Entries in Google (3)


USPTO and Google partner on opening patent and trademark data

The United States Patent and Trademark Office (USPTO) faces a major challenge: an overload of patent applications (including sinister patent trolls) and far too few analysts to evaluate them. The quantity problem is compounded by difficulty of searching for existing patents and prior art that might discourage additional applications. The result is absurd wait times for would-be patent holders, lots of mockable patent granting decisions, and an overall lack of transparency of the patent process to the public.

But USPTO lately deserves more credit. Nearly a year ago to the day, I wrote about Peer-to-Patent, an innovative effort to open up the difficult and time-consuming task of researching prior art to the scientific and technical communities who possess the subject matter expertise to quickly evaluate patent applications. It was an early #gov20 success and its creator, Beth Noveck, was soon plucked from her job in academia to become the United States Deputy CTO for Open Government.

Today, Google announced another step forward. The USPTO is partnering with Google to bring 10 terabytes of patent and trademark data to the web for easy download. Analysts now have the data necessary to perform exciting trends analysis and otherwise parse the information to demystify patent granting. This must be a costly endeavor and I applaud USPTO for letting Google bear the costs. It's not often that a government agency so fully relinquishes control of its data for public consumption and this is a major #gov20 win.


Trends point towards increased regulation of high tech success 

Earlier this week, EU regulators levied a record $1.45 billion fine on Intel for anticompetitive practices. Through a suit brought by main competitor AMD, the commission found that rebate conditions and predatory pricing schemes violated anti-trust laws.

This exorbitant fine is bad news for the Intel, but even worse news for consumers. In the current down economy, Intel is a bright light of innovation and success that has not only revolutionized the computing industry but provided a massive engine of job growth. According to the Association for Competitive Technology:

For the past 20 years, the microprocessor industry has delivered more innovation, more speed, more functionality, and lower prices...Over the past 10 years, the average price of Intel's PC microprocessors has dropped by 60 percent. When the only one complaining about the competitive situation is AMD, it raises serious concerns about the efficacy of this action.”

This aggressive action against a company that is currently resilient enough to actually have the cash on hand to pay the fine (CEO Paul Otelinni announced two days ago that Intel has a healthy $10 billion cash balance) may be a harbinger of increased regulatory activity this side of the Atlantic.

Indeed, Ms. Christine Varney, the new commissioner of the Justice Department's antitrust division is scheduled to make a speech on Monday at the Center for American Progress outlining her intent to revive antitrust actions. Technology is one of the industries specifically expected to be targeted.

The question is: Why are governments going after high tech?

From Chairman Craig Barrett:

"The antitrust rules and regulations seem designed for a different era. When you look at high-tech companies, with the high R&D budgets, specialization and market creation they need to hold their big market shares, it's so very different from the old world of oil companies and auto makers that the antitrust regulations were designed for. They are out of sync with reality."

So the EU and American regulators are going to go after Intel (and TechCrunch predicts Google is next) where the vitality and money can be found, while propping up moribund industries like cars and encouraging massive consolidation in finance. How is this a recipe for innovation and growth?


The future of corporate prediction markets

The Economist today discusses the "uncertain future" of prediction markets in corporate decision making. As the article states, "although they have spread beyond early-adopting companies in the technology industry, they have still not become mainstream management tools." A couple of challenges are raised:

  • Getting enough people to keep trading once the novelty wears off. While an important point (a prediction market, like all markets, can only thrive and reach equilibrium if there are enough players), a good incentivization structure (e.g., money, prizes) should both encourage participation and better results.
  • Keeping people interested if they can't see how the results are being used. If a company isn't planning on using the results of the prediction market to make real decisions, then why create it to begin with? Wells Fargo, for example, said that its most effective trials took place in areas where managers could "do something with their findings." This should be the standard, not the exception. Plenty of prediction markets out there now are simply fun and games, brag to the community ventures with no practical value (e.g., a current question on Hubdub: "Will Nadya Suleman's ex-boyfriend be confirmed as the father of her children?") Corporate markets run by businesses with a bottom line should have no tolerance for wasted time of their employees making silly bets. If the company is using the results, but it is just not clear to employees, then they have a simple strategic communication problem.
  • The wariness of bosses to rely on the recommendations of non-experts. This challenge seems to miss the point. Corporate prediction markets should be asking questions that the employees are experts in. Perhaps the junior staff does not have all of the program management knowledge of the managers who make the decisions, but they do have more insight into day-to-day operations. Gaining the collective judgment of employees who have windows into small pieces of the overall problem should, according to the Wisdom of Crowds argument, be worth more than any single expert.

The most successful corporate prediction markets ask specific questions that the employee pool can offer diverse, informed opinions upon. The Koch Industries example cited in the article, then seems to be a poor example. In Koch's prediction markets, employees can bet on the future prices of raw materials and the liklihood of bank nationalization. The truth is that an internal Koch market is probably not the best place to answer these big questions. There is no reason to believe that the collective wisdom of the chemical conglomerate on the future of banks should be better than the judgement of economic experts.

HP, by contrast, had great success focusing on something that their employees did have unique insight into: projecting future sales of printers. Their internal market is quite complex: “We want to reduce the wisdom of crowds to the wisdom of 12 or 13 people,” said Bernardo A. Huberman, director of the social computing lab at Hewlett-Packard. Among the techniques, he said, are preliminary tests to assess the “behavioral risk characteristics” of participants to shade predictions from people who are inherently risk seekers or risk averse.

Google has created the largest corporate prediction market in the world. Google economic analyst Bo Cowgill explains that the trading system lets Google management discover its employees' uncensored opinions: "If you let people bet on things anonymously, they will tell you what they really believe because they have money at stake," Cowgill said. "This is a conversation that’s happening without politics. Nobody knows who each other is, and nobody has any incentive to kiss up." Traders can bet on such questions as: Will a project be finished on time? How many users will Gmail have? (In addition to some unrelated, political questions).

The primary benefit of a prediction market is that it allows information to be shared efficiently and at little cost. Improved information in turn leads to better decision making by management. The important thing is to ask the right questions.

For more on the subject of corporate prediction markets, see Robin Hanson and Tyler Cowen.