Adobe, Inc.

Jordan Walker

William Brandon George

Ryan A Rossi

Eunyee Koh

Wei Zhang

Sungchul Kim

Tung Mai

Trevor Paulsen

Ritwik Sinha

Alan Wilson

Tong Yu

Shunan Guo

Benjamin R Gaines

Said Kobeissi

Prithvi Bhutani

Andrew Watanabe

Tyler Rasmussen

Samuel Nephi Grigg

Abhisek Trivedi

Kevin Wilson

Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Learning-based method for generating visualizations via natural language

This disclosure relates to methods, non-transitory computer readable media, and systems that provide an attribution user interface that integrates attribution models as native components within the interface to configure analytics visualizations. By integrating attribution models and corresponding functions as native components of a user interface, the disclosed methods, non-transitory computer readable media, and systems can implement attribution models as parameters of attribution distributions or of any attribution visualizations, where the attribution models function as event categories. For instance, the disclosed methods, non-transitory computer readable media, and systems can provide analytics tools to generate visualizations of different attribution distributions of events across dimension values (or other visualizations) based on different attribution models. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can also modify an attribution-distribution visualization extemporaneously given user inputs for a new event category, new dimension, new segment, or other parameter for the visualization.

Dynamically generating attribution-model visualizations for display in attribution user interfaces

The present disclosure covers systems and methods for collecting and analyzing analytics data for a plurality of online user interactions and aggregating the analytics data to determine sentiment scores and generate a presentation of a path of interactions. For example, the systems and methods analyze the analytics data to identify attributes of the online user interactions and determine, based on the identified attributes, a sentiment score for each of the plurality of online user interactions. In addition, the systems and methods aggregate the plurality of online user online user interactions to identify an interaction path commonly experienced by the users of the interactions. Further, the systems and methods generate and provide an interactive presentation including a visualization of the interaction path and associated ranges of sentiment scores associated with types of online user interactions that make up the interaction path.

Dynamic sentiment-based mapping of user journeys

Systems and methods for deploying tutorial content to websites via tag management systems are disclosed. In an example, a tutorial deployment application accesses a content segment and a condition for displaying the content segment. The application generates a tutorial object including code to display the content segment. The application integrates the tutorial object into the set of runtime tags by selecting a tag from a set of runtime tags that control operations of a website and links the tag with the tutorial object. Based on a performance of the operation, the condition is met, and the tag is executed, causing the tutorial object to be loaded and executed by the website and the content segment to be displayed by the website.

Integrating web-based tutorials into websites using a tag manager

Growing community of inventors

Lehi, UT, United States of America

Jordan Walker