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Big data may drive IP enforcement, businesses reveal

In-house sources at Peloton Technology and elsewhere explain that big data analysis tools could be increasingly used for analyses surrounding IP litigation as the technology becomes more reliable

AI dataBusinesses could increasingly rely on big data analysis tools to drive their enforcement decisions, according to in-house lawyers.

Sources say that the technology is not yet good enough to make entirely trustworthy predictions that would help a business decide whether to pursue litigation or not, but that such tools will become more valuable as they are developed.

They add that the technology could one day help firms to perform more accurate cost-benefit analyses and better measure the likelihood of litigation success based on factors such as portfolio strength and opponent tactics.

The head of IP strategy at a metals manufacturer says that big data will become a more important element in enforcement, as long as the artificial intelligence (AI) algorithms used in data analysis tools become reliable enough to effectively harvest data for litigation analysis purposes.

“I would not exclude the idea of using such a tool in the future,” he says. “I cannot see a concrete example of a good one on sale right now, but they could be more important one day. We’re just not there yet.”

A European patent attorney for a sportswear brand agrees that AI algorithms need to be improved before big data analysis tools can be properly exploited by businesses, but adds that he sees potential in the technology to analyse opponents’ strategies in court.

“You can get so much information now that could support you in a case,” he says. “If you have oral proceedings and information about the judge and the people involved on the other side, such as how successful they are and the kinds of arguments they are using, crunching that data could be useful.”

He says that the emergence of this technology will make it more important for companies to more proactively use the information they gather in such matters in the future, so that they do not lose out on opportunities that could be seized by competitors.

A senior scientist and patent agent at chemicals company adds that trawling through data and analysing it manually takes a lot of time and effort, and that using AI to handle that task could significantly streamline the process.

Sources point out that it is vital for companies to conduct some form of analysis before deciding to litigate or not; this is despite the fact that the process is often complicated and can strain resources.

“An analysis is always necessary,” says Carlos Rosario, IP attorney at vehicle tech company Peloton Technology in San Francisco. “You will often have people in a business who want to enter into litigation for emotional reasons because someone has copied their work.

“You need someone experienced with the costs, consequences and benefits of patent litigation to spell out why a court case may or may not be a good idea.”

The director of IP at a tools company agrees, adding that a business may choose whether to conduct either an in-depth or a quick analysis on a case-by-case basis, but that an overview of the situation should always be ascertained.

The ultimate goal of enforcement, he adds, is to benefit the company – and if a case is likely to cost the company more than it might get back in damages, there is normally little point in pursuing it. 

Augmented enforcement

Counsel say that perhaps the best way big data analysis can aid enforcement at the moment is by helping firms to better map out their portfolios.

“I can see the value of using such an analysis tool to put a value on a patent and enforcing said patent,” says the IP director at an energy company. “From the other side of things, it would also be good to be able to assess competitor patents – so it would act a bit like a double-edged sword.”

The IP director at a San Francisco-based tech company says that having a sense of what patents the firm owns and how they could be applicable against certain technologies and competitors creates a lot of data that could be analysed with big data tools.

“That is a foray into enforcement – ensuring that the company has good assets that are defensibly ready and that work for you,” he says.

He adds that even if a company is not enforcing its patents, it should be vigilant of what patents it has and how they match up competitively. Businesses, he says, must learn to update their portfolios with proper taxonomy and categorisation to develop understanding of how claims are evolving and how strong they are against a particular target. 

The sportswear European patent attorney agrees that effective portfolio analysis opens to doors to better enforcement, and adds that his firm is already using tools to evaluate its portfolio to help identify fields where it can get something for its patents.

“So far, the tools are okay but we are still mostly doing enforcement analysis in a traditional way,” he says.

Sources add that while the technology is developing, it may also be a useful tool to help augment enforcement analysis. The sportswear patent attorney says that these tools are currently complementing his analysis process, but it will be a while until the technology can carry out the process itself.

Rosario of Peloton Technology adds that while big data analysis would be useful to help navigate the rocky path to enforcement decisions, its directions should be checked.

“In the long run, you will need someone to do a sanity check on any results put out by an AI algorithm,” he says.

The tools firm IP director argues that data analysis tools can certainly help with enforcement and augment the process, but that it cannot add much to a manual analysis process.

“Perhaps the tech could help you see where it is convenient to litigate and put together some statistics, but other than that, I cannot really see how it would help. I would, however, be happy to be proved wrong.”

The chemicals patent agent adds that human intervention will likely be required a long way into the development process of big data technology. He points out that even when the technology gets to the point where it can do 80% of the work – which would be impressive – human beings would still need to do 20% of the analysis themselves.

“We currently base cost-benefit analyses completely on manual inputs because of the simple fact that we need to be 100% sure of what we are doing,” he adds.

Businesses don’t trust big data tools for enforcement yet, but their optimism for the technology suggests that they will soon. That outlook is encouraging because opportunities such tools could one day offer to reduce risk and save costs when deciding to start a case, or even during a case, should be seized. 


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