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Minneapolis, MN, United States of America

Fair Isaac Corporation

USPTO Granted Patents = 277

% Expired Patents = 10.1

CIPO Patents = 1

EPO Patents = 12

Trademarks = 20

Forward Citations = 12,481

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326 inventors (with patents filed for the assignee):

Scott Michael Zoldi (91 out of 96 patents)

Shafi Ur Rahman (24 out of 25 patents)

Joseph P Milana (19 out of 19 patents)

4. Gerald Fahner (15 out of 15 patents)

5. Stuart L Crawford (13 out of 14 patents)

6. Larry Peranich (11 out of 12 patents)

7. Sergei Tolmanov (9 out of 9 patents)

8. Matthew Kennel (9 out of 9 patents)

9. Shailesh Kumar (9 out of 9 patents)

10. Uwe Mayer (8 out of 9 patents)

11. Andrew Flint (8 out of 8 patents)

12. Navin Doshi (8 out of 9 patents)

13. Anu Kumar Pathria (8 out of 14 patents)

14. Michael Steele (7 out of 8 patents)

15. Kashyap Babu Rao Kolipaka (7 out of 7 patents)

Core Competencies:

Model Explanations

Uncertainty Quantification

Synthetic Data Generation

Decision Logic

Bias Analysis

Fraud Detection

Neural Networks

Data Governance

Privacy Enhancement

Feature Discovery

Transaction Scoring

Adaptive Analytics

277 patents: Sort by Forward Citations

1. 12743601 - Latent feature based model bias mitigation in artificial intelligence systems (2026-09-22),

2. 12737421 - Interpretable feature discovery with grammar-based Bayesian optimization (2026-09-15),

3. 12730789 - Composable modular platform (2026-09-08),

4. 12710955 - GRANULAR MANAGEMENT OF DEPLOYMENT UNITS IN A MODULARIZED SOFTWARE PLATFORM (2026-08-18),

5. 12699562 - Workflow templates for configuration packages (2026-08-04),

6. 12586006 - Deployment of self-contained decision logic (2026-03-24),

7. 12585996 - Explanatory dropout for machine learning models (2026-03-24),

8. 12585630 - Method and apparatus for analyzing coverage, bias, and model explanations in large dimensional modeling data (2026-03-24),

9. 12530600 - Synthetic data generation for machine learning models (2026-01-20),

10. 12511298 - Fast automatic explanation of scored observations (2025-12-30),

11. 12489646 - Blockchain-based model governance and auditable monitoring of machine learning models (2025-12-02),

12. 12475354 - Constrained designed and guided latent feature space contributions to machine learning models (2025-11-18),

13. 12468986 - Rank distillation for training supervised machine learning models (2025-11-11),

14. 12468524 - Extendible, recursive configuration packages that provide versioning trace-ability and testability (2025-11-11),

15. 12443908 - Data distillery for signal detection (2025-10-14),

Company Overview

Fair Isaac Corporation, headquartered in Minneapolis, MN, specializes in advanced machine learning models that enhance decision-making processes across various industries. The company focuses on core technology areas such as model explanations, uncertainty quantification, and synthetic data generation, which are crucial for developing robust analytical frameworks. Additionally, Fair Isaac emphasizes bias analysis and fraud detection, leveraging neural networks and data governance to ensure ethical and effective use of data in its solutions.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 13 2026

277 Patents

#9 in Minneapolis, MN

#40 in Minnesota

#1,575 in US

#3,187 in the World

Minneapolis, MN, United States of America

Fair Isaac Corporation

Forward Citations = 12,481

326 inventors:

Scott Michael Zoldi (91 out of 96 patents)

Shafi Ur Rahman (24 out of 25 patents)

Joseph P Milana (19 out of 19 patents)

4. Gerald Fahner (15 out of 15 patents)

5. Stuart L Crawford (13 out of 14 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Fair Isaac Corporation. Retrieved from https://idiyas.com/company/fair-isaac

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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