Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to context-aware environments. One example can include inwardly-facing cameras positioned around a periphery of an environment that defines a volume. The example can also include sensors positioned relative to the volume and configured to communicate with a user device in the volume. The example can also include an ambient perception component configured to track user locations in the volume and to detect user gestures relative to objects in the volume, and responsive to receiving a query from the user's device, to supplement the query with information derived from the objects.

Abstract: The discussion relates to inventory control. One example can analyze data from sensors to identify items and users in an inventory control environment. The example can detect co-location of an individual user and an individual item at a first location in the inventory control environment at a first time and at a second location in the inventory control environment at a second time.

Abstract: Identifying products in a physical store shopping environment. The method includes, using a first detection method, identifying that a given product likely belongs to a given set of products. The method further includes, using one or more other detection methods, determining that the product is likely a specific product from the given set of products.

Abstract: A “Context-Aware Crowdsourced Task Optimizer” provides various processes to optimize task recommendations for workers in mobile crowdsourcing scenarios by automatically identifying and recommending bundles of tasks compatible with workers' contexts (e.g., worker history, present or expected locations, travel paths, working hours, skill sets, capabilities of worker's mobile computing devices, etc.). The Context-Aware Crowdsourced Task Optimizer bundles tasks to both maximize expected numbers of completed tasks and to dynamically price tasks to maximize the system's utility, which is a function of task values and task completion rates. Advantageously, the resulting task identification and recommendation process incentivizes individual workers to perform more tasks in a shorter time period, thereby helping tasks to complete faster, even with smaller budgets.

Abstract: Identifying products in a physical store shopping environment. The method includes, using a first detection method, identifying that a given product likely belongs to a given set of products. The method further includes, using one or more other detection methods, determining that the product is likely a specific product from the given set of products.

Abstract: Providing discounts to users in a physical store location. The method includes detecting that a user has stopped at a given location in the physical store. The method further includes, based on the location, identifying a set of products. The method further includes, providing an identification of the set of products to an ad server. At the ad server an auction is initiated between different product promoters to identify ads to be provided to the user. The method further includes, receiving from the ad server one or more ads to be provided to the user based on the results of the ad auction. The method further includes, providing the one or more ads to the user.

Abstract: Providing product recommendations in a physical retail store. A method includes detecting that the user arrives at the physical retail store. The method further includes, in response, receiving information from a recommendation server for a particular user. The method further includes storing locally, the information from the recommendation server. The method further includes, detecting a plurality of user interactions for the user with products in the retail store as part of the shopping experience and prior to a check-out phase of the shopping experience. The method further includes based on the locally stored information and the user interaction, providing product recommendations.

Abstract: A “Context-Aware Crowdsourced Task Optimizer” provides various processes to optimize task recommendations for workers in mobile crowdsourcing scenarios by automatically identifying and recommending bundles of tasks compatible with workers' contexts (e.g., worker history, present or expected locations, travel paths, working hours, skill sets, capabilities of worker's mobile computing devices, etc.). The Context-Aware Crowdsourced Task Optimizer bundles tasks to both maximize expected numbers of completed tasks and to dynamically price tasks to maximize the system's utility, which is a function of task values and task completion rates. Advantageously, the resulting task identification and recommendation process incentivizes individual workers to perform more tasks in a shorter time period, thereby helping tasks to complete faster, even with smaller budgets.

Abstract: Energy management modeling and scheduling techniques are described for reducing the power consumed to execute an application on a multi-processor computing platform within a certain time period. In one embodiment, a sophisticated resource model which accounts for discrete operating modes for computing components/resources on a computing platform and transition costs for transitioning between each of the discrete modes is described. This resource model provides information for a specific heterogeneous multi-processor computing platform and an application being implemented on the platform in a form that can be processed by a selection module, typically utilizing an integer linear programming (ILP) solver or algorithm, to select a task schedule and operating configuration(s) for executing the application within a given time.

Abstract: Magnetic stripe-based transaction enabled mobile communication device embodiments are presented which generally involve a mobile communication device which has been configured to perform transactions that heretofore were completed using a magnetic stripe found on magnetic-stripe cards. In one general embodiment, a mobile communication device generates magnetic stripe data which is used to perform a magnetic stripe-based transaction. To this end, the mobile communication device includes a magnetic stripe device and a computing device. The computing device stores the magnetic stripe data, and the magnetic stripe device is employed to transfer the stored magnetic stripe information so that it can be used to conduct transactions as if a traditional magnetic stripe card were being used.

Abstract: The subject disclosure is directed towards a context-aware mobile crowd sourcing service/system. Context information is automatically collected for a mobile device via mobile-device sensors. When a task is received that specifies context-related criteria, a worker is selected for that task based at least in part upon the context information associated with that worker's mobile device. Sensors on the device may be leveraged to capture information related to performing the task. Also described is a cross-platform task configuration that allows a task to be written once and run on different mobile device platforms.

Abstract: The described implementations relate to predictive computing device energy management. One implementation measures resource usage of a computing device that employs a power policy. This implementation also estimates resource usage of the computing device having at least one different power policy without actually running the at least one different power policy on the computing device.

Abstract: A “Portable Card Generator” is implemented within a portable device, such as a mobile phone, and provides various techniques for writing secure account information from user selected accounts to a “wildcard” having rewritable magnetic stripes, rewritable RFID tags, and/or rewritable smartcard circuitry. The account information is retrieved by the portable device from local or remote stores of user accounts. Once that account information is written, the wildcard is then available for immediate use for credit card or debit-type payments, loyalty card use, etc. Consequently, by providing a credit card sized object having a rewriteable magnetic stripe, RFID tag, and/or smartcard circuitry, in combination with account information for various credit cards, debit cards, consumer loyalty cards, insurance cards, ID cards or badges, etc., the user is no longer required to physically carry those cards in order to use the corresponding accounts within existing card-based infrastructures.