Adobe, Inc.

Anandhavelu N

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Ritwik Sinha

Franck Dernoncourt

Shiv Kumar Saini

Nedim Lipka

Rajiv Jain

Vlad Ion Morariu

Jiuxiang Gu

Harsh Jhamtani

Cedric Huesler

Quan Hung Tran

Ashutosh Mehra

Shriram Venkatesh Shet Revankar

Payal Bajaj

Atanu R Sinha

Shriram Revankar

Kannan Iyer

Ani Nenkova

Shankar Srinivasan

Michael Rimer

Puneet Mathur

Padmanabhan Anandan

Verena Kaynig-Fittkau

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.

Extracting document hierarchy using a multimodal, layer-wise link prediction neural network

This disclosure relates to methods, non-transitory computer readable media, and systems that, based on a sparse textual segment, can use machine learning models to generate document variants that are both conforming to digital content guidelines and uniquely tailored for distribution to client devices of specific audiences via specific delivery channels. To create such variants, in some embodiments, the methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of content-guideline-conforming documents. Additionally, or alternatively, in certain implementations, the disclosed methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of audience-channel-specific documents.

Automatically generating digital enterprise content variants

Disclosed are various embodiments for automatically creating on a computer analytics tags for different object types of website objects in web pages with analytics tracking capability in a dynamic tag management system. In one implementation, user input is received identifying a website object for tagging in the web pages and keywords are identified based on the user input. Based on the keywords, multiple occurrences of the website object in the web are identified, wherein the multiple occurrences of the website object correspond to multiple object types. The computer automatically creates analytics tags for the website object corresponding to object types. Based on the website object, an expansion object is identified and the computer automatically creates an analytics tag for the expansion object.

Methods and systems for tag expansion by handling website object variations and automatic tag suggestions in dynamic tag management

The present disclosure is directed toward systems and methods for identifying contributing factors associated with a multi-variable metric anomaly. One or more embodiments described herein identify one or more contributing factors that led to an anomaly in a multi-variable metric by calculating linearizing weights such that the total deviation in the multi-variable metric can be written as a weighted sum of deviations for dimension elements associated with the multi-variable metric.

Identifying sources of anomalies in multi-variable metrics using linearization

The present disclosure is directed toward systems and methods for increasing an engagement level of a social media post among a community of social media users. For example, systems and method described herein involve building and training a data model that represents how a given community of social media users engages with social media posts. Furthermore, systems and method described herein utilize the trained data model to suggest one or more alternative word choices for use in a social media post, in order to increase or optimize the predicted level of engagement the social media post will receive from the community of social media users.

Providing alternate words to aid in drafting effective social media posts

Techniques are disclosed for automatically modeling and predicting moderator actions for online content. A model can be generated or updated based on the content received and the action or actions taken by the moderator in response to receiving the content. The model can be used to automatically predict which action, or combination of actions, are likely to be taken by the moderator when new content is received, and suggest those action(s) to the moderator. These suggestions can, among other things, simplify and speed up the decision-making process for the moderator.

Automatic moderation of online content

Computer-implemented methods and systems are disclosed for making a recommending providing a post on a social media forum. One exemplary embodiment involves utilizing machine-learning techniques to produce a model capable of determining optimal post recommendations from various posting factors. The model may be produced from historical post information regarding various posts made by, for instance, marketers on a social media forum and corresponding community interest responses to the posts made by the community of users associated with the social media forum. The model may be provided to a recommendation engine.

Optimal time to post for maximum social engagement

Growing community of inventors

Tamil Nadu, India

Anandhavelu N