Octimine - 2026 Pricing, Features, Reviews & Alternatives

Analyzing Big Data using traditional methods can be a tedious task, and sometimes, even an impossible one. The tools used for such processes must be tailored to cope with the large volume of information, while somehow still being able to generate meaningful results. These are some of the key goals which current solutions are aiming to achieve in the patent searching landscape.

Most new technologies focusing on prior-art searching, competitor analysis, freedom to operate and other IP-related aspects, have a core functionality revolving around the use of Artificial Intelligence. The quality of the results retrieved by these tools is directly proportional to the algorithm’s capability of accurately analyzing the data and extracting the relevant information.

Why Octimine can make a difference?

Before coming up with the current version of the algorithm used in our semantic search, the Octimine team has tried tens of thousands of different configurations. Running all these models gave us great confidence in the end result: an algorithm having the best recall rate for “X” documents. The feedback received from our existing users confirms that Octimine is standing out in the crowd thanks to its ease of use and high-quality results. The tool can do this by quickly identifying those documents with a particular relevancy for reasons of novelty.

Using machine learning and predictive analytics, Octimine can effectively process the natural language of the inventor and retrieve the most relevant prior-art. By extracting the meaning of the technical text, the tool can form a term vector, which is compared against our database. Depending on different factors, such as number of occurrences, or occurrences in conjunction with other keywords, Octimine is calculating a similarity index. Using this scoring system, noise is easily eliminated from the list of results and the searcher can focus on what matters most.