Historically, the interest in unlocking patent value by licensing has picked up momentum when IBM declared more than 1 Billion USD revenues purely from patent licensing.  This is why even after having successful products, companies with 50 or more active patents are looking at ways to unlock greater value from their IP portfolios. Nowadays, dedicated licensing teams are common across all technology driven mid to large sized organizations.

Seen in black and white from a business’s perspective, patents are filed to protect intellectual property and buy limited time to capitalize on the innovation. It offers some protection from competition and provides some space for the business to get it’s products out to market as soon as possible and make the most of what is developed. Within the stipulated time frame if a business isn’t able to capitalize on the patents, they could virtually lose out on their advantage which is why it makes sense to license the patents while they are active.

The raw material for effective licensing comes from patents itself. The balance information is mostly available on the internet. Patent Intelligence plays an important role when it comes to being able to quickly locate possible out-licensing opportunities.

So what are the different ways to go about gathering intelligence ?

The licensing-101 method is to look up the forward citations and then further analyze who owns those patents and then screen the patent claims to see if the innovations are incremental over your patents.

A more powerful and effective method is to look at more than just the forward citations and build a more comprehensive licensing set for analysis. This involves going multiple generations forward (atleast 2-3 generations) and going at least 1 or 2 generations backward and then going forward from them as well. Invention companies and universities, that invest in actively locating licensing candidates for technology IP held know the value of why it important to go back and then go forward. Usually when you do that you are in a better position to locate alternative areas of science where your technology may be applicable. This is also necessary if you have a technology  in search for a problem.

So as part of this method, if you have a source portfolio say P and you want to undertake a more effective licensing analysis then you should build a portfolio that contains (g+1)((g-1)P, (g+1)((g+1)((g-1)((g-1)P)))) where (g-1)P is a set of all backward citations of P and (g+1)((g-1)P) is a set of all forward citations of that set. Add to this atleast 2 forward generations of P and  atleast one generation back from the immediate forward generation i.e., (g-1)((g+1)P) and you now have a more effective set for analysis.

Typically it is not uncommon to run into a licensing set that has between 4000-10000 records. And once you have such a set, you can proceed with the next phase of analysis required to locate most relevant licensing candidates. This would include:

• Advanced keyword searching though the licensing set with highlighting across full/text and claims
• Similarity searching (quickly comparing overlaps in text portions between records)
• Clustering is important since many a time you are not sure about all the technologies or keywords you want to  search. Its better if important topics both big and small were directly located across the set and presented to you to review and relate. This is exactly what clustering does.
• Co-citation based clustering (i.e., grouping together documents that’s share a high level of common forward and backward citations) of records to see which are the most technologically linked records with your portfolio
• Ranking and marking patents as you proceed with identification (to avoid going through the same record twice)
• Classifying the licensing set across various US/IPC and ECLA classifications and even your custom categories to see the spread of the records in different product or business lines
• Finally you will also have to update the latest ownership information of the patent. US Assignments information can help to an extent, but in  many cases companies try to hide the ownership by having obscure holding company names and private LLC’s that are setup as a vehicle to own and operate the patents. A fair guess would be that 30% of all patents fall in such type of ownership. Ironically these are usually the more relevant lot. So in such a situation, once you discover that they are good licensing potentials then further time needs to be invested in online research, inventor look-up, corporate tree and any other method to pinpoint the exact ownership.

As you can see that because of the sheer number of records its useful to have the licensing set in a medium that helps you search or slice-n-dice the information quickly. The time factor is always important and being able to get quick patent intelligence can play an important role in identifying opportunities before others, being more informed and thereby being able to negotiate better too. Patent data analysis software can help immensely in your ability to efficiently go through large volumes of patent data and quickly get to the answers you need. You can also go a step further by analyzing patent families across countries, to understand  geographical spread and IP investments of potential licensees. This way you can rank potential partners based on the markets they have a strong presence in.

Software can help analyze complex citation relationships traversing a sequence of generations to help understand a company’s resources and value as a potential partner or licensor. The cost of filing and managing patent IP portfolios is a sizable one and ensuring maximum returns on this is the goal of every business. Investment into analysis tools like Patent iNSIGHT Pro can deliver to a business can help uncover hidden revenue opportunities and discover new revenue streams for underutilized IP portfolios and in the process pay for itself several times over. Good intelligence leads to better opportunities or at the very least, helps one see them.