Addressing and Identifying Challenges in Patenting Machine Learning Algorithms
The proliferation of artificial intelligence (AI) and other technological developments such as machine learning algorithms have fundamentally changed traditional processes in today's society. Companies of all sizes now rely on algorithms and automated processes to perform most of their daily transactions. With the increased use of such transformative technologies, innovators have intensified their efforts to secure control over their intellectual property rights. Among the fastest growing patent rights filings are those for machine learning algorithms.
However, along with the many opportunities surrounding this field of technology are the challenges and regulatory considerations for each jurisdiction which make it difficult for inventors to obtain a patent award.
In this LIVE Webcast, a panel of thought leaders and professionals brought together by The Knowledge Group will provide the audience with an in-depth analysis of the fundamentals as well as recent trends and developments in Patenting Machine Learning. Speakers will address common challenges and hurdles when patenting machine learning algorithms. They will also go beyond the basics and present the best practices and practical tips to avoid potential legal pitfalls.
Key topics include:
- Machine Learning Algorithms: Framework
- Variables in Machine Learning
- Minimizing Risk and Pitfalls
- Patenting Machine Learning: Identifying Challenges
- Significant Case Law
- Regional and Jurisdictional Regulations
Weintraub Tobin Chediak Coleman Grodin Law Corporation
- Machine learning typically involves algorithms and software implementations. If the algorithms can be viewed as merely solving mathematical problems, are they patent eligible? Should they be patent eligible?
- Patentees should delineate the differences between algorithms and software when seeking patent protection and when enforcing patents.
- Recent decisions involving machine learning and related AI patents inform approaches for patent protection of machine learning and related AI inventions.
- Patent versus trade secret protection: how to make the choice.
Oblon, McClelland, Maier & Neustadt, L.L.P
- Machine learning, and also deep learning, present different patentable features depending on who the stakeholder is.
- Claiming the algorithm for performing machine learning or deep learning is different than attempting to claim the end result of such learning (the formed AI), and in some cases patent protection is not the best option.
- The nature of machine learning and AI innovations creates a specific challenge in patent subject matter eligibility under 35 U.S.C. §101.
- Enforcement, inventorship, and written description/enablement are additional issues related to patenting machine learning or AI related inventions
Jo Dale Carothers is a shareholder and chair of the firm’s Intellectual Property group. She is an intellectual property litigator and registered patent attorney. Jo Dale advises clients on a wide range of intellectual property issues, including litigation, prosecution, licensing, contract disputes, and issues related to proceedings before the USPTO. Jo Dale has represented companies in litigation in numerous federal district courts and state courts across the country, the Federal Circuit Court of Appeals, and in Section 337 investigations in the United States International Trade Commission (ITC).
Jo Dale Carothers is a shareholder and chair of the firm’s Intellectual Property group. She is an intellectual property litigator …
Sameer Gokhale is a partner in the Oblon firm's Electrical Patent Prosecution group. He prosecutes patents for a diverse clientele in the electrical and mechanical fields, including printing and copying technology, semiconductors and semiconductor manufacturing, display systems, digital rights management, personal electronic devices, and biomedical devices. Additionally, as a wireless industry patent attorney, he has prosecuted patents for wireless communications technologies. Mr. Gokhale is a knowledge leader at Oblon in the topics of functional claiming and statutory subject matter. Mr. Gokhale has given several presentations on how recent changes in U.S. patent law impact patent prosecution and patent enforceability related to computer-based technologies, with an emphasis on emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Cloud Computing, Big Data, Blockchain, and all aspects of Industry 4.0.
Sameer Gokhale is a partner in the Oblon firm's Electrical Patent Prosecution group. He prosecutes patents for a diverse clientele …
Print and review course materials
Method Of Presentation:
NY Category of CLE Credit:
Areas of Professional Practice
Unlock All The Knowledge and Credit You Need
Leading Provider of Online Continuing Education
It's As Easy as 1, 2, 3
UNLIMITED 1 Year Pass for only $199
About Weintraub Tobin Chediak Coleman Grodin Law Corporation
With offices in Los Angeles, Newport Beach, Sacramento, San Diego, and San Francisco, Weintraub Tobin is an innovative provider of sophisticated legal services to dynamic businesses and business owners, as well as non-profits and individuals with litigation and business needs. For more information on the firm, visit www.weintraub.com.
About Oblon, McClelland, Maier & Neustadt, L.L.P
Oblon is one of the largest law firms in the United States focused exclusively on intellectual property law. The Firm has obtained more U.S. utility patents than any other firm in the world for over 27 consecutive years. Oblon's attorneys and professionals collectively offer decades of USPTO insight and expertise.