Secunderabad, India

Vitobha Munigala

USPTO Granted Patents = 7 

Average Co-Inventor Count = 3.9

ph-index = 1

Forward Citations = 3(Granted Patents)


Location History:

  • Hyderabad, IN (2020)
  • Bengaluru, IN (2022)
  • Secunderabad, IN (2022 - 2024)

Company Filing History:


Years Active: 2020-2025

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7 patents (USPTO):Explore Patents

Title: Vitobha Munigala: Innovator in Time Series Forecasting

Introduction

Vitobha Munigala is a prominent inventor based in Secunderabad, India. He has made significant contributions to the field of time series forecasting, holding a total of seven patents. His work focuses on developing methods and systems that enhance the understanding and accuracy of predictions made by complex models.

Latest Patents

One of his latest patents is titled "Explanation for time series forecasting models." This invention provides a method, system, and computer program product for explaining predictions made by black box time series models. The process involves identifying a black box model, predicting time instances, and generating explanations for the forecasts made by these models. Another notable patent is "Corpus quality processing for a specified task." This patent describes a system that enhances the quality of unstructured documents for specific tasks by applying quality metrics and transforming documents to address identified issues.

Career Highlights

Vitobha Munigala is currently employed at International Business Machines Corporation (IBM), where he continues to innovate and contribute to advancements in technology. His expertise in time series forecasting and document processing has positioned him as a valuable asset in his field.

Collaborations

He has collaborated with notable colleagues, including Diptikalyan Saha and Arvind Agarwal, to further enhance the impact of his inventions and research.

Conclusion

Vitobha Munigala's work in time series forecasting and document quality processing showcases his innovative spirit and dedication to advancing technology. His contributions are paving the way for more accurate predictive models and improved data processing methods.

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