Amazon Technologies, Inc.

Facebook, Inc.

Abhay Kumar Jha

Ashish Kumar Agrawal

Arindam Mandal

Emanuel Alexandre Strauss

Siddhartha Reddy Jonnalagadda

Prakash Krishnan

Raefer Christopher Gabriel

Shishir Sridhar Bharathi

Savas Parastatidis

Anushree Venkatesh

David Chi-Wai Tang

Yun Lei

Arjit Biswas

Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

Multi-session context

An online system enforces policies to content items that are distributed on its platform and blocks content items that violate one or more of those policies. To identify content items that are slightly varied from each other, the online system generates an embedding for each of the known content items that have already been determined to be noncompliant with one or more policies. The online system then groups the known noncompliant content items that are clustered together in the embedding space. The texts of the group of known noncompliant content items are converted to finite state automata and are merged to generate a common automaton. The common automaton is used to generate a common regular expression that is used to screen new content items. When a new content item matches the textual pattern defined by the common regular expression, the system may block the new content item.

Regular expression generation and screening of textual items

Growing community of inventors

Belmont, CA, United States of America

Abhay Kumar Jha