Microsoft Technology Licensing, LLC

Pramod Kumar Sharma

Robin Abraham

Leo Moreno Betthauser

Paul Pangilinan Del Villar

Jing Tian

Natalia Larios Delgado

Nilgoon Zarei

Mohamed Abdelrhman Mostafa Ali Elfeki

Liang Du

Maurice Diesendruck

Xiaofei Zeng

Yijian Xiang

Andy Daniel Martinez

Ziyao Li

J Brandon Smock

Min Xiao

Rohith Venkata Pesala

The present disclosure relates to selectively analyzing digital content items from a social networking system to generate a searchable graph object that facilitates visualization of correlations between entities of interest (e.g., brands, products, services, companies), concepts (e.g., domain-specific and/or general terms), hashtags, and other output classes. For example, systems described herein involve collecting images and/or videos that are publicly available via a social networking platform and evaluating content and metadata thereof to identify entities of interest therein and predict various concepts therein to generate a graph object that provides a searchable matrix. The systems herein provide a mechanism for processing a graph query that presents a relational graph showing correlations between the query and the various output classes.

Generating and presenting a searchable graph based on a graph query

A computer implemented method includes rendering a document page as an image; detecting tables, columns, and other associated table objects within the image via one or more table recognition models that model objects in the image as overlapping bounding boxes; transforming the set of objects into a structured representation of the table; extracting data from the objects into the structured representation; and exporting the table into the desired output format.

Inferring structure information from table images

The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.

Generating and presenting multi-dimensional representations for complex entities

A data processing system implements obtaining query parameters for a query for content items in a datastore, the query parameters including attributes of content items for which a search is to be conducted; obtaining a first set of content items from a content datastore based on the query parameters; analyzing the first set of content items using a first machine learning model trained to generate relevant content information that identifies a plurality of relevant content items included in the first set of content items; and analyzing the plurality of relevant content items using a second machine learning model configured to output novel content information, the novel content information including a plurality of content items predicted to be relevant and novel, the novel content information ranking the plurality of content items predicted to be relevant and novel based on a novelty score associated with each respective content item.

Contextualized novelty for personalized discovery

Growing community of inventors

Seattle, WA, United States of America

Pramod Kumar Sharma