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US Patent 10467339 Using machine learning and natural language processing to replace gender biased words within free-form text

Patent 10467339 was granted and assigned to SAP SE on November, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
SAP SE
SAP SE
Current Assignee
SAP SE
SAP SE
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10467339
Date of Patent
November 5, 2019
Patent Application Number
16022408
Date Filed
June 28, 2018
Patent Citations Received
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US Patent 12106050 Debiasing pre-trained sentence encoders with probabilistic dropouts
0
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US Patent 12001791 Systems and methods for screening data instances based on a target text of a target corpus
0
0
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US Patent 12079573 Tool for categorizing and extracting data from audio conversations
0
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US Patent 11537876 Targeted variation of machine learning input data
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US Patent 10885279 Determining states of content characteristics of electronic communications
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US Patent 11281858 Systems and methods for data classification
‌
US Patent 11714963 Content modification using natural language processing to include features of interest to various groups
0
...
Patent Primary Examiner
‌
Akwasi M Sarpong
Patent abstract

A classification engine classifies words into gender-biased or neutral classes and outputs classification labels and probabilities the words were correctly classified. A dictionary engine generates a set of gender-biased words based on their labels, if their probability exceeds a threshold, if they are defined within a set of definitions. For each such word, the dictionary engine parses the set of definitions to extract the most frequent meaning of that word in a part of speech; parses the set of definitions to extract a synonym for the most frequent meaning of that word in that part of speech; and parses the classification engine output to determine that synonym's classification label. A synonym is selected that is labeled in the neutral class as a replacement for the gender-biased word. A dictionary entry is generated that includes the word, the part of speech, and the synonym.

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