Monday 18 April 2016

One of the key elements in understanding a technology sector or a competitor’s activities is to measure and detect any significant changes over time that may indicate a declining interest or a new hot emerging area. 

But how do we spot the signal from the noise? What constitutes a significant change?  This depends on how we measure change which in turn depends on the measure of time we use.  In scientific literature, we have limited choice – publication date (but even that is changing with wide availability of electronic pre-prints).  In patent literature, publication date provides a measure of when an invention is publicly disclosed, but priority date is perhaps a truer measure of when the invention was made.  And is it better to look at individual dates, or use moving windows of time?

This presentation will consider these questions using a case study approach to determine the impacts and effectiveness of the different approaches.

Information that is relevant for researchers and decision makers in the Life Sciences comes from many different backgrounds: Scientific publications, patents, news, clinical reports, user-generated content, they all may be required to understand trends, opportunities and threats. A key to providing quick and comprehensive overview is having information from various source in one place and semantically enrich and normalize them and relate them to one another.

We present the key principles of a platform that serves that purpose and that provides users with insights into the scientific, clinical and competitive intelligence landscape of their respective area of interest. Forged in close collaboration with industry practitioners, the Luxid Biopharma Navigator is today used in production by hundreds of experts.

This joint presentation will focus on a project between CENTREDOC and the ARMASUISSE Science and Technology Foresight program to set up an optimized Patent Landscape process. The talk will outline the major bottlenecks identified in the existing process, the solutions considered and implemented by CENTREDOC, as well as the results achieved by ARMASUISSE in its capacity to anticipate and get the necessary understanding of emerging technologies. As technologies can be considered independently of the domain of application, creating a contributory platform providing structured information about technologies is of common general interest at governmental and industrial level, both national and international.

This presentation will give some guidelines on how to create a meaningful Patent Landscapes. Generating patent landscaping reports seems simple, but it isn’t. For making patent landscapes you have to take several different issues into consideration.
It’s important at the start to already have in mind what kind of landscape report you are going to prepare, and choose a topic of interest, but preferable not one that is too broad.  It’s also extremely important to have a clean (80-90% relevance) dataset that the landscape is based on; otherwise the outcome will be rubbish. And of course, do not use landscapes for questions that require a legal opinion (like Freedom-to-operate conclusions!!). Patent landscapes are not aimed to be as precise as other patent searches.
Some more important issues has to be taken in account and are presented.
 

This paper revisits some of the issues discussed in our 2013 presentation "Challenges in Visualizing Pharmaceutical Business Information," where we analyzed some of the unique challenges in visualizing competitive intelligence information for the pharmaceutical industry.  A key challenge for pharmaceutical companies is to evaluate the competitive landscape for drug launches many years in the future, based on a combination of publicly available drug pipeline and clinical trials data and internal company knowledge.  This information is often conveyed in hand-drawn PowerPoint slides, which are very time consuming to create and update as the competitive landscape changes.  In this paper we'll discuss approaches to developing a toolkit to facilitate the analysis and visualization of competitive drug launch timelines, and then show how to apply the same tools to a different problem -- forecasting the patent expiration landscape.

The use of ontologies to aid in the development of text search queries, the quality and relevance ranking of results, and the
categorization of patents has been well characterized. Similar work has been performed on non-patent scientific literature such as journal articles. We present here the employment of common life science ontologies to search both the entirety of the patent and non-patent literature corpora at the same time. The results of these searches can be readily studied in a single unified search result that allows for the annotation of key patent and non-patent documents in a mixed data-type environment.

SMEs represent a prime target for public authority awareness-raising policies especially as regards innovation and filing patents. Yet it is not always easy to get a handle on this population in terms of statistics, meaning that it is particularly difficult to systematically identify in the patent databases those SMEs that do file patent applications.

Two census of SMEs conducted in 1999 and 2007, organised jointly by Bpifrance and INPI, allow the INPI to yearly identify SMEs among the companies that filed a patent application at the French patent office.

This study reveals the importance of SMEs among the total patent applicant population. They show that in France, in 2014, SMEs represented 67% of the French corporate bodies that filed patent applications, but only accounted for 23% of the patent applications published in 2014. This share of SMEs and large companies in the patent applications of French corporate bodies are stable since 2011. The figures highlight the fact that in 2014, on average, an SME filed 1.4 patents, compared to 15.2 for a large company (more than 5,000 employees). We observe regional disparities: regions such as Alsace, Languedoc-Roussillon, Pays de la Loire and Poitou-Charentes are characterised by the highest share of patent application from SMEs.  And SMEs are more specialised in medical technologies.

Boehringer Ingelheim has been developing dedicated Life Science SEARCHCORPORA for startups, scientific literature and news tracking based on the Web Data Analysis platform Deep SEARCH 9.

Using the Deep SEARCH 9 approach, Boehringer Ingelheim is capable of tapping directly any web resources like online websites data bases, web sites or news feeds.

Use case 1: SEARCHCORPUS® for life science startups:
We find startup information we could not find in public search engines.

Use case 2: Life science news SEARCHCORPUS®:
100s of incoming mails and alerts are processed every day and websites and articles behind the news tags are crawled automatically.

The purpose of these applications is that Scientists can subscribe to the services to have compilations of results of personalized deep searches sent to them automatically or that they can alternatively use faceted search on the life science SEARCHCORPORA interactively.