Western Digital Technologies, Inc.

Hgst Netherlands, B.v.

Hitachi Global Storage Technologies Netherlands B.v.

International Business Machines Corporation

Sandisk Technologies Inc.

Other

Hitachi Global Storage Technologies

Hitachi Global Storage Technologies B.v.

Hitachi Global Storage Technologies Amsterdam B.v.

Toshiki Hirano

Shaomin Xiong

Tetsuo Semba

Darya Amin-Shahidi

Abhishek Dhanda

Thomas R Albrecht

Lidu Huang

Barry C Stipe

Jeffrey David Wilke

Matthew Taylor White

Vipin Ayanoor-Vitikkate

Haoyu Wu

Haruhide Takahashi

Vipin Ayanoor Vitikkate

Fu-Ying Huang

Satoshi Yamamoto

Long-Sheng Fan

Michael Stephen Bell

Henry Hung Yang

Damien Kah

Tzong-Shii Pan

Qinghua Zeng

Kirk Barrows Price

David Berman

Ullal Vasant Nayak

William Bernard Boyle

Timothy Carl Strand

Rajeev Nagabhirava

Xiaotian Sun

Jeff Wilke

Neale Marvin Jones

Qian Zhong

Jeffrey Scott Lille

Timothy C Reiley

Satya Prakash Arya

Tatsumi Tsuchiya

Nobumasa Nishiyama

Ian Robson McFadyen

Jin Hui Ou-Yang

Francis C Lee

Shinobu Hagiya

Tatsuo Nakamoto

Jia-Yang Juang

Na Wang

Hussam Zebian

Ramy Ayad

Yan Li

Zvonimir Z Bandic

Neil Leslie Robertson

Guoxiao Guo

Robert P Ryan

Ferdinand Hendriks

Bruno Jean Marchon

Michael Grobis

Bernhard E Knigge

Guy Charles Wicker

Surya Narayan Pattanaik

Akio Nishida

Nagesh K Vodrahalli

Jifang Tian

Federico Nardi

Mac D Apodaca

Xing-Cai Guo

Wing Chun Shum

Kiyoshi Satoh

Gary Allan Herbst

Thomas Edward Karis

Chiao-Ping (Roger) Ku

Linden James Crawforth

Kousaku Wakatsuki

Craig N Fukushima

David William Niss

Francis Chee-Shuen Lee

Gokul Kumar

Ailian Zhao

Jeffrey L Furlong

Michael Mostovoy

Hideto Imai

Hitoshi Tamura

Christian René Bonhôte

Shigenori Takada

Ryo Yoshida

Todd McNally

Tatsuo Hayakawa

Pritam Das

Vipin A Vitikkate

Timothy Skarzynski

Victor (Wing Shum

Keishi Takahashi

Tim R Glassburn

Jim Malina

Gwo-Jeng Lo

Chris Gimenez

Dave Niss

Akio Takatsuka

Systems and methods for site-based calibration of object detection rules, such as for surveillance video cameras, are described. Video data from a video image sensor may be processed using an object detector to determine object data for a detected object. The object data may be post-processed using a post-processing rule set to determine whether the detected object violates the post-processing rule set. Event notifications to a video surveillance application may be prevented responsive to the object data violating the post-processing rule set.

Site-based calibration of object detection rules

A disk retention system includes a spindle motor assembly with a spindle motor and an electromagnet retainer coupled and rotatable therewith, and a disk media assembly including a disk medium and a ferromagnetic material coupled therewith, whereby the disk is retained on the spindle motor by an adjustable attractive magnetic force between the ferromagnetic material of the disk assembly and the electromagnet retainer of the motor assembly. The motor assembly may further include a pair of stationary electrically-conductive liquid or liquid metal slip rings and a pair of rotatable electrical contacts each electrically coupled with a respective slip ring and with the electromagnet retainer, where the slip rings may utilize a Gallium-Indium-Tin alloy.

Rotating electromagnet for removable disk clamp

Systems and methods described herein provide for the use and storage of intermediate layer data within a neural network processing system. A first neural network, such as an object detection neural network may receive and process raw video image data to generate output utilized for metadata creation. Secondary neural networks may be configured to accept input data from one or more intermediate layers of the first neural network instead of the raw video image data. In this way, the initial data processed by the intermediate layers of the first neural network can be stored and utilized as a shortcut for processing additional features or attributes within the video image data. This alleviates the need to process video image data multiple times in different neural networks. The intermediate layer data can be stored in a different and often cheaper storage system and recalled faster and with fewer resources for future use.

Storage and processing of intermediate features in neural networks

A magnetic media library drive includes a first disk medium fixed to a spindle, a first read-write transducer configured to operate on the first disk, a second read-write transducer configured to operate on a surface of a single removable second disk medium installed over the first disk, and electronic controller circuitry configured to cause: while the first transducer is servo-positioning from a master servo pattern from the first disk, the second transducer servo-writes a product servo pattern, based on the master servo pattern, to the surface of the second disk. The removable second disk can be removed, turned over, and secured to the spindle, whereby the self-servo writing procedure can be performed on the other side of the second disk based on the master servo pattern on the first disk.

Servo-write for magnetic disk media archival data storage system

A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture identifies objects according to a first confidence threshold of the model and identifies candidate objects according to the first confidence threshold and a second confidence threshold. The model may track the motion of the candidate objects within a visual field, separate the candidate objects into false positive candidate objects and false negative candidate objects according to their tracked motions, and present at least a portion of the false positive candidate objects and false negative candidate objects for further annotation.

Data collection and user feedback in edge video devices

A removable disk clamp assembly for mounting disk media on a motor-driven spindle of a magnetic read-write device includes a disk clamp and a mechanism for generating a predetermined force to press the disk media to a flange of the spindle such that the predetermined force can be repeatedly overcome by an applied counterforce to remove the disk clamp from engagement with a hub of the spindle. Such a mechanism may include a spring-loaded removable disk clamp assembly or a magnetic removable disk clamp assembly, and whereby a vacuum-driven chuck may be employed for disk and clamp handling purposes, all of which are suitable for implementation and use in a read-write device configured for use in an archival data storage system library such as in a cleanroom storage system.

Removable disk clamp for read-write device in archival data storage library

Systems, methods, and data storage devices using a video group classifier based on an object tracker are described. A group classifier may be trained using machine learning to classify image objects from a video frame and assign a classifier identifier. An object tracker and the group classifier may be used to determine correspondence between tracker identifiers and classifier identifiers for assigning group identifiers. The object tracker may then be used to determine image objects, assign tracker identifiers, and track the movement of those image objects through a video data stream to associate tracker identifiers with the video frames. The tracker identifier may be used to assign a group identifier to each video frame based the correspondence between the tracker identifier and the classifier identifier.

Video group classification using object tracker

Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

Ranking images in an image group

Image group classifier in a user device

A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture may identify objects according to a confidence threshold of a model. The confidence threshold may be monitored over time, and the model may be updated if the confidence threshold drops below an acceptable level. The data for retraining is ideally generated substantially internal to the camera. A filter is generated to process the entire field data set stored on the camera to create a field data subset also stored on the camera. The field data subset may be run through the model to generate cases that may be used in further monitoring, training, and updating of the model.

Data collection and retraining in edge video devices

Systems and methods for audio privacy in network video surveillance systems are described. A video camera may include an image sensor and a microphone to generate a video stream. Responsive to detecting a human speaking condition in the video stream, the audio data may be selectively modified to mask a human voice component of the audio data for storing and/or displaying the surveillance video stream.

Audio privacy protection for surveillance systems

A data storage device including, in one implementation, a non-volatile memory device having a memory block including a number of memory dies, and a controller coupled to the non-volatile memory device. The controller is configured to monitor a temperature of the data storage device and determine whether the monitored temperature exceeds a first temperature threshold. The controller is also configured to perform a default thermal throttling operation based on the monitored temperature exceeding the first temperature threshold, determine whether the monitored temperature exceeds a second temperature threshold, and perform a customized thermal throttling operation based on the monitored temperature exceeding the second temperature threshold.

Customized thermal throttling using environmental conditions

Systems and methods for site-based estimation of storage requirements, such as for surveillance video cameras, are described. Product information, a data retention policy, and an intended recording mode about a camera of a surveillance system may be received through a user interface of a user device. A baseline storage value is determined based on the received product information and the data retention policy. A storage requirement may be calculated based on the baseline data storage value and a determined recording co-efficient value based on a sample scene at the camera location. Scene descriptors may be generated based on the sample scene to retrieve the recording co-efficient value from a lookup table.

Usage-based assessment for surveillance storage configuration

A surveillance system includes at least one image or video capture device and a controller configured to determine a change in location for the at least one image or video capture device from a first location to a second location. First image or video data is received from the at least one image or video capture device at the second location, and in response to the location change, a preconfigured neural network is obtained or weights for a neural network are obtained based at least in part on the received first image or video data. Second image or video data is received from the at least one image or video capture device at the second location and an inference operation is performed on the second image or video data by processing the second image or video data using the obtained weights or the obtained preconfigured neural network.

Smart video surveillance system using a neural network engine

In one embodiment, a sensor device includes a sensor configured to obtain sensor data and a communication interface configured to communicate with a set of secondary sensor devices. The set of secondary sensor devices comprise a set of secondary sensors and a set of secondary processing devices. The set of secondary sensors obtain additional sensor data. The set of secondary processing devices process the additional sensor data based on a set of secondary machine learning models, and generate activation data based on the additional sensor data. The sensor device also includes a processing device. The processing device is also configured to receive the activation data from the set of secondary sensor devices and cause the sensor to obtain first sensor data based on the activation data. The processing device is further configured to generate one or more inferences based on the first sensor data and a machine learning model.

Sensor system with low power sensor devices and high power sensor devices

Growing community of inventors

San Jose, CA, United States of America

Toshiki Hirano