Adobe, Inc.

Niranjan Shivanand Kumbi

Ajay Awatramani

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Omar Rahman

Vaidyanathan Venkatraman

Sreekanth Reddy

Ritwik Sinha

Rajan Madhavan

Kai Yeung Lau

Akangsha Sunil Bedmutha

Reddy Sreekanth

Sachin Soni

Viswanathan Swaminathan

Amit Gupta

Ajay Jain

Saayan Mitra

Sanjeev Tagra

Atanu Ranjan Sinha

Shiv Kumar Saini

Abhishek Jain

Xiang Chen

Gaurav Sinha

Eric Andrew Kienle

Navita Goyal

Aditya Bindal

Yifu Zheng

Alan Lai

Ayush Agarwal

Roodram Paneri

Lakshay Tanwar

Brajendra Kumar Bhujabal

Varinder Kumar

Badsah Mukherji

Uddhab Pant

Udit Kalani

Amit Doda

In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.

Bot activity detection for email tracking

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

A content generator system receives a request to generate content for a target entity, and one or more keywords. The content generator system retrieves, for the target entity, a current stage identifier linking the target entity to a current stage within a multi-stage objective. The content generator system generates an input vector including the current stage identifier, a target stage identifier, a token embedding comprising the one or more keywords, and a position embedding for each of the one or more keywords, the target stage identifier associated with a target stage within the multi-stage objective different from the current stage. The content generator system generates output text content for the target entity by applying a generative transformer network to the input vector. The content generator system transmits the output text content to a computing device associated with the target entity.

Content augmentation with machine generated content to meet content gaps during interaction with target entities

In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

Systems for predicting a terminal event

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

Growing community of inventors

Fremont, CA, United States of America

Niranjan Shivanand Kumbi