Choosing the right patent software for better results


Martin Bijman

Choosing the right patent software for better results


There have never been so many IP tools and technology aides on the market, but how do you decide which ones are going to help your business? Martin Bijman of TechInsights explains.

At the heart of any IP strategy is a combination of resources that helps the rights holders to achieve their goals. Patent analysis requires subject matter experts (SMEs) and is therefore subjective.

To work within the constraints of an IP strategy, a balance needs to be struck between the skills of the experts engaged, the available data and the use of increasingly powerful analytical tools.

Combining SMEs and patent tools

Ideally, an SME is knowledgeable in relevant patent technology and the associated jargon, is familiar with patent practice, and is available to manually conduct all patent and market-related research.

Investing an SME’s time in this manner, however, is costly, time-consuming, and can be impractical. A combination of analytical tools and expert knowledge is usually a more efficient and effective approach.

Many analytical tools focus on patents and can help to create IP value through a wide range of generated types of data. Patent owners should select an IP tool that accounts for and uses all their defined parameters and methodologies, and which ensures that the results provided correlate to their previous experience. The onus is on the end user to determine how to achieve this.

Patent owners should employ a combination of SMEs, data, tools and methodology, and should consider the following:

  • SMEs have varying degrees of skill and creativity; they may be limited by their data, tools and experience.
  • Data includes:

—Public metadata about the patent owner’s IP assets, and about the IP assets of market leaders, market contemporaries, and potential companies of interest;

—Technical data about the products of interest; and

—Aggregate proprietary data about the patent owner’s previous success in managing a portfolio, finding evidence of use or conducting other business.

Companies need tools to process this data. In the English language market, more than 50 commercial IP tools are actively marketed, as well as a handful of generic big data analysis platforms through which analysts can quickly create custom workflows and visualisations.

What defines value in an IP tool?

A valuable IP tool is one that is validated to produce analytical data that correlates to previous business success. Keeping track of the variations in IP tools is a considerable task, with existing tools continuing to improve and new tools enter the market. The amount of time a company spends keeping track of this depends on its needs. For most common use cases, a company will find a solution that it trusts and continue to use it until it falters or the company’s needs change.

Validation is key to selecting an IP tool. While some tools may appear to be useful, the IP team must be convinced that the results a tool produces are correct and meaningful. A starting point is to consider the tool manufacturer’s product literature and video blogs.

Next, consider a demo. Prepare a test bench of data that is known to correlate to the company’s recent business success. Then, consider a trial. Correlate the IP tool outcome based on the test bench and rate its price, accuracy, usability and performance. Ask the following questions:

  • Would the IP tool have accelerated your result?
  • Would it have been more economical than using only SMEs?
  • Would the outcome have been as good as the one that you recently achieved using a different solution?

If the result is positive, obtain a license for the tool and run it simultaneously with existing operations for a variety of project cases in order to develop trust in the combination of data, tool and methodology.

The brains behind a software tool’s search features

An IP tool’s search engine is crucial. Accurately finding the patents you want can be challenging. Patents use jargon and are written in a variety of styles and languages, spanning many industries, fashions and best practices in IP matters over several decades.

In addition, when a patent is written, the industry terms that will come to be associated with the innovation may not yet be established. The search capacity of an IP tool can range in sophistication, which affects the likelihood of providing incorrect or otherwise missed results.

Artificial intelligence (AI) extends beyond most search capabilities, it refines models using machine learning, neural networks, and other algorithms. Datasets used to train AI may include taxonomies, classifiers, natural language databases, concept databases and jargon databases. They can also leverage the experience and applied knowledge of SMEs through human-generated databases. 

Many AI-enabled patent evaluation tools focus on the language in the patents, rather than on how that language maps to technology. For a patent tool to be truly useful, it must also incorporate knowledge of the technology itself. For example, a tool that combines well-defined patent classifiers with technology data and artifacts will give you an improved result.

Programmability enables IP tools to adjust their search parameters and algorithms, often with the aim of finding push-button solutions. It is now common for IP tools to have the function “find more patents like this patent”.

To validate whether an IP tool is useful, patent owners may need to experiment with its capabilities regarding the data, search engine and adjustments of weighting, algorithms, sensitivities and assumptions. 

Portfolio ranking example

In some use cases patent owners may need to know how their portfolios are ranked in various areas of the market, in order to determine exposure, transaction opportunities or apportionment for licensing negotiations.

Alternatively, the aim may be to obtain a broader understanding of the market in preparation for a marketing or financial report or entering a new line of business. Traditionally, portfolio ranking involves counting patents in a variety of technical segments of the market.  A typical approach includes:

  • Creating taxonomies of market areas;
  • Employing an SME to brainstorm phrases in these areas;
  • Running an algorithm to search for n-grams of these phrases in patent metadata; and 
  • Counting the number of patents found from each portfolio.

This approach faces challenges in terms of quality and maintenance. The SME will create keywords based on experience and thoroughness. As patents use a wide range of jargon, a limited subset of the desired patents will be found, as well as considerable number of false positives.

Some may try to address the false positives
by broadening or narrowing the keywords, or by using a facet such as cooperative patent classifications—thus adding to the workflow. 

To keep pace with new technologies, the keywords must be updated regularly.   Additionally, machine learning can be used to find candidate patents that match these updated phrases.  However, to ensure accuracy, these candidate patents must be validated by the SME before they can be added to the taxonomy models.  Therefore, SMEs are continually needed to maintain such an approach.

Another approach to portfolio ranking involves weighing many factors. Some commercial tools support this through the programming of a formula based on patent metadata. This can improve the ranking, assuming that the methodology can also overcome the challenges outlined above. 

Let us consider an example formula that combines popular metrics with others that are not typically available in IP tools, such as:

  • Patent age;
  • Forward references;
  • Fundamental patents;
  • Previous licensing programs; 
  • Global coverage;
  • Applicable technology; and
  • Detectability

These metrics can be combined with the phrase approach to create less of a push-button solution. However, the key to determining how to combine this data depends on previous business success.

Summary and next steps

IP teams are required to deliver on a variety of business goals that are pursued through the development and use of potentially valuable IP.

SMEs are key to developing and assessing patent portfolios, which are subjective in nature, and IP
tools can help them to increase efficiency and improve on decisions.

Although some IP tools create push-button solutions, most are often combined with data and methodologies to address the use cases that further the aims of the owner.

When selecting an IP tool, the onus in on the user to ensure that the data, analytics and visualisations convey understanding that correlates to its recent business success and advances its ability to make better IP decisions.

It falls to the user of an IP tool to ensure that the data and analytics presented by the tool correlate to examples of previous success of the company’s use case and further advance its business goals. When selecting an IP tool, companies should consider the following steps:

Prepare a test bench of data known to correlate to recent business success.

For each IP tool candidate, consider a demo, then a trial. Correlate the trial outcome to the test bench and rate accuracy and performance.

If the result is positive, license the tool for a while and run it simultaneously with existing operations for a variety of project corner cases in order to build trust in the combination of data, tool and methodology. 

Martin Bijman is director of IP products at TechInsights. He can be contacted at:

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