Cisco Technology, Inc.

Ajith Chandran

Kabiraj Sethi

Neha Agarwal

Amitesh Shukla

Chandra Sekhara Rao Vempali

In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product. An outcome of the particular suggested solution for the particular computing-feature-specific problem on the particular computing product may then be fed back to the machine learning server as collected problem-solution data.

Feature-specific adaptive models for support tools

Methods and systems are disclosed which may provide MultiGig capability to a system where a physical layer device (PHY) or a network device does not have the capacity to support all available line speeds while operating in a single system-interface mode between MAC and PHY devices.

Growing community of inventors

San Jose, CA, United States of America

Ajith Chandran