Patents by Inventor Mouli RASTOGI

Mouli RASTOGI 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: 20220374769
    Abstract: Conventionally three main approaches are utilized for explainability of blackbox ML systems: proxy or shadow model approaches, model inspection approaches and data based approaches. Most of the research work on explainability has followed one of the above approaches with each having its own limitations and advantages. Embodiments of the present disclosure provide a method and system for explainable Machine learning (ML) using data and proxy model based hybrid approach to explain outcomes of a ML model. The hybrid approach is based on Local Interpretable Model-agnostic Explanations (LIME) using Formal Concept Analysis (FCA) for structured sampling of instances. The approach combines the benefits of using a data-based approach (FCA) and proxy model-based approach (LIME).
    Type: Application
    Filed: May 13, 2022
    Publication date: November 24, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: AMIT SANGROYA, LOVEKESH VIG, MOULI RASTOGI, CHANDRASEKHAR ANANTARAM
  • Publication number: 20220284215
    Abstract: This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 8, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Mouli RASTOGI, Syed Afshan ALI, Mrinal RAWAT, Lovekesh VIG, Puneet AGARWAL, Gautam SHROFF, Ashwin SRINIVASAN