Dell Products Inc.

Karthik Ranganathan

Sathish Kumar Bikumala

Amit Sawhney

Shalu Singh

Tejas Naren Tennur Narayanan

David Thomas Kirkpatrick

Mohammed Amin

Vasudev Ka

In some examples, a computing device may determine that an issue (e.g., crash, restart etc.) occurred, gather context data (e.g., logs, device profile, etc.) associated with the issue, and generate a contact address to technical support based on the context data. The computing device may upload the context data to a location accessible to a server. After a user of the computing device initiates a communication to technical support using the contact address, the server may automatically route the call, based on the contact address, to a particular technician that has experience addressing the issue. The server may retrieve the context data and use machine learning to determine recommendations to address the issue. The machine learning may prioritize the recommendations and provide the context data and the prioritized recommendations to enable the particular technician to quickly resolve the issue.

Dynamically creating a contact address to customer support based on information associated with a computing device

Methods and systems are disclosed that include receiving problem information from a user interface at a resolution identification system, receiving product information at the resolution identification system, and performing machine learning analysis of the problem information and the product information. The machine learning analysis produces one or more model outputs, and is performed by a machine learning system of the resolution identification system, using one or more machine learning models. Each of the one or more machine learning models produces a corresponding one of the one or more model outputs. Such a method can further include generating resolution information by performing an action identification operation using the one or more model outputs, and outputting the resolution information from the resolution identification system. The resolution information is output to the user interface.

Guided problem resolution using machine learning

In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of steps in a process used to close the case. The analysis may determine a length of time of each step and determine that a time to close the case or complete a particular step was at least a predetermined amount faster than average. The server may use machine learning to create a survey question to determine a technique used to close the case or complete the particular step faster than average and to determine one or more incentives to provide a technician that closed the case. An answer from the technician to the survey question may include the technique used to close the case or complete the particular step faster than average. The technique may be shared with other technicians.

Training a machine learning algorithm to create survey questions

Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Intelligent scoring model for assessing the skills of a customer support agent

A machine learning (ML) module that analyzes multiple language-influencing factors to correct textual content in a more meaningful and efficient manner. In the context of product support, the ML module considers the factors such as a customer's persona that shapes the words a customer chooses while speaking/writing to a customer support agent, current social trends that create new words in the social media/social platforms related to the customer's support issue, and the device used by the customer to input the textual content because different words may be input by the customer when using a smart phone versus a desktop/laptop personal computer with a traditional keyboard. The ML module also may analyze the agent's persona to modify agent's response to the customer because the agent's persona can influence the content of the agent's text. The ML module automatically corrects textual content in real-time before it is sent to the relevant recipient.

AI driven content correction built on personas

In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of a communication session between a user and a technician and the steps taken by the technician to resolve the issue. Machine learning may be used on results of the analysis to predict potential pain points. For example, steps that take longer than average and during which particular words spoken by the user increase in pitch and/or volume may be predicted to be potential pain points. The machine learning may create questions for inclusion in a custom survey based on the potential pain points. The custom survey may be presented to the user. The answers may be correlated with the potential pain points to determine actual pain points in the steps taken to resolve the issue.

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Round Rock, TX, United States of America

Karthik Ranganathan