Patents by Inventor Janani Venugopalan

Janani Venugopalan 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: 20230205953
    Abstract: Examples of techniques for machine learning-based system architecture determination are described herein. An aspect includes receiving a system architecture specification corresponding to a system design, and a plurality of topological variants of the system architecture specification. Another aspect includes determining a system architecture graph based on the system architecture specification. Another aspect includes classifying, by a neural network-based classifier, each of the topological variants as a feasible architecture or an infeasible architecture based on the system architecture graph. Another aspect includes identifying a subset of the feasible architectures as system design candidates based on performance predictions.
    Type: Application
    Filed: June 5, 2020
    Publication date: June 29, 2023
    Applicant: Siemens Industry Software NV
    Inventors: Janani Venugopalan, Wesley Reinhart, Lucia Mirabella, Mike Nicolai
  • Publication number: 20220383167
    Abstract: System and method for latent bias detection by artificial intelligence modeling of human decision making using time series prediction data and events data of survey participants along with personal characteristics data for the participants. A deep Bayesian model solves for a bias distribution that fits a modeled prediction distribution of time series event data and personal characteristics data to a prediction probability distribution derived by a recurrent neural network. Sets of group bias clusters are evaluated for key features of related personal characteristics. Causal graphs are defined from dependency graphs of the key features. Bias explainability is inferred by perturbation in the deep Bayesian model of a subset of features from the causal graph, determining which causal relationships are most sensitive to alter group membership of participants.
    Type: Application
    Filed: August 28, 2020
    Publication date: December 1, 2022
    Inventors: Janani Venugopalan, Sudipta Pathak, Wei Xia, Sanjeev Srivastava, Arun Ramamurthy
  • Publication number: 20220171907
    Abstract: A method includes receiving, via a first component in a production environment, a sensor measurement corresponding to a second component in the production environment. A first digital twin corresponding to the first component is identified, and a perception algorithm is applied to identify a component type associated with the second component. A second digital twin is selected based on the component type, and a third digital twin is selected that models interactions between the first digital twin and the second digital twin. The third digital twin is used to generate instructions for the first component that allow the first component to interact with the second component. The instructions may then be delivered to the first component.
    Type: Application
    Filed: March 18, 2019
    Publication date: June 2, 2022
    Inventors: Ti-chiun Chang, Pranav Srinivas Kumar, Reed Williams, Arun Innanje, Janani Venugopalan, Edward Slavin, III, Lucia Mirabella
  • Publication number: 20220137591
    Abstract: A design and manufacturing system includes a multi-axis machine tool including a cutting head able to support a plurality of available tools and a part support, the cutting head and part support fully controllable in at least two axes, a design system operable using a computer to generate a 3-D model of a part to be manufactured, and a machine learning model operable using the computer to analyze the part to be manufactured to identify features and develop a manufacturing plan at least partially based on the multi-axis machine tool and the plurality of available tools, the manufacturing plan including a type of tool used for each feature, a feed-rate for each type of tool for each feature, and a speed of the tool for each type of tool for each feature.
    Type: Application
    Filed: April 3, 2019
    Publication date: May 5, 2022
    Inventors: Janani Venugopalan, Erhan Arisoy, Guannan Ren, Avinash Kumar, Mehdi Hamadou, Matthias Loskyll
  • Publication number: 20220108185
    Abstract: Machine-learned networks provide generative design. Rather than emulate the typical human design process, an inverse model is machine trained to generate a design from requirements. A simulation model is machine trained to recover performance relative to the requirements for generated designs. These two machine-trained models are used in an optimization that creates further designs from the inverse model output design and tests those designs with the simulation model. The use of machine-trained models in this loop for exploring many different designs decreases the time to explore, so may result in a more optimal design or better starting designs for the design engineer.
    Type: Application
    Filed: March 22, 2019
    Publication date: April 7, 2022
    Inventors: Janani Venugopalan, Sanjeev Srivastava, Krzysztof Chalupka, Marcin Staniszewski, Frederic Villeneuve, Edward Slavin, III
  • Publication number: 20220092240
    Abstract: A system and method for accelerating topology optimization of a design includes a topology optimization module configured to determine state variables of the topology using a two-scale topology optimization using design variables for a coarse-scale mesh and a fine-scale mesh for a number of optimization steps. A machine learning module includes a fully connected deep neural network having a tunable number of hidden layers configured to execute an initial training of a machine learning-based model using the history data, determine a predicted sensitivity value related to the design variables using the trained machine learning model, execute an online update of the machine learning-based model using updated history data, and update the design variables based on the predicted sensitivity value. The model predictions reduce the number of two-scale optimizations for each optimization step to occur only for initial training and for online model updates.
    Type: Application
    Filed: January 29, 2020
    Publication date: March 24, 2022
    Inventors: Heng Chi, Yuyu Zhang, Tsz Ling Elaine Tang, Janani Venugopalan, Lucia Mirabella, Le Song, Glaucio Paulino