Patents by Inventor Ming-Jung Seow

Ming-Jung Seow 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).

  • Patent number: 11468660
    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 particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify 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 object into object type clusters based on the micro-feature vectors.
    Type: Grant
    Filed: July 17, 2020
    Date of Patent: October 11, 2022
    Assignee: Intellective Ai, Inc.
    Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
  • Publication number: 20220150267
    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: January 4, 2022
    Publication date: May 12, 2022
    Applicant: Intellective Ai, 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
  • Publication number: 20220121942
    Abstract: Self-supervised machine learning is performed based on metadata that is acquired in real time and/or offline, via a single data source or multiple data sources. A cognitive analytics system (CAS) performs learning, based on metadata associated with structured and/or un-structured data, to generate data representations for use in decision learning. A cognitive engine compares the data representations to learned patterns stored in memory, for example as weights. Data can be transformed into representations, and condition(s) may be generated based on new data received from a behavioral network. Codelets matching the condition(s) can then be executed, as part of cognitive analytics, to perform pattern association with stored weights and/or inferences.
    Type: Application
    Filed: October 20, 2021
    Publication date: April 21, 2022
    Inventor: Ming-Jung SEOW
  • Publication number: 20220114735
    Abstract: Techniques are disclosed for analyzing and learning behavior in an acquired stream of video frames. In one embodiment, a trajectory analyzer clusters trajectories of objects depicted in video frames and builds a trajectory model including the trajectory clusters, a prior probability of assigning a trajectory to each cluster, and an intra-cluster probability distribution indicating the probability that a trajectory mapping to each cluster is least various distances away from the cluster. Given a new trajectory, a score indicating how unusual the trajectory is may be computed based on the product of the probability of the trajectory mapping to a particular cluster and the intra-cluster probability of the trajectory being a computed distance from the cluster. The distance used to match the trajectory to the cluster and determine intra-cluster probability is computed using a parallel Needleman-Wunsch algorithm, with cells in antidiagonals of a matrix and connected sub-matrices being computed in parallel.
    Type: Application
    Filed: October 25, 2021
    Publication date: April 14, 2022
    Applicant: Intellective Ai, Inc.
    Inventors: Gang XU, Ming-Jung SEOW, Tao YANG, Wesley Kenneth COBB
  • Publication number: 20220075946
    Abstract: Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.
    Type: Application
    Filed: September 20, 2021
    Publication date: March 10, 2022
    Applicant: Intellective Ai, Inc.
    Inventors: Ming-Jung SEOW, Gang XU, Tao YANG, Wesley Kenneth COBB
  • Patent number: 11270218
    Abstract: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
    Type: Grant
    Filed: June 28, 2019
    Date of Patent: March 8, 2022
    Assignee: Intellective Ai, Inc.
    Inventors: Ming-Jung Seow, Gang Xu, Tao Yang, Wesley Kenneth Cobb
  • Publication number: 20220027556
    Abstract: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
    Type: Application
    Filed: August 9, 2021
    Publication date: January 27, 2022
    Applicant: Intellective Ai, Inc.
    Inventors: Ming-Jung SEOW, Gang XU, Tao YANG, Wesley K. COBB
  • Publication number: 20220012422
    Abstract: Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.
    Type: Application
    Filed: May 24, 2021
    Publication date: January 13, 2022
    Applicant: Intellective Ai, Inc.
    Inventors: Gang XU, Ming-Jung SEOW, Tao YANG, Wesley Kenneth COBB
  • Publication number: 20220006825
    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: Application
    Filed: July 6, 2021
    Publication date: January 6, 2022
    Applicant: Intellective 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
  • Publication number: 20210398418
    Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.
    Type: Application
    Filed: July 16, 2021
    Publication date: December 23, 2021
    Applicant: Intellective Ai, Inc.
    Inventors: Wesley Kenneth COBB, Ming-Jung SEOW, Gang XU, Kishor Adinath SAITWAL, Anthony AKINS, Kerry JOSEPH, Dennis G. URECH
  • Publication number: 20210165958
    Abstract: Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.
    Type: Application
    Filed: November 30, 2020
    Publication date: June 3, 2021
    Inventors: Gang XU, Tao YANG, Ming-Jung SEOW
  • Patent number: 11017168
    Abstract: Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.
    Type: Grant
    Filed: July 26, 2019
    Date of Patent: May 25, 2021
    Assignee: Intellective Ai, Inc.
    Inventors: Gang Xu, Ming-Jung Seow, Tao Yang, Wesley Kenneth Cobb
  • Publication number: 20210042556
    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 particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify 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 object into object type clusters based on the micro-feature vectors.
    Type: Application
    Filed: July 17, 2020
    Publication date: February 11, 2021
    Applicant: Intellective Ai, Inc.
    Inventors: Wesley Kenneth COBB, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU, Lon W. RISINGER, Jeff GRAHAM
  • Patent number: 10909322
    Abstract: Techniques are disclosed for generating anomaly scores for a neuro-linguistic model of input data obtained from one or more sources. According to one embodiment, generating anomaly scores includes receiving a stream of symbols generated from an ordered stream of normalized vectors generated from input data received from one or more sensor devices during a first time period. Upon receiving the stream of symbols, generating a set of words based on an occurrence of groups of symbols from the stream of symbols, determining a number of previous occurrences of a first word of the set of words, determining a number of previous occurrences of words of a same length as the first word, and determining a first anomaly score based on the number of previous occurrences of the first word and the number of previous occurrences of words of the same length as the first word.
    Type: Grant
    Filed: January 29, 2018
    Date of Patent: February 2, 2021
    Assignee: Intellective Ai, Inc.
    Inventors: Ming-Jung Seow, Gang Xu, Tao Yang, Wesley Kenneth Cobb
  • Publication number: 20210014258
    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: July 31, 2020
    Publication date: January 14, 2021
    Applicant: Intellective Ai, 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
  • Publication number: 20200410164
    Abstract: Techniques are disclosed for analyzing and learning behaviors based on acquired sensor data. A neuro-linguistic cognitive engine performs learning and analysis on linguistic content (e.g., identified alpha symbols, betas, and gammas) obtained by a linguistic model that clusters observations to generate the linguistic content. The neuro-linguistic cognitive engine compares new data to learned patterns stored in short and longer-term memories and determines whether to issue special event notifications indicating anomalous behavior. In one embodiment, condition(s) may be generated for new data and checked against inference nodes of an inference network. Inference nodes matching the condition(s) are executed to, e.g., compare the new data with the learned patterns, with output from the inference nodes being used to generate additional condition(s) that are again matched to inference nodes which may be executed.
    Type: Application
    Filed: September 15, 2020
    Publication date: December 31, 2020
    Applicant: Intellective Ai, Inc.
    Inventors: Ming-Jung SEOW, Tao YANG, Gang XU, Wesley Kenneth COBB
  • Patent number: 10855549
    Abstract: Techniques are disclosed for processing data collected from network components for analysis by a machine learning engine of a Cognitive AI System. A network data processing driver receives a stream of data from a data collector which obtains data from one or more network data sources. The driver normalizes the stream of data to one or more feature values each corresponding to the network data sources and generates a sample vector from the feature values. The sample vector is formatted to be analyzed by the machine learning engine.
    Type: Grant
    Filed: September 12, 2019
    Date of Patent: December 1, 2020
    Assignee: Intellective Ai, Inc.
    Inventors: Tao Yang, Ming-Jung Seow
  • Patent number: 10853661
    Abstract: Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.
    Type: Grant
    Filed: September 4, 2018
    Date of Patent: December 1, 2020
    Assignee: Intellective Ai, Inc.
    Inventors: Gang Xu, Tao Yang, Ming-Jung Seow
  • Patent number: 10853961
    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: Grant
    Filed: September 13, 2018
    Date of Patent: December 1, 2020
    Assignee: Intellective Ai, Inc.
    Inventors: Kishor Adinath Saitwal, Lon W. Risinger, Wesley Kenneth Cobb, Ming-Jung Seow, Gang Xu
  • Patent number: 10755131
    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: July 12, 2018
    Date of Patent: August 25, 2020
    Assignee: Intellective Ai, Inc.
    Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham