Adobe, Inc.

Honeywell International Inc.

International Business Machines Corporation

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Anandhavelu Natarajan

Vishwa Vinay

Natwar Modani

Cedric Huesler

Gaurav Verma

Tanya Goyal

Abhilasha Sancheti

Kundan Krishna

Harsh Jhamtani

Anandhavelu N

Shriram Revankar

Shiv Kumar Saini

Ravinder K Jain

Kushal Chawla

Pranav Ravindra Maneriker

Niranjan Shivanand Kumbi

Sanket Vaibhav Mehta

Hrituraj Singh

Navita Goyal

Anandha Velu Natarajan

Harshit Agrawal

Prabhat Mahapatra

Rizurekh Saha

Bakhtiyar Hussain Syed

Sumit Shekhar

Ritwik Sinha

Sopan Khosla

Paridhi Maheshwari

Srikrishna Karanam

Kuldeep Kulkarni

Aparna Garimella

Ajay Awatramani

Tripti Shukla

Vineet Gupta

Ankur Jain

Vinay Aggarwal

Bhanu Prakash Reddy Guda

Saumitra Sharma

Sreekanth Reddy

Suryateja Bv

Mithlesh Kumar

Arijit Pramanik

Chloe McConnell

Aniket Murhekar

Ivan Sandy Wyatt

Aaron James Gannon

Richard Snyder

Gaurush Hiranandani

Stefano Petrangeli

Mark Pearson

Moumita Sinha

Sripathi Ramachandra

Payal Bajaj

Anuj Jain

Atanu R Sinha

Jonas Dahl

Katarina Alexis Morowsky

Greg Carlucci

Vineet Sharma

Kannan Iyer

Ponnurangam Kumaraguru

Shankar Srinivasan

Balaraman Ravindran

Praneetha Vaddamanu

Rishiraj Saha Roy

Akshay Kumar

Akhash Nakkonda Amarnath

Akangsha Sunil Bedmutha

Shubham Gupta

Reddy Sreekanth

Shriram V S Revankar

Varadharajan Ramachandran

Nikhil Mohan Nainani

Kartik Sreenivasan

David Nicholas Bieselin

Anand Ganesan

Avery Burns

Japnik Singh

Bharat Polineni

Madhu Tadiparthi

Khushi Gupta

Padmanabhan Anandan

Sujith Sai Venna

Durga Prasad Maram

Dasireddy Sai Shritishma Reddy

Rajat Chaturvedi

Anish Valliyath Monsy

Aman Mishra

Suryateja B V

Keerti Harpavat

Pranav Goel

Trikay Nalamada

Samagra Sharma

Systems and methods for automatically generating graphic design documents are described. Embodiments include identifying an input text that includes a plurality of phrases; obtaining one or more images based on the input text; encoding an image of the one or more images in a vector space using a multimodal encoder to obtain a vector image representation; encoding a phrase from the plurality of phrases in the vector space using the multimodal encoder to obtain a vector text representation; selecting an image text combination including the image and the phrase by comparing the vector image representation and the vector text representation; selecting a design template from a plurality of candidate design templates based on the image text combination; and generating a document based on the design template, wherein the document includes the at least one image and the at least one phrase.

Automatically generating graphic design variants from input text

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

Embodiments are disclosed for a digital design system trained to segment unstructured text into topically coherent segments. The method may include receiving unstructured text, the unstructured text including a sequence of sentences. The disclosed systems and methods further comprise generating, by a neural network, a hierarchically segmented tree structure representing the unstructured text. The tree structure comprises a plurality of tree structure nodes, where a node of the tree structure nodes represents a sentence from the sequence of sentences. The segments and sub-segments of the unstructured text can then be determined based on node data for nodes of the hierarchically segmented tree structure. Using the determined segments and sub-segments of the unstructured text, a modified representation of the unstructured text can be displayed.

Hierarchical segmentation of unstructured text using neural networks

Embodiments are disclosed for multichannel content recommendation. The method may include receiving an input collection comprising a plurality of images. The method may include extracting a set of feature channels from each of the images. The method may include generating, by a trained machine learning model, an intent channel of the input collection from the set of feature channels. The method may include retrieving, from a content library, a plurality of search result images that include a channel that matches the intent channel. The method may include generating a recommended set of images based on the intent channel and the set of feature channels.

Multichannel content recommendation system

In some embodiments, a multimodal computing system receives a query and identifies, from source documents, text passages and images that are relevant to the query. The multimodal computing system accesses a multimodal question-answering model that includes a textual stream of language models and a visual stream of language models. Each of the textual stream and the visual stream contains a set of transformer-based models and each transformer-based model includes a cross-attention layer using data generated by both the textual stream and visual stream of language models as an input. The multimodal computing system identifies text relevant to the query by applying the textual stream to the text passages and computes, using the visual stream, relevance scores of the images to the query, respectively. The multimodal computing system further generates a response to the query by including the text and/or an image according to the relevance scores.

Modality adaptive information retrieval

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

Systems and methods for video processing are configured. Embodiments of the present disclosure receive a procedural document comprising a plurality of instructions; extract a plurality of key concepts for an instruction of the plurality of instructions; compute an information coverage distribution for each of a plurality of candidate multi-media assets, wherein the information coverage distribution indicates whether a corresponding multi-media asset relates to each of the plurality of key concepts; select a set of multi-media assets for the instruction based on the information coverage distribution; and generate a multi-media presentation describing the procedural document by combining the set of multi-media assets based on a presentation template.

Auto-generating video to illustrate a procedural document

An illustrator system determines, for each feature of a set of features, a feature representation for an electronic document displayed via a user interface, based on a plurality of elements of the electronic document. The system receives a selection from among the set of features of (1) a query feature and of (2) a target feature and determines, for each replacement template of a set of replacement templates, a compatibility score based on the feature representation for the electronic document determined for the query feature and a target feature representation of the replacement template determined for the target feature, the representations being determined in a joint representation space. The system selects one or more replacement electronic documents based on the determined compatibility scores. The system displays a preview for each replacement electronic document and displays a particular replacement electronic document responsive to receiving a selection of a preview.

Cross view template recommendation system

Embodiments are disclosed for generating an intelligent change summary are described. In some embodiments, a method of generating an intelligent change summary includes obtaining a representation of a plurality of versions of a document, determining a distance score based on a comparison of a first of version of the document and a second version of the document, the distance score representing a magnitude of changes made from the first version of the document to the second version of the document, and generating a change summary of the document based on the distance score.

Intelligent change summarization for designers

Techniques for template generation from image content include extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

Semantically-guided template generation from image content

Rewriting text in the writing style of a target author is described. A stylistic rewriting system receives input text and an indication of the target author. The system trains a language model to understand the target author's writing style using a corpus of text associated with the target author. The language model may be transformer-based, and is first trained on a different corpus of text associated with a range of different authors to understand linguistic nuances of a particular language. Copies of the language model are then cascaded into an encoder-decoder framework, which is further trained using a masked language modeling objective and a noisy version of the target author corpus. After training, the encoder-decoder framework of the trained language model automatically rewrites input text in the writing style of the target author and outputs the rewritten text as stylized text.

Stylistic text rewriting for a target author

Enhanced techniques and circuitry are presented herein for providing responses to user questions from among digital documentation sources spanning various documentation formats, versions, and types. One example includes a method comprising receiving a user question directed to subject having a documentation corpus, determining a set of passages of the documentation corpus related to the user question, ranking the set of passages according to relevance to the user question, forming semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity, and providing a response to the user question based at least on a selected semantic cluster.

Semantic cluster formation in deep learning intelligent assistants

Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Tagging documents with security policies

In some embodiments, a style transfer computing system receives, from a computing device, an input text and a request to transfer the input text to a target style combination including a set of target styles. The system applies a style transfer language model associated with the target style combination to the input text to generate a transferred text in the target style combination. The style transfer language model comprises a cascaded language model configured to generate the transferred text. The cascaded language model is trained using a set of discriminator models corresponding to the set of target styles. The system provides, to the computing device, the transferred text.

Multi-dimensional language style transfer

Embodiments of the present disclosure are directed to a system, methods, and computer-readable media for facilitating stylistic expression transfers in machine translation of source sequence data. Using integrated loss functions for style transfer along with content preservation and/or cross entropy, source sequence data is processed by an autoencoder trained to reduce loss values across the loss functions at each time step encoded for the source sequence data. The target sequence data generated by the autoencoder therefore exhibits reduced loss values for the integrated loss functions at each time step, thereby improving content preservation and providing for stylistic expression transfer.

Systems and methods for transferring stylistic expression in machine translation of sequence data

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Bengaluru, India

Balaji Vasan Srinivasan