.g., location, time, accessory state, etc.) data associated with accessory state transitions to determine triggers for automatically invoking a scene. The user device can present suggest scenes and/or triggers to the user of the user device.

Abstract: The localization of a vehicle is determined using less expensive and computationally robust equipment compared to conventional methods. Localization is determined by estimating the position of a vehicle relative to a map of the environment, and the process thereof includes using a map of the surrounding environment of the vehicle, a model of the motion of the frame of reference of the vehicle (e.g., ego motion), sensor data from the surrounding environment, and a process to match sensory data to the map. Localization also includes a process to estimate the position based on the sensor data, the motion of the frame of reference of the vehicle, and/or the map. Such methods and systems enable the use of less expensive components while achieving useful results for a variety of applications, such as autonomous vehicles.

Abstract: In some implementations, a resident device can automatically generate scene suggestions and/or trigger suggestions based on historical data aggregated from multiple user devices. For example, a resident device can be a user device that is (e.g., mostly) kept in the home environment. Therefore, the resident device is able to collect accessory state and/or context data when another user device may not be connected to the accessories in the home environment. Moreover, the resident device can collect and/or aggregate accessory state data, context data, and user device state data from across multiple user devices. The resident device can automatically determine the accessories and/or corresponding accessory states for a scene and/or scene triggers based on historical patterns of use represented in the aggregated historical accessory state data. The user device can present suggest scenes and/or triggers to the user of the user device.

Abstract: The subject technology extracts features from each log line of a log file. The subject technology determines, based on the features, a sequence of log lines. The subject technology determines probabilities of log lines occurring within a window of time from a respective log line from the sequence of log lines, and determines probabilities of periods of time within the window of time that a next log line will occur after the respective log line. The subject technology segments log lines from the log file into sequences of log lines based on the probabilities of the set of log lines occurring within the window of time and the probabilities of periods of time that the next log line occurs after the respective log line. The subject technology determines a predicted subsequent log line, and detects an anomaly when an actual subsequent log line differs from the predicted subsequent log line.

Abstract: A network controller, a user equipment (UE) device, and associated methods for conducting intelligent enhanced multimedia broadcast multicast services (eMBMS). A network controller receives user preference information and location information from a plurality of UEs. The network controller selects one or more data packages to transmit using eMBMS in one or more broadcast regions based on the user preference information and the location information. The one or more data packages may comprise an updated machine learning model in a distributed learning application. The network controller provides an indication to a remote device to broadcast the one or more data packages in the respective one or more broadcast regions using eMBMS.

Abstract: In some implementations, a user device can automatically generate scene suggestions and/or trigger suggestions. For example, a scene can be a collection of accessories and corresponding accessory states. When the scene is invoked, the user device can cause the accessories associated with the scene to assume the corresponding accessory states. The user device can automatically determine the accessories and/or corresponding accessory states for a scene based on historical patterns of use represented in historical accessory state data. The user device can automatically determine triggers for automatically invoking scenes based on historical patterns of use. For example, the user device can analyze historical context (e.g., location, time, accessory state, etc.) data associated with accessory state transitions to determine triggers for automatically invoking a scene. The user device can present suggest scenes and/or triggers to the user of the user device.

Abstract: In some implementations, a user device (or resident device) can notify the user of anomalous accessory states. For example, the user device can determine which accessory states and contexts represent normal accessory states in the respective contexts. Similarly to scene suggestions, the user device can analyze historical accessory state data and context data to determine an accessory state pattern that indicates a normal state of an accessory for a given context. The user device can compare the current state and/or context of an accessory to historical accessory state data to determine when the current state of the accessory is abnormal for the current context. If the current accessory state is abnormal for the current context, the user device can present a notification to the user informing the user of the anomalous accessory state.

Abstract: In some implementations, a resident device can automatically generate scene suggestions and/or trigger suggestions based on historical data aggregated from multiple user devices. For example, a resident device can be a user device that is (e.g., mostly) kept in the home environment. Therefore, the resident device is able to collect accessory state and/or context data when another user device may not be connected to the accessories in the home environment. Moreover, the resident device can collect and/or aggregate accessory state data, context data, and user device state data from across multiple user devices. The resident device can automatically determine the accessories and/or corresponding accessory states for a scene and/or scene triggers based on historical patterns of use represented in the aggregated historical accessory state data. The user device can present suggest scenes and/or triggers to the user of the user device.

Abstract: A method and system for displaying an item for communication with a contact stored in an electronic device are provided. The method includes storing, by the electronic device, a frequency of usage of at least one item for each contact in a database. Further, the method includes receiving an interaction event performed on the at least one contact stored in the electronic device. Furthermore, the method includes identifying the at least one item for communication with the at least one contact from the database and displaying the at least one identified item, based on the frequency of the usage of the at least one identified item, as a response to the at least one interaction event performed on the at least one contact.