Patents by Inventor Wesley Kenneth Cobb

Wesley Kenneth Cobb has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • TREND ANALYSIS FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM
    Publication number: 20170286856
    Abstract: Techniques are disclosed for detecting changes in a neuro-linguistic model.
    Type: Application
    Filed: April 5, 2016
    Publication date: October 5, 2017
    Inventors: Ming-Jung SEOW, Gang XU, Tao YANG, Wesley Kenneth COBB
  • ANOMALY SCORE ADJUSTMENT ACROSS ANOMALY GENERATORS
    Publication number: 20170286863
    Abstract: Techniques are disclosed for generating an anomaly score for a neuro-linguistic model of input data obtained from one or more sources. According to one embodiment, generating an anomaly score comprises receiving a score indicating how often a characteristic is observed in the input data. Upon receiving the score, comparing the score with an unusual score model to determine an unusualness score and comparing the unusualness score with an anomaly score model based on one or more unusual score models to generate the anomaly score indicating an overall unusualness for the input data.
    Type: Application
    Filed: April 5, 2016
    Publication date: October 5, 2017
    Inventors: Ming-Jung SEOW, Gang XU, Tao YANG, Wesley Kenneth COBB
  • LOGICAL SENSOR GENERATION IN A BEHAVIORAL RECOGNITION SYSTEM
    Publication number: 20170286800
    Abstract: Techniques are disclosed for generating logical sensors for an image driver. The image driver monitors values corresponding to at least a first feature in one or more regions of a first image in a stream of images received by a first sensor. The image driver identifies at least a first correlation between at least a first and second value of the monitored values. The image driver generates a logical sensor based on the identified correlations. The logical sensor samples one or more features corresponding to the identified correlation from a second image in the stream of images.
    Type: Application
    Filed: April 5, 2016
    Publication date: October 5, 2017
    Inventors: Kishor Adinath SAITWAL, Lon W. RISINGER, Wesley Kenneth COBB
  • SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM
    Publication number: 20170228598
    Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
    Type: Application
    Filed: April 21, 2017
    Publication date: August 10, 2017
    Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis G. URECH, David S. FRIEDLANDER, Gang XU, Ming-Jung SEOW, Lon W. RISINGER, David M. SOLUM, Tao YANG, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL
  • Anomalous stationary object detection and reporting
    Patent number: 9723271
    Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include receiving data for an object within the scene and determining whether the object has remained substantially stationary within the scene for at least a threshold period. If the object is determined to have remained stationary for at least the threshold period, a rareness score is calculated for the object to indicate a likelihood of the object being stationary to an observed degree at an observed location. The rareness score may use a learning model to take into account previous stationary and/or non-stationary behavior of objects within the scene. In general, the learning model may be updated based on observed stationary and/or non-stationary behaviors of the objects. If the rareness score meets reporting conditions, the stationary object event may be reported.
    Type: Grant
    Filed: June 28, 2013
    Date of Patent: August 1, 2017
    Assignee: OMNI AI, INC.
    Inventors: Gang Xu, Wesley Kenneth Cobb
  • COGNITIVE INFORMATION SECURITY USING A BEHAVIORAL RECOGNITION SYSTEM
    Publication number: 20170163672
    Abstract: Embodiments presented herein describe a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.
    Type: Application
    Filed: November 29, 2016
    Publication date: June 8, 2017
    Inventors: Wesley Kenneth COBB, Ming-Jung SEOW, Curtis Edward COLE, JR., Cody Shay FALCON, Benjamin A. KONOSKY, Charles Richard MORGAN, Aaron POFFENBERGER, Thong Toan NGUYEN
  • Image stabilization techniques for video surveillance systems
    Patent number: 9674442
    Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may provide image stabilization of a video stream obtained from a camera. An image stabilization module in the behavioral recognition system obtains a reference image from the video stream. The image stabilization module identifies alignment regions within the reference image based on the regions of the image that are dense with features. Upon determining that the tracked features of a current image is out of alignment with the reference image, the image stabilization module uses the most feature dense alignment region to estimate an affine transformation matrix to apply to the entire current image to warp the image into proper alignment.
    Type: Grant
    Filed: January 5, 2016
    Date of Patent: June 6, 2017
    Assignee: OMNI AI, INC.
    Inventors: Kishor Adinath Saitwal, Wesley Kenneth Cobb, Tao Yang
  • Semantic representation module of a machine-learning engine in a video analysis system
    Patent number: 9665774
    Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
    Type: Grant
    Filed: October 28, 2016
    Date of Patent: May 30, 2017
    Assignee: Avigilon Patent Holding 1 Corporation
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
  • IMAGE DRIVER THAT SAMPLES HIGH-RESOLUTION IMAGE DATA
    Publication number: 20170148183
    Abstract: Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.
    Type: Application
    Filed: November 25, 2015
    Publication date: May 25, 2017
    Inventors: Kishor Adinath SAITWAL, Lon W. RISINGER, Wesley Kenneth COBB, Ming-Jung SEOW, Gang XU
  • Cognitive neuro-linguistic behavior recognition system for multi-sensor data fusion
    Patent number: 9639521
    Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.
    Type: Grant
    Filed: August 11, 2014
    Date of Patent: May 2, 2017
    Assignee: Omni AI, Inc.
    Inventors: Ming-Jung Seow, Wesley Kenneth Cobb, Gang Xu, Tao Yang, Aaron Poffenberger, Lon W. Risinger, Kishor Adinath Saitwal, Michael S. Yantosca, David M. Solum, Alex David Hemsath, Dennis G. Urech, Duy Trong Nguyen, Charles Richard Morgan
  • Pixel-level based micro-feature extraction
    Patent number: 9633275
    Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: April 25, 2017
    Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
  • DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM
    Publication number: 20170083790
    Abstract: Techniques are disclosed which provide a detected object tracker for a video analytics system. As disclosed, the detected object tracker provides a robust foreground object tracking component for a video analytics system which allow other components of the video analytics system to more accurately evaluate the behavior of a given object (as well as to learn to identify different instances or occurrences of the same object) over time. More generally, techniques are disclosed for identifying what pixels of successive video frames depict the same foreground object. Logic implementing certain functions of the detected object tracker can be executed on either a conventional processor (e.g., a CPU) or a hardware acceleration processing device (e.g., a GPU), allowing multiple camera feeds to be evaluated in parallel.
    Type: Application
    Filed: September 23, 2015
    Publication date: March 23, 2017
    Inventors: Lon RISINGER, Kishor Adinath SAITWAL, Wesley Kenneth COBB
  • DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM
    Publication number: 20170083766
    Abstract: Techniques are disclosed which provide a detected object tracker for a video analytics system. As disclosed, the detected object tracker provides a robust foreground object tracking component for a video analytics system which allow other components of the video analytics system to more accurately evaluate the behavior of a given object (as well as to learn to identify different instances or occurrences of the same object) over time. More generally, techniques are disclosed for identifying what pixels of successive video frames depict the same foreground object. Logic implementing certain functions of the detected object tracker can be executed on either a conventional processor (e.g., a CPU) or a hardware acceleration processing device (e.g., a GPU), allowing multiple camera feeds to be evaluated in parallel.
    Type: Application
    Filed: September 23, 2015
    Publication date: March 23, 2017
    Inventors: Lon RISINGER, Kishor Adinath SAITWAL, Wesley Kenneth COBB
  • DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM
    Publication number: 20170083764
    Abstract: Techniques are disclosed which provide a detected object tracker for a video analytics system. As disclosed, the detected object tracker provides a robust foreground object tracking component for a video analytics system which allow other components of the video analytics system to more accurately evaluate the behavior of a given object (as well as to learn to identify different instances or occurrences of the same object) over time. More generally, techniques are disclosed for identifying what pixels of successive video frames depict the same foreground object. Logic implementing certain functions of the detected object tracker can be executed on either a conventional processor (e.g., a CPU) or a hardware acceleration processing device (e.g., a GPU), allowing multiple camera feeds to be evaluated in parallel.
    Type: Application
    Filed: September 23, 2015
    Publication date: March 23, 2017
    Inventors: Lon RISINGER, Kishor Adinath SAITWAL, Wesley Kenneth COBB
  • DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM
    Publication number: 20170083765
    Abstract: Techniques are disclosed which provide a detected object tracker for a video analytics system. As disclosed, the detected object tracker provides a robust foreground object tracking component for a video analytics system which allow other components of the video analytics system to more accurately evaluate the behavior of a given object (as well as to learn to identify different instances or occurrences of the same object) over time. More generally, techniques are disclosed for identifying what pixels of successive video frames depict the same foreground object. Logic implementing certain functions of the detected object tracker can be executed on either a conventional processor (e.g., a CPU) or a hardware acceleration processing device (e.g., a GPU), allowing multiple camera feeds to be evaluated in parallel.
    Type: Application
    Filed: September 23, 2015
    Publication date: March 23, 2017
    Inventors: Lon RISINGER, Kishor Adinath SAITWAL, Wesley Kenneth COBB
  • SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM
    Publication number: 20170046576
    Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
    Type: Application
    Filed: October 28, 2016
    Publication date: February 16, 2017
    Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis G. URECH, David S. FRIEDLANDER, Gang XU, Ming-Jung SEOW, Lon W. RISINGER, David M. SOLUM, Tao YANG, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL
  • METHOD AND SYSTEM FOR DETECTING SEA-SURFACE OIL
    Publication number: 20160350908
    Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to detect and evaluate the presence of sea-surface oil on the water surrounding an offshore oil platform. The computer vision engine may be configured to segment image data into detected patches or blobs of surface oil (foreground) present in the field of view of an infrared camera (or cameras). A machine learning engine may evaluate the detected patches of surface oil to learn to distinguish between sea-surface oil incident to the operation of an offshore platform and the appearance of surface oil that should be investigated by platform personnel.
    Type: Application
    Filed: August 9, 2016
    Publication date: December 1, 2016
    Inventor: Wesley Kenneth COBB
  • Cognitive information security using a behavioral recognition system
    Patent number: 9507768
    Abstract: Embodiments presented herein describe a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.
    Type: Grant
    Filed: August 11, 2014
    Date of Patent: November 29, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Curtis Edward Cole, Jr., Cody Shay Falcon, Benjamin A. Konosky, Charles Richard Morgan, Aaron Poffenberger, Thong Toan Nguyen
  • Semantic representation module of a machine-learning engine in a video analysis system
    Patent number: 9489569
    Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
    Type: Grant
    Filed: January 11, 2016
    Date of Patent: November 8, 2016
    Assignee: 9051147 CANADA INC.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
  • Dynamic absorption window for foreground background detector
    Patent number: 9471844
    Abstract: Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
    Type: Grant
    Filed: October 29, 2014
    Date of Patent: October 18, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Kishor Adinath Saitwal, Lon Risinger, Wesley Kenneth Cobb