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Chicago, IL, United States of America

Tempus Labs, Inc.

USPTO Granted Patents = 64

Trademarks = 25

Forward Citations = 376

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96 inventors (with patents filed for the assignee):

Aly Azeem Khan (15 out of 21 patents)

Hailey B Lefkofsky (11 out of 13 patents)

Caroline Epstein (10 out of 11 patents)

4. Stephen Yip (10 out of 15 patents)

5. Jonathan Ozeran (9 out of 16 patents)

6. Nike Tsiapera Beaubier (9 out of 9 patents)

7. Irvin Ho (9 out of 11 patents)

8. Lingdao Sha (9 out of 11 patents)

9. Boleslaw Osinski (9 out of 11 patents)

10. Ashraf Hafez (9 out of 11 patents)

11. Eric Lefkofsky (9 out of 14 patents)

12. Andrew J Kruger (7 out of 9 patents)

13. Michael Carlson (7 out of 9 patents)

14. Abel Greenwald (7 out of 9 patents)

15. Caleb Willis (7 out of 9 patents)

Core Competencies:

Biomarkers

Histopathology

Machine Learning

Cancer Diagnostics

Cardiac Event Prediction

Cohort Analysis

Metastasis Prediction

Liquid Biopsy

64 patents: Sort by Forward Citations

1. 12725682 - Systems, methods, and articles for imputing directed temporal measurements (2026-09-01),

2. 11935152 - Determining biomarkers from histopathology slide images (2024-03-19),

3. 11875903 - Method and process for predicting and analyzing patient cohort response, progression, and survival (2024-01-16),

4. 11869668 - Artificial intelligence based cardiac event predictor systems and methods (2024-01-09),

5. 11848107 - Predicting likelihood and site of metastasis from patient records (2023-12-19),

6. 11830587 - Method and process for predicting and analyzing patient cohort response, progression, and survival (2023-11-28),

7. 11769572 - Method and process for predicting and analyzing patient cohort response, progression, and survival (2023-09-26),

8. 11756688 - ECG-based cardiovascular disease detection systems and related methods (2023-09-12),

9. 11742064 - Automated quality assurance testing of structured clinical data (2023-08-29),

10. 11741365 - Generalizable and interpretable deep learning framework for predicting MSI from histopathology slide images (2023-08-29),

11. 11727674 - Systems and methods for generating histology image training datasets for machine learning models (2023-08-15),

12. 11715565 - Evaluating effect of event on condition using propensity scoring (2023-08-01),

13. 11715467 - Collaborative artificial intelligence method and system (2023-08-01),

14. 11705226 - Data based cancer research and treatment systems and methods (2023-07-18),

15. 11699507 - Method and process for predicting and analyzing patient cohort response, progression, and survival (2023-07-11),

Company Overview

Tempus Labs, Inc. is a technology company based in Chicago, specializing in the application of artificial intelligence to healthcare. The organization focuses on leveraging advanced machine learning techniques to enhance cancer diagnostics and improve patient outcomes. Tempus develops solutions that analyze biomarkers and histopathology data, enabling precise cohort analysis and predictions related to metastasis and cardiac events. Their innovative approach includes the use of liquid biopsy technology, positioning them at the forefront of personalized medicine.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 20 2026

64 Patents

#90 in Chicago, IL

#318 in Illinois

#5,686 in US

#11,484 in the World

Chicago, IL, United States of America

Tempus Labs, Inc.

Forward Citations = 376

96 inventors:

Aly Azeem Khan (15 out of 21 patents)

Hailey B Lefkofsky (11 out of 13 patents)

Caroline Epstein (10 out of 11 patents)

4. Stephen Yip (10 out of 15 patents)

5. Jonathan Ozeran (9 out of 16 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Tempus Labs, Inc.. Retrieved from https://idiyas.com/company/tempus-labs

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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