Artificial Intelligence (AI) has been a technology with promise for decades. The ability to manipulate huge volumes of data quickly and efficiently, identifying patterns and quickly analyzing the most optimal solution can be applied to thousands of day-to-day scenarios. However, it is set to come of age in the era of big data and real time decisions where AI can provide solutions to age old issues and challenges. <\/p>\n
Consider, as an example, traffic management. The first traffic management system in London was a manually operated gas-lit traffic signal, which promptly exploded two months after its introduction. Since this inauspicious start, a complex network of road closures, traffic management systems, traffic lights and pedestrian crossings have served to drive increased complexity into travelling in the City. Today traffic travels slower than ever, despite the plethora of new systems being added to better manage the system. <\/p>\n
AI has the potential to change this. It can harvest data on traffic volumes, historical trends and current blockages to quickly calculate the most optimal solution for traffic in London. It can do this in near real time, constantly tweaking and managing flow to deliver the best possible solution. <\/p>\n
This is why AI is increasingly the go to technology for organisations wanting to solve highly complex and data heavy challenges. Digital retailers are using AI-powered robots to run warehouses. Utilities are using AI to forecast electricity demand. Mobile networks are deploying AI to manage an ever-increasing demand for data. We stand on the threshold of a new age of AI powered technology. <\/p>\n
The Intellectual Property (IP) industry is another market where AI could have a profound effect. Traditionally powered by paper, manual searches and lengthy decision-making processes, AI can be deployed to simplify day-to-day tasks and deliver increased insight from IP data. <\/p>\n
IP administrative tasks are one of the most time intensive and risky areas of IP. Law firms and corporate IP departments may, at any time, cover thousands of individual items of IP data, across hundreds of jurisdictions, dealing with thousands of different products. Historically this has been a significantly manual and slow process. <\/p>\n
Consider one single patent that a company has applied for protection for in many different countries. A network of agents, familiar with the specific processes required to gain protection in specific countries, will each help the company achieve their goal. Along the way, hundreds of items of paperwork will be generated, in multiple languages, each with their own challenges and opportunities. <\/p>\n
All of this information would currently be assessed manually and then input into an IP management system. Naturally enough this could easily result in many data processing errors. Now consider this across multiple patents. The opportunities for error are almost limitless. Yet for many companies IP remains its most valuable asset. A simple error in inputting a renewal date could risk losing an asset worth millions to a company. It is worth noting that the World Intellectual Property Organisation (WIPO) estimates around a quarter of patent information is wrong. The risks are therefore very evident. <\/p>\n
In addition, considerable time and cost accrues from the manual labour involved in inputting data. This is activity that, if it can be automated, frees law firms and IP experts to focus on more strategic issues. AI, which is highly adept at processing large sets of data quickly and accurately, can help both efficiency and accuracy. This also enables law firms and IP professionals to take on a more strategic role within the organisation, generating insight from data to help shape future company performance, whilst leaving the more mundane aspects of IP management to computers. <\/p>\n
By automating the submission of data and ensuring that every single item of IP has a unique identifier, correspondence from the various patent offices and agent networks can be simply sorted and searchable on demand. An AI engine can then be deployed to identify relevant information in correspondence, resulting in faster and more accurate outcomes. <\/p>\n
The number of IP assets globally is growing. According to the WIPO there was a 7.8% growth in patent filings between 2014 and 2015. This upward trend in filings has continued for at least 20 years. Therefore, IP documentation and resources are growing. Finding relevant information in this vast amount of data is becoming more difficult. Historically, searches have been carried out manually, with static search databases being the only support tools. <\/p>\n
AI and Machine Learning (ML) can not only automate the process of searching huge databases but also store and use previously collected data to improve the accuracy of future searches. AI can also be used to provide insight into a geographical or vertical market. Consider a company looking to exploit IP in new regions. It may wish to consider the best countries to file for protection. Insight into the strengths and weaknesses of markets in certain countries could be cross referenced with competitive IP data to deliver an instant overview of the most beneficial geographies to apply for further protection. Research that would have previously taken months to achieve can be managed in minutes by deploying AI in an effective way. <\/p>\n
A large IP portfolio is bound to have both strengths and weakness. Indeed, one of the weaknesses may be the sheer scope of the portfolio. As a patent portfolio increases in size, it becomes difficult to effectively oversee and draw insight from the portfolio. As a result, firms are not only limited in managing processes such as renewals, but also in using insight to gain a competitive advantage. <\/p>\n
Many IP professionals are already analysing the value of their patent portfolio. Which patents are most effective? Which deliver most licencing revenues? In which countries? What is the value of IP to a business compared to the cost of renewal? By analysing large sets of data, AI is able to indicate where a companys portfolio of IP is strongest and weakest. <\/p>\n
This can, in turn shape future investment decisions in research and development, help companies understand their relative strengths and weaknesses in terms of their competitors and enable companies to understand more about the potential opportunities in new markets. <\/p>\n
AI is now delivering real value to companies that need to solve complex issues. Within IP management, AI can empower IP professionals. Day-to-day IP tasks can be time consuming, but AI technology enables professionals the time to focus on more strategic decisions in their portfolio. It will also drive improved accuracy while reducing the risk of IP insight and intelligence moving on as employees do. For IP professionals, the real opportunity however comes from the insight that AI can provide into otherwise impenetrable and inaccessible volumes of data. AI will help IP professionals generate business insight that can open up new markets, accurately value an IP portfolio and deliver a better understanding of what and where the next generation of IP investment should come from. <\/p>\n
Tyron Stading is the Chief Data Officer for CPA Global, where he is responsible for creating unified data integration and analytics across all of our products and services. In 2006, Tyron founded and served as CTO for Innography, the US-based IP analytics software provider that CPA Global acquired in 2015. He was previously employed at IBM and several other high technology start-ups. Tyron earned a Computer Science degree from Stanford University and an MBA from University of Texas at Austin. Tyron has published multiple research papers on intellectual property and personally filed more than 50 patents. <\/p>\n
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The Role of Artificial Intelligence in Intellectual Property - IPWatchdog.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Artificial Intelligence (AI) has been a technology with promise for decades. The ability to manipulate huge volumes of data quickly and efficiently, identifying patterns and quickly analyzing the most optimal solution can be applied to thousands of day-to-day scenarios. However, it is set to come of age in the era of big data and real time decisions where AI can provide solutions to age old issues and challenges. Continue reading