Generally, artificial intelligence (AI) is the automation of a human being can do, or the simulation of intelligent human behavior by a machine. In other words, AI performs what can be done with vastly more data and processing of incoming information. Unfortunately, claiming AI is in the United States. Europe and China have already updated their patent examination procedures for AI. If the United States maintains its current examination procedure Alice and Mayo framework established by the Supreme Court, will we be leaving this industry behind?
AI is the umbrella term that encompasses four main categories: reactive AI, limited memory AI, theory of mind AI and self-aware AI. Reactive AI includes machines that operate based on the present data inputted; its decisions take into account only the current situation. Reactive AI don’t make inferences based on the data inputted. Examples of reactive AI include spam filters, Netflix show recommendations and computer chess players.
Limited memory is capable of improving its decision-making processes over time. This is the category in which the vast majority of research and development and patenting is taking place. Examples of limited memory use include environmentally competent vehicles
The machines grow more prescient in the next two categories – these include AI that can understand human emotions and make decisions based on understanding the theory of mind AI. Even more futuristic are self-aware AI – these machines are capable of processing the mental states of others and emotions, as well as having their own. Think about the robots in Wall-E or, more darkly, in I Robot.
When the original patent laws were drafted, lawmakers did not anticipate that one day we might have machines with decision-making capabilities that would mirror that of humans. As the result is, the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO) and the China National Intellectual Property Administration (CNIPA) which includes processing information and making decisions based on said information and data. The idea is to prevent the patent system from being abused in this way. But, in the view of the emerging technologies in the field of AI, each of these offices has sought to update their examination procedure at best try to capture some of this subject matter.
The CNIPA prohibits patenting methods for mental activities. Recently, the CNIPA issued Draft Examination Guidelines on examining inventions related to the improvement of algorithms for artificial intelligence (such as deep learning, classification and clustering and big data processing). The CNIPA proposes looking at improvements to algorithms and large data processing, hardware computing efficiency or execution effect. The CNIPA considers improvements to data storage size, data transmission rate and hardware processing.
In the March of this year, the EPO’s 2022 Guidelines for Examination came into effect, which state explicitly, “[a] ” The EPO is explicitly state that mathematical formulas can be patented if used in specific technical implementation. Specific bandwidth. EPO has published a series of mathematical formulas that contribute to showing technical effect.
The USPTO issued its latest Guidance on examination back in 2019. Notably, technical improvements in the US basically exclude end user benefits, which is different from the new CNIPA and EPO practice which allows user benefits to be considered. Also unique to the United States is our Supreme Court, which occasionally intervenes on patent matters, especially with the Alice and Mayo decisions, which supersede any type of USPTO guidance. The USPTO Guidance was constructed within the confines of the abstract idea / law of nature framework of Alice and MayoHowever, the CNIPA and the EPO Guidelines have not been patented when implemented by a machine, and have been designed to improve their processing speed. So, in terms of examination procedure for machine processes and machine intelligence, we are failing a bit behind.
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