At&t Intellectual Property I, L.p.

Parth Manoj Sutaria

Abhay Dabholkar

Vijayan Nagarajan

Nutan M Ranoliya

Caitlin Rose Gruis

Concepts and technologies disclosed herein are directed to optimizing and reducing redundant dispatch tickets via a network knowledge graph. According to one aspect disclosed herein, a network knowledge graph generation system ('NKGGS') can construct a machine learning model to determine a probability of an installation job needing a helper job to fulfill a service order. The NKGGS can execute the machine learning model to determine the probability. The machine learning model can determine the probability of the installation job needing the helper job to fulfill the service order based upon a network knowledge graph and a dependency score. The NKGGS can cluster the installation job with a plurality of installation jobs.

Optimizing and reducing redundant dispatch tickets via network knowledge graph

Techniques for desirably translating a document image to an editable electronic textual document are presented. Utilizing respective applications, a document processing management component (DPMC) can convert the document image to a grayscale document image, remove noise from such image, rotate such image to reduce or eliminate any skewing of such image, and perform character recognition on the rotated grayscale document image to extract the textual information from such document to generate an electronic textual document. DPMC can associate a document identifier with the electronic textual document, and such document and document identifier can be stored in a data store. When such document is related to a device or other item, a code or textual string can be associated with the device or item, wherein a communication device can scan the code or textual string. In response, DPMC can retrieve such document, or information relating thereto, from the data store.

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Wylie, TX, United States of America

Parth Manoj Sutaria