Google Inc.

Arris Enterprises LLC

Motorola Mobility LLC

Carnegie Mellon University

Arris Technology, Inc.

Jianguo Li

Paul C Davis

Di You

Mir Farooq Ali

Dale William Russell

Guohua Hao

James R Talley

Haifeng Li

Heng-Tze Cheng

James A Rumpler

Ioannis Kompatsiaris

Symeon Papadopoulos

Dorothea Tsatsou

Mir F Ali

Systems, methods, and devices for determining contexts and determining associated health profiles using information received from multiple health sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of time and location. Health sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The health sensor data can include health sensor readings that are implicit indications of health for the context. The sensor data can also include user reported data with explicit descriptions of health for the context. The health sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine a health profile for the context that can be output to one or more users or entities.

Context health determination system

Disclosed are techniques () and apparatuses () for drawing an inference using multiple sensors. These techniques and apparatuses enable a computing device to choose () a set of sensors that are capable of providing information for an inference, invoke () the chosen set of sensors to provide the information, receive () the information from at least a subset of the chosen sensors, and draw (), based on the received information, the inference. In some cases, the set of sensors are chosen such that time or resources of the computing device can be conserved.

Drawing an inference of a usage context of a computing device using multiple sensors

Systems, methods, and devices for determining contexts and determining associated emotion profiles using information received from multiple emotion sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of times and locations. Emotion sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The emotion sensor data can include emotion sensor readings that are implicit indications of an emotion for the context. The sensor data can also include user reported data with explicit descriptors of an emotion for the context. The emotion sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine an emotion profile for the context that can be output to one or more users or entities.

Context emotion determination system

A system and method for identifying and labeling locations frequented by a user of a device, where the system and method track geographic positions and environmental or contextual factors as the user moves about, and identify locations of interest to the user via a clustering procedure. As the device collects contextual data, the system and method label each identified location to create a location model. This model allows the device to label new locations as they arise. The model may be periodically updated by separately processing geographic position data gathered after the model was created to determine if the cluster locations and labels remain accurate.

Identifying personalized meaningful locations

A method for automatic recognition of human activity is provided and includes the steps of decomposing human activity into a plurality of fundamental component attributes needed to perform an activity and defining ontologies of fundamental component attributes from the plurality of the fundamental component attributes identified during the decomposing step for each of a plurality of different targeted activities. The method also includes the steps of converting a data stream captured during a performance of an activity performed by a human into a sequence of fundamental component attributes and classifying the performed activity as one of the plurality of different targeted activities based on a closest match of the sequence of fundamental component attributes obtained during the converting step to at least a part of one of the ontologies of fundamental component attributes defined during the defining step. A system for performing the method is also disclosed.

System and method for activity recognition

Disclosed are a system and method for constructing and using a predictive model to generate a prediction signal, also referred to as a classification signal when the signal indicates one of a plurality of distinct classes. In various embodiments, the disclosed technique reduces a size of a predictive Support Vector Model by extracting certain values beforehand and storing only weighting values. The technique does not sacrifice generalization performance but does significantly reduce the model size and accelerate prediction performance. The described system applies to most kernel functions, whether linear or nonlinear.

Efficient prediction

Systems, methods, and devices for determining contexts and determining associated demographic profiles using information received from multiple demographic sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of time and location. Demographic sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The demographic sensor data can include demographic sensor readings that are implicit indications of a demographic for the context. The sensor data can also include user reported data with explicit descriptions of a demographic for the context. The demographic sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine a demographic profile for the context that can be output to one or more users or entities.

Context demographic determination system

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the 'statistically most informative' feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be 'aggregated' together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something &#x201c;interesting&#x201d; has happened to the entity.

Hierarchical behavioral profile

Disclosed are methods and apparatus for processing linguistic expressions (e.g., opinionated text documents). The linguistic expressions are processed by, firstly, detecting topics of interest discussed in the linguistic expressions. The sentiment, or sentiments, of an originator with respect to each of the topics detected in the linguistic expressions is then assessed. The originators are then grouped (or clustered) into one or more groups based on the similarities between the originators' respective sets of detected topics and corresponding sentiments. Semantic information is then associated with a given group. Finally, for a given member of a given group, a profile is created or updated. This profile comprises attributes that may be based on a degree of membership of the given member to the given group and the semantic information associated with the given group.

Communication processing

Disclosed are methods in which ontologies are automatically (i.e., with little or no human interaction) constructed from data. The constructed ontology may be provided for use by an application or device. An ontology may be constructed as follows. Firstly, a lexical graph is constructed. Secondly, a concept-detection process is performed on the lexical graph to detect concepts (prominent terms) in the lexical graph. Thirdly, a clustering method is performed on the lexical graph to form groups of concepts. Fourthly, relationships between the concepts are determined based on the interconnections of the concepts in the groups. Lastly, the concepts and detected relationships therebetween are structured using a formal ontology specification.

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Chicago, IL, United States of America

Jianguo Li