MCRANK – Can Machine learning/Artificial intelligence find out the patent what is missed by humans?

Keywords - Patent ranking, Patent sorting, Hybrid patent search

A complete automated system to find the missing patent cannot be achieved 100%, MCRPL has adopted a hybrid approach. MCRPL has launched and tested a powerful AI based algorithm, that can aide human efforts from missing out critical patents and can be added as a quality mechanism.

MCRPL has launched the MC RANK tool for internal patent engineers. The tool keeps improving based on the feedback that is fed to the tool to improve the Algorithm.  Over the last 5 years that tool has evolved to a stage where the key relevant patents comes up in top 75 patents out of ~700 patents that is uploaded for review in the tool. 

The company’s innovative approach to data analysis has helped businesses across a range of industries to make informed decisions based on reliable and accurate information. By providing access to comprehensive and up-to-date data, MC has enabled organizations to stay ahead of the competition and drive growth in their respective fields. MC has been building the world’s top 7 databases and has analyzed over 25 Million patent documents accounting for a total of 45% data of these databases.


What is the impact of missing a key patent in any IP search?

Patents are key to a company’s real technology worth. Patents are essential to accurately understand technological activities across countries and over time. Access to accurate, reliable, and actionable insights through a patent search study is essential for successfully attaining know-how of any technological developments.

Are you missing patents that could cost you millions?

If you have a pile of 80,000 patents and you are looking for the best and the most relevant patent in the same, what will be your strategy to identify them? Manual analysis most definitely will not be a sane approach to analyse all these 80,000 patents, so what can be done?

Getting all relevant patents in that pile is always challenging by using conventional automatic/ semi-automatic methods, i.e. IPC/ CPC class-operator-based, assignee/ inventor based, etc. To overcome this challenge, MC RANK, a proprietary tool developed by Molecular Connections’ patent experts and IT experts to strengthen the quality of the patent results.

MC RANK’s algorithm, semantics, and ontology, expertly curated by our knowledgeable experts will most certainly provide you with a complete search picture, minimizing risk and maximizing opportunity in your IP strategy. With its proprietary text-based analytics and algorithms, you can be rest assured that all the relevant patents are rightly and accurately captured in any search project irrespective of the technology involved.

MC RANK has been a powerful tool for the patent industry and one of the most innovative in its category.  A unique algorithm in MC RANK compares textual proximity and  similarity among different document collections e.g. patents and literature.

How MCRANK help overcome your challenges?

Challenges with manual analysis:

Volume of prior art: The huge volume of prior art can be overwhelming, particularly in fast-moving technical field.

Difficulty of search terms: Patent search terms can be difficult to identify and may not be intuitive.

On the other hand, AI-powered patent ranking tools use machine learning algorithms to analyse vast amounts of patent data and extract valuable insights. However, AI-powered tools have their limitations. While they can process vast amounts of data quickly and efficiently, they may lack the context and nuance that human expertise provides. This is where the hybrid AI + human  analysis comes into play.

Improves patent results and quality

Manual analysis of patents can be a time-consuming and complex task, and there are several factors that can contribute to relevant patents being missed.

MC RANK is an in-house tool that complements and enhances the manual analysis process. It utilizes a keyword refinement method to help users generate more accurate results and speed up the analysis process, ultimately improving the quality of the output.

Lessens risk

Missed patents can lead to litigation in several ways. In general, the failure to identify relevant patents during the patent search process can result in legal disputes and can lead to increased costs for companies. That’s why it is essential to conduct a thorough patent search and analysis to identify all relevant or best patents before developing and commercializing a product or technology.

MC RANK provides a semantic-based approach that can help to narrow down the search results to only those patents that are relevant to the concept.

Saves time

The normal landscape/ prior art search approach includes running advance search strings, additional searches including similarity search, citation search, assignee search that are used extensively while narrowing down on the most relevant results. These are usually prolonged methods and still there are chances we might miss certain patents.

MC RANK is an easy to use tool that reduces hours of work of tagging huge chunk of patents most of which would be noise or junk patents and generate relevant results under 15 seconds.

Gain insight by getting ranked list of relevant patent results

Various insights can be easily derived through a search run via MCRANK. Usually while working manually on a patentability search the focus remains on identifying the most relevant art vis-à-vis an invention disclosure. However, using the proprietary MCRANK algorithm the following other insights can also be quickly and accurately identified:

From development of comprehensive search strategies to analysing according to the objective of patent search that can be summarised with some key questions:

  • Is the technology patentable? How quickly can the relevant art be identified without spending too much manual effort?
  • Who are the top players and how can we quickly identify their current technological developments in the similar field as that of the invention disclosure?

The benefits of using the MC RANK tool based on hybrid AI and  human analysis are manifold. It can help companies identify opportunities to license or purchase patents, avoid infringement risks, and stay ahead of their competitors. Additionally, it can save time and resources that would otherwise be spent manually analysing patent data. 

How MCRANK works?

MC RANK is built on a hybrid approach to deliver high-quality results consistently.

  1. Understanding clients’ needs before extracting patents after formulating accurate search strategies on various patent databases
  2. Building search strategies for several search concepts
  3. Analysing the retrieved patent/ non-patent data sets with MC RANK followed by extracted all other important key terms which might have been missed in the search strategy formulation phase
  4. Rebuilding the search strategies using those important key terms identified through MC RANK
  5. Tagging the data set manually by human experts
  6. Ranking the tagged documents automatically on MC RANK
  7. Arranging the patents in the order of relevancy by MC RANK
  8. Diving deep into the technology using the advance search feature of MC RANK tool 
Patent Ranking
Patent ranking tool
Fig. 1 Representation of filtering best patents using MC RANK Tool

MC RANK tool can be a powerful asset for businesses looking to protect their intellectual property and stay ahead of their competitors. By combining the speed and efficiency of AI with the expertise of human analysts, companies can gain valuable insights into the patent or technology landscapes or patent searches and make informed decisions about their patent portfolios.

Interested in MCRANK – Can Machine learning/Artificial intelligence find out the patent what is missed by humans? ? Reach out to us.