Marvell Asia Pte., Ltd.

Marvell International Limited

Marvellworld Trade Ltd.

Konstantin Kudryavtsev

Mats Oberg

Nedeljko Varnica

Noam Mizrahi

Leo Jiang

Christophe Therene

Manish Shrivastava

Gregory Burd

Nitin Nangare

Ruwan Ratnayake

Igor Polivanyi

Wen Lung Chang

Quynh Chau

Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Storage edge controller with a metadata computational engine

The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Artificial intelligence-enabled search for a storage system

A storage control device coupled to a storage device and located remotely from a host device receives media object data from the host device. The storage control device identifies a type of the media object data and select, based on the identified type, a computational model from among a plurality of computational models for use by a computational engine of the storage control device. The computational engine uses the selected computational model to generate metadata describing the media object data. The metadata is stored in the storage device so as to be selectively retrievable from the storage device separately from the media object data.

Systems and methods for generating metadata describing unstructured data objects at the storage edge

Various embodiments provide a Gray code detector that is not designed to look for a specific defect signature, but rather looks for wedges that are in error or close to being in error, as determined through a probabilistic analysis that considers the mean and sigma of a minimum Viterbi Metric Margin (minVMM) distribution of a servo wedge relative to a defined threshold for a Wedge Error Rate (WER).

Servo gray code quality detector

Growing community of inventors

Sunnyvale, CA, United States of America

Konstantin Kudryavtsev