Avaya Inc.

Audris Mockus

Randy L Hackbarth

John D Palframan

Biswajyoti Pal

Brett Alan Shockley

An electronic communication is received from a first communication device by a multi-tasking operating system. For example, a customer has entered a service request at a web site, which is received by the multi-tasking operating system. The electronic communication comprises a plurality of items of information associated with a service request. An accuracy level for the plurality of items of information is determined. The accuracy level for the plurality of items is based on a history of prior electronic communications. In response to determining the accuracy level for the plurality of items of information associated with the service request, a communication system associated with a contact center and/or a contact center agent is identified. In response to identifying the communication system associated with the contact center and/or contact center agent, the electronic communication is routed to the communication system associated with the contact center and/or contact center agent.

Electronic communication routing based data accuracy

Disclosed herein are systems, methods, and computer-readable storage media for identifying and remediating risky source files. An example system configured to practice the method can gather data describing each file in a source code repository, and generate, using a weighted algorithm based on empirical relationships between the data and customer-found defects, a risk score for each file in the source code repository, wherein the weighted algorithm prioritizes factors based on predictiveness of defects. Then the system can generate a list of files having risk scores above a threshold, and make risk-mitigation recommendations based on the risk scores. A file can include a single file or a collection of files such as a module. The system can identify, for each file in the list of files having risk scores above the threshold, a respective risk type, and make the risk-mitigation recommendation for each file based on the respective risk type.

System and method for prioritizing and remediating defect risk in source code

Systems, methods, and computer-readable storage media for predicting escalation risks in customer support contexts. A system implementing the example method can identify instances of customer escalation from a corpus of customer support sales, customer support, and product software development data, and determine, for the instances of customer escalation, sets of trigger conditions that were at least partially responsible for respective instances of customer escalation. Then the system can classify each of the instances of customer escalation by problem type based on the set of trigger conditions to yield classifications, and add the classifications, the sets of trigger conditions, and the instances of customer escalation to a database. Then the system can compare the database to support data to identify customers or support tasks that are likely to be escalated, and the system or a user can allocate additional support resources to proactively prevent escalations.

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Summit, NJ, United States of America

Audris Mockus