Matthew Charles Petrillo

US Patent:

20210117678, Apr 22, 2021

Filed:

Oct 16, 2019

Appl. No.:

16/655117

Inventors:

- Burbank CA, US
Matthew C. Petrillo - Sandy Hook CT, US
Monica Alfaro Vendrell - Barcelona, ES
Daniel Fojo - Barcelona, ES
Albert Aparicio - Barcelona, ES
Francese Josep Guitart Bravo - Lleida, ES
Jordi Badia Pujol - Madrid, ES
Marc Junyent Martin - Barcelona, ES
Anthony M. Accardo - Los Angeles CA, US

International Classification:

G06K 9/00
G06K 9/62
G06N 5/04
G06N 5/02
G06N 20/00

Abstract:

According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

US Patent:

20210019576, Jan 21, 2021

Filed:

Jul 15, 2019

Appl. No.:

16/512223

Inventors:

- Burbank CA, US
Matthew C. Petrillo - Sandy Hook CT, US
Marc Junyent Martin - Barcelona, ES
Anthony M. Accardo - Los Angeles CA, US
Avner Swerdlow - Los Angeles CA, US
Monica Alfaro Vendrell - Barcelona, ES

International Classification:

G06K 9/62
G06K 9/00

Abstract:

According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

US Patent:

20190297392, Sep 26, 2019

Filed:

Mar 23, 2018

Appl. No.:

15/934871

Inventors:

- Burbank CA, US
Matthew C. Petrillo - Sandy Hook CT, US
Monica Alfaro Vendrell - Barcelona, ES
Marc Junyent Martin - Barcelona, ES
Katharine S. Ettinger - Santa Monica CA, US
Evan A. Binder - Calabasas CA, US
Anthony M. Accardo - Los Angeles CA, US
Avner Swerdlow - Los Angeles CA, US

International Classification:

H04N 21/84
H04N 21/845
G06F 17/30

Abstract:

According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

US Patent:

20190034822, Jan 31, 2019

Filed:

Jul 27, 2017

Appl. No.:

15/661084

Inventors:

- Burbank CA, US
Marc JUNYENT MARTIN - Barcelona, ES
Matthew C. PETRILLO - Sandy Hook CT, US
Monica ALFARO VENDRELL - Barcelona, ES
Pablo Beltran SANCHIDRIAN - Barcelona, ES
Avner SWERDLOW - Los Angeles CA, US
Katharine S. ETTINGER - Santa Monica CA, US
Evan A. BINDER - Calabasas CA, US
Anthony M. ACCARDO - Los Angeles CA, US

International Classification:

G06N 99/00
G06N 5/02
G06K 9/00
G06K 9/62

Abstract:

Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

US Patent:

20180343496, Nov 29, 2018

Filed:

Aug 3, 2018

Appl. No.:

16/054846

Inventors:

- Burbank CA, US
Matthew Petrillo - Sandy Hook CT, US
Monica Alfaro Vendrell - Barcelona, ES
Pablo Beltran Sanchidrian - Barcelona, ES
Marc Junyent Martin - Barcelona, ES
Avner Swerdlow - Los Angeles CA, US
Katharine S. Ettinger - Santa Monica CA, US
Anthony M. Accardo - Los Angeles CA, US

International Classification:

H04N 21/45
H04N 21/44
H04N 21/435

Abstract:

According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

US Patent:

20180005041, Jan 4, 2018

Filed:

Jun 30, 2016

Appl. No.:

15/199677

Inventors:

- Burbank CA, US
Jack Luu - Huntington Beach CA, US
Alan Pao - Santa Monica CA, US
Matthew Petrillo - Sandy Hook CT, US
Anthony M. Accardo - Los Angeles CA, US
Alexis Lindquist - Los Angeles CA, US
Miquel Angel Farre Guiu - Bern, CH
Katharine S. Ettinger - Santa Monica CA, US
Lena Volodarsky Bareket - Los Angeles CA, US

International Classification:

G06K 9/00
G11B 27/34
G11B 27/02
H04N 21/845
H04N 21/81
H04N 21/472

Abstract:

There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

US Patent:

20180005157, Jan 4, 2018

Filed:

Jun 30, 2016

Appl. No.:

15/199717

Inventors:

- Burbank CA, US
Jack Luu - Huntington Beach CA, US
Alan Pao - Santa Monica CA, US
Matthew Petrillo - Sandy Hook CT, US
Anthony M. Accardo - Los Angeles CA, US
Miquel Angel Farre Guiu - Bern, CH
Lena Volodarsky Bareket - Los Angeles CA, US
Katharine S. Ettinger - Santa Monica CA, US

International Classification:

G06Q 10/06
G06F 17/30

Abstract:

There are provided media asset tagging systems and method. Such a system includes a hardware processor, and a system memory storing a workflow management software code including a tagging application template and a multi-contributor synthesis module. The hardware processor executes the workflow management software code to provide a workflow management interface, to receive a media asset identification data and a workflow rules data, and to generate custom tagging applications based on the workflow rules data. The hardware processor further executes the workflow management software code to receive tagging data for the media asset, determine at least a first constraint for tagging the media asset, receive additional tagging data for, and determine at least a second constraint for tagging the media asset. The media asset is then tagged based on the tagging data and the additional tagging data, subject to the constraints.