Maximize Your Patent Portfolio Using Helferich-Style Claims

By Michael Woodward, Harrity Associate

Patent owners often obtain patents to protect products, as well as complementary products or use cases associated with those products. However, when selling or licensing the patented products, a patent owner may inadvertently extinguish potential revenue streams associated with the complementary use cases due to the doctrine of patent exhaustion.

Patent exhaustion follows the basic idea that if a company sells or licenses a patented product to a buyer, the company cannot sue the buyer (or a third party that the buyer provides the patented product to under the license) for patent infringement for using the product. Patent owners should take care when preparing and licensing patents to ensure that infringement claims for complementary products or use cases associated with patented products are not exhausted by the sale or licensing of the patented products, as shown by the Federal Circuit case of Helferich Patent Licensing v. New York Times, 778 F.3D 1293 (Fed. Cir. 2015)…

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Data-Driven Decision Making for Patent Portfolio Managers

By Bret Tingey, Associate | IPWatchdog.com

A patent portfolio can be one of the greatest assets that a company owns. A patent portfolio may boost a company’s valuation for a stock offering or a sale, may be licensed for recurring revenue, may be sold to raise cash, or may be enforced to seek monetary damages or to exclude competitors from the market. However, growing a patent portfolio can be expensive. For this reason, many companies hire a portfolio manager to balance the value and expense of growing and maintaining the patent portfolio.

A portfolio manager may be responsible for making decisions including a total target number of patent filings, target numbers of patent filings for different technology areas of the company, patent firms to use for filing and prosecuting patent applications, or how to manage costs per issued patent. The portfolio manager can improve the decision-making process by using data-driven decision making. This article shows some examples of data-driven decision making.

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