Microsoft Technology Licensing, LLC

Michel Goraczko

Jie Jerry Liu

Dimitrios Lymberopoulos

Di Wang

Zhengyou Zhang

Yasser Baseer Asmi

Abhishek Abhishek

Yi Lu

Khawar Zuberi

Rouzbeh Aminpour

Tony Capone

Ali Dalloul

Suman Kumar Nath

Aman Kansal

Feng Zhao

Nissanka Arachchige Bodhi Priyantha

Lei Zhang

Changhu Wang

Gerald Reuben DeJean

Yuxiao Hu

Mohammed Shoaib

Nissanka Bodhi Priyantha

Marcel Gavriliu

Azalia Mirhoseini

Slobodan Matic

Nissanka Arachchige B Priyantha

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.

Context-awareness

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.

Multi-signal based shopping cart content recognition in brick-and-mortar retail stores

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. While such optimization problems are NP-hard, the Context-Aware Crowdsourced Task Optimizer exploits monotonicity and submodularity of various objective functions to provide computationally feasible task identification and recommendation algorithms with tight optimality bounds.

Optimizing task recommendations in context-aware mobile crowdsourcing

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.

Resource modeling and scheduling for extensible computing platforms

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.

Magnetic stripe-based transactions using mobile communication devices

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.

Predictive computing device power management

The claimed subject matter provides a system and/or a method that facilitates managing energy consumption with an energy sink device in order to reduce energy costs. An energy sink device can consume a portion of energy. An interface component can receive a portion of data related to at least one of a real time energy rate or a user presence. An energy manager can dynamically control the energy sink device for efficient consumption of the portion of energy for reduced energy costs based at least in part upon the evaluation of the portion of received data. The energy manager can leverage the real time energy rate and the user presence in order to employ at least one of a power on the energy sink device, a power off the energy sink device, or an adjust a setting for the energy sink device.

Energy cost reduction and ad delivery

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Seattle, WA, United States of America

Michel Goraczko