Patents by Inventor Fuk Ho Pius Ng

Fuk Ho Pius Ng 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).

  • Publication number: 20240232713
    Abstract: Techniques and mechanisms described herein provide automated processes for integrating supervised and unsupervised classification results of a test data observation with training data observations in a feature space. Novelty of the test data observation relative to the feature space may be measured using one or more distance metrics. Novelty of a test data observation may be further refined by comparison to a confusion matrix segment determined based on a supervised model. Based on the novelty information, the supervised and/or unsupervised models may be updated, for instance via incremental or batch training.
    Type: Application
    Filed: August 31, 2023
    Publication date: July 11, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Sudharani Sivaraj, Ananda Shekappa Sonnada, Nagarjun Pogakula Surya Prakash
  • Publication number: 20240236447
    Abstract: A method to autonomously obtain and process images to predict grape quality includes acquiring first image data, detecting an object based on the first image data, determining a location of the object based on the first image data, acquiring second image data based on the location of the object, and analyzing the second image data to determine a characteristic of the object. The second image data includes hyperspectral image data.
    Type: Application
    Filed: January 5, 2023
    Publication date: July 11, 2024
    Inventors: Srikanth KADIYALA, Shan WAN, Sai BHARATHWAJ, Mitani MUNEHISA, Nagarjun Pogakula SURYA, Fuk Ho Pius NG
  • Publication number: 20240232714
    Abstract: In a training phase, training data may be used to train a supervised machine learning prediction model and an unsupervised machine learning segmentation model. Then, in a testing phase, the supervised machine learning prediction model may be used to predict a target outcome for a test data observation. Also, the unsupervised machine learning segmentation model may be used to evaluate the novelty of the test data observation relative to the training data.
    Type: Application
    Filed: September 11, 2023
    Publication date: July 11, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Sudharani Sivaraj, Ananda Shekappa Sonnada, Nagarjun Pogakula Surya Prakash
  • Publication number: 20240232651
    Abstract: One or more structural equations modeling a physical process over time may be sampled using simulated parameter values to generate input data signal values. A noise generator may be applied to the input data signal values to generate noise values. The noise values and the input data signal values may be combined to determined noisy data signal values. These noisy data signal values may in turn be used in combination with one or more states to train a prediction model.
    Type: Application
    Filed: October 19, 2022
    Publication date: July 11, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Nagarjun Pogakula Surya Prakash, Ananda Shekappa Sonnada
  • Publication number: 20240233302
    Abstract: A mobile camera apparatus includes a cartesian arm that is able to move along three axes, a first camera to generate first image data, and a second camera to generate second image data and is attached to the cartesian arm. The cartesian arm is operable to move the second camera along the three axes, and the second image data includes hyperspectral image data.
    Type: Application
    Filed: January 5, 2023
    Publication date: July 11, 2024
    Inventors: Dharma Hariharan BABU, Fuk Ho Pius NG, Shan WAN, Srikanth KADIYALA
  • Patent number: 11981028
    Abstract: A robotic arm mount assembly includes a first mount with a main body, the main body including a first end and a second end opposed to the first end in an axial direction, the first end including a first mounting portion. The robotic arm mount assembly also includes a motor including a motor body and a motor shaft attached to the motor body, a pinion gear attached to the motor shaft, a rack to be driven by the pinion gear, and a second mount including an extension portion and a second mounting portion.
    Type: Grant
    Filed: October 7, 2022
    Date of Patent: May 14, 2024
    Assignee: KUBOTA CORPORATION
    Inventors: Roatchanatam Anattasakul, Fuk Ho Pius Ng
  • Publication number: 20240135200
    Abstract: One or more structural equations modeling a physical process over time may be sampled using simulated parameter values to generate input data signal values. A noise generator may be applied to the input data signal values to generate noise values. The noise values and the input data signal values may be combined to determined noisy data signal values. These noisy data signal values may in turn be used in combination with one or more states to train a prediction model.
    Type: Application
    Filed: October 18, 2022
    Publication date: April 25, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Nagarjun Pogakula Surya Prakash, Ananda Shekappa Sonnada
  • Publication number: 20240116173
    Abstract: A robotic arm mount assembly includes a first mount with a main body, the main body including a first end and a second end opposed to the first end in an axial direction, the first end including a first mounting portion. The robotic arm mount assembly also includes a motor including a motor body and a motor shaft attached to the motor body, a pinion gear attached to the motor shaft, a rack to be driven by the pinion gear, and a second mount including an extension portion and a second mounting portion.
    Type: Application
    Filed: October 7, 2022
    Publication date: April 11, 2024
    Inventors: Roatchanatam ANATTASAKUL, Fuk Ho Pius NG
  • Patent number: 11954929
    Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.
    Type: Grant
    Filed: March 17, 2023
    Date of Patent: April 9, 2024
    Assignee: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
  • Publication number: 20240053739
    Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.
    Type: Application
    Filed: March 17, 2023
    Publication date: February 15, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
  • Publication number: 20240054800
    Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.
    Type: Application
    Filed: March 17, 2023
    Publication date: February 15, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
  • Publication number: 20240027974
    Abstract: A first plurality of predictor values occurring during or before a first time interval may be received. An estimated outcome value may be determined for a second time interval by applying a prediction model via a processor to the first plurality of predictor values. A designated outcome value occurring during the second time interval and a second plurality of predictor values occurring during or before the second time interval may be received. An error value may be determined based on the estimated outcome value and the designated outcome value. A drift value for a second time interval may be determined by fitting a function to the second plurality of predictor values. The prediction model may be updated when it is determined that the drift value exceeds a designated drift threshold or that the error value exceeds a designated error threshold.
    Type: Application
    Filed: June 30, 2023
    Publication date: January 25, 2024
    Applicant: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Dushyanth Gokhale
  • Patent number: 11783233
    Abstract: A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.
    Type: Grant
    Filed: January 11, 2023
    Date of Patent: October 10, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Nagarjun Pogakula Surya Prakash, Ananda Shekappa Sonnada
  • Patent number: 11740905
    Abstract: In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.
    Type: Grant
    Filed: July 25, 2022
    Date of Patent: August 29, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Dushyanth Gokhale
  • Patent number: 11676036
    Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.
    Type: Grant
    Filed: May 21, 2020
    Date of Patent: June 13, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Nagarjun Pogakula Surya, Gomathi Sankar, Fuk Ho Pius Ng, Satish Padmanabhan
  • Patent number: 11648847
    Abstract: Described herein are methods and systems for remote charging of work vehicles using recharge vehicles. A recharge vehicle is equipped with power storage that has a sufficient capacity for propelling the recharge vehicle between a charging station (used for charging the recharge vehicle) and a work location (of the work vehicle) and also for charging the work vehicle at the work location. In some examples, the charging of the work vehicle and even the connection between the vehicles are formed while the work vehicle continues to operate. This approach helps to maximize the operating time of the work vehicle. Furthermore, this approach relaxes the charge rate requirement for charging the recharge vehicle and also for charging the work vehicle as well as the location of the charging station (for charging the recharge vehicle). In some examples, work vehicles and/or recharge vehicles are autonomous vehicles and use vehicle-to-vehicle coordination features.
    Type: Grant
    Filed: February 16, 2022
    Date of Patent: May 16, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Fuk Ho Pius Ng, Ian Wright
  • Patent number: 11635753
    Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.
    Type: Grant
    Filed: August 15, 2022
    Date of Patent: April 25, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
  • Patent number: 11636697
    Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.
    Type: Grant
    Filed: August 9, 2022
    Date of Patent: April 25, 2023
    Assignee: DIMAAG-AI, Inc.
    Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
  • Publication number: 20220261628
    Abstract: A method of processing data for an artificial intelligence (AI) system includes extracting features of the data to produce a lower dimensional representation of the data points; grouping the lower dimensional representation into clusters using a clustering algorithm; comparing the classes of data points within the clusters; and identifying unrepresented, under-represented, or misrepresented data.
    Type: Application
    Filed: February 15, 2021
    Publication date: August 18, 2022
    Inventors: Nagarjun Pogakula Surya, Gomathi Sankar, Fuk Ho Pius Ng, Satish Padmanabhan, Manikandan Manikam
  • Publication number: 20210365793
    Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.
    Type: Application
    Filed: May 21, 2020
    Publication date: November 25, 2021
    Inventors: Nagarjun Pogakula Surya, Gomathi Sankar, Fuk Ho Pius Ng, Satish Padmanabhan