Adobe, Inc.

Nikhil Belsare

Alexander R Hood

Vasanthi Holtcamp

Greg Collison

Albert Lim

Antoine Barbier

Eunyee Koh

Fan Du

Sungchul Kim

Tak Yeon Lee

Ryan A Rossi

Piyush Gupta

Handong Zhao

Sumit Shekhar

Sana Malik Lee

Saurabh Mahapatra

Thomas M Jacobs

Yikun Xian

Subrahmanya Kumar Giliyaru

Duy-Trung Trong Dinh

Zening Qu

Andrew Thomson

Caroline Jiwon Kim

Binit Kumar Sinha

Silky Kedawat

Gaurav Makkar

Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

Lifelong schema matching

The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

Dynamic cross-platform ask interface and natural language processing model

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

Facilitating generation of data visualizations via natural language processing

Systems and methods are disclosed for enabling incremental reach for an advertising campaign, across multiple screens/channels. In some embodiments, a base TV/media plan is uploaded, and targeted exposed viewers are monitored. Unexposed target viewers are identified. Additional media channels that unexposed viewers use are found. Unexposed viewers are matched with pricing and media avails from one or more media directories. Media avails are then analyzed along with incremental on-target impressions. Targeted avails for unexposed viewers are then determined, based on lowest incremental cost and largest incremental reach are added to a current cross-screen plan. An analysis to find new unexposed viewers is then re-run to determine the next-most cost effective avails. The above steps are repeated or looped until a selected advertisement budget has been allocated. User interface embodiments for these methods are also disclosed.

System and method for executing an advertising campaign that incrementally reaches unexposed target viewers

Growing community of inventors

Foster City, CA, United States of America

Nikhil Belsare