Rouse is driven by delivering excellent IP related advice to its clients and has built a great reputation over the years. Consequently, Rouse attracted a wide range of clients, operating across many different industries and technological domains.
Due to the diversity of its clientele, Rouse faces significant challenges. In order to advise its clients well, Rouse needs the ability to quickly gather IP related intelligence across many domains. As its analysts cannot be experts in everything, they need a tool that helps them get expert level results regardless.
Traditionally, patent search tools allow you to either perform manual Boolean searches, which require you to specify every single search term, or they provide an unsupervised machine learning algorithm that categorized patents for you. Neither of these solutions work very well.
With Boolean search, you must be extremely skilled in a topic to know all relevant search terms, and even then, you are likely to get noisy results. On the opposite end of the spectrum, unsupervised machine learning provides the issue that the machine chooses the categorization of patents for you, without you having any control over the process. Both options lead to poor intelligence gathering, which harms the quality of the advice you can give to your clients.
"We have tried all sorts of tools but it is always a problem to get reliable results."
- Erik Oskarsson, Principal Rouse Sweden
Creating patent strategies is one of Rouse’s specialties. In this case study, we investigate how Rouse used Focus to gather all the intelligence it needed for creating a killer patent strategy for its client.