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US Patent 11537801 Structured text translation

Patent 11537801 was granted and assigned to Salesforce on December, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Salesforce
Salesforce
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Current Assignee
Salesforce
Salesforce
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
115378010
Patent Inventor Names
Caiming Xiong0
Kazuma Hashimoto0
Raffaella Buschiazzo0
Richard Socher0
Teresa Marshall0
James Bradbury0
Date of Patent
December 27, 2022
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Patent Application Number
172146910
Date Filed
March 26, 2021
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Patent Citations
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US Patent 10542270 Dense video captioning
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US Patent 10282663 Three-dimensional (3D) convolution with 3D batch normalization
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US Patent 10346721 Training a neural network using augmented training datasets
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US Patent 10380236 Machine learning system for annotating unstructured text
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US Patent 10474709 Deep reinforced model for abstractive summarization
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US Patent 10521465 Deep reinforced model for abstractive summarization
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US Patent 10565306 Sentinel gate for modulating auxiliary information in a long short-term memory (LSTM) neural network
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US Patent 10565318 Neural machine translation with latent tree attention
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Patent Primary Examiner
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Yogeshkumar Patel
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CPC Code
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G06F 40/154
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G06N 3/08
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G06N 3/0454
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G06N 3/0481
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G06N 3/084
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G06N 3/088
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G06F 40/47
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G06F 40/42
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Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

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