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TL;DR
- An R&D blind spot is a bet made without evidence that was already public. Most of that evidence sits in the patent record.
- Six cost the most: the route nobody brought to the table, judging by maturity, following filing volume, trusting the room, the study that went stale, and competitors moving where you don't look.
- Forecasting with patent software is hard because it counts activity. Counts lag the market, keyword searches miss and over-include, and one invention filed in 15 countries reads as 15.
- What predicts the winner is how fast a technology improves. MIT research found citation-based measures track that rate; simple patent counts don't.
- Close the gaps by scoping by meaning, reading every family, pairing TRL with Technology Improvement Rate, and keeping it running.
What is a blind spot in R&D strategy?
A blind spot is a technology decision made without evidence that already existed. It is rarely a secret. The competing route, the faster-improving rival, the competitor building a position: almost all of it is in the patent record years before it reaches the market. Without a way to read that record, it stays invisible until it arrives as a surprise.
Patents are one of the few globally consistent records of invention: structured, standardised and linked by citations. That makes patent analysis the natural place to look for what an R&D strategy is missing.
What causes blind spots in R&D strategy without a patent analysis platform?
Six blind spots come up again and again. Each has a cause you can name, and each is closed by evidence the team can already reach.
1. The route nobody brought to the table
The most dangerous options are the ones that never make the shortlist. A team evaluates what it knows: the routes its experts work on, the suppliers who pitch, the technologies in the trade press. A route developing in an adjacent field, or filed in another language, never enters the comparison.
“First, it helps make sure you see everything, so you are not surprised by something emerging from left field that you did not see coming.”
Christophe Perthuisot, Former SVP of R&D, Moët Hennessy (LVMH) · previously Danone. Read his story.
2. Judging by maturity instead of speed
Technology readiness level (TRL) tells you how ready a technology is today. It says nothing about how fast it is getting better. A mature route that improves slowly can lose to an early route that improves fast, and a maturity-only view backs the wrong one. Why TRL tells you how ready, not how fast.
3. Following filing volume
Filing volume shows where the industry is putting its effort. That is the consensus, and the consensus is often wrong. After Washington funded a $1.2B Hydrogen Fuel Initiative in 2003, hydrogen out-filed every other vehicle technology for 25 years. Meanwhile NMC batteries were improving at 54% a year against hydrogen's 21%, from the first data point in 2001, and never gave the lead back.
GetFocus replayed seven historical disruptions using only the patents public at each point in time. In every one, filing volume pointed at the losing technology. See the seven cases.
4. Trusting the room
Expert judgment is essential, and it is anchored in what each expert already knows and works on. Without an independent measure, the most confident view in the room sets the roadmap, and routes outside everyone's specialty get discounted by default. How expert bias shapes R&D decisions.
5. The study that went stale
A one-off landscape study is a snapshot. It can take six months to produce and starts ageing the day it lands, while the technologies it describes keep moving. A decision taken a year later rests on a picture of a field that no longer exists.
6. Competitors moving where you don't look
Competitors file patents before they launch products. A team that watches only products, press releases and conferences learns about a competitor's new position once it is already built. How R&D teams gather competitive intelligence.
| Blind spot | What it looks like | What closes it |
|---|---|---|
| The route nobody brought to the table | The shortlist holds only what the team already knows | Retrieve every route by meaning, in any language |
| Judging by maturity | The most mature option wins by default | Pair TRL with improvement rate |
| Following filing volume | The roadmap follows where the industry files most | Measure how fast each route improves, not how much it files |
| Trusting the room | The most confident expert sets the direction | An independent measure experts can interrogate |
| The study that went stale | A year-old snapshot drives this year's bet | Re-measure monthly, review quarterly |
| Competitors moving where you don't look | A rival's position shows up at launch | Watch competitor filings by technology route |
Why is technology forecasting with patent software so difficult for R&D teams?
The patent record holds the answer. Most patent software is built to search and count it, not to forecast from it. Five problems get in the way.
1. Patent counts lag the market
Counted one by one, patents show up late. Applications are generally published 18 months after first filing, and the breakthrough patents themselves arrive after the work that made them possible. A count-based trend chart tells you what the industry has already decided.
2. Keyword searches miss and over-include at the same time
A Boolean string can only match the words an inventor happened to use. Inventors describe the same idea in different terms and different languages, so relevant families are missed. The same string pulls in filings that mention the technology only in passing. Both errors flow into every number downstream.
3. Counting publications instead of inventions
The same invention filed in fifteen countries is one invention. A tool that counts publications counts it fifteen times, so a company that files widely looks like a company that invents more. Counting patent families, one row per invention, removes that distortion.
4. Most tools measure activity, not improvement
Filing growth, citation totals, top assignees and funding rounds all describe activity. The question an R&D leader needs answered is which technology is improving fastest, because where two technologies do the same job, the faster improver has historically taken over.
MIT researchers tested patent measures against measured improvement rates across 28 technology domains. Simple patent counts did not predict how fast a domain improved. Citation-based measures did.
5. A rate only means something next to its rivals
Improvement rates vary enormously between fields. In MIT's data they ran from about 3% a year for milling machines to 62% a year for optical telecom. A chart of one technology on its own can't tell you whether it is winning. Every competing route has to be measured from the same corpus, with the same method, on the same chart.
Fields where patenting is thin add one more trap: no signal looks like no progress. That gap has to be flagged when the study is scoped, before it can distort a verdict.
How do you close these blind spots?
Four practices close the gaps above.
- Scope by meaning. Retrieve every patent family related to the concept, in any language, then have each one read in full and ruled in or out against a written definition.
- Put every route on one chart. Measure all competing routes from one corpus with one method, including the ones nobody proposed.
- Pair maturity with speed. Plot TRL against Technology Improvement Rate (TIR), computed from the citation network. Each route lands on one of four verdicts: fund, monitor closely, phase out, or ignore and watch.
- Keep it running. Re-measure every month and review every quarter, so the picture moves when the field does.
This is how GetFocus works. AI agents read the patent families. The forecast itself is an explainable regression on two citation metrics, built on peer-reviewed MIT research and validated at R² ≈ 0.7 on 150+ domains. See the method, or read how AI patent analytics differs from patent search tools.
Frequently asked questions
Can patent data really show a blind spot before the market does?
Yes. The vast majority of technical progress reaches the patent literature, and the citation network forms years before breakthrough patents are filed. Reading that network, rather than counting filings, is what gives the early signal.
Isn't filing volume a good proxy for momentum?
No. Filing volume measures effort, which is the consensus. In all seven historical disruptions GetFocus replayed, filing volume pointed at the technology that lost.
What about fields where little is patented?
Where coverage is genuinely thin for a route, flag it during scoping, before it can distort a verdict. In those fields, no signal can look like no progress.
How often should a technology landscape be refreshed?
Continuously. A one-off study starts ageing the day it lands. A monthly re-measure with a quarterly review keeps the picture moving with the field.
Sources
- Benson, C. L. & Magee, C. L. (2015). Quantitative determination of technological improvement from patent data. PLOS ONE 10(4): e0121635. doi:10.1371/journal.pone.0121635
- GetFocus. Seven disruptions, replayed against the patent record.
- GetFocus. Customer story: Christophe Perthuisot.
