Adobe, Inc.

Manoj Kilaru

Atanu Ranjan Sinha

Ryan A Rossi

Vishwa Vinay

Fan Du

Nedim Lipka

Natwar Modani

Iftikhar Ahamath Burhanuddin

Prakhar Gupta

Deepali Jain

Aparna Garimella

Saurabh Mahapatra

Aurghya Maiti

Rachit Bansal

Aneesh Shetty

Shaurya Goel

Deepanshu Bansal

Vidit Jain

Pratyush Garg

Harkanwar Singh

Madhav Goel

Garvit Bhardwaj

Aniket Raj

Akhash N A

Vishesh Kaushik

Muskan Agarwal

Tirupati Saketh Chandra

Priyanshu Agarwal

Titas Chakraborty

Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

Facilitating generation of fillable document templates

In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.

Generating occurrence contexts for objects in digital content collections

Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Leading indicators and monitor business KPIs and metrics for preemptive action

A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

Facilitating changes to online computing environment by assessing impacts of actions using a knowledge base representation

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Bengalaru, India

Manoj Kilaru