Unlocking value from your IP using Patent Citation Intelligence

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.

Patent Analysis Software – Going from 3000 search results to the relevant 30

Have you ever tried an internet search on Google or Yahoo hoping that there will be some results on what you were looking for only to find there were so many results that you found yourself overwhelmed? Overload of information is a common and consistent challenge. The same applies to innovators, researchers and those who work with patent databases and need to analyze information. On the plus side, there is virtually unlimited access to a vast amount of patent and IP data in the form of online databases and other technical publications. This abundance of data is also the downside for many as it can prove very challenging to find specific information across a very large group of patents.

Most sources of IP data and patent information provide search capabilities and while this is an important component of any database, it’s not necessarily the most efficient way of finding what you are looking for in patent data. It’s important to understand the difference and benefits of both “search” and “text clustering”. Searching is necessary, however since searches match keywords and identify results based on the hits they can end up returning too many records many of which may not fall in the context of what you’re looking for.

Text clustering technology however, identifies meaningful clusters of text or segments of information within the patent data which is more along the lines of how researchers would look through the data. It scans, identifies and then ranks relevant topics or concepts within the data which can help the researcher interpret the information better. By looking through a generated set of topics from the search results the user can quickly identify those he would like to set aside for deeper review and those he would like to ignore or mark irrelevant. That’s because clusters can represent both: topics you want and topics you don’t want. In either case you are rapidly narrowing down your search to the relevant few.

One must however set their expectations right since there isnt (and never can be) ‘the one set of right clusters’ for a set of patent records. Most solutions that provide clustering capabilities do not give any flexibility to the user to tune the way clustering is done thereby keeping the clustering process as black-box and not allowing any refinement in the generated set of clusters. That’s because it is assumed that the generated set of topics can only be used for trend analysis and not for exploring a.k.a “digging through” or narrowing down a large patent result set. However with the right set of tuning parameters a user can quickly instruct the clustering engine to focus on the “broader topics”,  or just the “finer topics”, or to keep broader topics at first level and finer topics under them, or to give more weightage to topics or concepts containing a particular set of words. With such flexibility a user can now run the clustering engine more than once, each time with a different setting, to rapidly dissect a large patent set and comprehend its various facets. This flexibility has been the cornerstone of the text clustering capabilities provided in Patent iNSIGHT Pro and a wide range of parameters can be tuned to influence the clustering process at each step.

Below is an example of the use of this technology taken from our White Paper on Text Clustering

In this sample set, we did a simple search for the word “skateboard” in Title, Abstract and Claims of patents across key countries and then de‐duplicated the results to only unique families. This resulted in 552 unique inventions.

Text clustering was then performed using Patent iNSIGHT Pro* over the Title, Abstract and Claims sections of these patents and the results obtained are illustrated below. We have used the sub‐topics on Skateboards used in Wikipedia as a sample for cross‐reference.

For more download the white paper on Text Clustering HERE

The results are automatically categorized making it easier to narrow down on a category or set of patents and the data retrieved for analysis is far more refined. In effect, the way to better efficiency in managing larger amounts of patent data and being able to analyze the information quicker lies in the automation factor of text clustering technology.

Searching through an IP database, reading through the text of the hundreds of results and then analyzing the information manually would not only be slow but very tedious in most cases. While the benefits of smarter patent data analysis software go beyond this, for helping one find the information they need and presenting patent data in a clearer light, it’s an invaluable investment with visible returns. So while you build on the large sources of IP data you have access to and gather more data, also explore the right software tools that will help you quickly narrow down and get the most from your data. With the right patent data analysis software, even a 30,000 search result set can be managed efficiently without being overwhelmed by the volume of data.

Citations In Patent Data And Why They Need Your Attention

A friend of mine who is an avid blogger shared an interesting story of how he landed himself in a tight situation just last week.  He received an email over the weekend from the owner of a copyrighted image he used on his blog without his permission and was now threatened with being sued and facing a fine for violating the copyright laws mentioned on the owners website. This came as a big surprise since he found the image on popular photo sharing platform Flickr, contacted the owner to seek permission, confirmed the photo was published under the “creative commons license” and made sure he complied with every requirement including giving credit to the owner with a link to the original photo and yet now he finds himself in a soup. It turns out, although he sourced it from Flickr respecting every rule and following the right process, the photo was originally copyrighted on another website and another photographer had claims on the image. Despite all precautions and doing nothing wrong, there was practically no way knowing how many people had claims on that image or coming across their websites and tracing the path of the ownership sources.

Luckily for him, it was a clear case of him being lead to believe the image was free to use just because someone else had published it under that license but our discussion on the fiasco brought about an interesting point. He said to me “I contacted the person I thought was the owner and did everything right, how was I supposed to know who had claims on the image before he published it and perhaps who owned it even before that guy?”

Now that’s where we can draw a parallel between his situation and the thousands of us who work with intellectual property and patents. Patents, unlike copyrighted images, have citations and there are several situations where innovators and researchers need to take the time to research patent citation history just to make sure they do not infringe on existing patents. Yet, many a time they find themselves in a sticky situation much like that friend of mine where a competitor or (worse) a Non-practicing entity (NPE) / troll sues them for infringement.

Today, having a strong portfolio of patents behind a successful product is good but not enough since there still lies the threat of NPE’s and trolls whom you cannot counter-sue. Companies with a portfolio of patents in a technology area must look at backward citation mining in addition to regular search when conducting infringement/FTO analysis for their own patents in order to identify risks to their portfolios. Going one generation back ‘(G-1)P’ from your portfolio ‘P’ is not enough and you must go at least 2-3 generations back or (G-1)(G-1)(G-1)P and then go forward from there. A comprehensive backward citation research would perhaps include {(G-1)P,  (G-1)((G-1)P),  (G-1)((G-1)((G-1)P)),  (G+1)((G-1)P),  (G+1)((G-1)((G-1)((G-1)P)))}. The same technique also applies to Invalidation Research and can help locate critical invalidating prior art for a blocking patent owned by a competitor or someone else.

In citation research, patent volumes tends to quickly become unmanageable if you are working with a larger group of patents and looking through all the data for specific relationships. Citation analysis is one such area where these relationships need to be seen clearly so that nothing is overlooked and the researcher has a very clear picture of everyone who has patents and claims on anything that they are working closely with. Patent data analysis software such as Patent iNSIGHT Pro comes with citation analysis components which can create citation trees and clearly display those links in a graphical format which is easy to interpret. Using both forward and reverse citation graphs one can see clear relationships across a group of patents and the chances of missing important citations is greatly reduced. Multi-generation citation sets can be created from a starting point which itself can be a single patent or a whole portfolio. These citation sets can be compared easily via intuitive tables and charts to help both quantitative analysis and a more minute claims analysis.

Apart from infringement or invalidation research, getting an overview of the history of and invention across a time line and following its evolution and usage can provide valuable R&D insights that can help make better product and research strategy decisions while also making clear the pattern of ownership and inventors associated with the work.

Despite the clear differences in copyrights and patents, citation analysis which provides insights that can help minimize unpleasant surprises like my friend had. With the right tools, it is possible to look deeper into the data and see the broader picture with a group of patents. It can help avoid any oversights and potentially expensive mistakes which were not intentional but just happened. While one perhaps can never to enough homework on IP data, it definitely pays to be as careful, calculated and informed as possible.

Patent Insights – Guiding The Way For Open Innovation

Patents have been associated with protecting products and research from competition and others in the field who may benefit directly from your IP. It was for the longest time linked to keeping the competition away and not for colluding with them but that view has expanded. Today, collaboration is increasingly becoming the preferred path to innovation for many companies. As R&D teams seek external teams to collaborate in ongoing research, patents are key to protecting new IP and advancing collaborative innovation.

For instance, Pharamalicensing.com which aids open innovation in the life sciences stream carried the following post for a licensing partner:

“US based researchers are seeking licensing partners for the further development of their antigen which is currently being tested for the treatment of ovarian cancer.

Full description

Our US based clients are developing an antigen intended for the treatment of ovarian cancer by DC vaccine production. The antigen which is currently in preparation for Phase I trials can be used in any type of technology platform (e.g. recombinant vectors, liposomes, gene therapy). The antigen may be used for the creation of a therapeutic vaccine.”

Whether it’s licensing certain components or partnering to develop something completely new, the joint approach makes business sense in a number of situations and it’s not just restricted to pharma. Out-licensing and In-licensing are no longer the only approaches to open-innovation. Crowdsourcing is a relatively new trend which involves companies announcing their R&D needs as problems or challenges and offering a reward anyone who can solve them. Examples of online services that coordinate such needs are innocentive.com andtekscout.com.

Bigger companies with large patent portfolio are going a step ahead by forgoing short term licensing benefits and opening up access to their patent portfolios in hope of increasing the market size of the end products and thereby benefiting all parties. A recent article onChinaIPMagazine covering IBMs successful IP strategy made a similar point when they said:

“Mr. Saber also said, “IBM has also been a leader in supporting open source and collaborative innovation, IBM has pledged hundreds of patents in support of open source, healthcare and education initiatives.  In July 2007, IBM also announced granting access to its entire patent portfolio of 40,000 patents in support of more than 150 standards designed to make software interoperable under certain conditions.  This is a prime example of using our IP assets for the collective good.

In summary, IBM’s IP Law department has been leading the discussion worldwide in shaping the IP community’s thinking about the business value of patents and by demonstrating that patents needn’t be a blunt instrument of litigation, but an effective tool for supporting and encouraging collaboration, open standards and innovation.”

So how can you leverage IP to guide your R&D collaboration strategy ? Studying IP portfolios of other companies in the area of research and further analyzing such patent data in context of the market intelligence can be instrumental in not just deciding whether to pursue a collaborative approach for any project but also in bringing about the right partnerships. So, once the right set of patents within the area of interest have been identified, they can be analyzed in sets and compared to reveal who the best options to license from might be, who are the innovators with the right amount of experience for a particular initiative, which companies may be keen on collaborating within the space and which are the other areas of research which may inter-twine and help increase the company IP portfolio value. These answers can help develop new products faster, get them out to market quicker or facilitate building newer IP to complement an existing portfolio.

Such processes usually require speeding up the task of patent analysis and companies soon realize that the “Search > Narrow-down > Read” approach may not work when scouting for collaborators or licensees. Analysis solutions as a result have become a necessary tool in helping companies quickly quantify research and come up with critical insights that also serves to give them an edge when negotiating a partnership deal with external partners .

As, more and more companies across the board come to a juncture where they have to decide between a completely proprietary path or go the collaborative way on a certain initiative, IP analyses and insights will go a long way in driving the right partnerships and paving the road to collaborative innovation.