About

Mark Lemley is the William H. Neukom Professor of Law at Stanford Law School and the Director of the Stanford Program in Law, Science and Technology. He is also a Senior Fellow at the Stanford Institute for Economic Policy Research and affiliated faculty in the Symbolic Systems program. His teaching areas include intellectual property, patent law, trademark law, antitrust, the law of robotics and AI, video game law, and remedies. Lemley is the author of 11 books and 218 articles, including the two-volume treatise IP and Antitrust. His works have been cited over 300 times by courts, including 19 times by the United States Supreme Court, and more than 40,000 times in books and academic articles, making him the most-cited scholar in IP law and one of the ten most cited legal scholars of all time. He has published numerous influential articles in top law reviews and other prestigious journals, with his work translated into multiple languages. Lemley has testified before Congress, filed over 70 amicus briefs in the U.S. Supreme Court and other courts, and litigated extensively in federal courts, arguing 30 federal appellate cases and participating in more than three dozen Supreme Court cases. He is a cofounder of Lex Machina, Inc., a litigation data and analytics company acquired by Lexis. Recognized with multiple awards, including California Lawyer’s Attorney of the Year twice and the World Technology Award for Law, Lemley has been named a Young Global Leader by the Davos World Economic Forum and has received numerous other honors. He clerked for Judge Dorothy Nelson on the Ninth Circuit and has practiced law with several firms, including Fish & Richardson and Keker & Van Nest. Previously, he taught at Berkeley Law School and the University of Texas School of Law. In his personal time, he enjoys cooking, travel, yoga, and video games.

Research topics

• Political Science
• Economics
• Business
• Law and economics
• Law
• Virology
• Philosophy
• Medicine
• Natural resource economics
• Environmental ethics
• Microeconomics
• Biology
• Epistemology
• Ecology

Selected publications

• 26-A-17309-ACC INTEGRATED PET MYOCARDIAL PERFUSION PHENOTYPING PREDICTS LV REMODELING AND OUTCOMES IN PATIENTS WITH AND WITHOUT LBBB

  Journal of the American College of Cardiology · 2026-03-27

  article
  PublisherDOI

• Extracting memorized pieces of (copyrighted) books from open-weight language models

  ArXiv.org · 2025-05-18

  preprintOpen access

  Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language models (LLMs) memorize protected expression from books in their training data. We show that these polarized positions dramatically oversimplify the relationship between memorization and copyright. To do so, we develop a technique to measure memorization of books, which we apply to 200 books and 14 open-weight LLMs. Through over 3000 experiments, we show that memorization varies both by model and book. With respect to our specific extraction methodology, we find that most LLMs do not memorize most books -- either in whole or in part; however, there are notable exceptions. For instance, Llama 3.1 70B entirely memorizes some books, like Harry Potter and the Sorcerer's Stone; memorization is so extensive that one can deterministically extract the whole book almost verbatim using the book's first few words as an initial prompt. We discuss why our results have significant implications for copyright cases, though not ones that unambiguously favor either side.

  PublisherOA PDFDOI

• Dynamic frame-by-frame motion correction for ¹⁸ F-flurpiridaz PET-MPI using convolution neural network

  medRxiv · 2025-07-01 · 1 citations

  preprintOpen access

  Abstract Purpose Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET. Methods The method employs a 3D ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32 sites of a Phase III clinical trial ( NCT01347710 ). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental MBF and MFR. Results The area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC MBF, manual-MC MBF from Operators (AUC=0.897,0.892 and 0.889, respectively; p>0.05), standard non-AI automatic MC (AUC=0.877; p>0.05) and significantly higher than No-MC (AUC=0.835; p<0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were ±0.49ml/g/min (mean difference = 0.00) for MFR and ±0.24ml/g/min (mean difference = 0.00) for MBF. Conclusion DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18 F-flurpiridaz PET-MPI.

  PublisherOA PDFDOI

• General Purpose Deep Learning Attenuation Correction Improves Diagnostic Accuracy of SPECT MPI

  JACC. Cardiovascular imaging · 2025-08-08 · 4 citations

  article
  PublisherDOI

• Intellectual Property in the New Technological Age 2025: Volume I Chapters 1&2--Perspectives, Trade Secrets

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Incremental Prognostic Value of Subendocardial Myocardial Flow Reserve in Patients with Normal Transmural Myocardial Flow Reserve

  Journal of Nuclear Cardiology · 2025-08-01

  article
  PublisherDOI

• Patent Law's Era of Normalcy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

  npj Digital Medicine · 2025-03-13 · 6 citations

  articleOpen access

  Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74-0.87]), which was higher than for coronary calcium (0.64 [0.57-0.71]) or perfusion (0.62 [0.55-0.70]), with p < 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.

  PublisherOA PDFDOI

• Extracting memorized pieces of (copyrighted) books from open-weight language models

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Subendocardial quantification enhances coronary artery disease detection in 18F-flurpiridaz PET

  European Journal of Nuclear Medicine and Molecular Imaging · 2025-03-05 · 5 citations

  articleOpen access

  PURPOSE: F-flurpiridaz, is set to enter clinical use soon following its recent regulatory approval. We developed an approach for evaluating subendocardial analysis for stress total perfusion deficit (TPD) and ischemic TPD, assessed its performance for detection of coronary artery disease (CAD) and compared these measures to transmural analysis and expert physician assessments. METHODS: F-flurpiridaz phase III clinical trial (NCT01347710) were used. The subendocardial layer was automatically defined on the left ventricular contours and used for the derivation of polar maps. Areas under the receiver operating characteristic curve (AUC) for quantitative and visual measures were evaluated for detecting CAD, defined as ≥ 50% stenosis by invasive coronary angiography. RESULTS: In total, 753 cases were analyzed, with a median age of 63 (interquartile range 56,69) and 69% male. AUC for detecting ≥ 50% stenosis was higher for subendocardial than transmural analysis for stress (0.795 vs. 0.762, respectively; p = 0.013) and ischemic (0.795 vs. 0.767, respectively; p = 0.049) TPD. Subendocardial and transmural TPD achieved diagnostic performance greater than or comparable to that of the readers' assessments in the total population as well as across subgroups of interest. CONCLUSION: Subendocardial analysis of ischemic perfusion improves the detection of CAD compared to transmural quantitative analysis or expert visual reading. These measures can be derived automatically with minimal user interaction. Integrating TPD quantitative measures could standardize the diagnostic approach for this novel tracer.

  PublisherOA PDFDOI

Frequent coauthors

• Peter S. Menell

  37 shared

• Robert P. Merges

  32 shared

• Dan L. Burk

  25 shared

• John R. Allison

  The University of Texas at Austin

  16 shared

• Herbert Hovenkamp

  University of Pennsylvania

  13 shared

• Mark Weston Janis

  13 shared

• Daralyn J. Durie

  13 shared

• Carl Shapiro

  University of California, Berkeley

  12 shared

Labs

• Stanford Law and Technology LabPI

Awards & honors

• California Lawyer’s Attorney of the Year (twice)
• California State Bar’s inaugural IP Vanguard Award
• 2018 World Technology Award for Law
• P.J. Federico Award from the Patent and Trademark Office Soc…
• Young Global Leader by the Davos World Economic Forum