According to the World Intellectual Property Organisation (WIPO), approximately 2.7 million patent applications were filed globally in 2014. Patent applications have grown by an average of 6.5% per year over the past 10 years (WIPO 2015, World Intellectual Property Indicators, Economics & Statistics Series). The rapid growth of patent applications continues to create a huge pile of data that contains potentially valuable information for a variety of uses. As shown by many fields, such as social media, internet clickstream data and machine data (the Internet of Things), big data offers tremendous opportunities if the right algorithms are found to extract valuable information from vast amount of available data. Only this creates meaningful knowledge that can be exploited to generate new business or to make better decisions (eg, with regard to finding the right strategy for the firm). Therefore, the key challenge is to develop smart analytics tools that elicit meaningful knowledge out of the huge amount of data which can be compiled efficiently by applying new digitisation technologies today. General Electric CEO Jeff Immelt emphasises the importance of analytics in the modern economy: ”If you woke up as an industrial company today, you will wake up as a software and analytics company tomorrow.”
Data analytics is also an important and growing area in the IP field. Recent research based on a sample of 158 technology-based firms from the United States and Germany reveals that firms that use patent analytics outperform their peers because they achieve higher overall firm performance (profitability) and extract higher strategic and financial value from their patent portfolios (Ernst, H, Conley, J, Omland, N, 2016, "How to create commercial value from patents: the role of patent management", R&D Management, Vol 46, pp 677-690). The key challenge is to find the relevant information out of the big pile of patent documents that can be retrieved from multiple data sources around the world. That is the essential task of smart patent analytics.
PatentSight offers smart patent analytics solutions that provide reliable, unique and relevant insights into the patent landscape for both decision makers and patent experts. Reliability is linked to the data quality, which is a fundamental prerequisite of any good analytics solution. Ownership changes are checked and tracked over time to ensure that patent ownership is correct, and advanced heuristics are used to judge the legal status of patents with the highest possible precision.
Another important aspect is the distinction of patents with regard to their varying degrees of quality or relevance. Without this differentiation, the huge noise in patent data makes the generation of meaningful insights impossible. The Patent Asset IndexTM is now the standard metric to assess the quality and relevance of single patent families and entire patent portfolios (Ernst, H, Omland, N, 2011, "The Patent Asset Index – A New Approach to Benchmark Patent Portfolios", World Patent Information, Vol 33, pp 34-41). The Patent Asset Index is used by many established firms across multiple industries.
Relevance is defined through the eyes of the users, and ultimately the user can experience relevance of patent analytics in multiple ways, including for:
- R&D strategy;
- portfolio management;
- M&A; and
- trend identification and forecasting.
Users benefit from smart patent analytics solutions in these fields in the following ways:
- better and faster decision making (eg, on strategy, investments and M&A);
- increased licensing revenues;
- higher returns from investment in R&D and patents;
- improved patent strategy and stronger overall patent portfolios;
- a better negotiating position (eg, cross-licensing deals, M&A transactions); and
- better pricing decisions (with regard to the value of patents or entire patent portfolios).
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This is a co-published article whose content has not been commissioned or written by the IAM editorial team, but which has been proofed and edited to run in accordance with the IAM style guide.