Microsoft Technology Licensing, LLC

Andrey Kolobov

Eric J Horvitz

David Maxwell Chickering

Geoffrey G Zweig

Jason D Williams

Carlos Garcia Jurado Suarez

Aparna Lakshmiratan

Nobal Bikram Niraula

Yuval Peres

Cheng Lu

Pradeep Dasigi

Ashish Kapoor

Semiha Ece Kamar Eden

Debadeepta Dey

Richard A Caruana

The technology described herein builds an optimal refresh schedule by minimizing a cost function constrained by an available refresh bandwidth. The cost function receives an importance score for a content item and a change rate for the content item as input in order to optimize the schedule. The cost function is considered optimized when a refresh schedule is found that minimizes the cost while using the available bandwidth and no more. The technology can build an optimized schedule to refresh content with incomplete change data, content with complete change data, or a mixture of content with and without complete change data. It can also re-learn content item change rates from its own schedule execution history and re-compute the refresh schedule, ensuring that this schedule takes into account the latest trends in content item updates.

Efficient freshness crawl scheduling

Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Generation of language understanding systems and methods

Various technologies pertaining to dynamically identifying travel segments to be taken by a traveler traveling in a region are described herein, where observations about travel segments in the region are sparse and subject to alteration. A computer-implemented graph can be loaded into a memory, where the computer-implemented graph is representative of the region. The computer-implemented graph includes nodes that represent locations in the region and edges that represent travel segments of the region, where the edges have costs assigned thereto, and further where there is a defined statistical relationship between the costs. When an observation about a travel path is received, using the computer-implemented graph, inferences can be made about costs of traversing other travel paths in the region.

Travel path identification based upon statistical relationships between path costs

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Bellevue, WA, United States of America

Andrey Kolobov