For the Inventor, By the Inventor

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San Ramon, CA, United States of America

Cylance Inc.

USPTO Granted Patents = 112

EPO Patents = 30

Trademarks = 14

Forward Citations = 477

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72 inventors (with patents filed for the assignee):

Derek A Soeder (46 out of 46 patents)

Matthew Wolff (46 out of 46 patents)

Ryan Permeh (34 out of 34 patents)

4. Xuan Zhao (25 out of 25 patents)

5. Andrew Davis (23 out of 40 patents)

6. Michael Thomas Wojnowicz (20 out of 20 patents)

7. Jian Luan (15 out of 15 patents)

8. Brian Michael Wallace (15 out of 15 patents)

9. David Neill Beveridge (15 out of 16 patents)

10. John Brock (14 out of 14 patents)

11. Glenn Chisholm (12 out of 12 patents)

12. Mahdi Azarafrooz (9 out of 9 patents)

13. Samuel John Oswald (9 out of 11 patents)

14. Homer Valentine Strong (9 out of 9 patents)

15. Eric G Petersen (9 out of 35 patents)

Core Competencies:

Malware Detection

User Authentication

Threat Analysis

Machine Learning

Endpoint Protection

Anomaly Detection

Feature Hashing

Data Fingerprinting

Dynamic Analysis

Classification Models

Security Frameworks

Signal Fingerprinting

112 patents: Sort by Forward Citations

1. 12737279 - Detecting unwanted software using a machine learning operation (2026-09-15),

2. 12731005 - Processing labeled data in a machine learning operation (2026-09-08),

3. 12724874 - Malware process blocking technique (2026-09-01),

4. 12061692 - Methods and systems for fingerprinting malicious behavior (2024-08-13),

5. 11928213 - Malware detection (2024-03-12),

6. 11893096 - Computer user authentication using machine learning (2024-02-06),

7. 11880391 - Clustering software codes in scalable manner (2024-01-23),

8. 11797826 - Machine learning model for analysis of instruction sequences (2023-10-24),

9. 11709922 - Password-less software system user authentication (2023-07-25),

10. 11657317 - Automated systems and methods for generative multimodel multiclass classification and similarity analysis using machine learning (2023-05-23),

11. 11637858 - Detecting malware with deep generative models (2023-04-25),

12. 11636202 - Projected vector overflow penalty as mitigation for machine learning model string stuffing (2023-04-25),

13. 11620471 - Clustering analysis for deduplication of training set samples for machine learning based computer threat analysis (2023-04-04),

14. 11604871 - Projected vector modification as mitigation for machine learning model string stuffing (2023-03-14),

15. 11586975 - Machine learning model score obfuscation using multiple classifiers (2023-02-21),

Company Overview

Cylance Inc., based in San Ramon, California, specializes in machine learning security solutions aimed at enhancing cybersecurity measures. The company focuses on developing advanced technologies for malware detection, user authentication, and threat analysis, leveraging machine learning to provide robust endpoint protection. Their innovative approaches include anomaly detection, feature hashing, and data fingerprinting, positioning them as a leader in the cybersecurity landscape, dedicated to safeguarding organizations against evolving digital threats.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 20 2026

112 Patents

#5 in San Ramon, CA

#775 in California

#3,431 in US

#7,058 in the World

San Ramon, CA, United States of America

Cylance Inc.

Forward Citations = 477

72 inventors:

Derek A Soeder (46 out of 46 patents)

Matthew Wolff (46 out of 46 patents)

Ryan Permeh (34 out of 34 patents)

4. Xuan Zhao (25 out of 25 patents)

5. Andrew Davis (23 out of 40 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Cylance Inc.. Retrieved from https://idiyas.com/company/cylance

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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