Yahoo! Inc.

Excalibur Ip, LLC

Yahoo Holdings, Inc.

Oath Inc.

Verizon Media Inc.

Excalibur Lp, Lcc

Supreeth Rao

Sudharsan Vasudevan

Utkarsh Shrivastava

Matt Earls

Anurag Ojha

Saurabh Dixit

Jianning Dong

Gopal Mor

Aditi Sinha

Varun Bhagwan

Kaushal Kurapati

Shivakumar Ningappa

Patrick Mason

Sundeep Narravula

Ram Sriharsha

Tim Tully

Sunil Kumar Gupta

Reynold Xin

Chetan Nadgire

Seema Kamath

Swapnil Patel

An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Keyword recommendation

An advertising and data analysis platform may need to mine through vast amounts of data to come up with insights into advertising effectiveness, and measure and improve the effectiveness of advertising reach. Distributed network data analytics may be applied to ad matching/targeting, such that an in-memory cluster computing environment may be used with advertising data. For example, HADOOP may be utilized for distributed processing of the vast amounts of data and the HADOOP distributed file system (HDFS) is used for organizing communications and storage of that data. Satellite clusters or nodes may be generated that also utilize HDFS. For example, a SPARK or SHARK satellite cluster may be arranged to further utilize the HDFS of the HADOOP clusters.

Spark satellite clusters to HADOOP data stores

A system may measure the virality of content items on a network. The virality may be measured for Internet content using indices. Indices may be generated that represent the share or news worthiness of content. An indexer may monitor the content items and generate a score which may be used to identify which content items are most likely to receive a user access request or referral from the remote server. A logic generator may display visual elements as graphical representations of the index score.

System and method for measuring the virality of internet content using indices

A cross-device messaging integration capability is disclosed, which allows a user using a first device to indicate, using a first user computing device, an intent to perform one or more messaging actions at one or more second user computing device(s). The first and second devices may be different devices. The user may receive a reminder or other notification of the user's intent at the second device(s).

Cross-device integration system and method

A system and method for generating a personalized trends module includes steps of: for a given user, producing a social timeline by logging content posted on the given user's accounts on social media sites; analyzing the social timeline for recently posted content to derive an interim summary of first trending topics for the given user; receiving from a content personalization platform an in-stream feed of second trending topics based on the user's recent on-line activity including page views, queries, and clicks; augmenting the social timeline with the second trending topics from the in-stream feed to produce an interim list of third trending topics; ranking the third trending topics by source category using a frequency index; selecting the highest ranking third trending topics from each source category; and presenting a personalized trends module with positions allocated to the highest ranking third trending topics.

Personal trends module

An system can include a server that includes or is associated with a keyword price recommendation module. The module can be configured to recommend an optimal price for a keyword, per referral event, in a search engine marketing campaign. For making such a recommendation, the module can be configured to determine revenue generated per referral event for the keyword according to a number of referral events for the keyword for a plurality of users and revenue generated for the keyword by the plurality of users. It can also be configured to select a return on investment percentage for the keyword, and determine the optimal price for the keyword according to the revenue generated per referral event and the return on investment percentage. Additionally or alternatively, the keyword price recommendation module can be configured to calculate the return on investment percentage for the keyword according to at least one market condition.

Keyword price recommendation

Methods and systems for providing content on a webpage includes rendering a webpage having a plurality of page areas. Each of the plurality of page areas is associated with an entity having display content. Selection of one of the plurality of page areas is detected. The selection causes a landing page to be rendered in place of the webpage. A return to the webpage from the landing page is detected. In response to the return, the selected page area of the webpage is modified to include at least one additional entity having associated display content.

System and method for contextually enriching content of a referrer page when returning from a landing page

A revenue share analysis module can determine individual session revenues for content items provided by content providers. The module can also determine total session content revenues for each of the content items according to the determined individual session revenues, and determine respective content values for each of the content items according to the determined total session revenues for each of the content items. It also may determine total session provider revenues for each of the content providers according to the determined total session revenues for each of the content items, and determine respective provider values for each of the content providers according to the determined total session revenues for each of the content providers. Also, it can determine revenue share offers according to the content values and/or the provider values.

Revenue share analysis

A system and method for automatic draft email notification includes the following steps or acts performed while a user is engaged with a first email in an active email session: retrieving a value from the first email to use as a search term; accessing draft emails associated with the user; determining if the search term from the first email matches a corresponding string in the draft emails associated with the user; and presenting the draft emails that match the search term as a listing of selectable draft emails.

Automatic draft email notification

A system can provide unique user metrics associated with a node in a hierarchy and the node's decedents in real time. This can occur by merging sketches of the node and its decedents to form a merged sketch, and retrieving the unique user metrics from the merged sketch. The sketches and the merged sketch may each be a separate data form, such as a hash. The hierarchy may include an online content taxonomy and the sketches of each node may include unique user metrics associated with the node, such as user interaction metrics associated with a category of content of the node. The system may build sketches for the nodes of a hierarchy, associate each sketch with its ancestor sketches, invert the associations, tie corresponding descendant sketches of an ancestor sketch based on the inversions, and merge the descendant sketches with their ancestor sketches to generate the merged sketches.

Recursive unique user metrics in real time

Software on a content-aggregation website obtains a resource associated with a podcast from a website publishing the podcast and stores it e resource on the content-aggregation website. The software adds the resource as a leaf node to a taxonomy generated by the content-aggregation website. The addition is based on data associated with the podcast. The non-leaf nodes in the taxonomy are categories of content. The software determines that a user of the content-aggregation website is qualified as to at least one category that includes the resource as a leaf node. The determination is based at least in part on feedback from the user that includes a viewing or listening history for the user. Then the software serves the resource to the user in a content stream published by the content-aggregation website, based at least in part on a personalization score associated with the resource.

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Sunnyvale, CA, United States of America

Supreeth Rao