Adobe, Inc.

William Brandon George

Kevin Gary Smith

John Bevil Bates

James Meyer

Trevor Paulsen

Joshua Butikofer

Kyle W Smith

Benjamin R Gaines

Edward L Hewett

Jordan Walker

Pearce Aurigemma

Balaji Krishnamurthy

Vishwa Vinay

Nikaash Puri

Mihir Naware

Iftikhar Ahamath Burhanuddin

Ritwik Sinha

Alan Wilson

Lauren Dest

Kumar Mrityunjay Singh

Yaman Kumar

Nathan A Purser

Kunal Kumar Jain

Hyman Chung

Said Kobeissi

Brandon Pack

Matthew William Rozen

Carl Sandquist

Kristopher Paries

Pranjal Prasoon

Somesh Singh

Alexander Klimetschek

Ian M Hansen

Prithvi Bhutani

Suman Basetty

Clinton Hansen Goudie-Nice

James Patrick O'Reilly

Abhinav Chauhan

Mihai Corlan

Timothy Chia-chi Liu

Walter Chang

Ryan A Rossi

Eunyee Koh

Wei Zhang

Saayan Mitra

Fan Du

Sungchul Kim

Handong Zhao

Tung Mai

Somdeb Sarkhel

Sunav Choudhary

Tong Yu

Sanket Vaibhav Mehta

Shunan Guo

Ivan Andrus

James Harold Brown

Chris Challis

Joshua Aaron Hansen

Tyler Rasmussen

Samuel Nephi Grigg

Joseph Ward

Abhisek Trivedi

Matthew Bryan

Bret B Gundersen

Eric V Anderson

Harvineet Singh

Alexandra Strawn

In some embodiments, a contact stream is generated or modified based on configuration data received from a machine-learning model. Multiple contact items are selected for a contact stream, to be delivered to a user device via electronic communication channels. In addition, a success metric is identified indicating an engagement with the contact stream or an action performed following the engagement. A machine-learning model is applied to the contact items, where the machine-learning model is trained to identify relationships among actions in an online environment and configuration parameters that control delivery of contact streams. The machine-learning model provides an output indicating configuration data or a success probability for the contact stream. The configuration data includes configuration parameter values computed by the machine-learning model for achieving the identified success metric. The success probability indicates a probability computed by the machine-learning model for achieving the identified success metric.

Automatic generation and modification of contact streams via machine-learning analytics

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

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

Generating and executing automatic suggestions to modify data of ingested data collections without additional data ingestion

The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.

Identifying contributing factors to a metric anomaly

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

This disclosure generally covers systems and methods that create sequential segments from analytics data to enable investigation of events that occurred before or after a certain sequence of events—that is, pre-sequence or post-sequence events. In particular, certain embodiments of the disclosed systems and methods receive a segment query of certain analytics data to identify events that occurred before or after a defined sequence of events within a network and—in response to the segment query—provide a query result that identifies pre-sequence events or post-sequence events. By providing such query results, the disclosed systems and methods enable users to examine correlations between a sequence of events and any pre-sequence or post-sequence events, including any data associated with those events at a granular level.

Generating sequential segments with pre-sequence or post-sequence analytics data

In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Systems for generating sequential supporting answer reports

Systems and methods for software management are described. One or more embodiments of the present disclosure receive first organization data about a first organization that uses a first software system and second organization data about a second organization that uses a second software system; receive first event data from the first organization and second event data from the second organization; generate first converted event data and second converted event data by converting the first event data and the second event data to a common data format; predict organization output based on using the first software system and based on using the second software system; compute a first rating for the first software system and a second rating for the second software system for use in the third organization; and installing the first software system in a computer system of a third organization based on the first rating.

Systems industry benchmarking and corporation systems tool recommendation

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

The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

Determining audience segments of users that contributed to a metric anomaly

The present disclosure is directed toward systems and methods that allow users to efficiently and effectively create and identify segments of usage patterns. For example, systems and methods described herein allow marketers to query and return sequential segments including sequence conditions based on user-defined dimension item values. Furthermore, systems and methods described allow marketers to query and return sequential segments including sequential events based on user-defined dimension variables. In addition to the foregoing, systems and methods described herein allow marketers to query and return sequential segments defined by repeated events performed at given regularity or frequency.

Generating segments based on intelligent sequential data

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

Growing community of inventors

Pleasant Grove, UT, United States of America

William Brandon George