Why an analysis of the patent landscape is essential
Every company, big or small, relies on research and development for its growth. The success of a product and, in turn, of the company, depends on the strategic planning of these R&D activities. Now, a key to this strategic planning is PLA or patent landscape analysis, which allows one to navigate areas of growth and future developments.
Landscape analysis is primarily a tool for outlining the scope and opportunities in a particular sector so that you can focus your research where there is maximum benefit. PLA also helps map regions where the market potential is good for your product and whether your product will stand a chance among others.
To demonstrate how a PLA works, let’s talk about this Big Data Analytics report. Big data is the term for a collection of data sets so large and complex that they become difficult to process using off-the-shelf database management tools or traditional data processing applications. Challenges include capturing, preserving, storing, searching, sharing, transferring, analyzing, and viewing. Big data requires exceptional technologies to efficiently process large amounts of data within tolerable elapsed times. More accurate analytics can lead to more confident decision making. And better decisions can mean greater operational efficiencies, cost reductions and risk reduction. A number of recent technological advances allow organizations to take full advantage of big data and big data analytics.
The primary objective of this assignment was to conduct a “Patent Landscape Analysis” on “Big Data Analytics” to identify patent documents (including granted patents, published patent applications) that exclusively disclose Big Data analytics techniques. Data, the software/platforms used and the application areas of all types of Big Data. In other words, the analysis covers families of patents that reveal the details related to Big Data Analytics.
The task was carried out in two phases: first, a background analysis to identify relevant keywords, forming strategies based on those keywords, obtaining relevant patent documents, and preparing the final report that was sent to the client for feedback.
The second phase involved incorporating customer feedback into the analysis. Based on those comments, the relevant patent documents were grouped into five main segments:
1) Application areas
2) Software/Platforms
3) SQL/Non-SQL
4) Analysis techniques
5) Data
The analysis of the trend of patent activity was carried out based on all the previous segments.
This PLA points to certain characteristics of patenting activity in Big Data Analytics, such as the fact that the US geography appears to be dominating the technology space. The PCT route has 543 applications and XYZ has the most PCT applications, i.e. 22. It also shows that for the patent families that have the oldest priority year (2009-2013), Microsoft has overtaken IBM in terms of of patent applications. In terms of blank space analysis, we find that connectomics, history/archaeology, and meteorology have the fewest number of patent families; there is the possibility of having more dedicated patents related to these application areas. Crowdsourcing and machine learning (data mining) are the big data analysis techniques that could be used in the future for patenting activity. So all of this valuable data analysis will help analyze growth areas and is useful if you are looking at the future of a new technology or looking to branch out into new technology avenues. With this kind of compilation of a variety of literature available to present technology trends, you will get a clear idea of the way forward.
An analysis of the patent landscape has been and will continue to be a valuable tool for companies to base their research and development strategies and a better position in the market. Hiring professionals in the field would not only grow your market but also help you avoid wasting your resources on fruitless research.
