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For the Inventor, By the Inventor

Cary, NC, United States of America

Deovrat Vijay Kakde

This inventor holds 13 USPTO granted patents and 2 published patent applications. Top assignee: Sas Institute Inc.. Active years: 2014-2023.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Deovrat Vijay Kakde. Retrieved from https://idiyas.com/inventor/deovrat-vijay-kakde

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 13 

% Patents Active = 100.0

Average Co-Inventor Count = 3.7

ph-index = 4

Forward Citations = 42(Granted Patents)


Location History:

  • Pune, IN (2014)
  • Cary, NC (US) (2017 - 2023)

Company Filing History:

goldMedal13 out of 810 
 
Sas Institute Inc.
 patents

Years Active: 2014-2023

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13 patents (USPTO):Explore Patents
1. 11846979 - Anomaly detection and diagnostics based on multivariate analysis (2023-12-19),
2. 11630973 - Quality prediction using process data (2023-04-18),
3. 11501116 - Quality prediction using process data (2022-11-15),
4. 10684618 - Event monitoring system using frequency segments (2020-06-16),
5. 10656638 - Visualization to support event monitoring system (2020-05-19),
6. 10558207 - Event monitoring system (2020-02-11),
7. 10509847 - Local outlier factor hyperparameter tuning for data outlier detection (2019-12-17),
8. 10482353 - Bandwidth selection in support vector data description for classification or outlier detection (2019-11-19),
9. 9990592 - Kernel parameter selection in support vector data description for outlier identification (2018-06-05),
10. 9830558 - Fast training of support vector data description using sampling (2017-11-28),
11. 9639809 - Monitoring system based on a support vector data description (2017-05-02),
12. 9536208 - Kernel parameter selection in support vector data description for outlier identification (2017-01-03),
13. 8671364 - Techniques to present hierarchical information using a multivariable decomposition visualization (2014-03-11),

Title: Deovrat Vijay Kakde: Innovator in Anomaly Detection and Quality Prediction

Introduction

Deovrat Vijay Kakde is a prominent inventor based in Cary, NC (US). He has made significant contributions to the fields of anomaly detection and quality prediction, holding a total of 13 patents. His innovative work has the potential to enhance various industrial processes through advanced analytical techniques.

Latest Patents

Kakde's latest patents include groundbreaking technologies such as "Anomaly detection and diagnostics based on multivariate analysis" and "Quality prediction using process data." The first patent focuses on detecting and diagnosing anomalies in target objects using improved Mahalanobis-Taguchi system (MTS) techniques. This system can analyze a set of measurements and determine an abnormal condition when the Mahalanobis distance exceeds a predetermined threshold. The second patent involves a machine learning model that predicts quality risks in bonding operations by analyzing process data from previous operations. This predictive capability allows for better control and optimization of manufacturing processes.

Career Highlights

Deovrat Kakde is currently employed at SAS Institute Inc., where he applies his expertise in data analysis and machine learning to develop innovative solutions. His work at SAS has positioned him as a key player in the field of data-driven decision-making.

Collaborations

Kakde collaborates with talented professionals such as Arin Chaudhuri and Anya Mary McGuirk, contributing to a dynamic and innovative work environment.

Conclusion

Deovrat Vijay Kakde's contributions to anomaly detection and quality prediction demonstrate his commitment to advancing technology in industrial applications. His innovative patents and collaborative efforts continue to shape the future of data analysis and process optimization.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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