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US Patent 11288453 Key-word identification

Patent 11288453 was granted and assigned to Pinterest on March, 2022 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Pinterest
Pinterest
Current Assignee
Pinterest
Pinterest
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11288453
Patent Inventor Names
Yunsong Guo0
Anant Srinivas Subramanian0
Heath Vinicombe0
Yu Liu0
Date of Patent
March 29, 2022
Patent Application Number
16361871
Date Filed
March 22, 2019
Patent Citations
‌
US Patent 10496752 Consumer insights analysis using word embeddings
Patent Citations Received
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US Patent 12086851 Similarity detection based on token distinctiveness
0
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US Patent 11768945 Machine learning system for determining a security vulnerability in computer software
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US Patent 11907643 Dynamic persona-based document navigation
0
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US Patent 11941064 Machine learning comparison of receipts and invoices
0
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US Patent 11966699 Intent classification using non-correlated features
0
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US Patent 11972424 Detection of evasive item listings
0
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US Patent 11977841 Classification of documents
0
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US Patent 11526688 Discovering ranked domain relevant terms using knowledge
0
Patent Primary Examiner
‌
Seong-Ah A Shin

Systems and methods are set forth for identifying key-words and key-phrases, collectively referred to as key-terms, from a document. A document is accessed and the document is tokenized, each token corresponding to a word or phrase occurring within the document. Term frequencies of the terms of the tokens may be determined and TF-IDF scores may be generated according to the term frequencies. Embedding vectors for the terms of the tokens may be generated and a document embedding vector may be generated according to the embedding vectors of the documents. A similarity score may be determined for each token according to the embedding vector of a token and the document embedding vector. Additionally, an overall score may be determined for each token according to the term of the token, a TF-IDF score, similarity scores, and the like. Terms from the highest scoring tokens are selected as the key-terms for the document.

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