Patents by Inventor Shashank Prasad RAO

Shashank Prasad RAO 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: 11567996
    Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient aggregate user-document interaction monitoring in collaborative document server systems. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to perform aggregate user-document interaction monitoring in collaborative document server systems using one or more of collaborative document graph-based user interfaces, collaborative document graph-based interface objects, edge-wise visual effect objects, collaborative document node objects, collaborative document node elements, document transition edge objects, and document transition edge elements.
    Type: Grant
    Filed: December 28, 2020
    Date of Patent: January 31, 2023
    Assignees: ATLASSIAN PTY LTD, ATLASSIAN, INC.
    Inventors: Shashank Prasad Rao, Bharat Agarwal, Avinash Agrawal, Viraj Sinha
  • Publication number: 20230024040
    Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to accurately and concisely generate one or more action item logs of one or more document data objects. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an action item log of a document data object comprising one or more semantically complete or incomplete units of text data, by generating content segmentation units, determining action item presence predictions, generating action item sets from each content segmentation unit within a candidate action item subset, aggregating the action item sets to create an action item log, and storing the action item log.
    Type: Application
    Filed: July 26, 2021
    Publication date: January 26, 2023
    Inventors: Karthik Muralidharan, Shashank Prasad Rao, Krishna Sai, Sri Vardhamanan A, Bailur Arjun Kini
  • Publication number: 20230027310
    Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.
    Type: Application
    Filed: July 26, 2021
    Publication date: January 26, 2023
    Inventors: Karthik Muralidharan, Shashank Prasad Rao, Krishna Sai
  • Publication number: 20230004835
    Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.
    Type: Application
    Filed: November 15, 2021
    Publication date: January 5, 2023
    Inventors: Mayank SAWHNEY, Shashank Prasad RAO, Akshar PRASAD, Saloni KHANDELWAL
  • Publication number: 20220207086
    Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient aggregate user-document interaction monitoring in collaborative document server systems. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to perform aggregate user-document interaction monitoring in collaborative document server systems using one or more of collaborative document graph-based user interfaces, collaborative document graph-based interface objects, edge-wise visual effect objects, collaborative document node objects, collaborative document node elements, document transition edge objects, and document transition edge elements.
    Type: Application
    Filed: December 28, 2020
    Publication date: June 30, 2022
    Inventors: Shashank Prasad RAO, Bharat AGARWAL, Avinash AGRAWAL, Viraj SINHA
  • Publication number: 20220198156
    Abstract: Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, a feature combination machine learning model, and a clustering machine learning model.
    Type: Application
    Filed: December 18, 2020
    Publication date: June 23, 2022
    Inventors: Shashank Prasad RAO, Karthik MURALIDHARAN