Accuracy and Speed in Patent Infringement Searching Need Not Be “One or the Other”

“Faster and better results at lower cost!”

A claim so common it blends in with the hum of office chatter. We’d like to believe that patent searches could be turned around within a day or two, but it typically takes much longer.  We’d like to believe that the work returned to us includes everything we need to know.  But, at some level, we sense there are shortcomings.

This is particularly true in the case of freedom-to-operate investigations (FTO).  Searchers often fail to consider certain product features.  Claims are not always afforded their full scope.  And sometimes key patents are just plain missed.  Despite relying on highly competent and experienced analysts, “faster, better and cheaper” just does not seem a reality.

But a departure from conventional methods can overcome the trade-off between speed and accuracy. We can achieve both.  Clearstone Elements is that departure.

Why is there a speed/accuracy trade-off when using conventional search methods?

Imagine for a moment that you have been asked to carry out an FTO analysis.  Let’s also say that, as is often the case, you must complete this analysis under budget, say $3,500.  Assume a billing rate of $75/hour (you have overhead and a boss) and that you’re able to review a patent every 6 minutes.  That means you only have budget to manually review about 470 patents.

This might seem like a reasonably-sized lot.  However, experienced FTO searchers would likely feel that it’s not nearly enough to guarantee a high level of recall (i.e., the ratio of the number of relevant patents retrieved to the total number of relevant patents), say of about 95%.  This will of course vary case-by-case.  But, for many competitive industries, you’d need a starting set in the range of about 2,500-5,000 patents to achieve a sufficiently high recall.

Why must so many patents be manually reviewed in FTO?

Certainly, products don’t typically infringe thousands of patents.  Else, industries would come to a grinding halt.  Indeed, out of a robust set of patents to be reviewed, a searcher is likely to only uncover a handful of relevant ones.

The short answer to the above question is that it’s very difficult for a searcher to anticipate whether a patent is relevant without actually reading and understanding its legal claims.  Products may be described in countless ways, many of which will go unnoticed to a searcher.  Claims of a patent may be written in much broader terms than used to describe the specific embodiments of the patent’s technical disclosure.  For these and other reasons (see here for more information), conventional search tools are imprecise blunt instruments in the FTO context.  Thus, a broad net must be cast.

It should now be obvious why inaccuracy seeps in.

A searcher will typically approach a search with the best of intentions, considering every angle of attack.  Patent classes will be reviewed, important ones selected and queried.  Key assignees will be queried.  Citation, clustering, and family analysis will be conducted on patents flagged as relevant.  Natural language queries, perhaps using semantic analysis, will also be applied relating to key product features.

These tools are all great at robustly representing technology areas.  However, most likely, this aggregation will far exceed, in number, what could be reviewed under budget in an FTO context.  Accordingly, the searcher will necessarily have to take additional steps to downsize this set.  And this is where it goes awry.  The searcher may do as follows:

  1. limit all results by requiring the inclusion of certain key terms;
  2. exclude unknown or small-scale assignees;
  3. exclude patents based on Title, Abstract and/or Drawings; and/or
  4. limit patents by relevancy score cut-off (if relevancy scores are provided).

These processes are generally arbitrary in nature and bare little correlation with relevance.  They are simply not sound bases for reducing a large aggregation of patents in an FTO search.  Inevitably, they cause omission of critical patents.

How does Clearstone Elements overcome this dilemma?

Clearstone Elements is a fundamentally unique search platform.  In Clearstone Elements, associations between patents and technical attributes are memorialized, forming specialized data files called workspaces.  Notably, these technical attributes are associated with patents on the basis of their claims and using human analysis.  These attributes (or elements) are then presented to a searcher as an interactive taxonomy.  A searcher may than effortlessly eliminate swaths of patents from a robust patent set by selecting elements not embodied by the product undergoing search (more on this process here).  In contrast to the arbitrary procedures discussed above, the Clearstone process is objective, reliable, and deliberate.

In fact, current users of Clearstone Elements are typically able to reduce a robust patent set by 90-95%.  This means that, once a workspace is put in place, provided the same constraints as in the above example, a searcher using Clearstone Elements is actually able to objectively consider upwards of 4,700-9,400 patents.  We are essentially empowering searchers to cast significantly broader nets with significantly reduced manual effort.  By replacing the conventional tools that are blunt and arbitrary with Clearstone Element’s logic-based reduction process, “faster, cheaper and more accurate” is no longer an empty promise.

Why Semantic Searching Fails For Freedom-to-Operate (FTO) and What You Should Be Doing Instead (PART 2)

Hammer-and-ScrewPart 2 of 3: Why Semantic Searching Fails for FTO

This three-part series explains why conventional techniques, particularly “semantics-based” searching, fall short for freedom-to-operate (FTO) searching and analysis.  It then puts forth a solution for avoiding these problems. Part I was an introduction to the differences between the searches. Part II identifies the deficiencies of semantic searching in relation to FTO analysis. Part III explains how these deficiencies can be overcome. Click here to download a PDF of the entire series.

 

I.  How Semantic Search Platforms Work

There are countless patent searching software platforms available. Each has unique features, but broad commonalities exist. Available platforms tend to offer some combination of natural language, Boolean, classification and semantic searching. Semantic searching is the primary focus of this discussion, as it is the most evolved.

Semantic patent searching generally refers to automatically enhancing a text-based query to better represent its underlying meaning, thereby better identifying conceptually related references. This process generally includes: (1) supplementing terms of a text-based query with their synonyms; and (2) assessing the proximity of resulting patents to the determined underlying meaning of the text-based query. Semantic platforms are often touted as critical add-ons to natural language searching. They are said to account for discrepancies in word form and lexicography between the text of queries and patent disclosures.

Based on this, it would seem that semantic searching is powerful and effective. Well, it is…  for some types of searches (e.g., patentability or invalidity searches). However, it is surprisingly ineffective for FTO. And this has everything to do with the distinctiveness of FTO as discussed in Part 1.

II.  The Effect of Semantic Platforms on FTO

Semantic platforms, by their nature, assume a certain paradigm. They purport to interpolate the underlying meaning of a text-based query. This is great in cases where an analyst knows which technical concepts are relevant. For example, in a patentability or invalidity search, the analyst has a specific claim under review with specifically-recited elements. FTO searches do not fit this paradigm.

Consider the distinctions discussed in Part 1 of this series:

(1) In FTO, relevance of patent results is determined by claim scope, not description. The technical aspects described by a patent’s disclosure are distinct from its claims.

In a patentability search, the semantic platform will return precisely what the searcher desires – patents describing the subject concept of the query.

For FTO, the platform will not. Some patents describing a product feature under review may contain claims covering such feature. However, the vast majority will not. The claims will instead be drawn narrower by requiring additional aspects and specificity. Accordingly, semantic engines necessarily output a high proportion of non-relevant patents (i.e., they are “noisy”).

The reverse scenario is also problematic. Many patents will exist that do not describe a specific product feature, yet will have claims sufficiently broad to cover the feature. Semantic engines will rarely identify these types of patents. Even if identified, they are likely to be assigned a low relevancy rank given their much broader scope. This makes sense in a patentability search, but not in an FTO context.

For this reason, semantic platforms suffer two deficiencies at opposite ends of the spectrum: (1) they are under-inclusive as they are prone to missing relevant broad patents; and (2) they are over-inclusive due to their noisiness with respect to patents with narrow or otherwise non-relevant claims.

(2) Products tell a thousand stories. Products, due to their physical existence, can be described in thousands of ways. Each way could be a basis for infringement. Patentability searching, instead, is more discrete.

Semantic search tools force analysts to play an arbitrary game of “guess the element.” They require that analysts examine features of a product and pick out just the right ones worthy of review. Even for experienced analysts, this exercise is more conjury than skill. It is simply impossible to accurately predict which aspects of a product are likely to be the basis of infringement in an FTO analysis.

In practical terms, semantic platforms unduly force analysts to pit accuracy against timeliness. If an analyst is selective, many relevant references will inevitably be missed. If, on the other hand, the analyst is cautious and queries many product features, the results will be unworkably noisy.

 (3) Missing patents in an FTO search could be dire. Finding relevant patents in an FTO search is no indication whether additional relevant patents exist. An entire technology space must be cleared. In patentability searching, producing a few close results is more acceptable.

Because of points (1) and (2) above, semantic-based results are likely to contain a large number of patents, perhaps ranked by purported relevance. In a patentability search, an analyst may be comfortable reviewing only the first tier of patent references (e.g., the top one-hundred or so). However, the purpose of FTO is to assess and minimize liability risk. Reviewing only the first arbitrary tier of references would undermine this mission. FTO is not concerned with which patents most predictably cover a product; FTO means ensuring that no patents cover the product.

III.  Summing Up Semantics

Conducting FTO searches using semantic platforms produces noisy results that are also prone to significant omission of relevant patents. This presents the analyst with a dilemma. The analyst must choose between: (1) reviewing a compact set of references that is likely incomplete; or (2) reviewing a comprehensive set of references that likely contains a significant amount of noise.

If interested in whether these findings relate to you, perform a simple test. Dig up your last comprehensive FTO search. Review the patent references that you ultimately deemed relevant. Do they generally fall within the same patent classes (as opposed to being scattered over the classification map)? Do they all pertain to a predictable technical feature (as opposed to relating to the product in unexpected ways)? Do you believe they could have all been retrieved using just a few keywords? If your responses are generally “no,” then your experience is quite typical. If your responses are generally “yes,” you’ve experienced a surprising amount of luck. I suggest buying a lottery ticket.

The illustration below shows how semantic search platforms handle patentability and FTO searches differently in terms of accuracy and cost (“cost” essentially being a proxy measure for work time). A high proportion of missed references results in an inaccurate search. A high proportion of noise results in a costly search. The darker shaded regions represent where industry cases typically fall.

semantic graphics

The point here is that semantic platforms can deliver effective results for patentability searches at a reasonable cost but, when it comes to FTO searching, the effectiveness of the platforms is limited even at great cost.

This all leads to the question of whether FTO searches are innately high-cost/low-accuracy processes or if we are just not handling them correctly. Many in the patent industry seem resigned to the belief that improving FTO is a futile endeavor. This point-of-view is understandable but incorrect. FTO can be made accurate and low-cost. It just takes a fresh approach.

More on streamlining FTO in Part 3: What You Should be Doing Instead.

Why Semantic Searching Fails For Freedom-to-Operate (FTO) and What You Should Be Doing Instead (PART 1)

Hammer-and-ScrewPart 1 of 3: Introduction to the differences between Patentability and FTO searching

This three-part series explains why conventional techniques, particularly “semantics-based” searching, fall short for freedom-to-operate (FTO) searching and analysis.  It then puts forth a solution for avoiding these problems. Part I is an introduction to the differences between the searches. Part II will identify the deficiencies of semantic searching in relation to FTO analysis. Part III will explain how these deficiencies can be overcome. Click here to download a PDF of the entire series.

Not all patent searches are the same.

This seems an obvious point. But how well understood are the conceptual distinctions between the various types of patent searches? We are quite familiar with a “patentability search,” which attempts to answer the question:

Is this concept novel and non-obvious? 

We are also familiar with an “invalidity search,” which attempts to answer the question:

Should this patented invention have been considered novel and non-obvious? 

These types of searches are conceptually similar, and may be collectively referred to as “patentability searches.” Now consider, in contrast, the question posed in an FTO search:

Is this product likely to infringe an active patent?

Based on these different underlying questions, three critical distinctions between FTO and patentability emerge.

1.  In FTO, relevance of patent results is determined by claim scope, not description.

Patents necessarily include a technical description and legal claims. While the technical description must enable the claimed inventions, the actual scope of what is claimed may vary significantly from what is described in the technical description. For example, practitioners generally aim for detailed technical descriptions yet broad all-encompassing claims.

In the vast majority of FTO cases, the claims of patents that describe features of a product undergoing FTO do not actually cover those features. Usually these claims are significantly narrower in scope. Other times, the subject matter of the claims is simply directed to other disclosed aspects.

The reverse scenario is also a significant concern. Patents that do not describe features of a product undergoing FTO could certainly have claims that cover one or more of its features. For example, consider a cup having a handle. A patent may never describe a handle, but may claim a cup with circumferentially asymmetric mass distribution. A handle could likely fall within those bounds. These scenarios are quite common.

2.  Products tell a thousand stories.

Questions of patentability are often limited to a concept or a fixed set of concepts. The question hinges on a specific claim that is, by definition, a single textual sentence. On the other hand, FTO analysis centers on actual products. A product, by its physical presence, could be described in thousands of ways. For example, even a simple device implicates all of its structural components, its mass characteristics, its geometric characteristics, processes underlying its manufacture, and processes involving its use.

Anticipating all the ways in which a product can be described is serious guesswork. Opting to focusing on some ways and not others is an arbitrary exercise.

3.  Missing patents in an FTO search could be dire.

A final distinction between these types of searches lies in the consequences of missing key patent references. Missing key patent references in a patentability search is certainly not desirable. However, I would venture a guess that, if they were forced to choose, most companies would prefer to have a potentially invalid patent issue than a potentially infringing product launch.

Also, finding some relevant patents in a patentability search is helpful. In fact, perhaps, in an invalidity investigation, a few good references is all it takes; no need to lose sleep over the prospect of other patents lurking about. In other words, there are pro rata rewards to locating relevant patents in patentability searches; the more relevant patents we find, the better we understand the landscape of a feature.

Not true for an FTO search. Finding relevant patents “along the way” does not bring an analyst any closer to the finish line or provide any greater satisfaction that their work is complete. Finding some patents of concern is little indication of whether other patents exist that may also be of concern. That one missed patent could spell complete disaster for a product line or, worst case, a business.


These observations may not be news to experienced patent analysts, who have long understood the unique difficulties associated with FTO patent searching and analysis. What is notable, however, is that conventional analytical tools have not evolved to recognize these distinctions. They apply virtually the same processes to both patentability and FTO, despite their compelling distinctions.

These shortcomings will be discussed further in Part 2: Why Semantic Searching Fails for FTO.