Welcome!

@DXWorldExpo Authors: Yeshim Deniz, Pat Romanski, Liz McMillan, Elizabeth White, Zakia Bouachraoui

Related Topics: @DXWorldExpo, Cognitive Computing , Machine Learning

@DXWorldExpo: Article

Patent Data Quality | @CloudExpo #BigData #Analytics #AI #MachineLearning

Is clean data a pipe dream?

The United States Patent and Trademark Office (USPTO) recently announced an expansion of PatentsView, its visualization tool for US patents. First launched a few years ago, the intent behind the tool was to make 40 years of patent filing data available for free to those interested in examining "the dynamics of inventor patenting activity over time." In spite of being limited to patents (not applications) and with a focus only on the US, it offers some interesting visualizations around locations and citations.

In a blog post last month, USPTO director Michelle Lee said the PatentView tool is based on "the highest-quality patent data available," connecting 40 years' worth of information about inventors, their organizations, and their locations in unprecedented ways. The newly revamped interface presents three user-friendly starting points - relationship, locations, and comparison visualizations - which allow for deeper exploration and detailed views. However, through no fault of their own, the USPTO dataset is rife with spelling errors, doesn't reflect patent reassignments, and doesn't resolve company subsidiaries or acquisitions.

This issue is not unique to the USPTO. Other PTO offices around the world face similar barriers to presenting "clean" data. The first issue, spelling errors, merely reflects the fact that assignee information (among other fields like inventor names) is manually entered and hence prone to error and inconsistency. For example, "International Business Machines" has been spelled 1,200 different ways as a patent assignee over the last two decades in the USPTO data set.

In addition, PTO data doesn't get corrected or updated based on later corrections or patent reassignments. For example, patent US8176440 was originally - and incorrectly - assigned to Silicon Labs. My company, Innography, filed a certificate of correction to update the assignment, yet the USPTO data and PatentsView still don't reflect this. In fact, Innography research shows that nearly 20 percent of US patents are reassigned in their lifetimes, translating into a significant number of company portfolio errors based on this factor alone.

Finally, PTO data also doesn't reflect when companies purchase each other, when there's a spinoff, or when a subsidiary files patents. Microsoft, for example, now owns all LinkedIn's patents, even if the reassignments haven't been processed.

As a result, PTO data falls far short of reflecting reality, where patents and companies are bought and sold every day, and where data-entry errors exist and are corrected. The accuracy of the data is very low when it comes to representing company patent portfolios in the real world.

The Cost of Free Data
The USPTO aims to increase the transparency of patenting and invention processes. But if the quality of data and search results is questionable, what good is it to IP practitioners?

There is rich information available through the patenting process, including economic research, prior-art searching, and discovery of broader trends around filing patterns. However, it was never intended to be used as-is to inform strategic business decisions such as in and out licensing, merger and acquisition activities, or portfolio pruning and maintenance decisions.

It makes sense for PTOs to offer their data for free as a way to engage the community's interest in patenting processes. However, too many lightweight patent analytics tools use this flawed data verbatim to tout their "data quality" to IP professionals.

Many patent analyses start with a company's patent portfolio, such as competitive benchmarking, acquisition analysis, and negotiation preparation. In addition, just about every board-level question about patents requires accurate patent ownership information: "Are we ahead of or behind this competitor?" "What companies should we be worried about in this technology area?"

Poor data quality makes it difficult, if not impossible, to answer those questions accurately. To create the most accurate data set possible, companies must use other sources of information to crosscheck and improve patent data accuracy.

Innography data scientists process more than 2,000 company acquisitions annually, and our user base suggests another 5,000 updates each year. As a result, Innography has created more than 10 million data-correction rules over the last decade, which are continuously updated via machine learning and crowdsourcing.

Company leaders must be able to use patent reports to assess market opportunities and make strategic business decisions. This requires an IP analytics solution that reflects real-world changes, and doesn't rely on poor data quality from outdated PTO assignee information.

More Stories By Tyron Stading

Tyron Stading is president and founder of Innography, and chief data officer for CPA Global. He has been named one of the “World’s Leading IP Strategists" by IAM, and one of National Law Journal's "50 Intellectual Property Trailblazers & Pioneers". Before Innography, Tyron was an IBM worldwide industry solutions manager in the telecommunications and utilities sector, and worked at several start-ups focused on mobile communications and networks security. He has published multiple research papers and filed more than three dozen patents. Tyron has a BS in Computer Science from Stanford University and an MS in Technology Commercialization from The University of Texas.

DXWorldEXPO Digital Transformation Stories
Today, we have more data to manage than ever. We also have better algorithms that help us access our data faster. Cloud is the driving force behind many of the data warehouse advancements we have enjoyed in recent years. But what are the best practices for storing data in the cloud for machine learning and data science applications?
DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.
Bill Schmarzo, author of "Big Data: Understanding How Data Powers Big Business" and "Big Data MBA: Driving Business Strategies with Data Science" is responsible for guiding the technology strategy within Hitachi Vantara for IoT and Analytics. Bill brings a balanced business-technology approach that focuses on business outcomes to drive data, analytics and technology decisions that underpin an organization's digital transformation strategy.
@DevOpsSummit at Cloud Expo, taking place November 12-13 in New York City, NY, is co-located with 22nd international CloudEXPO | first international DXWorldEXPO and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time t...
Headquartered in Plainsboro, NJ, Synametrics Technologies has provided IT professionals and computer systems developers since 1997. Based on the success of their initial product offerings (WinSQL and DeltaCopy), the company continues to create and hone innovative products that help its customers get more from their computer applications, databases and infrastructure. To date, over one million users around the world have chosen Synametrics solutions to help power their accelerated business or per...
DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
For years the world's most security-focused and distributed organizations - banks, military/defense agencies, global enterprises - have sought to adopt cloud technologies that can reduce costs, future-proof against data growth, and improve user productivity. The challenges of cloud transformation for these kinds of secure organizations have centered around data security, migration from legacy systems, and performance. In our presentation, we will discuss the notion that cloud computing, properl...
CloudEXPO New York 2018, colocated with DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.