Qualcomm Incorporated

Tourmaline Labs, Inc.

Leonard Henry Grokop

Sanjiv Nanda

Shankar Sadasivam

Junyi Li

Mehmet Yavuz

Xinzhou Wu

Rajiv Laroia

Thomas J Richardson

Vidya Narayanan

Saurabh R Tavildar

Chirag Sureshbhai Patel

Anthony Sarah

Sumeeth Nagaraja

Vinay Chande

Farhad Meshkati

Edward Harrison Teague

Saurabha Rangrao Tavildar

Lukas Daniel Kuhn

Alexandros Manolakos

Li Ding

Jin Won Lee

Rashmi Kulkarni

Disha Ahuja

Bhuwan Dhingra

James William Dolter

Eric Martin Hall

James W Dolter

Systems and methods herein enable a mobile device to detect that a user is traveling in association with a vehicle based at least on motion data. In some embodiments, accelerometer data is used. Motion data is leveraged in combination with various observations regarding vehicular movement to determine whether or not a mobile device is located in or on the vehicle. For instance, before entering the state of vehicular movement, it can be determined that the user is first in a walking state (e.g., walking to the car, bus, etc., and entering it). Likewise, after exiting the state of vehicular movement, the user re-enters the walking state (e.g., after stepping out of the car, bus, etc., the user again begins walking). Further, it can be determined that when the user is in the walking state, the accelerometer signals appear different to any accelerometer signals seen in the vehicular movement state.

Techniques for determination of a motion state of a mobile device

Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.

Sequential feature computation for power efficient classification

Systems and methodologies are described that facilitate identifying peers based upon encoded signals during peer discovery in a peer to peer network. For example, direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval; thus, a particular segment selected for transmission can signal a portion of the identifier, while a remainder can be signaled based upon tones communicated within the selected segment. Moreover, a subset of symbols within the resource can be reserved (e.g., unused) to enable identifying and/or correcting timing offset. Further, signaling can be effectuated over a plurality of peer discovery intervals such that partial identifiers communicated during each of the peer discovery intervals can be linked (e.g., based upon overlapping bits and/or bloom filter information).

Coding Methods of communicating identifiers in peer discovery in a peer-to-peer network

Systems and methods for monitoring the proximity of a personal item are provided. Systems and methods as provided herein allow a computing device to monitor the proximity of a personal item by generating an alert when wireless communications between the computing device and the personal item are lost in an unsafe zone to remind a user of the computing device about the personal item in an attempt to prevent leaving the personal item in the unsafe zone, where it may be susceptible to theft or loss. The provided systems and methods also automatically assign safe zones by analyzing clusters of location data points obtained by the computing device over time to determine a home location and an office location, assigning the home location and the office location as safe zones, and assigning all other locations as unsafe. A user may further manually designate locations as safe zones.

Systems and methods for monitoring a proximity of a personal item and automatically assigning safe and unsafe zones

Methods, systems, computer-readable media, and apparatuses for determining indoor/outdoor state of a mobile device are presented. In some embodiments, a mobile device may maintain an indoor/outdoor state. The mobile device may include at least one first sensor and at least one second sensor, the first sensor associated with higher power consumption than the second sensor. The mobile device may gate off the first sensor and using the second sensor to obtain a sensor reading, if the second sensor can generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may use the first sensor to obtain a sensor reading, if the second sensor cannot generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may update the indoor/outdoor state of the mobile device based on a reading received from one of the first and the second sensors.

Low power always-on determination of indoor versus outdoor state

Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.

Context labels for data clusters

Disclosed is an apparatus and method for speaker equalization in a mobile device. In one embodiment, placement of a mobile device including a speaker is determined and speaker equalization is performed in accordance with the determined placement.

Speaker equalization for mobile devices

Systems and methods for providing application-controlled, power-efficient context (state) classification are described herein. An apparatus for performing context classification with adjustable granularity as described herein includes a classifier controller configured to receive a request for a context classification and a granularity input associated with the request; and a context classifier communicatively coupled to the classifier controller and configured to receive the request and the granularity input from the classifier controller, to select a resource usage level for the context classification based on the granularity input, wherein a granularity input indicating a higher granularity level is associated with a higher resource usage level and a granularity input indicating a lower granularity level is associated with a lower resource usage level, and to perform the context classification at the selected resource usage level.

Application-controlled granularity for power-efficient classification

The disclosure is directed to modifying the operation of one or more hardware subsystems when a new context awareness service begins. An aspect determines a power budget for a plurality of operating context awareness services including the new context awareness service, wherein the power budget is based on a power requirement for each of the plurality of context awareness services, and wherein the power requirement for each of the plurality of context awareness services is based on power utilizations of the one or more hardware subsystems corresponding to the plurality of context awareness services, and allocates power resources to the one or more hardware subsystems based on importances of the plurality of context awareness services and/or the one or more hardware subsystems, wherein the allocation of the power resources is performed within the power budget.

Dynamic power management of context aware services

Methods, systems, computer-readable media, and apparatuses for inferring context are provided. In one potential implementation, first context information associated with a first duration is identified, second context information is accessed to determine a context segmentation boundary; and the first context information and the second context information is then aggregated to generate an inferred segmented aggregated context. In a further implementation, the first context information is used to average inferred contexts, and the context segmentation boundary is used to reset a start time for averaging the first context information.

Aggregate context inferences using multiple context streams

A method of generating demographic statistics related to an event includes using sensor data acquired at a mobile device to estimate a value for a category of a demographic model of a user of the mobile device; using sensor data acquired at the mobile device to detect an occurrence of the event in which the user of the mobile device participates; and associating the estimated value for the category of the demographic model of the user of the mobile device with the event.

Estimating demographic statistics of media viewership via context aware mobile devices

Components, methods, and apparatuses are provided that may be used to obtain a plurality of time-referenced features extracted from signals received from one or more inertial sensors of a mobile device, wherein the time-referenced features are provided according to heterogeneous time scales. The time-referenced features are combined over a decision window to infer an activity of a user co-located with the mobile device.

Methods, devices, and apparatuses for activity classification using temporal scaling of time-referenced features

Information about a context of a mobile device is inferred using measurements from light detectors of the mobile device. The light detectors may be located on multiple different sides of the mobile device. In some embodiments, measurements from one or more cameras may be used to infer context.

Method and apparatus for characterizing context of a mobile device

A position estimate for a mobile device is generated using data from motion sensors, such as accelerometers, magnetometers, and/or gyroscopes, and data from light sensors, such as an ambient light sensor, proximity sensor and/or camera intensity sensor. A plurality of proposed positions with associated likelihoods is generated by analyzing information from the motion sensors and a list of candidate positions is produced based on information from the light sensors. At least one of the plurality of proposed positions is eliminated using the list of candidate positions and a position estimate for the mobile device is determined based on the remaining proposed positions and associated likelihoods. The proposed positions may be generated by extracting features from the information from the motion sensors and using models to generate likelihoods for the proposed positions. The likelihoods may be filtered over time. Additionally, a confidence metric may be generated for the estimated position.

Device position estimates from motion and ambient light classifiers

A method of computing an orientation of a mobile device in a vehicle includes collecting accelerometer data from the mobile device. The accelerometer data are subdivided into frames, each frame being a quantity of time in which a sample data point is taken. Statistics are calculated for each frame, including (i) a mean and (ii) a standard deviation of the magnitude of the acceleration vectors. Device usage delimiters, marking start and end points of a coherent block, are computed, the coherent block being consecutive frames in which the mobile device stays in the same orientation relative to the vehicle. A gravity vector is estimated using the statistics in coherent blocks. A nullspace is computed from the gravity vector, the nullspace being a plane orthogonal to the gravity vector. The accelerometer data is projected onto the nullspace, resulting in an estimated orientation of the mobile device in the vehicle.

Growing community of inventors

San Diego, CA, United States of America

Leonard Henry Grokop