Why a Patent Landscape Analysis Is Essential

Every enterprise, big or small, thrives on Research and development for its growth. Success of a product and in turn the company, depends on 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 for growth and future developments.

Landscape analysis is mainly 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 maps regions where the market potential is good for your product and whether your product will stands a chance amongst 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 it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies, cost reductions and reduced risk. A number of recent technology advancements enable organizations to make the most of big data and big data analytics.

The main objective of this assignment was to conduct a “Patent Landscape Analysis” on “Big Data Analytics” to identify the patent documents (including granted patents, published patent applications) that exclusively disclose the analyzing techniques of Big Data, the softwares /platforms used and the application areas of all types of Big Data. In other words, the analysis covers patent families that disclose the details pertaining to Big Data Analytics.

The assignment was carried out in two phases, first being background analysis to identify relevant keywords, forming strategies based on those keywords, obtaining relevant patent documents and preparing the final report which was submitted to the client for feedback.

The second phase involved incorporating client feedback into the analysis. Based on that feedback, the relevant patent documents were clustered under five major segments:

1) Application Areas

2) Softwares/ Platforms

3) SQL/NoSQL

4) Analyzing Techniques

5) Data

Patent activity trend analysis was carried out based on all the above segments.

This PLA points out certain features of the patenting activity in Big Data Analytics like the fact that the US geography seems to be dominating the technology space. The PCT route has 543 applications with XYZ having the highest PCT applications i.e 22. It also shows that for the Patent Families having earliest priority year (2009-2013) Microsoft has overtaken IBM in terms of Patent filings. In terms of white space analysis, we observe that connectomics, historical/archaeology and meteorology have the lowest number of patent families; there is scope of having more dedicated patents relating to these application areas. Crowd sourcing and machine learning (data mining) are the analyzing techniques for big data which could be used in the future for patenting activity. So, all this valuable data analysis will help analyze growth areas and comes in handy if you are looking at the future of a new technology or looking to diversify into new avenues of technology. With this sort of compilation of a variety of literature available for a technology- filing trends, you get a clear idea of the road ahead.

A patent landscape analysis has and will continue to be a valuable tool for enterprises to base their research and development strategies and a better market position. Hiring professionals in the field would not only make your market grow but would also help you avoid wasting your resources on unfruitful researches.

Source by Jason Smith Benson

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