Google Inc.

Interactive Flow Studies Corporation

Bilgehan Uygar Oztekin

Taher H Haveliwala

Glen M Jeh

Sepandar D Kamvar

Tessa Macduff

Pei-Wen Andy Chiu

Murat Okcay

Mehran Sahami

Kedar Dhamdhere

Sugato Basu

Finnegan Southey

Daria Antonova

Zhenyu Mao

Adam J Klein

In a method of generating a search boost vector, a first set of content location identifiers for first content are provided. A second set of content location identifiers for second content are identified. The second content is related to the first content in accordance with one or more predefined criteria. A search boost vector is generated by determining weights corresponding to content location identifiers in the second set of content location identifiers. The search boost vector may also be generated by determining weights corresponding to content location identifiers in the first set of content location identifiers. The predefined criteria may include one or more additional content location identifiers that are co-visited with one or more content location identifiers for the first set of content location identifiers. Co-visits are by a community of users during an interval of time.

Search boost vector based on co-visitation information

A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.

Generating improved document classification data using historical search results

A method of processing a search query includes, for each search context of a plurality of search contexts, for each scoring primitive of a plurality of scoring primitives, and for a set of previously executed search queries that are consistent with the search context, determining a correlation between the scoring primitive and actual user selections of results of the previously executed search queries by a plurality of users. For each search context, machine learning is performed on the correlations to identify a predicted performance function comprising a weighted subset of the scoring primitives that meet predefined predictive quality criteria. Executing a user submitted search query includes associating the user submitted search query with a respective search context, and ordering at least a portion of the search results in accordance with the predicted performance function for the search context for the user submitted search query.

Reordering search query results in accordance with search context specific predicted performance functions

In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Generating website profiles based on queries from webistes and user activities on the search results

This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained. A computational unit is provided to perform computational fluid flow dynamics analysis on fluid flow models. The computed data can then be compared to the test data from the fluid flow analysis device.

Fluid flow computation, visualization, and analysis

This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained.

Fluid flow visualization and analysis

In a method of generating a search boost vector, a first set of content location identifiers for first content are provided. A second set of content location identifiers for second content are identified. The second content is related to the first content in accordance with one or more first categories corresponding to the first content. The one or more first categories may be determined by applying a text classifier to the first content. The second content may also be identified based on one or more user profiles and/or one or more content site profiles. The search boost vector is generated using weights corresponding to the content location identifiers in the first set of content location identifiers and/or the second set of content location identifiers. The one or more search boost vectors may be used to boost one or more search results.

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Mountain View, CA, United States of America

Bilgehan Uygar Oztekin