Adobe, Inc.

Ajay Awatramani

Niranjan Shivanand Kumbi

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Rajan Madhavan

Omar Rahman

Vaidyanathan Venkatraman

Reddy Sreekanth

Sachin Soni

Amit Gupta

Ajay Jain

Sanjeev Tagra

Abhishek Jain

Eric Andrew Kienle

Sreekanth Reddy

Akangsha Sunil Bedmutha

Kai Yeung Lau

Aditya Bindal

Brajendra Kumar Bhujabal

Badsah Mukherji

Varinder Kumar

Uddhab Pant

Lakshay Tanwar

Certain embodiments involve using machine-learning methods to generate a recommendation for sequential content items. A method involves accessing a content item associated with an interaction stage in an online environment. A stage graph, which includes a ratio of interactions, of the content item is generated. An additional content item that includes additional stage-transition content is identified. A sequencing function outcome indicating a portion of the ratio of interactions is determined. A transition probability of receiving an interaction with stage-transition content and an additional interaction with the additional stage-transition content is calculated. A content provider system is caused to provide a recipient device with interactive content that includes the additional content item.

Machine-learning techniques applied to interaction data for determining sequential content and facilitating interactions in online environments

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.

Generating a recommended target audience based on determining a predicted attendance utilizing a machine learning approach

In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.

Systems for role classification

Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.

Method to explain factors influencing AI predictions with deep neural networks

Techniques and systems are described for content interest from interaction information. Keywords are extracted from digital content, and relevance values are determined based on the keywords that captures both the statistical and semantic significance of topics in the digital content through use of a network representation. Interest values for an entity are determined based on the relevance values and an interaction dataset, which capture both the statistical and semantic significance of the topics with respect to the entity. The interest values may be utilized to control output of digital content to a client device.

Content interest from interaction information

The present disclosure relates to systems, non-transitory computer-readable media, and methods for initiating electronic chats based on conversation workflows identified in response to detected user actions in connection with an embedded document container displaying a PDF file. In particular, in one or more embodiments, the disclosed systems detect user interactions with a PDF file displayed by a document container embedded in a webpage. The disclosed systems can determine whether the detected user interactions include or indicate a conversation workflow trigger associated with a conversation workflow. The disclosed systems can further generate electronic messages based on the conversation workflow and provide the generated electronic messages to the user in connection with the webpage where the document container is embedded.

Providing personalized chat communications within portable document format documents

An improved marketing automation system can optimize governance of server resources by managing the execution of campaigns. The marketing automation system can develop intelligence around a given customer's inflow of incoming campaigns, the execution time of the campaigns, and general resource utilization over time. The marketing automation system can learn to predict an expected number and type of campaigns for a pre-defined window of time. This intelligence can be leveraged to ensure that one or more executors remain available to execute predicted high priority campaigns upon placement into an execution queue. Further, this intelligence can be applied such that predicted dormant executors can be used to execute low priority tasks. In this way, the marketing automation system minimizes queue time until execution for high priority campaigns while optimizing use of server resources.

Dynamic allocation of execution resources

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Fremont, CA, United States of America

Ajay Awatramani