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Google Bought 28% of the Patents It Liked During Its Patent Purchase Experiment

It paid $250,000 for one but declined a $3.5 billion offer

1 min read
Google Bought 28% of the Patents It Liked During Its Patent Purchase Experiment
Illustration: Randi Klett; Images: Google and iStockphoto

Google received “thousands” of submissions to its experimental Patent Purchase Promotion, which launched in April and closed last week. Out of that rather vague number, the company bought 28 percent of the patents it deemed were “relevant” to its business, according to Kurt Brasch, senior product licensing manager.

The program, which offered a chance for anybody to sell patents to Google at a price set by the patent holder, was an experiment in keeping patents out of the hands of trolls.

The number of submissions was “well beyond what we expected,” says Brasch. “We were very, very happy with the overall program.”

Some other stats Google shared:

  • The median price of submissions was about $150,000.
  • There were several submissions priced at more than $1 billion, including one for $3.5 billion
  • One half of the submissions came in at under $100,000
  • The lowest price Google paid for a patent was $3,000; the highest was $250,000
  • 25 percent of all submissions came from individual inventors, the rest from operating companies
  • Of the 75 percent from operating companies, about a third were handled by brokers

Google was surprised at the big response from both individual inventors and brokers. It also received many inquiries from operating companies who wanted to know how the program was progressing.

“Clearly there is interest in what we learned,” says Brasch, lessons that the company intends to share after it has a chance to more closely analyze the data. 

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read
Image of a computer rendering.

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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