Abstract: Methods, devices, systems, and non-transitory process-readable storage media for a computing device to use machine learning to dynamically configure an application and/or complex algorithms associated with the application. An aspect method performed by a processor of the computing device may include operations for performing an application that calls a library function associated with a complex algorithm, obtaining signals indicating user responses to performance of the application, determining whether a user tolerates the performance of the application based on the obtained signals indicating the user responses, adjusting a configuration of the application to improve a subsequent performance of the application in response to determining the user does not tolerate the performance of the application, and storing data indicating the user responses to the performance of the application and other external variables for use in subsequent evaluations of user inputs.

Abstract: Mobile computing devices may be equipped with hardware components configured to monitor key assets of the mobile device at a low level (e.g., firmware level, hardware level, etc.). The hardware component may also be configured to dynamically determine the key assets that are to be monitored in the mobile device, monitor the access or use of these key assets by monitoring data flows, transactions, or operations in a system data bus of the mobile device, and report suspicious activities to a comprehensive behavioral monitoring and analysis system of the mobile device. The comprehensive behavioral monitoring and analysis system may then use this information to quickly identify and respond to malicious or performance degrading activities of the mobile device.

Abstract: Systems, apparatus and methods for estimating a location of a mobile device are presented. Before computing a location estimate, the mobile device groups a plurality of access points into two or more categories (for example, a first list of access points having a first characteristic and a second list of access points having a second characteristic). Round-trip time (RTT) measurements are computed for access points in the first list. A Short Interframe Space (SIFS) value may be determined for each access point in the first list or generally SIFT representing the first list as a whole. The RTT measurements are compensated with the appropriate SIFS value. The mobile device then computes its location or position fix estimate using the compensated RTT values while excluding less accurate RTT values from other access points. As a result, the location estimate eliminates adverse influent from some access points.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for detecting location changes and monitoring assistance data via scanning for use in or with a mobile device. Briefly, in accordance with at least one implementation, a method may include obtaining, at a mobile device, a rough estimate of a location of the mobile device; identifying a plurality of transmitters within a signal acquisition range of the roughly estimated location; transmitting probe requests addressed to at least some of the transmitters; and selectively initiating a passive scan at a receiver of the mobile device if a number of responses to the probe requests received from the transmitters is less than a threshold number.

Abstract: Described are devices, methods, techniques and systems for locating a portable services access transceiver (PSAT) for use in aiding emergency “911” services. In one implementation, one or more conditions indicative of movement of a PSAT may initiate a process for obtaining a new estimated location of the PSAT. In another implementation, a location of a PSAT may be determined or updated using indoor navigation techniques.

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.

Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

Abstract: The various aspects provide for a computing device and methods implemented by the device to ensure that an application executing on the device and seeking root access will not cause malicious behavior while after receiving root access. Before giving the application root access, the computing device may identify operations the application intends to execute while having root access, determine whether executing the operations will cause malicious behavior by simulating execution of the operations, and pre-approve those operations after determining that executing those operations will not result in malicious behavior. Further, after giving the application root access, the computing device may only allow the application to perform pre-approved operations by quickly checking the application's pending operations against the pre-approved operations before allowing the application to perform those operations.

Abstract: A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, or otherwise respond to malicious or performance-degrading behaviors of the computing device. As part of these operations, the processor may generate user-persona information that characterizes the user based on that user's activities, preferences, age, occupation, habits, moods, emotional states, personality, device usage patterns, etc. The processor may use the user-persona information to dynamically determine the number of device features that are monitored or evaluated in the computing device, to identify the device features that are most relevant to determining whether the device behavior is not consistent with a pattern of ordinary usage of the computing device by the user, and to better identify or respond to non-benign behaviors of the computing device.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for detecting location changes and monitoring assistance data via scanning for use in or with a mobile device. Briefly, in accordance with at least one implementation, a method may include obtaining, at a mobile device, a rough estimate of a location of the mobile device; identifying a plurality of transmitters within a signal acquisition range of the roughly estimated location; transmitting probe requests addressed to at least some of the transmitters; and selectively initiating a passive scan at a receiver of the mobile device if a number of responses to the probe requests received from the transmitters is less than a threshold number.

Abstract: Apparatuses and methods for adjusting wireless-derived positions of a mobile station using a motion sensor are presented. One method includes estimating a position of a mobile station based upon wireless signal measurements and measuring a movement of the mobile station using a relative motion sensor. The method further includes detecting a displacement of the mobile station based upon the measured movement, determining that the displacement is below a threshold and then adjusting the estimated position of the mobile station using information from the relative motion sensor. An apparatus includes a wireless transceiver, a relative motion sensor, a processor coupled to the wireless transceiver and the relative motion sensor, and a memory coupled to the processor. The memory stores executable instructions and data for causing the processor to execute methods for adjusting wireless-derived positions using a motion sensor.

Abstract: Described are devices, methods, techniques and systems for locating a portable services access transceiver (PSAT) for use in aiding emergency “911” services. In one implementation, one or more conditions indicative of movement of a PSAT may initiate a process for obtaining a new estimated location of the PSAT. In another implementation, a location of a PSAT may be determined or updated using indoor navigation techniques.

Abstract: The various aspects provide a method for recognizing and preventing malicious behavior on a mobile computing device before it occurs by monitoring and modifying instructions pending in the mobile computing device's hardware pipeline (i.e., queued instructions). In the various aspects, a mobile computing device may preemptively determine whether executing a set of queued instructions will result in a malicious configuration given the mobile computing device's current configuration. When the mobile computing device determines that executing the queued instructions will result in a malicious configuration, the mobile computing device may stop execution of the queued instructions or take other actions to preempt the malicious behavior before the queued instructions are executed.

Abstract: Systems, apparatus and methods for estimating a location of a mobile device are presented. Before computing a location estimate, the mobile device groups a plurality of access points into two or more categories (for example, a first list of access points having a first characteristic and a second list of access points having a second characteristic). Round-trip time (RTT) measurements are computed for access points in the first list. A Short Interframe Space (SIFS) value may be determined for each access point in the first list or generally SIFT representing the first list as a whole. The RTT measurements are compensated with the appropriate SIFS value. The mobile device then computes its location or position fix estimate using the compensated RTT values while excluding less accurate RTT values from other access points. As a result, the location estimate eliminates adverse influent from some access points.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for detecting location changes and monitoring assistance data via scanning for use in or with a mobile device. Briefly, in accordance with at least one implementation, a method may include obtaining, at a mobile device, a rough estimate of a location of the mobile device; identifying a plurality of transmitters within a signal acquisition range of the roughly estimated location; transmitting probe requests addressed to at least some of the transmitters; and selectively initiating a passive scan at a receiver of the mobile device if a number of responses to the probe requests received from the transmitters is less than a threshold number.

Abstract: Systems, apparatus and methods for estimating a location of a mobile device are presented. Before computing a location estimate, the mobile device groups a plurality of access points into two or more categories (for example, a first list of access points having a first characteristic and a second list of access points having a second characteristic). Round-trip time (RTT) measurements are computed for access points in the first list. A Short Interframe Space (SIFS) value may be determined for each access point in the first list or generally SIFT representing the first list as a whole. The RTT measurements are compensated with the appropriate SIFS value. The mobile device then computes its location or position fix estimate using the compensated RTT values while excluding less accurate RTT values from other access points. As a result, the location estimate eliminates adverse influent from some access points.

Abstract: Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to support mobile device positioning through the use of adaptive passive scanning and/or adaptive active probing techniques. For example, a mobile device may acquire signals from wireless transceivers, identify wireless transceivers based, at least in part, on the acquired signal(s), determine a received signal strength measurement for each of the wireless transceivers based, at least in part, on the acquired signal(s), and determine a transmission power of a probe signal to be transmitted to at least one of the wireless transceivers based, at least in part, on at least one of the received signal strength measurements.

Abstract: The various aspects provide a method for recognizing and preventing malicious behavior on a mobile computing device before it occurs by monitoring and modifying instructions pending in the mobile computing device's hardware pipeline (i.e., queued instructions). In the various aspects, a mobile computing device may preemptively determine whether executing a set of queued instructions will result in a malicious configuration given the mobile computing device's current configuration. When the mobile computing device determines that executing the queued instructions will result in a malicious configuration, the mobile computing device may stop execution of the queued instructions or take other actions to preempt the malicious behavior before the queued instructions are executed.

Abstract: An example method for determining a processing time for wirelessly determining a position of a mobile station includes: measuring a round trip time delay to each of multiple wireless access points; estimating an initial processing time for each of the wireless access points; calculating the position of the mobile station based upon the measured round trip time delays and estimated processing times; and updating the initial processing time for each of the wireless access points based upon the calculated position of the mobile station.

Abstract: Various aspects provide methods implemented by at least one processor executing on a mobile communication device to efficiently identify, classify, model, prevent, and/or correct the non-benign (e.g., performance degrading) conditions and/or behaviors that are related to an application operating on the device. Specifically, in various aspects, the mobile computing device may derive or extract application-specific features by performing a binary analysis of an application and may determine the application's category (e.g., a “games,” “entertainment,” or “news” category) based on the application-specific features. The mobile computing device may also obtain a classifier model associated with the application's category that includes various conditions, features, behaviors and corrective actions that may be used to quickly identify and correct non-benign behaviors (e.g., undesirable, malicious, and/or performance-degrading behaviors) occurring on the mobile computing device that are related to the application.