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US Patent 10255085 Interactive graphical user interface with override guidance

Patent 10255085 was granted and assigned to Sas (company) on April, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Sas (company)
Sas (company)
0
Current Assignee
Sas (company)
Sas (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
102550850
Patent Inventor Names
Bahadir Aral0
Rebecca Gallagher0
Roger Lee Baldridge, Jr.0
Varunraj Valsaraj0
Jinxin Yi0
Date of Patent
April 9, 2019
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Patent Application Number
161829300
Date Filed
November 7, 2018
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Patent Citations Received
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US Patent 12021713 Anomaly detection
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US Patent 10846643 Method and system for predicting task completion of a time period based on task completion rates and data trend of prior time periods in view of attributes of tasks using machine learning models
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US Patent 10983682 Interactive graphical user-interface for analyzing and manipulating time-series projections
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US Patent 11250357 Method for estimating amount of task objects required to reach target completed tasks
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US Patent 11609187 Artificial neural network-based method for selecting surface type of object
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US Patent 11650164 Artificial neural network-based method for selecting surface type of object
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US Patent 11328583 Dynamic gauges for displaying present and predicted machine status
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US Patent 11861470 Simplistic machine learning model generation tool for predictive data analytics
0
...
Patent Primary Examiner
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Haoshian Shih
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Patent abstract

One exemplary system can receive a selection of a dataset via a graphical user interface (GUI). The dataset can represent a time-series projection. The system can feed the dataset into a first machine-learning model to obtain an output indicating whether the time-series projection has a data value that should be overridden with an override value. If the first machine-learning model indicates that the time-series projection has the data value that should be overridden, the system can feed the data value as input to a second machine-learning model to obtain an output indicating whether the override value should be greater than or less than the data value. The system can then render a visual directionality cue within the GUI based on the output from the second machine-learning model. The visual directionality cue can provide guidance for overriding the data value.

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