Mikhail V Parakhin

20110252012, Oct 13, 2011

Apr 9, 2010

12/757095

Satya Pradeep Kanduri - Redmond WA, US
Marcelo De Barros - Redmond WA, US
Mikhail Parakhin - Redmond WA, US
Cynthia Yu - Redmond WA, US
Qiang Wu - Redmond WA, US

MICROSOFT CORPORATION - Redmond WA

G06F 17/30
G06F 15/18

707706, 707731, 706 12, 707E17108

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.

20120163709, Jun 28, 2012

Dec 22, 2010

12/975684

Marcelo De Barros - Redmond WA, US
Satya Pradeep Kanduri - Bellevue WA, US
Nabeel Kaushal - Seattle WA, US
Mikhail Parakhin - Redmond WA, US
Manish Mittal - Redmond WA, US
Adam Edlavitch - Redmond WA, US

MICROSOFT CORPORATION - Redmond WA

G06K 9/00

382162

Outlier images—those images that differ substantially from other images in a set—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.

20130173572, Jul 4, 2013

Dec 30, 2011

13/341856

Lawrence Colagiovanni - Issaquah WA, US
Arun Sacheti - Sammamish WA, US
Marcelo De Barros - Redmond WA, US
Nektarios Ioannides - Bellevue WA, US
Mikhail Parakhin - Redmond WA, US
Meenaz Merchant - Sammamish WA, US

MICROSOFT CORPORATION - Redmond WA

G06F 17/30

707706, 707769, 707E17108

Information from social networks may be used to identify a user's interests and predilections, and the information may be used to affect search results. In one example, social networks have pages that correspond to real entities, such as manufacturers and merchants. Entity pages in social networks are mapped to their corresponding real entities, and information that users leave on the pages (e.g., “likes”, or textual reviews) are extracted to determine users' sentiments about the entities. When users search for products with a search engine, user sentiment is then used to guide the results. Social networks' information about users (e.g., their affinities, such as schools, workplaces, interests) may be used to determine the relevance of specific users' sentiments—e.g., sentiments of users who went to a particular school may be used to influence search results, when the search is requested by someone who went to the same school.

20130198186, Aug 1, 2013

Jan 28, 2012

13/360664

Mikhail Parakhin - Redmond WA, US
Dmitry Korolev - Bellevue WA, US
Alexey Poyarkov - Kirkland WA, US

MICROSOFT CORPORATION - Redmond WA

G06F 17/30

707737, 707E17089

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.

20230085967, Mar 23, 2023

Nov 21, 2022

17/991163

- Redmond WA, US
Nagareddy S. Reddy - Sammamish WA, US
Mikhail Parakhin - Redmond WA, US

G06F 16/22
G06F 16/13
G06F 16/9535

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.

20210109911, Apr 15, 2021

Dec 21, 2020

17/128856

- Redmond WA, US
Nagareddy S. Reddy - Sammamish WA, US
Mikhail Parakhin - Redmond WA, US

G06F 16/22
G06F 16/13
G06F 16/9535

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.

20180181603, Jun 28, 2018

Feb 21, 2018

15/901020

- Redmond WA, US
Nagareddy S. Reddy - Sammamish WA, US
Mikhail Parakhin - Redmond WA, US

G06F 17/30

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.

20160078133, Mar 17, 2016

Sep 15, 2014

14/487032

- Redmond WA, US
Arun Sacheti - Sammamish WA, US
Yandong Guo - Bellevue WA, US
Mikhail Valeryevich Parakhin - Seattle WA, US

G06F 17/30
G06F 3/0482
G06F 3/0484
G06F 3/0485

One or more techniques and/or systems are provided for constructing a content interface layout. For example a search query may be received from a user of a device. A content interest context for the user may be identified (e.g., a time of day, a season, a user location, a user social network post about planting a garden, device type of the user, etc.). Content interfaces (e.g., a gardening image collection, a tomato planting instructional app, a soil related topic search suggestion, etc.) may be populated within content interface slots of a search results canvas for the search query based upon user engagement metrics and the content interest context. In this way, content interfaces, comprising supplemental content, may be assigned to content interface slots so that such content interfaces have a high probability of being relevant to and/or engaged by the user.