The Suitability of Websites for Participation in an Advertising Network

Three new Google patent applications involving paid search:

Reviewing the suitability of websites for participation in an advertising network

The way in which Websites are reviewed for use in an advertising network may be improved by (a) accepting a collection including one or more documents, (b) determining whether or not the collection complies with policies of an advertising network, and (c) approving the collection if it was determined that the collection complies with the policies. The collection may be added to the advertising network if the collection is approved such that (e.g., content-targeted) advertisements may be served in association with renderings of documents included in the collection. The collection may be a Website including one or more Webpages. The policy may concern (A) content of the one or more documents of the collection, (B) usability of a Website wherein the collection of one or more documents is a Website including one or more Webpages, and/or (C) a possible fraud or deception on the advertising network or participants of the advertising network by the collection.

In addition to policing a site for advertising policy violations (such as hate-centric documents, sites selling alcoholic beverages other than wine, etc.), this patent application calls for a proactive approach to sites that violate Google’s policies. It also explicitly refers to a “quality score” for web pages:

[0059] Website quality scoring may be determined using defined quality criteria. Some examples of quality criteria of a Website include: usage data from the advertising network or other sources (e.g., impressions, selections, user geolocation, conversions, and derivatives of such information); Website (in advertising network, or not) popularity (e.g., as measured by Google toolbar for example); Website spam (e.g., so-called “link farms”, where Websites are set up having little content of relevance but many links to each other to boost search engine scores, invalid content being on a page such as a pornographic Website having a black background and white text relating to porn, but black (and therefore hidden) text referring to digital cameras or other popular queries, to boost search engine scores, etc.); etc.

It also serves notice on “pay-to-click” (made for adsense) sites.

Prioritizing ad review, by using expected revenue for example, in an advertising system

Ads are reviewed manually once they have reached a certain expected revenue threshold (instead of attempting to review manually all incoming ads as was done in the past). This review process considers the fact that the many advertisers submit hundreds or even thousands of ads with each individually returning relatively little revenue. Such a review process should greatly reduce the number of ads, or at least to prioritize the order of ads, pending manual approval. The threshold may be set to the approximate cost of manually approving an ad, thereby reducing ad reviews pending manual review significantly. A classification of the ad (e.g., forbidden, suspicious, unchecked, an ad category, etc.), which may be determined by automated means for example, may also be used when prioritizing the order of ads pending manual approval. Revenue-based scores may also be used to control a review of an advertisement. For example, such scores may be used to select one of a plurality of review protocols.

Determining and/or managing offers such as bids for advertising

Offers, such as bids in an advertising network, may be determined and/or managed by accepting an ad budget and at least one ad serving constraint, and then generating offer information using the ad budget and the serving constraint(s). The offer may be generated by obtaining, for each of the ad serving constraint(s), a plurality of points, wherein each point includes a cost per event value and an event quantity value. These points collectively define a landscape. A convex landscape for each of the ad serving constraint(s) is then determined from the landscape(s). One or more points from at least one of the convex landscapes is then used to generate the offer information.

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