Google Inc.

Massachusetts Institute of Technology

Ching Law

Gokul Rajaram

Andrew Maxwell

David Gehrking

Rama Ranganath

Sumit Agarwal

Deepak Jindal

Brian Axe

Leora Ruth Wiseman

Kayi Lee

Ting Liu

Kai-Yeung S Siu

David H Ko

Jonathan Michael Krafcik

Leora Wiseman

Chi Yung Tse

One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Suggesting targeting information for ads, such as websites and/or categories of websites for example

Suggesting targeting information for ads; such as websites and/or categories of websites for example

A Website may be automatically categorized by accepting Website information, determining a set of scored clusters for the Website using the Website information, and determining at least one category of a predefined taxonomy using at least some of the set of clusters.

Categorizing objects, such as documents and/or clusters, with respect to a taxonomy and data structures derived from such categorization

A computer-implemented method, system, and computer-readable medium includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Placement attribute targeting

Methods, computer-readable media, and systems for placement attribute targeting are described. In one example, an advertiser can specify attributes of one or more web pages on which their advertisements can be displayed. Based on a page attribute of the web page that a user accesses, one or more advertisements can be transmitted to the user for display, where the advertisements are chosen such that the page attribute matches the attribute specified by the advertiser. In some implementations, a computer-implemented method includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

A server system receives, respectively, a first character string from a first user and a second character string from a second user. There are one or more differences between the first and second character strings. The server system obtains from a plurality of previously submitted complete queries, respectively, a first set of predicted complete queries corresponding to the first character string and a second set of predicted complete queries corresponding to the second character string. There are one or more identical queries in both the first and second sets. The server system conveys at least a first subset of the first set to the first user and at least a second subset of the second set to the second user. Both the first subset and the second subset include a respective identical query.

Autocompletion for partially entered query

A Website may be automatically categorized by (a) accepting Website information, (b) determining a set of scored clusters (e.g., semantic, term co-occurrence, etc.) for the Website using the Website information, and (c) determining at least one category (e.g., a vertical category) of a predefined taxonomy using at least some of the set of clusters.

Methods and systems for distributing interest categories within a hierarchical classification, wherein a parent interest category is identified and the hierarchical classification is filtered. A sub-interest category is extracted, based on the filtering, and a weighting associated with the sub-interest category is transferred into the parent interest category. The weighting is distributed in the hierarchical classification.

Distributing interest categories within a hierarchical classification

A demographic attribute value of a sink online document may be determined by (a) accepting a value(s) of the demographic attribute of a source online document, (b) accepting, for each of the source online documents having an accepted demographic attribute value, a probability that a user will visit or has visited the sink online document if the user visited the source online document, and (c) determining the demographic attribute value of the sink online document using the demographic attribute value of each of the source online documents and the probabilities. A demographic attribute value of the sink online document may also be determined using the above information and using the demographic attribute value of each of the source online documents and the probabilities that a user will visit or has visited the sink online document if the user visited the other online document.

Determining a demographic attribute value of an online document visited by users

A demographic attribute value of a sink online document (such as Websites or Web pages) may be determined given a set of users who have visited at least one of the source documents and the sink document, by (a) accepting a value(s) of the demographic attribute, each of which values is associated with a source online document (where each of the source online documents has a value for the demographic attribute and has been visited by at least one user of the given set), (b) determining an estimate of the demographic attribute value of each of the users of the given set using the accepted demographic attribute value of each of the source online documents visited by the user, and (c) determining the demographic attribute value of the sink online document using the determined estimate of the demographic attribute value of each of the users of the given set.

Probabilistic inference of site demographics from aggregate user internet usage and source demographic information

The invention features a method and system for identifying a plurality of tags using an efficient memoryless protocol. The system includes a reader and a plurality of tags. The reader is adapted to maintain an ordered set of query strings; select a string from the set of query strings; broadcast a query message containing the selected string or a portion of the selected string to the tags; and receive a response from one of the tags. The tags operate without batteries and are adapted to respond to the selected string broadcast by the reader. Accordingly, the tag identification methods are efficient in terms of both time and communication complexities.

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Ching Law