Rapid workflow design using machine learning

A system and method for rapid workflow design utilizing machine learning includes a scanner, a convolutional neural network, a converter, a graph generator, and a controller. The scanner may convert a paper copy of a process flow into a pixelated image. The convolutional neural network is configured...

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description A system and method for rapid workflow design utilizing machine learning includes a scanner, a convolutional neural network, a converter, a graph generator, and a controller. The scanner may convert a paper copy of a process flow into a pixelated image. The convolutional neural network is configured to segment the pixelated image into objects including at least one of an action, a decision, a connector, or a combination thereof. The converter is configured to transform the objects into a connectivity matrix. The graph generator is configured to transform the connectivity matrix into a rectilinear graph. The controller is configured to identify automated actions, semi-automatic actions, and actions requiring operator input from graphical icons provided on the paper copy and, based on the rectilinear graph, to serialize the automated actions, the semi-automatic actions, and the actions requiring operator input as control commands to a printing system.
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The controller is configured to identify automated actions, semi-automatic actions, and actions requiring operator input from graphical icons provided on the paper copy and, based on the rectilinear graph, to serialize the automated actions, the semi-automatic actions, and the actions requiring operator input as control commands to a printing system.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; HANDLING RECORD CARRIERS ; PHYSICS ; PICTORIAL COMMUNICATION, e.g. TELEVISION ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20200818&amp;DB=EPODOC&amp;CC=US&amp;NR=10750036B1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20200818&amp;DB=EPODOC&amp;CC=US&amp;NR=10750036B1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>de Beus, Eric</creatorcontrib><title>Rapid workflow design using machine learning</title><description>A system and method for rapid workflow design utilizing machine learning includes a scanner, a convolutional neural network, a converter, a graph generator, and a controller. 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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
HANDLING RECORD CARRIERS
PHYSICS
PICTORIAL COMMUNICATION, e.g. TELEVISION
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
title Rapid workflow design using machine learning
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