Jeffrey Heuer

This inventor holds 9 USPTO granted patents and 2 published patent applications. Top assignees: Kenna Security, Inc., Risk I/o, Inc.. Active years: 2015-2024.

USPTO Granted Patents = 9 (Patent Applications = 2)

% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 5

Forward Citations = 172(Granted Patents)

Location History:

Chicago, IL (US) (2016 - 2019)
New York, NY (US) (2015 - 2024)

Company Filing History:

where one patent can have more than one assignee

Years Active: 2015-2024

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9 patents (USPTO):Sort by Forward CitationsExplore Patents

1. 12079346 - Exploit prediction based on machine learning (2024-09-03),

2. 11275844 - Exploit prediction based on machine learning (2022-03-15),

3. 10762212 - Exploit prediction based on machine learning (2020-09-01),

4. 10305925 - Ordered computer vulnerability remediation reporting (2019-05-28),

5. 10114954 - Exploit prediction based on machine learning (2018-10-30),

6. 9825981 - Ordered computer vulnerability remediation reporting (2017-11-21),

7. 9270695 - Identifying vulnerabilities of computing assets based on breach data (2016-02-23),

8. 8984643 - Ordered computer vulnerability remediation reporting (2015-03-17),

9. 8966639 - Internet breach correlation (2015-02-24),

Title: Innovations of Jeffrey Heuer

Introduction

Jeffrey Heuer is a notable inventor based in New York, NY (US), recognized for his contributions to the field of machine learning and cybersecurity. With a total of nine patents to his name, Heuer has made significant strides in predicting exploits based on advanced algorithms.

Latest Patents

One of his latest patents focuses on exploit prediction based on machine learning. This innovation involves generating one or more models by selecting training data that includes a variety of features, particularly a prevalence feature for each vulnerability within a first set of vulnerabilities. These models are designed to predict whether an exploit will be developed for a vulnerability and whether it will be utilized in an attack. The models are then applied to input data that comprises the prevalence feature for each vulnerability in a second set. The output data generated from this application indicates predictions regarding the development of exploits for each vulnerability in the second set, as well as the potential use of yet-to-be-developed exploits in attacks.

Career Highlights

Throughout his career, Jeffrey Heuer has worked with prominent companies such as Kenna Security, Inc. and Risk I/O, Inc. His experience in these organizations has allowed him to refine his skills and contribute to significant advancements in cybersecurity.

Collaborations

Heuer has collaborated with notable professionals in the field, including Edward T Bellis and Michael Roytman. These partnerships have further enhanced his innovative capabilities and broadened his impact on the industry.

Conclusion

Jeffrey Heuer's work in machine learning and cybersecurity exemplifies the importance of innovation in predicting and preventing cyber threats. His contributions continue to shape the landscape of technology and security.