Standards and Fair, Reasonable, and Non-Discriminatory (FRAND) Licensing
Because there are often multiple patents in technology products, standards organizations have arisen to help coordinate technical issues and patent rights. This allows individual components to interoperate and communicate effectively to a common specification (i.e., Bluetooth, 4G LTE, WiFi, MPEG).
Such coordination is encouraged as procompetitive by various competition authorities, including the Department of Justice in its IP Licensing Guidelines. The standards organizations require each patent contributor to license its rights on FRAND terms once the patent is declared essential to the standard.
Cornerstone Research has extensive expertise in analyzing standard essential patents (SEPs) and FRAND issues. Cornerstone Research provided the key analysis and testimony in the seminal Microsoft v. Motorola Mobility case. Many subsequent cases on these issues have also involved Cornerstone Research support and experts.
Technology industries tend to involve a large number of patents. Instead of one patent resulting in a single protected product, a typical product can require rights to thousands of patents. When the ownership of these patents is fragmented, including rights held by non-practicing entities, licensing coordination can be difficult to resolve.
For example, if one patent blocks an entire product from going to market, the economics of apportionment—the “joint products” problem—becomes predominant. Cornerstone Research has assessed such risks and quantified appropriate licensing rights in these matters.
Copyrights in technology industries involve utilitarian and functional software as well as electronic data. Cornerstone Research is experienced in valuing the infringement of such copyrights, whether from a lost profits or royalty perspective. An emerging issue in software technology is the value of application programming interfaces (APIs), which involves the economics of access rights and avoided costs—areas where we have applied latest research and analyses.
Trade Secrets, Big Data, and Artificial Intelligence (AI)
Technology industries that depend on big data usually implicate trade secrets, as the data itself is not protected by patents or copyrights. Organizations collect data, often over a long time and at a large capital expense, in order to provide an essential training basis for AI systems and neural nets. As only the results of AI are observable, the data sets cannot be reverse engineered, and so these data are only protectable as trade secrets. If such trade secrets are stolen or publicly disclosed, however, their value can be diffused or destroyed. Cornerstone Research has experience in a variety of trade secret matters, including those involved with proprietary big data sets.