Adobe, Inc.

Saurabh Mahapatra

Fan Du

Atanu Ranjan Sinha

Ryan A Rossi

Eunyee Koh

Vasanthi Holtcamp

Aurghya Maiti

Niyati Himanshu Chhaya

Sungchul Kim

Sumit Shekhar

Iftikhar Ahamath Burhanuddin

Sana Malik Lee

Sonali Surange

Tak Yeon Lee

Shunan Guo

Aparna Garimella

Jane Elizabeth Hoffswell

Nikhil Belsare

Sapthotharan Krishnan Nair

Sagnik Mukherjee

Rachit Bansal

Manoj Kilaru

Sharmila Reddy Nangi

John Anderson

Zening Qu

Arpit Ajay Narechania

Harshita Chopra

Atharv Tyagi

Sarthak Kapoor

Saili Myana

Tirupati Saketh Chandra

Atishay Ganesh

Titas Chakraborty

Aneesh Shetty

Andrew Thomson

Snehal Raj

Jay Mundra

Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: 'quality'—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, an interactive tree view may visually represent a nested attribute schema and attribute quality or consumption metrics to facilitate discovery of bad data before ingesting into a data lake.

Interactive tree representing attribute quality or consumption metrics for data ingestion and other applications

Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.

Systems and methods for content customization

A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.

Auto-creation of custom models for text summarization

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 for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Leading indicators and monitor business KPIs and metrics for preemptive action

Growing community of inventors

Sunnyvale, CA, United States of America

Saurabh Mahapatra