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For the Inventor, By the Inventor

Plano, TX, United States of America

Milan Shah

This inventor holds 7 USPTO granted patents and 4 published patent applications. Top assignees: Citibank, N.a., Citigroup Technology, Inc.. Active years: 2023-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Milan Shah. Retrieved from https://idiyas.com/inventor/milan-shah-u069th6q

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

USPTO Granted Patents = 7 

% Patents Active = 85.7

Average Co-Inventor Count = 3.3

ph-index = 2

Forward Citations = 8(Granted Patents)


Company Filing History:

goldMedal5 out of 549 
 
Citibank, N.a.
 patents
silverMedal2 out of 49 
 
Citigroup Technology, Inc.
 patents

Years Active: 2023-2026

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7 patents (USPTO):Explore Patents
1. 12613875 - Systems and methods for maintaining sequential relationships in aggregating time-series data streams using time shifted event triggers (2026-04-28),
2. 12333395 - Systems and methods for cohort-based predictions in clustered time-series data in order to detect significant rate-of-change events (2025-06-17),
3. 12165035 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogonous time-series data (2024-12-10),
4. 12164525 - Systems and methods for aggregating time-series data streams based on potential state characteristics following aggregation (2024-12-10),
5. 11948065 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogeneous time-series data (2024-04-02),
6. 11868860 - Systems and methods for cohort-based predictions in clustered time-series data in order to detect significant rate-of-change events (2024-01-09),
7. 11704540 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogenous time series-data (2023-07-18),

Title: Milan Shah: Innovator in Artificial Intelligence and Data Aggregation

Introduction

Milan Shah is a prominent inventor based in Plano, TX (US), known for his contributions to artificial intelligence and data aggregation technologies. With a total of 7 patents to his name, he has made significant strides in developing systems that enhance predictive analytics and data processing.

Latest Patents

Milan's latest patents include innovative systems and methods for cohort-based predictions in clustered time-series data. These systems utilize artificial intelligence models to predict the effects of anticipated events on the current state of a system. The technology aims to generate timely and relevant responses by accurately determining predicted events in real-time. Another notable patent focuses on aggregating time-series data streams based on potential state characteristics. This invention improves data aggregation related to artificial intelligence applications, specifically in predicting effects when aggregating time-series data.

Career Highlights

Throughout his career, Milan has worked with notable companies such as Citibank, N.A. and Citigroup Technology, Inc. His experience in these organizations has allowed him to refine his skills in technology and innovation, contributing to his success as an inventor.

Collaborations

Milan has collaborated with esteemed professionals in his field, including Ernst Wilhelm Spannhake, II and Thomas Francis Gianelle. These partnerships have further enriched his work and expanded his impact on the industry.

Conclusion

Milan Shah's innovative work in artificial intelligence and data aggregation has positioned him as a key figure in the field. His patents reflect a commitment to advancing technology and improving predictive analytics.

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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