IBM today announced a demonstration project with the US Patent and Trademark Office (USPTO) to test an artificial intelligence (AI)-based intellectual property (IP) analytics tool, IBM IP Advisor with Watson® Demonstration System. By leveraging conversational AI technology with IBM Watson Assistant and content insight mining and guided navigation solutions with IBM Watson Discovery, the system is designed to help users discover and analyze relevant patent data more efficiently, saving more time and enabling strategic value-added activities.
Information that is publicly known before the effective filing date of a U.S. patent application, such as current and comparable U.S. patents and published patent applications, is called prior art. When an inventor has an idea for a new product, a study of the prior art can be useful to understand the landscape of the field of the invention, to evaluate not only the state of the art but also the competitors and innovation opportunities in the field. With the continuous increase in the number of patent applications filed and the growing amount of patent data, it can be difficult and time-consuming to find relevant and accurate information and analyze the state of the art, especially for new inventors, dealers, and consumers of the “high street” not imbued with patent law. IBM wants to help meet this challenge and promote greater participation in the innovation process with IBM IP Advisor with Watson® Demonstration System.
By enabling natural language query processing with IBM Watson Discovery technology, the demo system can help users find relevant information using their terminology, without the need to use specific terms and complex methods. And by using IBM Watson Assistant’s conversational AI technology as a guide, users can ask questions about embedded virtual agents to further inform and simplify their patent analysis.
The IBM IP Advisor with Watson® Demonstration System uses a subset of publicly available US patent data and is available for public testing and commentary on the USPTO Open Data Portal through November 30, 2022.