Adamit, Israel

Igor Kostirev-Kronos

This inventor holds 2 USPTO granted patents and 1 published patent application. Top assignee: International Business Machines Corporation. Active years: 2023.

USPTO Granted Patents = 2 

% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2023

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2 patents (USPTO):Explore Patents

Title: Igor Kostirev-Kronos: Innovator in Time-Series Data Uncertainty Reduction

Introduction

Igor Kostirev-Kronos is a notable inventor based in Adamit, IL. He has made significant contributions to the field of data analysis, particularly in the area of time-series data uncertainty reduction. With a total of 2 patents to his name, Kostirev-Kronos is recognized for his innovative approaches to complex data challenges.

Latest Patents

His latest patents focus on methods for reducing uncertainty in time-series data. One of his key inventions involves generating an initial accumulated signal based on an event classifier prediction score. This score is derived from an event predicator that analyzes time-series data to estimate the probability of a target event occurring. The process includes imposing signal leakage on the initial accumulated signal, and it also features an alert system that activates when the initial accumulated signal exceeds a predetermined alert threshold.

Career Highlights

Igor Kostirev-Kronos is currently employed at International Business Machines Corporation, commonly known as IBM. His work at IBM has allowed him to explore and develop advanced methodologies that enhance the reliability of data predictions.

Collaborations

Throughout his career, Kostirev-Kronos has collaborated with talented individuals such as Edward Vitkin and Alex Melament. These partnerships have contributed to the advancement of his research and the successful implementation of his inventions.

Conclusion

Igor Kostirev-Kronos stands out as an influential inventor in the realm of data analysis, particularly with his innovative patents aimed at reducing uncertainty in time-series data. His contributions continue to shape the future of data prediction technologies.

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