Adobe, Inc.

Sap Se

Omar Rahman

Atanu Ranjan Sinha

Niranjan Shivanand Kumbi

Vaidyanathan Venkatraman

Shiv Kumar Saini

Ajay Awatramani

Rajan Madhavan

Kai Yeung Lau

Sungchul Kim

Ritwik Sinha

Han Li

Xiang Chen

Gaurav Sinha

Julio C Navas

Tu Truong

Fuming Wu

Gautam Choudhary

Hanxiang Chen

Nazanin Zaker Habibabadi

Akangsha Sunil Bedmutha

Brajendra Kumar Bhujabal

Badsah Mukherji

Liana Vagharshakian

Camille Girabawe

Scott Trafton

Vivek Sinha

Ajain Kuzhimattathil

Goutham Srivatsav Arra

Jean Bernard Hishamunda

Amit Doda

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

Artificial intelligence approaches for predicting conversion activity probability scores and key personas for 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

Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

Provisioning interactive content based on predicted user-engagement levels

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

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

In an example, a machine learning algorithm is used to train an entity risk evaluation model to output an entity risk score based on transaction data in a computer network. Entity risk scores for various entities may be stored in a database, and retrieved and displayed upon user interaction with one or more reports involving corresponding entities.

Machine learning models for evaluating entities in a high-volume computer network

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San Jose, CA, United States of America

Omar Rahman