Samsung Electronics Co., Ltd.

Emmanuel M Tapia

Jun Yang

Saeed Moghaddam

Zhixian Yan

One embodiment provides a system comprising at least one learner application module for receiving feedback data from at least one user via at least one electronic device, about information presented to the users by a presenter. The system further comprises at least one sensor for capturing contextual data associated with the users. The system further comprises a recommendation provider for collecting feedback data and contextual data from each learner application module and each sensor, respectively. The recommendation provider determines at least one personalized recommendation for each user based on the collected data and the presented information.

Context-aware recommendation system for adaptive learning

A method for using Bluetooth&#xae; information for inferring user context and computing sociability is presented. The method includes receiving wireless sensor data from wireless sensor communication for an electronic device. Information units are formed using the wireless sensor data. User context is inferred using the information units. An entropy metric is computed based on the inferred user context. Social profiling is created based on the entropy metric and one or more user context characteristics.

Context recognition and social profiling using mobile devices

A method and system for monitoring resource information and user activity. The method includes acquiring one or more data streams from one or more resource meters and one or more electronic device sensors. Discrete events are computed from each data stream. A sequence of discrete sensor-meter event itemsets are extracted based on the events. Frequent sensor-meter event itemsets are discovered from the sequence of discrete event itemsets that occur together, and a frequency of occurrence of each frequent co-occurrence itemset is discovered. Rising sensor-meter event itemsets and falling sensor-meter event itemsets are matched based on appliance state models and the frequency of occurrence of each sensor-meter event itemset. Each individual fixture is identified. Each fixture cluster is classified to a fixture category. Based on the matched fixture events, fixture clusters, and categories, resource usage information and user activities are determined for each fixture usage event identified.

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Newark, CA, United States of America

Emmanuel M Tapia