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US Patent 11989511 System and method for term disambiguation

Patent 11989511 was granted and assigned to Verantos, Inc. on May, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
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Verantos, Inc.
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Current Assignee
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Verantos, Inc.
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119895110
Patent Inventor Names
Daniel Riskin0
Anand Shroff0
Date of Patent
May 21, 2024
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Patent Application Number
183418960
Date Filed
June 27, 2023
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Patent Citations
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US Patent 7028038 Method for generating training data for medical text abbreviation and acronym normalization
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US Patent 7681147 System for determining probable meanings of inputted words
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US Patent 9015031 Predicting lexical answer types in open domain question and answering (QA) systems
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US Patent 9213687 Compassion, variety and cohesion for methods of text analytics, writing, search, user interfaces
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US Patent 9292494 Conceptual world representation natural language understanding system and method
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US Patent 9720901 Automated text-evaluation of user generated text
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US Patent 10460229 Determining word senses using neural networks
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US Patent 10592505 Natural language interfaces for databases using autonomous agents and thesauri
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Patent Primary Examiner
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Abdelali Serrou
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CPC Code
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G06F 16/3331
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G06F 16/3347
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G06F 16/95
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G06F 16/955
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G06F 40/146
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G06F 40/205
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G06F 40/216
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G06F 40/253
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Patent abstract

Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

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