Palo Alto Research Center Inc.

Google Inc.

Brainoft Inc.

Xerox Corporation

Maurice Kyojin Chu

Kurt Edward Partridge

James M A Begole

Daniel H Greene

Ashutosh Saxena

Jinjing Zhou

Wei Zhang

Rui Zhang

Juan Liu

James E Reich

Bryan Thomas Preas

Haitham Hindi

Alan Lee Gardner, Iii

Nitin S Parekh

Brendan Berman

Takashi Matsumoto

Robert Roy Price

Robert Thomas Krivacic

Oliver Brdiczka

Richard Chow

Mark Webster Newman

Lara S Crawford

Mary Catherine McCorkindale

Angana Ghosh

John Hanley

Deng Deng

Kunal Lad

Kai Kunze

Ashwini Venkatesh

Sensor information is received. Based at least in part on at least one state detected using the sensor information, an expected value for automatically performing an action associated with a device controlled by a device controller system is evaluated as compared to an expected value for potentially performing, subject to a user interactive qualification, the action associated with the device controlled by the device controller. It is determined that the expected value of automatically performing the action is insufficient to trigger automatically performing the action without being subject to the user interactive qualification. A confirmatory indication is identified. Based at least in part on the confirmatory indication, an instruction is sent to the device to perform the action.

Interactive environmental controller

Sensor data is received from a physical environment sensor. A component region associated with at least a portion of the sensor data is identified. A physical environment has been defined to include a plurality of component regions. Context information associated with the identified component region is obtained. The context information is utilized in association with a prediction model.

Utilizing context information of environment component regions for event/activity prediction

Certain implementations of the disclosed technology may include systems and methods for providing notifications relating to context-based features of a mobile device. According to an example implementation, a method is provided for receiving an indication of contextual information and an indication of historical information. The method also includes determining an environmental context of the mobile device from the contextual information and the historical information. The method also includes determining whether a usage criteria associated with a context-based feature associated with the environmental context has been met. The method also includes outputting an indication of the determination that the context-based feature has not met the usage criteria, such that the mobile device outputs a notification related to the context-based feature.

Systems and methods for contextual discovery of device functions

Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Application analytics reporting

A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Simulation of near-field communications

One embodiment of the present invention provides a system for generating a classifier to detect patterns in a data sequence. During operation, the system receives the data sequence, which represents a sequence of measurements of a phenomenon. The system transforms the data sequence into a feature sequence that is of a higher dimensionality than a dimensionality of the data sequence, and the feature sequence is a sequence of feature vectors each created from contiguous members of the data sequence. Next, the system generates a graph where each node of the graph corresponds to a feature vector. The system converts the generated graph into a two-dimensional graph. Subsequently, the system displays, to a user, the two-dimensional graph. The system receives user input indicating that a region of the two-dimensional graph corresponds to a pattern associated with the feature sequence, and then generates a classifier based on the received user input.

Method and system for analyzing sequential data based on sparsity and sequential adjacency

One embodiment of the present invention provides a method and apparatus for enabling extensibility in sensing systems. The distributed sensing system comprises a number of sensor nodes, a device database, a sensing module registry, a sensing needs monitor, and an automatic composer. The device database is in communication with at least one sensor node and configured to maintain physical information on at least one sensor node. The sensing module registry is configured to maintain a registry of a number of software modules that are available for extracting information from sensor data. The sensing needs monitor is configured to maintain a list of context pairs that represent a number of sensing needs of a sensing application. The automatic composer is configured to generate a composition of software modules, at runtime, to realize the sensing needs of users and applications.

System and method for enabling extensibility in sensing systems

One embodiment of the present invention provides a system for provisioning physical resources shared by a plurality of jobs. During operation, the system establishes resource-usage models for the jobs, ranks the jobs based on quality of service (QoS) requirements associated with the jobs, and provisions the jobs for a predetermined time interval in such a way that any unused reservations associated with a first subset of jobs having higher QoS rankings are distributed to other remaining jobs with preference given to a second subset of jobs having a highest QoS ranking among the other remaining jobs. Provisioning the jobs involves making reservations for the jobs based on the resource-usage model and corresponding QoS requirements associated with the jobs.

Long-term resource provisioning with cascading allocations

Embodiments of the disclosed technology provide a system and method for automatically filling paper form with electronic data. The system receives from a user a scanned image of a paper form that includes a plurality of fields. The system then retrieves a schema map that maps the plurality of fields in the paper form to a plurality of fields in an electronic data record associated with the user. Next, the system retrieves data values of the fields in the electronic data record based on the schema map. The system also synthesizes the retrieved data values and the scanned image to create an image of the paper form in which the fields are filled with the retrieved data values.

System for automatically filling in paper forms with electronic data

A recommender system determines a probability threshold for an activity-prediction model, and uses the probability threshold to predict whether a user is performing a target activity. To determine the probability threshold, the system computes a set of activity probabilities based on contextual information for a set of historical activities, and based on an activity-prediction model for a target activity. The system then compares a set of probability thresholds with the set of activity probabilities to determine a prediction success rate for each probability threshold. The system computes a utility score for each probability threshold based on the prediction success rates and a utility function, and selects a probability threshold whose utility score is optimal amongst the utility scores of the set of thresholds and greater than or equal to a baseline utility score. The system then assigns the probability threshold to the activity-prediction model.

Automatically triggering predictions in recommendation systems based on an activity-probability threshold

A computer-implemented method of managing resources in a virtual machine environment can include determining a specification of provisioning success corresponding to each of multiple jobs in the virtual machine environment, determining a prediction of resource needs corresponding to each of the jobs, and determining a resource specification corresponding to each of the jobs based on the specification of provisioning success and the prediction of resource needs.

Statistical packing of resource requirements in data centers

A performance-efficient activity-modeling system generates a group-activity model for a population group using information from an optimal subset of users of the population group. During operation, the system computes utility scores for a set of users based on a utility-scoring function, such that a respective utility score indicates a usefulness or penalty of collecting a corresponding user's contextual information. The system then selects, from the set of users, a subset of users with highest utility scores, and receives user information from each of the selected users. The system generates the group-activity model based on the user information received from the selected users.

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Burlingame, CA, United States of America

Maurice Kyojin Chu