Alanya, Turkey

Iaroslav Elistratov

This inventor holds 1 USPTO granted patent and 3 published patent applications. Top assignee: Themis AI, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Iaroslav Elistratov. Retrieved from https://idiyas.com/inventor/iaroslav-elistratov

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

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

Loading Chart...
1 patent (USPTO):Explore Patents

Title: Iaroslav Elistratov: Innovator in Uncertainty-Aware Modeling

Introduction

Iaroslav Elistratov is an inventor based in Alanya, Turkey. He is known for his contributions to the field of modeling and optimization, particularly in the context of deep learning and risk-aware systems. Although he currently holds no granted patents, his innovative ideas are reflected in his latest patent applications.

Latest Patent Applications

Iaroslav Elistratov has submitted several notable patent applications. One of his latest applications is titled "UNCERTAINTY-AWARE SEQUENCE MODELING." This application describes systems and methods aimed at improving the accuracy of model output generation. The method involves obtaining a risk-aware model and user input, applying the model, and iteratively regenerating the user input based on risk values compared to a threshold. This process continues until specific processing conditions are met.

Another significant application is "SYSTEMS AND METHODS FOR DEEP LEARNING MODEL OPTIMIZATION." This application outlines methods for optimizing a user model by identifying sub-optimal combinations of operations. The process includes generating modifications for these combinations and returning a transformed model that enhances performance.

Conclusion

Iaroslav Elistratov is an innovative thinker whose latest patent applications reflect his commitment to advancing modeling techniques in the realm of deep learning. His work has the potential to significantly impact the accuracy and efficiency of model outputs in various applications.

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]
Loading…