Emc IP Holding Company LLC

Chao Su

Bina K Thakkar

Deepak Gowda

Aditya Krishnan

Roopa A Luktuke

Wenjin Liu

Shou-Huey Jiang

Techniques are provided for assessing uniqueness of information using string-based collection frequency techniques. One method comprises obtaining multiple collections of documents from at least one data source; determining a collection frequency for a given character string based on a number of the collections comprising the given character string relative to a total number of the collections; assigning a uniqueness rating to the given character string based at least in part on a comparison of the collection frequency of the given character string to a collection frequency of one or more additional character strings in one or more of the plurality of collections; and performing an automated action using the given character string based on the assigned uniqueness rating. The automated action may comprise protecting the given character string and/or identifying the given character string as important information satisfying one or more importance criteria.

Information uniqueness assessment using string-based collection frequency

Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Machine learning-based recommendation engine for storage system usage within an enterprise

Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Determining user retention values using machine learning and heuristic techniques

Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Growing community of inventors

Cary, NC, United States of America

Chao Su