Adobe, Inc.

Prithvi Bhutani

William Brandon George

Said Kobeissi

Ryan A Rossi

Eunyee Koh

Wei Zhang

Fan Du

Sungchul Kim

Ritwik Sinha

John Bevil Bates

Tung Mai

Sunav Choudhary

Tong Yu

Shunan Guo

Sanket Vaibhav Mehta

Ivan Andrus

Jordan Walker

Chris Challis

Harvineet Singh

Tyler Rasmussen

Samuel Nephi Grigg

Abhisek Trivedi

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

Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Intelligent customer journey mining and mapping

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Lynnwood, WA, United States of America

Prithvi Bhutani