Yu U Zhao, 666063 Southcrest Way, Saint Louis, MO 63129

2020028, Sep 10, 2020

Sep 24, 2019

16/581621

- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Yu Zhao - Costa Mesa CA, US

G06K 9/62
G06T 7/136
G06K 9/00
G06N 3/08
G06T 11/20

Provided herein are embodiments of systems and methods for classifying one or more objects in an image. One of the methods includes: receiving object classification results of the image from one or more classification engines, the object classification results comprise classification of one or more objects and confidence scores associated with the one or more objects; aggregating the object classification results from the one or more classification engines to generate a list of confidence scores associated with each of the one or more objects, the list of confidence scores comprises one or more confidence scores from one or more classification engines; calculating an overall certainty score for each of the one or more objects based at least on the list of confidence scores; and generating a first orchestrated classification result based the overall certainty score for each of the one or more object.

2020007, Mar 5, 2020

Mar 6, 2019

16/294781

- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US

G10L 15/32
G06F 17/27
G10L 15/02
G10L 15/18
G06N 3/08
G10L 15/16
G10L 15/30
G06N 5/00
G06N 20/00
G06N 3/04

Methods and systems for training an engine prediction neural network is disclosed. One of the methods can include: extracting image features of a first ground truth image using outputs of one or more layers of an image classification neural network; classifying the first ground truth image using a plurality of candidate neural networks; determining a classification accuracy score of a classification result of the first ground truth image for each candidate neural network of the plurality of candidate neural networks; and training the engine prediction neural network to predict the best candidate engine by associating the image features of the first ground truth image with the classification accuracy score of each candidate neural network.

2020006, Feb 27, 2020

Feb 27, 2019

16/287892

- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US

G10L 15/32
G06F 17/27
G10L 15/02
G10L 15/18
G06N 3/08
G10L 15/16
G10L 15/30
G06N 5/00
G06N 20/00
G06N 3/04

Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods can use classification results from one or more engines of different classes to select a different engine for the original classification task. For example, given an audio segment with associated metadata and image data, the disclosed interclass method can use the classification results from a topic classification of metadata and/or an image classification result of the image data as inputs for selecting a new transcription engine to transcribe the audio segment.

2020005, Feb 20, 2020

Feb 22, 2019

16/283222

- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US

G10L 15/32
G10L 15/30
G10L 15/16
G06N 3/08
G10L 15/18
G10L 15/02
G06F 17/27
G06N 3/04
G06N 20/00
G06N 5/00

Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods includes receiving, from a first transcription engine, one or more transcription results of one or more audio segments of the multimedia file; identifying a first transcription result for a first audio segment having a low confidence of accuracy; identifying a first image data of the multimedia file corresponding to the first segment; receiving, from an image classification engine trained to classify image data, an image classification result of one or more portions of the first image data in response to requesting the image classification engine to classify the first image data; and selecting, based at least on the image classification result of the one or more portions of the first image data, a second transcription engine to re-classify the first audio segment.