For the Inventor, By the Inventor

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San Francisco, CA, United States of America

Sift Science, Inc.

USPTO Granted Patents = 43

Forward Citations = 319

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56 inventors (with patents filed for the assignee):

Fred Sadaghiani (19 out of 19 patents)

Wei Liu (17 out of 18 patents)

Jacob Burnim (12 out of 12 patents)

4. Alex Paino (12 out of 12 patents)

5. Keren Gu (11 out of 11 patents)

6. Noah Grant (8 out of 8 patents)

7. Eugenia Ho (7 out of 7 patents)

8. Hui Wang (6 out of 6 patents)

9. Pradhan Bagur Umesh (5 out of 5 patents)

10. Rishabh Kothari (4 out of 5 patents)

11. Kostyantyn Gurnov (4 out of 5 patents)

12. Vera Dadok (4 out of 4 patents)

13. Yuan Zhuang (4 out of 4 patents)

14. Irving Chen (4 out of 4 patents)

15. Gary Lee (4 out of 5 patents)

Core Competencies:

Digital Threat Assessment

Machine Learning

Fraud Detection

Automated Decisioning

Anomaly Detection

Content Clustering

Threat Scoring

Data Labeling

User Threat Detection

Automated Workflows

Probationary Workflows

Malicious Account Testing

43 patents: Sort by Forward Citations

1. 12375500 - Systems and methods for digital threat assessment and mitigation using t-digest score distribution representations and percentile-based threat scoring in a digital threat mitigation platform (2025-07-29),

2. 12273356 - Systems and methods for digital threat assessment and mitigation using t-digest score distribution representations and percentile-based threat scoring in a digital threat mitigation platform (2025-04-08),

3. 12143402 - Systems and methods for accelerating a disposition of digital dispute events in a machine learning-based digital threat mitigation platform (2024-11-12),

4. 12047401 - Systems and methods for machine learning-based detection of an automated fraud attack or an automated abuse attack (2024-07-23),

5. 11916927 - Systems and methods for accelerating a disposition of digital dispute events in a machine learning-based digital threat mitigation platform (2024-02-27),

6. 11887126 - Systems and methods for generating a probationary automated-decisioning workflow in a machine learning-task oriented digital threat or digital abuse mitigation system (2024-01-30),

7. 11841941 - Systems and methods for accelerated detection and replacement of anomalous machine learning-based ensembles and intelligent generation of anomalous artifacts for anomalous ensembles (2023-12-12),

8. 11777962 - Systems and methods for machine learning-based detection of an automated fraud attack or an automated abuse attack (2023-10-03),

9. 11720668 - Systems and methods for accelerated detection and replacement of anomalous machine learning-based digital threat scoring ensembles and intelligent generation of anomalous artifacts for anomalous ensembles (2023-08-08),

10. 11645386 - Systems and methods for automated labeling of subscriber digital event data in a machine learning-based digital threat mitigation platform (2023-05-09),

11. 11620653 - Systems and methods for configuring and implementing a malicious account testing machine learning model in a machine learning-based digital threat mitigation platform (2023-04-04),

12. 11573882 - Systems and methods for optimizing a machine learning-informed automated decisioning workflow in a machine learning task-oriented digital threat mitigation platform (2023-02-07),

13. 11528290 - Systems and methods for machine learning-based digital content clustering, digital content threat detection, and digital content threat remediation in machine learning-based digital threat mitigation platform (2022-12-13),

14. 11496501 - Systems and methods for an adaptive sampling of unlabeled data samples for constructing an informative training data corpus that improves a training and predictive accuracy of a machine learning model (2022-11-08),

15. 11481490 - Systems and methods for automated labeling of subscriber digital event data in a machine learning-based digital threat mitigation platform (2022-10-25),

Company Overview

Sift Science, Inc., based in San Francisco, CA, specializes in digital threat mitigation through advanced machine learning technologies. The company focuses on fraud detection and automated decisioning, providing solutions that enhance security and streamline operations for businesses. Their expertise extends to anomaly detection and content threat detection, enabling organizations to identify and respond to potential risks effectively. Sift's innovative approach includes automated labeling and probationary workflows, ensuring a robust defense against evolving digital threats.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 20 2026

43 Patents

#106 in San Francisco, CA

#1,901 in California

#8,104 in US

#16,047 in the World

San Francisco, CA, United States of America

Sift Science, Inc.

Forward Citations = 319

56 inventors:

Fred Sadaghiani (19 out of 19 patents)

Wei Liu (17 out of 18 patents)

Jacob Burnim (12 out of 12 patents)

4. Alex Paino (12 out of 12 patents)

5. Keren Gu (11 out of 11 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Sift Science, Inc.. Retrieved from https://idiyas.com/company/sift-science

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

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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