Dell Products Inc.

Shalu Singh

Amit Sawhney

Sathish Kumar Bikumala

Karthik Ranganathan

Tejas Naren Tennur Narayanan

Gautam Kaura

Anish Arora

Prateek Mathur

Mohammed Amin

David Thomas Kirkpatrick

Mallory Anne Kolodzey

Techniques are provided for image-based search and prediction for physical defect investigations. One method comprises obtaining, for multiple investigations of physical defects associated with corresponding devices, (i) images of the physical defects, (ii) descriptions of the physical defects, and (iii) descriptions of a manual classification of the physical defects; storing the images for each investigation in an image repository, wherein each stored image comprises a set of tags based on the corresponding description of the physical defect and the corresponding description of the manual classification of the physical defect; and training an image classification model using the stored images, each with the corresponding description of the physical defect and the corresponding manual classification of the physical defect. A given investigation of a physical defect can be resolved using the image repository and/or the trained image classification model.

Image-based search and prediction system for physical defect investigations

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

The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.

Generating test scenarios by detecting failure patterns and themes in customer experiences

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

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Austin, TX, United States of America

Shalu Singh