Microsoft Technology Licensing, LLC

Mikhail Parakhin

Pragyana K Mishra

Nagareddy S Reddy

Marcelo De Barros

Satya Pradeep Kanduri

Qiang Wu

Manish Mittal

Dmitry Korolev

Nabeel Kaushal

Cynthia Yu

Adam Edlavitch

Alexey Poyarkov

A pervasive search architecture that indexes personal content of a querying user and made accessible to the user by other users. A compute node of a personal content location facilitates index generation and serve of the index. The index is generated for personal content stored at the personal content location. For a given content location, the index may encapsulate content stored in a set of locations with access permissions. The indexing application runs periodically at the personal content location and incrementally indexes content that is added to the shared locations. The same application allows the user to configure locations with the desired access permissions for participation in the search.

Pervasive search architecture

Architecture that automatically determines relationships between vector spaces of disparate media types, and outputs ranker signals based on these relationships, all in a single process. The architecture improves search result relevance by simultaneously clustering queries and documents, and enables the training of a model for creating one or more ranker signals using simultaneous clustering of queries and documents in their respective spaces.

Determination of relationships between collections of disparate media types

A web search system uses humans to rank the relevance of results returned for various sample search queries. The search results may be divided into groups allowing training and validation with the ranked results. Consistent guidelines for human evaluation allow consistent results across a number of people performing the ranking. After a machine learning categorization tool, such as MART, has been programmed and validated, it may be used to provide an absolute rank of relevance for documents returned, rather than a simple relative ranking, based, for example, on key word matches and click counts. Documents with lower relevance rankings may be excluded from consideration when developing related refinements, such as category and price sorting.

Shopping search engines

Outlier images&#x2014;those images that differ substantially from other images in a set&#x2014;can be automatically identified. One or more penalty values can be assigned to each image that quantifies how different that image is from others in the set. A threshold can be determined based on the set of penalty values. Each image whose penalty values are above the threshold is an outlier image. The penalty values can be the sum of per-pixel penalty values multiplied by the number of pixels with nonzero penalty values. A per-pixel penalty value can be the difference between a color value for that pixel and a predetermined range of color values, based on corresponding pixels in other images. The per-pixel penalty value can be determined for each component color and then optionally summed together. The threshold penalty values can be adjusted to provide for greater, or less, sensitivity to differences among the images.

Automated identification of image outliers

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Mikhail Parakhin