Gaming, one of the few categories to have AI related filings dating back over 30 years, has been an important part of the evolution of AI technology and continues to be an integral part of its future. AI winning at Checkers, Chess, and Go are all well-cited examples of computers beating humans at games that require strategic thinking, but video games go a step further with expanding the application of AI and creating algorithms for AI that respond to and anticipate the player’s movements as well as strategic actions. Also, the platforms both Google and Elon Musk just released are gaming platforms. Deepmind has open-sourced its game code and Musk’s OpenAI is a computer training ground that helps software play games. Therefore, gaming was a logical place to run a patent search.
By looking at early patents in gaming applications, we are able to trace a line to more modern applications of AI, in particular, those with more commercial applicability. In doing so, we highlight the fact that research in “model” areas brings value once the supporting technology provides an applicable infrastructure for their use as long as the patents defined are broad enough. In particular, we would expect that areas such as manufacturing, advertising, and navigation/auto have been influenced by these factors, with gaming itself now also being a significant market where this technology is key.
So, what does an Atari patent for simulating vehicle behavior for multidimensional video games have to do with electrophonic musical instruments, text to speech technology, speech synthesis, and speech processing using neural networks? The AI technology described in that Atari patent is highly cited and has helped shape innovations in those other seemingly unrelated areas. This particular patent was selected because it represents the very early stages of gaming AI, in this case, a process to simulate the behaviour of opponents in a racing game, (likely a game in the Sprint series, the first game to include CPU controlled cars – http://hackaday.com/2016/04/28/forty-year-old-arcade-game-reveals-secrets-of-robot-path-planning/ (fast forward to 11 minutes for the patent reference).
Following the trajectory of this single vehicle behavior patent over the past 36 years and looking at its citations, we can examine the impact of this single filing on subsequent technology developments, as any future citations (classified X and Y) indicate a direct link to prior art. In theory, this could be expanded to cover a company’s portfolio, or to look at a wider sphere of influence by looking at ALL citations, however, as a simple example, this single patent was chosen.
For each stage of citation, any X or Y citing patents of the previous stage was added to the examined dataset, with 9 stages and ~250 patents eventually captured as direct “descendants” of the original patent. (For a deeper explanation of our process see footnote below or contact us)
A selection of these technologies is presented two ways below. The first is an interactive timeline graphic allowing you to drag the tab along the timeline and see at what point different offshoots started taking shape.
The second is an infographic illustrating in one image the various offshoots of the original Atari patent and the years at which they started to sprout. These visual representations show the range of impact this one AI-related patent has had in the 36 years since it was filed and how quickly its sphere of influence has grown since the mid-90s.
As exemplified by the Atari patent and all its “children”, key patents for AI may not necessarily be those with the most direct link to current market applications. This will be important information during strategic patent acquisitions and investment due-diligence. However, if the market applications don’t get commercial traction soon, some of the older seminal patents will have expired. So, timing is key for patent value and understanding the market will help dictate what innovations to patent and when. Too late is not good but too early will see the patent expire before the market is mature enough to bring value.
For each patent in the dataset, the IPC codes of the patents that it cited were analyzed and the most common were chosen as a representative of the technology area that had most influenced its development. In reality, the situation is more complex and no single IPC code will have been the only influence, but by restricting it to a single code it is much easier to identify (and visualize) key filing trends.
The IPC codes of the initial patent (“parent codes”) were then linked to any IPC codes (“child codes”) listed as being most directly influenced by them, creating a branching tree of technologies based on areas of influence. For each area the timeframe of filing is also listed, allowing the year in which the initial branch from the parent code was made and at which point that child codes were no longer having a direct influence to be seen. Any future filings in those codes would extend those branches and open them up to influence new technologies.
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