Using Biological SWARM Based AI to Streamline the Patent Industry (SWARM SEARCH)

Hunter J Heiferling CEO Bluetronix Inc.

Patents are complex publications that contain many data elements that are essential for describing an invention disclosed, how the patent’s claims can be used by the public and to protect the inventor as well. There are more than twenty million active patents worldwide, ten million in the US alone from almost 200 national patent offices worldwide. Today the cost is in billions of dollars to be spent on the filing, maintaining, protecting, commercializing and general research regarding the patent space. We think artificial intelligence (AI) based upon search and optimization in distributed manner can transform the patent industry by reducing costs, reducing search time for prior art, and making more solid patents.

Patents date back many hundreds of years. What has evolved in the last 20 years is the major importance and value of patents, and now even more intangible IP based assets. Today for instance: many companies today see their intangible assets now account for more value than their fixed, assets in many cases. Because of these dynamics, therefore patent information has become the largest untapped repository of scientific and technical information. Patents are of public domain – publication is the exchange for the granted time-limited monopoly. The problem felt by all there is no efficient way to search patent information quickly and effectively today. To put it very bluntly, a very frustrating inefficient process to say the least. Patent information resides today in less than hundred or so patent offices and in countless languages. The problem is as most IP patent attorneys will say today patent drafting is now really a mixture of both art and science.

In a sense this is a big data application, there is greater value to be gained by combining many sources of data, rather than studying each as an isolation model. This is where AI combined with machine learning algorithms are able to bring possibly great value in efficiency, accuracy and show direct time savings. Therefore, AI will tabulate, sort and analyze better and faster than current human based manual based search models and current used search models used today. As all will attest to today’s way is labor extensive, time consuming and often inaccurate.

The patent application process includes several laborious steps including researching the prior art (earlier public documents related to the inventive idea in the application in examination), determining priority dates, and creating claim language that correctly describes the invention. When the inventor presents the invention to a patent examiner, he is obliged to specify the prior art of which he is aware and make his case as to why his invention is novel and non obvious in light of the prior art. The examiner then reviews the prior art and does his own search to determine if the patent application’s claims are novel and non obvious. This human analysis is often constrained by the resources of patent office at any given time: the number of examiners, the data sources available and various time constraints.

There are issues that exist in the marketplace relating to accessing value to active patents. Patent transactions are estimated to exceed 175 billion per year, but today only involve about few % of active patents. Illiquidity is a norm in the industry because of the natural complexity of the IP assets themselves (the complexity of publication. This often requires a high resource process to analyze patents. Most parties struggle to determine the value of patents that exist in a particular technology area and the specific aspects of the technology cover for commercialization and investors look to know if the patent is valid, valuable, defensible, and distinct enough to protect and sponsor.

Patent & Prior Art Search AI methods to be utilized:

1. Logic-based tools: Swarm intelligence (form of AI) tools that are used for knowledge representation and problem-solving for greater accuracy.
2. Knowledge-based tools: Swarm Intelligence (form of AI) tools based on ontologies and huge databases of notions, information, and rules to find patterns.
3. Probabilistic methods: Swarm Intelligence tools that allow independent agents to act in incomplete information scenarios to search, find, access, and learn.
4. Machine learning: Swarm Intelligence tools that allow computers to learn from data search and to streamline greater search results time after time.
5. Embodied intelligence: AI toolbox, which assumes that a body (or at least a partial set of functions is required for higher intelligence and learning.
6. Search and optimization: Swarm Intelligence that allow intelligent search with many possible solutions.