Google Inc.

Arris Enterprises LLC

Cheng Li

Marc Alexander Najork

Jinsong Zheng

Michael Bendersky

Mingyang Zhang

Ting Yao

Liu Yang

Eric Brunnett-Lazarte

Ying Hai Chen

Alexander Medvinsky

Pravir Kumar Gupta

Tat Keung Chan

Xin Qiu

Okan Kolak

Bo Wang

Jason A Pasion

Peter John Hodgson

Deniz Binay

Nitin Mangesh Shetti

Javier Alejandro Rey

Spurthi Amba Hombaiah

Patrick Dizon

Xiaozhou Fu

Dhruv Amin

Te-Lin Wu

Fredelyn A Benavidez

Peter Hodgson

Systems and methods for document processing that can process and understand the layout, text size, text style, and multimedia of a document can generate more accurate and informed document representations. The layout of a document paired with text size and style can indicate what portions of a document are possibly more important, and the understanding of that importance can help with understanding of the document. Systems and methods utilizing a hierarchical framework that processes the block-level and the document-level of a document can capitalize on these indicators to generate a better document representation.

Layout-aware multimodal pretraining for multimodal document understanding

The systems and method disclosed herein provide two paradigms for managing distribution of at least one license to one or more features defined in feature categories. A first embodiment is one that uses mixed single unit monthly credits, while a second embodiment uses separate fixed term credits.

Method and apparatus for license credit management

Systems and methods of the present disclosure are directed to a method for predicting semantic similarity between documents. The method can include obtaining a first document and a second document. The method can include parsing the first document into a plurality of first textual blocks and the second document into a plurality of second textual blocks. The method can include processing each of the plurality of first textual blocks and the second textual blocks with a machine-learned semantic document encoding model to obtain a first document encoding and a second document encoding. The method can include determining a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding.

Systems and methods for machine-learned prediction of semantic similarity between documents

A method and system provide the ability to authenticate client services. A private key and a client certificate are created and delivered to a client. Based on the private key and the certificate, a client account is created for the client on a server. One or more signing or feature licensing configurations are created and authorized on the server for the client account. The client certificate and a request to perform a requested client service are received on the server from a client. The request includes configuration information for the requested client service. The server verifies the client certificate and determines whether the client is authorized to perform the requested client service. The determination is based on the configuration information and the one or more authorized client operations. Upon determining that the client is authorized to perform the requested client service, the request is processed the authorization is sent to the client. Upon a determination that the client is not authorized to perform the requested client service, the requested client service is denied.

Certificate-based client authentication and authorization for automated interface

Techniques are described herein for leveraging information about a user to enable a personal assistant module to make various inferences about what actions that may be responsive to a user declaration. In various implementations, upon identifying a user declaration received at a computing device, a plurality of candidate responsive actions that can be initiated by the computing device in response to the user declaration may be identified. A single candidate responsive action may then be non-deterministically (e.g., randomly, stochastically) selected to be exclusively initiated on the computing device in response to the user declaration.

Nondeterministic task initiation by a personal assistant module

Disclosed is an arrangement to enable customers to provision devices with feature licenses that enable specified features in the devices. The arrangement includes a feature-licensing system for performing feature-licensing processes to provision the devices with feature licenses and a feature-licensing process configuration system.

Generic feature-licensing framework

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San Diego, CA, United States of America

Cheng Li