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US Patent 10438212 Ensemble machine learning based predicting customer tickets escalation

Patent 10438212 was granted and assigned to CA (journal) on October, 2019 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
CA (journal)
CA (journal)
1
Current Assignee
CA (journal)
CA (journal)
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
104382121
Patent Inventor Names
Narayanabhatla Lalithakishore1
Abdul Khader Jilani1
Date of Patent
October 8, 2019
1
Patent Application Number
140710961
Date Filed
November 4, 2013
1
Patent Citations Received
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US Patent 11995133 Systems and methods for automatically identifying unmet technical needs and/or technical problems
2
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US Patent 11501237 Optimized estimates for support characteristics for operational systems
3
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US Patent 11507836 Federated learning using local ground truth estimation
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US Patent 11842256 Ensemble training in a distributed marketplace
6
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US Patent 11861518 High fidelity predictions of service ticket escalation
7
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US Patent 11928182 Artificial intelligence system supporting semi-supervised learning with iterative stacking
8
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US Patent 11941082 Technologies for classifying feedback using machine learning models
9
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US Patent 11475063 Device, system and method for providing indications of a discrepancy between a video and a textual description of an incident
10
...
Patent Primary Examiner
‌
Jonathan P Ouellette
1
Patent abstract

A system receives data relating to a ticket from a customer of a business organization. The system processes the data using an ensemble machine learning artificial intelligence processor. The ensemble machine learning artificial intelligence processor includes a natural text language processor for analyzing text exchanged between the customer and an agent of the business organization. The ensemble machine artificial intelligence processor also includes a plurality of multivariate machine learning processors for analyzing a handling of the ticket by the business organization. The system generates results from the analysis of the natural language text processor and the analyses of the plurality of multivariate machine learning processors, and provides the results to a meta-learner processor. The meta-learner processor is operable to generate a likelihood percentage of an escalation of the ticket based on prior escalation predictions of the natural language text processor and prior predictions of the multivariate machine learning processors. The system displays the likelihood percentage of an escalation of the ticket on a computer display device.

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