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US Patent 10963504 Zero-shot event detection using semantic embedding

Patent 10963504 was granted and assigned to SRI International on March, 2021 by the United States Patent and Trademark Office.

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Patent
Patent
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Patent attributes

Current Assignee
SRI International
SRI International
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
109635040
Patent Inventor Names
Hui Cheng0
Harpreet Sawhney0
Jingen Liu0
Mohamed Elhoseiny0
Date of Patent
March 30, 2021
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Patent Application Number
160774490
Date Filed
February 13, 2017
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Patent Citations
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US Patent 10417350 Artificial intelligence system for automated adaptation of text-based classification models for multiple languages
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US Patent 10504010 Systems and methods for fast novel visual concept learning from sentence descriptions of images
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US Patent 10510000 Intelligent control with hierarchical stacked neural networks
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US Patent 10599700 Systems and methods for narrative detection and frame detection using generalized concepts and relations
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US Patent 10546233 Method and system to predict and interpret conceptual knowledge in the brain
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US Patent 10599774 Evaluating content items based upon semantic similarity of text
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US Patent 10007679 Enhanced max margin learning on multimodal data mining in a multimedia database
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US Patent 10068024 Method and apparatus for correlating and viewing disparate data
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Patent Citations Received
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US Patent 11989511 System and method for term disambiguation
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Patent Primary Examiner
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Hanh B Thai
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Patent abstract

Zero-shot content detection includes building/training a semantic space by embedding word-based document descriptions of a plurality of documents into a multi-dimensional space using a semantic embedding technique; detecting a plurality of features in the multimodal content by applying feature detection algorithms to the multimodal content; determining respective word-based concept descriptions for concepts identified in the multimodal content using the detected features; embedding the respective word-based concept descriptions into the semantic space; and in response to a content detection action, (i) embedding/mapping words representative of the content detection action into the semantic space, (ii) automatically determining, without the use of training examples, concepts in the semantic space relevant to the content detection action based on the embedded words, and (iii) identifying portions of the multimodal content responsive to the content detection action based on the concepts in the semantic space determined to be relevant to the content detection action.

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