Adchemy, Inc.

Zeta Global Corp.

Walmart Apollo, LLC

Richard Edward Chatwin

Matthew Robert McEachen

Murthy V Nukala

Benjamin David Foster

Veeravich Thi Thumasathit

Timothy J Catlin

Ayman Farahat

David Robert Kauchak

Shyam Kapur

Shie Mannor

Timothy J O Catlin

Kiran Garaga Lokeswarappa

Vineet Abhishek

Alan Coleman

Jaymin Daniel Mankowitz

Yashaswini Kotresh

Scott Campbell Johnston

Srinidhi Ramesh Kondaji

Steve Na

In one embodiment, a method includes accessing a current-visitor context of a current visitor to a web page in a current web-browsing session. The current-visitor context includes one or more data associated with or concerning the current visitor. The method includes selecting based on the current-visitor context a particular one of multiple possible instances of the web page for presentation to the current visitor. The particular one of the multiple possible instances of the web page is substantially most likely to generate a highest expected outcome from interaction with the web page by the current visitor as indicated by the current-visitor context.

Using visitor context and web page features to select web pages for display

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a source classifier with labeled source training data of a first product category from a website of an online retailer, clustering target data for a second product category into a plurality of clusters, inserting into each cluster labeled source training data of the first product category, assigning a domain discriminator score to each cluster, determining whether each cluster comprises an agreement cluster or a disagreement cluster using the domain discriminator score, receiving a product search request for a product of the second category from a user of the web site, and coordinating a display of the product on the web site to promote the product.

Systems and methods for promoting products in product search results using transfer learning with active sampling

In some embodiments, a method can comprise analyzing user interaction with one or more items. In some embodiments, analyzing user interaction with the one or more items can comprise determining a number of impressions for each item of the one or more items. In many embodiments, the method can further comprise determining at least one undiscovered item of the one or more items based at least in part on the analyzing user interaction with the one or more items, estimating a performance of the at least one undiscovered item of the one or more items, promoting the at least one undiscovered item of the one or more items based at least in part on the estimating the performance of the at least one undiscovered item of the one or more items. Other embodiments of related methods and systems are also provided.

Systems and methods for item discoverability

In one embodiment, a method includes accessing a current-visitor context of a current visitor to a web page in a current web-browsing session. The current-visitor context includes one or more data associated with or concerning the current visitor. The method includes selecting based on the current-visitor context a particular one of multiple possible instances of the web page for presentation to the current visitor. The particular one of the multiple possible instances of the web page is substantially most likely to generate a highest expected outcome from interaction with the web page bye the current visitor as indicated by the current-visitor context.

In one embodiment, a method includes receiving, in real-time, user information associated with a user of a client computing device. One or more user features are derived from the user information associated with the user. The one or more user features include one or more user intents. The one or more user intents are matched with one or more of a plurality of advertiser intents. The user information is determined to be accepted. In response to accepting the user information, an advertisement is generated based on the matching of the one or more user intents and the one or more of the plurality of advertiser intents.

Real-time bidding and advertising content generation

The present invention is directed towards systems and method for generating a bid amount for one or more keywords for a display of an advertisement associated with a given keyword. The method comprises calculating estimates of one or more advertising metrics for one or more keywords associated with one or more advertisements displayed at one or more positions. Target positions are identified for the one or more keywords at which a return on investment earned by the display of one or more advertisements in response to a query that matches a given keyword exceeds a threshold by a given probability on the basis of the estimates to select a given target position that provides a greatest estimated profit. Bid amounts are generated for the one or more keywords in order to obtain the display of associated advertisements at the given target position.

System and method for generating optimized bids for advertisement keywords

In one embodiment, a method includes accessing online advertising data including metric values for one or more online advertising spaces; identifying pairs of the user variables; for each of the pairs, calculating a statistical score for each of the online advertising metrics and calculating a weighted score based on all the statistical scores for the pair; selecting one of the pairs based on its weighted score; and performing a merge test on the selected pair; and merging the user variables in the selected one of the pairs with each other if the selected one of the pairs passes the merge test.

Grouping user features based on performance measures

In one embodiment, a method includes accessing metric values for one or more online advertising spaces for placing one or more online advertisements; generating a first user variable subgroup and a second user variable subgroup from a user variable group; deriving first metric values for the first user variable subgroup; deriving second metric values for the second user variable subgroup; for each of the online advertising metrics, using a particular statistical model to calculate a statistical score based on the first and second metric values with respect to the online advertising metric and the first and second user variable subgroups; performing a split test on the first user variable subgroup and the second user variable subgroup; and splitting the user variable group into two separate user variable groups if the first and second variable subgroups pass the split test.

The present invention is directed towards systems and methods for generating bids for one or more keywords and advertisement groups. The method of the present invention comprises generating a data set with one or more entries, a given entry identifying one or more advertising metric values associated with an advertisement group and keyword. One or more keywords are selected from the one or more entries in the data set, and a bid is generated for the one or more selected keywords using the one or more advertising metric values associated with the one or more selected keywords. The one or more advertising metric values of the one or more entries associated with a given advertisement group exclusive of the one or more advertising metric values associated with the one or more selected keywords is aggregated. A bid is generated for the one or more advertisement groups using the one or more aggregated advertising metric values associated with the one or more advertisement groups.

System and method for generating target bids for advertisement group keywords

In one embodiment, a method includes accessing information regarding a visitor to a website; accessing a plurality of filters that are each associated with one of a plurality of lead buyers and being defined by a set of lead criteria specified by the associated lead buyer; and, for each of the filters, determining whether the information regarding the visitor satisfies the set of lead criteria defining the filter and, if the information regarding the visitor satisfies the set of lead criteria defining the filter, selecting the filter as a filter for which the visitor qualifies as a lead. The method includes, for each of the filters for which the visitor qualifies as a lead, accessing information indicating a potential revenue to be obtained by a lead matcher from the lead buyer associated with the filter.

Matching visitors as leads to lead buyers

Embodiments of a web page optimization system are described. The system utilizes an accumulated body of visitor data regarding interaction with one or more web pages to determine an optimized web page to display to a visitor. Various types of session data, including network traffic data for the client computer, demographic information about the visitor, and general and specific session data of other visitors in similar browsing sessions are defined and compiled. Continuous full-path optimization from initial visitor interaction with the web page is provided through a visitor context analysis component that analyzes certain parameters related to how the visitor has accessed the web page. A real-time web page optimizer component introduces variations in one or more features of a web page to create a new web page, conducts structured tests of the new web page, compares the performance of the web page with one or more known served web pages and removes from presentation eligibility those pages with inferior performance. This facilitates real-time decision making of which web page to serve to a visitor based on an accumulated visitor context. It further allows the authoring of web pages that are optimized with respect to eliciting a desired user interaction with the web page.

Web page server process using visitor context and page features to select optimized web pages for display

Embodiments of a data representation system for describing specific data sets, such as documents, web pages, or search engine queries, based on data tokens, such as words or n-grams, contained in a collection of documents are described. Such a system can be used in any type of information retrieval application, such as a document, web page, or online advertisement serving process, based on an information request, such as a query executed through an Internet search engine. For example, when a search is performed at a search engine, a content provider uses the system to represent the search query and compares the query representation against representations of a set of content in order to identify, retrieve and aggregate the content from the set most relevant to the search query, in the form of a web page or other data unit for display or access through the web browser.

Growing community of inventors

Sunnyvale, CA, United States of America

Richard Edward Chatwin