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Can apply for an AI patent for new inventions? Not according to the EU Patent Office

The EU patent office says that only people can be inventors because they reject applications for drink containers and signaling equipment made by artificial intelligence

  • Researchers in England have filed two patent applications on behalf of an AI
  • The AI ​​has invented two unique devices: a beverage container and a signal device
  • The applications were rejected because there was no human inventor

The European Union Patent Office has issued a new ruling rejecting two patent applications filed on behalf of artificial intelligence programs.

The two inventions were made as part of a multidisciplinary research project organized at the University of Surrey in the United Kingdom.

The researchers used an artificial intelligence called DABUS, or “device for the autonomous bootstrapping of unified consciousness.”

The European Union Patent Office has rejected two patent applications filed on behalf of an AI, saying that patents can only be granted to human inventors

The European Union Patent Office has rejected two patent applications filed on behalf of an AI, saying that patents can only be granted to human inventors

DABUS created two unique, useful ideas that were submitted to the patent office: the first was a new type of drink; and the second was a signal device to help search and rescue teams find a goal.

According to a report techdirt, the EU Patent Office rejected both applications “because they do not meet the EPC’s requirement that an inventor designated in the application must be a human, not a machine.”

One of the researchers, Ryan Abbott from the University of Surrey, totally disagreed with the decision.

For Abbott. Refusing to credit the ownership of the inventions because they missed a human inventor was not only an outdated way of thinking, but a major obstacle that would stand in the way of a new era of spectacular human endeavors.

Abbott had previously argued that it would be inappropriate to assign patent ownership of an AI-powered invention to someone other than the AI ​​itself.

One of the inventions submitted to the Patent Office created by the AI ​​was for a beverage container (pictured above), which made the AI ​​with “flanges” to make it easier

“If I have my Ph.D. learn student and they continue to make a final complex idea, that doesn’t make me an inventor of their patent, so it shouldn’t be with a machine, “he said in October.

He believes the best approach would be to credit the AI ​​as the inventor of the patents, and then credit the human owner of the AI ​​as the assigned license to make or take advantage of the patent.

HOW DO ARTIFICIAL INTELLIGENCE LEARN?

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works to learn.

ANNs can be trained to recognize patterns in information – including speech, text data or visual images – and form the basis for a large number of developments in AI in recent years.

Conventional AI uses input to ‘teach’ an algorithm on a certain subject by giving it huge amounts of information.

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works to learn. ANNs can be trained to recognize patterns in information - including speech, text data, or visual images

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works to learn. ANNs can be trained to recognize patterns in information - including speech, text data, or visual images

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works to learn. ANNs can be trained to recognize patterns in information – including speech, text data, or visual images

Practical applications include Google’s language translation services, Facebook’s face recognition software, and Snapchat’s live image-changing filters.

Entering this data can be extremely time-consuming and is limited to one type of knowledge.

A new breed of ANNs called Adversarial Neural Networks contrasts the minds of two AI bots, allowing them to learn from each other.

This approach is designed to speed up the learning process and refine the output of AI systems.

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