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US Patent 10909313 Personalized summary generation of data visualizations

Patent 10909313 was granted and assigned to Sas (company) on February, 2021 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Sas (company)
Sas (company)
Current Assignee
Sas (company)
Sas (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10909313
Date of Patent
February 2, 2021
Patent Application Number
15630462
Date Filed
June 22, 2017
Patent Citations Received
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US Patent 11995114 Natural language processing operations performed on multi-segment documents
0
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US Patent 11321954 Computer-vision techniques for time-series recognition and analysis
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US Patent 11394799 Methods, systems, apparatuses, and devices for facilitating for generation of an interactive story based on non-interactive data
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US Patent 11941393 Systems and methods for managing a software repository
0
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US Patent 11042712 Simplifying and/or paraphrasing complex textual content by jointly learning semantic alignment and simplicity
Patent Primary Examiner
‌
Abedelali Serrou
Patent abstract

Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.

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