The theme of the 2014 PIUG Annual Conference is Patent Knowledge & IP Strategy: Riding the Waves of Change to Achieve Business Success, and it will be held from Saturday, April 26 through Thursday, May 1, 2014 at the Hyatt Regency in Orange County, California. The PIUG Annual Conference is the world’s premier patent information destination and the conference will feature world-renowned experts on patent information for technology research and planning, for legal organizations, for patent offices and for overall corporate IP management (technology licensing and related activities). The meeting will include many opportunities for discussion and part of the activities on the Tuesday of the conference will be dedicated to visiting the exhibit hall to learn about the new patent information tools and developments within the industry. Many professionally enriching workshops will also be held in conjunction with this conference (before, during and after the technical sessions).
The early bird deadline is March 7th, 2014, and interested attendees can register online via the conference registration website.
According to the program page there are a number of presentations that will be of high interest to patent analysis practitioners attending the meeting including:
Multivariate Patent Similarity Detection by Kas Kasravi of Hewlett-Packard
There is an increasing demand for more accurate detection of similarities among patents, leading to better prior art search, market gap analysis, infringement detection, discovery, and litigation support. Ample patent data is readily available, but detection of similarities among patents is difficult, generally resulting in high false-positive and false-negative errors. We describe a multivariate approach for detection of similarities among patents. In particular, the application of text mining, link analysis, and clustering to patent data (text, classification, citations and dates) provides a robust method for higher search accuracy fault-tolerance than keyword searching. Further, visualization of the multiple variables can further assist the patent searchers.
Machine Learning Approaches for Quantifying and Predicting Patent Quality by Yan Liu from the University of Southern California
The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents.
Patent Overlay Mapping: Visualizing Technological Distance by Luciano Kay from the University of California Santa Barbara
Technological distance, or the extent to which a set of patents reflects different types of technologies, is an important measure of new knowledge creation and innovative opportunity. This distance is often proxied by patent classes, with patents in a given patent class being considered more similar to one another than to those in other patent classes. A challenge in relying on patent classifications is that, as technology advances, different patent classes may actually involve the same technological area. This presentation will address this issue and propose an alternative approach using a new global patent map that represents all technological categories, and a method to locate patent data of individual organizations and technological fields on the global map. This second patent overlay map technique is shown to be of potential interest to support competitive intelligence and policy decision-making. The global patent map is based on similarities in citing-to-cited relationships between categories of the International Patent Classification (IPC) of European Patent Office (EPO) patents from 2000 to 2006. This patent dataset, extracted from PatStat database, represents more than 760,000 patent records in more than 400 IPC categories. To illustrate the kind of analytical support offered by this approach, the presentation will discuss examples based on the overlay of nanotechnology-related patenting activities of two companies and two different nanotechnology subfields on to the global patent map. The exercise shows the potential of patent overlay maps to visualize technological areas and support decision-making. The methods also shows that IPC categories that are similar to one another based on co-citation (and thus close in the global patent map) are not necessarily in the same hierarchical IPC branch, thus revealing new relationships between technologies that are classified as pertaining to different (and sometimes distant) subject areas in the IPC.
Analytics Panel – Patent Counsels’ Views on Patent Analytics/Citation Analysis moderated by John Arenivar of 3M
How do patent attorneys use patent analytics and patent citation based analysis to address business needs? A panel of senior patent counsel from leading multi-national corporations including Qualcomm, Sony and CoreLogic will be discussing their views. The moderated panel will cover the benefits, traps and place of analytics in IP decisions. Questions to the panel can be submitted prior to the conference to firstname.lastname@example.org to be taken up by the moderator or posed directly to the panel during the event.
The Keynote presentation will be provided by Dr. Stephen Boyer from IBM Almaden Research Center who will be talking about The Past Present & Future of Computer Curation of Patents & Scientific Literature
Initially, patents and related literature were indexed with text search engines to enable full-text searching of content. These traditional search systems find relevant documents if fed explicit queries, but they do not provide the added value of generating new scientific hypotheses. Technologies have since evolved to selectively identify and cross-correlate specific entities in both text and images of documents. For example, chemicals, genes, and diseases are identified and transformed into appropriate machine-readable formats that are then integrated with similarly processed data from a wide range of sources (databases, web services, journals, clinical trials, etc ). The result is a computer system that searches on molecular structures, gene sequences, and entity attributes to reveal relationships within the contents to be exploited in new ways. These data are then analyzed across multiple sources to build networks that reveal and predict relationships not apparent from the text of historical documents. One goal in the computer curation of text and structures is development of mining technologies to accelerate the discovery of new relationships and properties that are not obvious from directly reading the documents. In sum, the discovery value that results from computer curation across platforms far exceeds the sum of the parts.
The PIUG Annual Conference also offers several options for attending workshops, and for interacting with colleagues and leading experts during the six days of meetings.