Tuesday 9 April 2019

For almost 100 years IUPAC has been well known around the world for its efforts in standardizing nomenclature in chemistry. At the start of the present century it became clear to all involved in chemical structure representation work that with the extensive use of computers and electronic information in all aspects of chemistry and related sciences, an IUPAC standard was necessary.  From this critical need the InChI project was launched by IUPAC in cooperation with the US standards agency - NIST.  The result of this effort has been the development, maintenance, and expansion of capabilities of the open source non‑proprietary International Chemical Identifier - InChI first by NIST and now by the InChI Trust, a not-for-profit UK charity.  

This brief presentation of InChI will highlight on‑going efforts to strengthen and extend this standard for chemical structures and its hashed‑form, the InChIKey. Information standards are critical to enable effective and efficient communication of scientific content. Validation and reproducibility of research results are critical to advances in science. Without a chemical structure standard, it was becoming impossible to find and share all the reported results needed for a particular purpose. The costs of experiments are ever increasing; hence the need for increased efficiency in labs around the world. Open Access, Open Data and Open Standards are areas that are expanding rapidly and are facilitating faster and more effective research discovery. However, before you can share data about a chemical you need to find where the information has been made available on the Internet. Collaborative, interoperable, and global dissemination standards are essential in a more networked world.

This presentation wraps up the new Silicon Valley gateway to disruptive technologies. It outlines multiple in-depth monitoring, ranging from the Deloitte approach on AI to KPMG on IoT, AI and Robotics, and game changing start-ups covering a large variety of sectors from longevity boosters, to net-zero building, to faster IoT optimization.  

One of the key findings of the “AI artificial intelligence 2018 index” is that Europe, and not the U.S. or China, is releasing most AI papers. AI is being commercialized at a fast pace, and the state-of-the-art technology is improving, including the accuracy of object recognition and machine translation. 

We are about to see strides in our daily lives: using deep learning-based vision AI software for face analytics, emotion recognition, body pose, and human action and activity recognition for the automotive industry (Eyeris), hearing aids and hearables with machine learning for unprecedented energy efficiency, size reduction, and cost savings (iHEAR), HSBC employees using smartwatches to enhance customer service (Samsung), customers pushing carts that measure heart rate (Walmart), facial recognition into our car (Visteon) and the ubiquity of voice-command (Toshiba, Honda and SoundHound).  It is remarkable to see ABB partnering with San Francisco-based Salesforce ($8 billion in revenue in 2017 and 30,000 employees).  “With Salesforce, ABB is unifying its CRM globally, across every region, brand and department, to embrace the opportunities created by the Fourth Industrial Revolution”. 

It becomes obvious that Machine Learning, Machine Translation, Artificial Intelligence (AI) and Deep Learning in the area of Scientific Information are not something that each and every company can do on its own in its backyard; a joint effort is necessary to implement these emerging technologies in scientific data analysis.

With the digital revolution the provision of scientific information is fully entered in a new mode of operation.  

The fast advances of technologies is transforming the way of providing information towards scientists and opening new opportunities. 

Data and Information storage has moved from a paper-based, manual affair to an activity in which computers are necessary.  

As a result, a vast amount of scientific information is being daily produced and collected, a research organization has not enough resources to collect everything. 

With emphasizing data-intensive thinking e-Science is moving towards data Intensive technologies and is becoming a new technology driver and requires the re-thinking of infrastructure architecture components, solutions and processes for Scientific Information Provision.  

A new systematic approach for tackling the challenges of data-intensive computing, providing scientists and decision makers with practical tools for dealing and exploring the needed data and information.

Firms 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 different kinds of firms that do file patent applications.

Two census of firms conducted in 1999 and 2007, organised jointly by Bpifrance and INPI, allow the INPI to yearly identify the different kinds of firms among the companies that filed a patent application at the French patent office. This study reveals the importance of the different kinds of firms among the total patent applicant population and firms’ behaviours.

Human beings have too much information to process and we use heuristics to filter information and make decisions quickly; policymakers process evidence and the environment in which they operate in exactly the same way. The world of intellectual property is complex; analysis of IP databases is inherently full of caveats, assumptions and ambiguities. There is a natural disconnect between the two meaning effective communication to support evidence-based decision making is key. In recent years the digital age has given us increasingly beautiful and dynamic visualisations; when these are thrown in to the mix, do these help or hinder the art of effective communication? 

Exemplified compounds resulting from a substructure search are key to making filings based on novelty and inventive step. Conversations with our attorney clients revealed that they prefer to see a table of structures with hyperlinks to references, rather than a list of references with the structures beneath them. To provide this structure-led display we have had to rely on internal macros written many years ago, to reorganize the output from Chemical Abstracts, but the macros are not supported by GSK lT and often crash with large volumes of data. Analytical table tools in third party software have not been able to provide a solution either.  

We use BizInt Smart Charts for Patents to deliver reports, but it did not have the capability to include hit structures and was reference rather than structure oriented. We have now further collaborated with BizInt to develop a method enabling us to present our structures in an easy to read tabular format hyperlinked to their references. These tables are now used in our search reports for the chemistry attorneys, giving a succinct view of the compounds exemplified within a publication (patent and literature) to facilitate decision making.

 BizInt for data compilation, selection and Chart Vizualisation and VantagePoint for specific graphic data representations can help for competitive intelligence analysis.

·        Pipeline and clinical trials data

·        Structure, reliability and updating of data

·        Need to query and export data from different sources

·        Added values of verification and visualization of information.

·        Description of BizInt and VantagePoint

·        Practical examples of the use of these 2 tools for the realization of competitive intelligence reports

When I first started delivering patent landscapes, I was taught that there are three steps to a patent landscape analysis; create the data set; clean the data set; analyse the data set.

In an R&D environment, this met the expectations of project scientists. Despite this, Patent Landscaping never became the strategic tool I knew it should be. Is that because the process lacked the key step: reporting and communicating to senior stakeholders? Short of time and attention, they want to know what a landscape means; preferably in one PowerPoint slide.

How do we communicate with senior stakeholders effectively and ensure that Patent Landscapes play an effective part in strategic decision making?