On Technology Transfer Metrics, 5: Metrics relative to mission

Who it is that most wants technology change. Or, more particularly, who is it that we ought to want to make technology change? My bet is that the top of that list is not occupied by patent bureaucrats. It’s not, “Oh, our modern innovation of today is now driven by the deeply held desires of bureaucrats that technology will change and in ways that enrich them and allow them to take credit for desiring such technology change and taking action to bring it about.” I doubt, too, that we would look to patent speculators and patent attorneys as the key people to desire technology change. It’s fine that they do what they do, but it’s difficult to see federal innovation policy being driven by folks hoping to exploit patent positions for profit as the thing the public most wants. “Gosh, we will all be better off when patent speculators have cornered the market for any new thing and can extract monopoly rents from us for twenty years–or keep things off the market and well they should if we refuse to pay up!” Just doesn’t seem plausible somehow.

It gets even uglier when we notice that university administrators attempt to turn partnering with patent speculators into some odd sort of public virtue. “By partnering with patent speculators, our university furthers its public mission by taking a tiny share–which could be worth millions–of their monopoly pricing and scarcity management, gaining more money to build out research facilities that the public then will be asked to maintain, having never had to ask the public whether the facilities ought to be built in the first place.” One rather wonders whose side the university administrators are on.

Jim Collins wrote a popular business book, Good to Great. In it, he aimed to identify the attributes that made some companies “great” while others were less so. Then he tried to connect the greatness part to management practices. There’s all sorts of pitfalls in doing such a thing–the companies that were taken to be “great” had “great” stock performance over an extended period of time. The management practices that were in place during these runs of success may not have been the ones that started the runs, or may not even differ from management practices at merely good or even decidedly not-good companies. And it turns out that many of the “great” companies turned not-good, even failed, in the years after Good to Great came out. Even with these pitfalls, though, Collins was concerned about how to apply Good to Great thinking to “social sector” activities–organizations like symphony orchestra and the Girl Scouts. So Collins writes Good to Great and the Social Sectors. In this booklet, Collins lays out an argument summed up by his super title: “Why business thinking is not the answer”:

A great organization is one that delivers superior performance and makes a distinctive impact over a long period of time. For a business, financial returns are a perfectly legitimate measure of performance. For a social sector organization, however, performance must be assessed relative to mission, not financial returns.

What if your outputs are inherently not measurable? The basic idea is still the same: separate inputs from outputs, and hold yourself accountable for progress in outputs, even if those outputs defy measurement. (5)

University technology transfer exists within social sector organizations. It doesn’t have to be this way, or only this way, and of course is not this way–technology moves all the time without formal university administrative “assistance.” It moves counter to policy-demanded procedures. It moves without notice. Indeed, formal policies demanding all technology move through administrative procedures may drive technology transfer underground–transfer that idea about technology before it is manifest in technology, or, simply, don’t do any inventing or composing or collecting or building where a university could claim ownership and financial control over it.

As a social sector activity, then, technology transfer may have financial inputs, but outputs may not be measurable. Collins goes on to try to place financial performance in a broader context–“time, money, and brand”–and that has pitfalls of its own, but the general observation Collins makes is worth considering. In a Taylorist world of managing for volume outputs and efficient production, making money is a matter of producing more of what one wants to sell, at lower costs. But more patents do not mean more transfer. They mean, by default, more technology excluded from general practice. And we are not talking only about the new technology that someone has invented–we are talking about anything that can be claimed based on that inventing. Thus, a patent may claim applications of an invention, even though the applications have not been tested. A patent may claim a bunch of variations, even though the variations have not been tested. A patent may claim functional equivalents, even if those equivalents have not even been identified. A patent, then, may be used to block or disrupt or render useless independent research, the application of the invention, or people doing the same thing but using a different method–and in all these cases, the patent blocks activity that does not in any way depend on “transfer” of the technology.

More patents does not necessarily get one more money. And money may be got with patents plenty of ways that don’t involve any transfer of technology. Patent trolling, for, instance, involves going after people for infringing claims of a patent–but those people may not have learned their practice from a patent or publication. They may have figured out things on their own. In doing so, however, they may have come close enough to claims of some patent or another that it’s plausible for a patent owner to shake them down–paying for a license might be cheaper than the cost of defending an infringement suit. Make money with a patent–easy peasy once people are working in a given area and will come up with similar stuff to what the patent claims. No transfer needed. Just wait, then pounce.

Where does this get us? That technology transfer is not well characterized by counting things in isolation. The outcomes of technology transfer–among other ways that technology changes–are, let’s say, primarily qualitative. There are things that can be counted, but counts taken in isolation don’t matter. More patents does not mean more transfer. It easily could mean less. More licenses does not mean more transfer. Licenses could come from trolling, from one technology everyone wants while a bunch of other technologies languish. More money does not mean more transfer or effective transfer. Money can come from trolling–no transfer–or from patent cost reimbursements, from upfront and annual payments to maintain licenses, or realized equity, and none of those payments necessarily reflects use of technology released under a license. Indeed, those payments may be in lieu of use: “If you don’t use the licensed technology, then you have to pay us these amounts or we terminate the license.” Such payments maintain a license without use.

A measure that a licensed technology is being used is an earned royalty on that use, such as a royalty paid on sales. Even there, the amount paid is not a measure of transfer, but rather is a measure of the leverage in the licensing deal that runs alongside the transfer and the market power of the licensee to command a high price. And even there, that there is money to be paid at all suggests less effective transfer and less interesting outcomes–one company may be willing to pay a premium to prevent other companies from gaining access to a patented invention. Not just to the part of the claimed invention that the one company will use, but to all the parts of the claimed invention, the many unused parts. How is that good technology transfer, especially for the unused parts of the claimed invention now saddled with an exclusive license where the university accepts money in lieu of use of those claimed but unused parts of the patented invention?

Metrics in social sector activities might point to problems rather than successes. If no one comes to hear an orchestra, that’s a problem. But what is the cause? Poor publicity? Ticket prices too high? Lousy venue? Poor performances? Less popular playlist? Scheduled against hockey? One can’t reason from low attendance to a response. Same for tickets sold, or money received from ticket sales. Could be great performances, free tickets, and still be scheduled against hockey. Low attendance might mean that this orchestra has a huge problem and may have to fold or move–but that is in the nature of social sector activities.

There aren’t any available metrics for technology change. There is no reason to create such metrics only to ascertain the role of bureaucrats setting up speculative patent betting pools as a way to transfer technology. “How can we create better metrics to improve policy for bureaucrats setting up speculative patent betting pools, especially for speculating on public health?” Is that the compelling question here? It sure shouldn’t be.

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