Dell Products Inc.

Ming Qian

Anne-Marie McReynolds

Christopher O'Bryon Hill

Hanna Yehuda

Nicole Reineke

Jaeyoo Jung

Anne Marie McReynolds

Christopher Hill

Methods and systems for managing customer-encountered issues are disclosed. To manage the customer-encountered issues, a multiphase optimization process may be implemented to select a service agent to resolve each customer-encountered issue. The multiphase analysis may include a process of identifying service agents qualified to attempt to resolve each customer-encountered issue. The multiphase optimization may also include a process of ranking the qualified service agents based on their past performance and experience. The multiphase optimization may also include a process for estimating the likelihood of each of the qualified service agents resolving each customer-encountered issue within prescribed goals.

System and method for managing issues through resource optimization

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring an interaction of a user with a data center monitoring and management console throughout a plurality of developer journey steps, the interaction of the user including interaction with a plurality of application program interfaces; analyzing the interaction with the plurality of application program interfaces associated with each of the plurality of developer journey steps; and, generating an application program interface readiness score for each of the plurality of application program interfaces based upon the analyzing.

System for determining data center application program interface readiness

Techniques for training and/or using a machine learning (ML) algorithm to generate search results are disclosed. An ML algorithm is configured (i) to identify a search starting event and a search terminating event for a search session, where the search session includes multiple navigations across multiple different webpages, and (ii) to derive a dynamic score for the search session, where the dynamic score reflects whether the search session successfully identified an end result that was initially unknown at a time when the search starting event occurred. The trained ML algorithm can then be used during later search sessions to promote better search results and help users identify end targets or results in a faster and more intuitive manner.

Machine learning on search sessions to provide intelligent search results

Methods and systems for managing customer encountered issue resolution are disclosed. To manage the customer encountered issue resolution, tickets for the customer encountered issues may be managed to reduce suffering and reduce the rate of ticket churn. To do so, customer pain levels may be identified and used to prioritize resource deployment for ticket resolution. The tickets may be classified into groups for resolution via automated or human-assisted processes, which may be limited in capacity. To ascertain the pain levels of the customers, communications from the customers may be emotionally analyzed to identify emotions present in the customers. The identified emotions may be used to identify whether performed remediation processes for resolving the customer encountered issues were successful, and to what extent they were successful. Information obtained through previous ticket resolutions may be used to prioritize which approach to use to resolve subsequently encountered tickets for customer encountered issues.

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Allston, MA, United States of America

Ming Qian