Making the most of the latest data analytics tools

The middle ground

As a medium-sized public company with a few hundred granted patents and applications, how should you use data analytics to grow your portfolio? To get expert insight into the issue, we asked three leading thinkers how best to approach the challenge.

Jay Yonamine is the head of data science and operations, global patents and Michelle Park is senior patent counsel at Google. Over the past few years they have emerged as leading thinkers in the developing patent analytics space. Here is what they had to say:

• Collect key data:
  • Analyse your existing portfolio and that of your competitors – The two most important attributes are general composition metrics (eg, the number of assets in which jurisdictions and trends in filings and expiration or maintenance fee payments) and how well the specific assets relate to specific business needs around key technologies. All of the aforementioned portfolio composition metrics and many estimates of technology relevance are made available by various IP-focused data and SaaS providers.
• Analyse the data to devise a strategy:
  • Establish a general portfolio size model – While no magic, one-size-fits-all formula exists, it is still important to have a general framework in place to determine optimal portfolio composition. This helps to justify strategy to executives, explain rationales to other team members and provide year-over-year consistency.
  • Understand business goals – The general model framework should map directly to business goals, which generally involve protecting core differentiating technology while continuing to invest in growing more speculative, long-term emerging technologies, as well as maintaining a sufficiently strong position relative to third parties.
  • Refresh analysis and strategy on an annual basis – Portfolio management requires constant attention to keep pace with many impossible-to-predict changes, including case law, market dynamics and internal innovation rates. The data should be regularly refreshed and applied using the consistent model framework to account for these changes and update the strategy accordingly.
• Build technical resources as required:
  • Adopt appropriate tools – As you move from a few hundred to a few thousand assets, an off-the-shelf modern IP management system (IPMS) will be necessary. This takes time and energy to initially set up and optimise, but it is critical to portfolio management at scale. Additionally, it is important to standardise analytics and reports to increase accuracy and reduce turnaround times. Most IPMS systems will have business intelligence capabilities that allow for analytical dashboard creation.
  • Hire an expert – It is important to have at least one person – preferably in-house, although a full-time contractor will work if the person is highly experienced and empowered – who understands product management and analytics to manage the software, drive ongoing data quality initiatives and maintain continuous analyses.
  • Establish consistent workflows – As a portfolio grows, attorneys will need to establish one or more taxonomies and enforce consistent annotation procedures, such as labelling assets within the taxonomies. This will allow for quick analytics on the number of patents with important specific business attributes. It will also dramatically reduce the time taken to identify key assets, thus enabling faster decision making and more informed portfolio management decisions.

Ahsan Shaikh is a partner at McDermott, Will & Emery and in recent years has become a leading voice on patent data issues, helping to organise a Silicon Valley group of in-house counsel that specifically addresses best practices in data analytics. Here is his advice on how a medium-sized business can use analytics to grow its portfolio:

• Generate a listing of the company’s registered patents that maps priority relationships between them and categorises these according to technology classifications. This gives a sense of the business products’ coverage.
• Apply the mapping technique against:
  • a mapping of your top competitors’ patent assets;
  • your company’s product roadmap; and
  • a technology trends or market map. This will help to identify areas in which to grow out your patent portfolio.
• Generate a ranking of the company’s patents that are most frequently cited as blocking references against competitors. This will give a sense of which assets are valuable and against whom. Continue to pay maintenance fees for these patents. If they include family members, then keep filing continuation patent applications off them.
• Determine which references are most frequently cited as blocking references against your pending patent applications. If possible, acquire them anonymously through a middle-person to free up your path to allowance and your position against competitors. If you do not plan to acquire these assets, develop a response strategy to prosecute around them.
• Identify which inventors are most commonly cited against your high-value patents or patent applications and determine whether you can hire those inventors in order to boost your valuable technology patent pursuits.

The argument for an automated, data-first approach

Over the course of his career, David Andrews, chief IP analytics officer at Aon, has played a prominent role in shaping the patent analytics landscape. After spending 20 years at Microsoft, where he worked as a software developer and then as a patent attorney, Andrews eventually left to launch a data science and legal services company called Legal Analytics, which specialises in patent acquisition, valuation, landscape and portfolio management. Here he explains how to adopt a data-centric approach to portfolio development:

Step one: where are you going and how do you get there with data?

The first thing to do is to take stock of what you have and then create an idea of what is needed. Then, I would create metrics and tools to ensure that I build a portfolio that is sufficient to satisfy the business’s goals and at the best cost possible.

Each patent in the portfolio represents a variable value to the company, but that value can be difficult to measure through traditional one-at-a-time methods. There are a number of approaches to automated valuation, but one way to determine value is to estimate the chances of prevailing at trial on both infringement and validity, and then understand what sort of damages, as well as any ongoing royalty revenues, may be available.

It is possible to conduct this on a patent-by-patent basis for a few hundred assets, but when people focus on a single patent at a time, they often become susceptible to cognitive biases that make judgements unreliable. Data analytics allows us to see the forest and the trees in one glance, so the portfolio can be built with the combined view of the patents you own, as well as those held by competitors. Data analysts can build machine-learning models that identify patents that address specific technologies, and the collection of these represents a product or revenue stream which the patents in this space protect. These models are useful because they can be re-run and will produce the same results given the same set of data.

Figure 1 is an example that shows network virtualisation portfolios of the competitors in this space. The view is dynamic and in this instance has been filtered to show the priority dates and expiration dates of three companies in the overall field. The size of the box and the height of the line in the graph represent a patent count weighted by quality factors where patents that are predicted to be above average in district court litigation are weighted more heavily than average or below average patents. From this graph it is clear that Nicira is a relatively new filer whose portfolio will last for a long time. If Juniper is worried about a conflict with Nicira, it would make sense to file more patents.

Once the universe of patents has been identified, natural language processing and statistical techniques can be used to parse the claims of each patent and family to identify the superstars and the assets that may not be worth continued investment. It is also possible to use this tool to see how technologies, products and revenue streams are protected by the patents, and which are threatened by competitors through a quality and quantity viewpoint. Further, it shows what areas need more investment and where competitors are expanding. If an acquisition is necessary, then this technology is capable of finding suitable candidates that might be available without having to wait for patents to hit the market.

The ability to see the bigger picture allows attorneys to make decisions that are individually appropriate while supporting the larger mission of the company. Automated analytics makes this practical through real-time decision making. This is how to use data analytics to understand what the company should be working towards – the next step is to measure efficiency in getting there and commit to continuous improvement in both cost and quality.

Step two: keeping costs to a minimum

The only way to improve both cost and quality at the same time is to have continual measurements of each. Cost is relatively easy to track, using either financial data or prosecution information. Quality is more difficult and patent professionals often do not have a starting place to understand the quality of their portfolio in the aggregate. Machine-learning technologies can provide an immediate measurement of quality while humans review the portfolio and add to or replace computer-derived measures of quality. Every time a human adjustment is made the computer learns how to improve the estimate. While computers and algorithms can greatly reduce the burden on patent attorneys, ultimately, the decisions should always rest with humans. Informing decision making with data and analytics makes attorneys faster and better at their jobs.

Following this data-first approach, the new portfolio should be shaped to how you want it to support the goals of the business and you should be able to articulate why the money spent on each case is worth it given the overall portfolio and business aims.

Action plan

Data is not an end to a means, but a means to an end. While analytics are designed to simplify the decision-making process, having a confirmed strategy and a clear idea of what you want to accomplish is essential before you start to play around with figures.

To get the most out of patent analytics platforms, attorneys should:

• ask questions to determine exactly what the data shows and what the potential limitations are;
• check that the visualisations deliver value and help to inform decision making; and
• hire an expert to point them in the right direction.