Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario.

The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption. But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality -- there is not a lot of intelligence built-in.

Machine learning enables us to build systems that can predict the world around us: like what movies we'd like to watch, how much traffic we'll experience on our morning commute, or what words we'll type next in our emails. There are many types of models and tasks. Face detection models transform raw image pixels into high level signals (like the presence and position of eyes, noses, and ears) and then use those signals to locate all faces in an image. Time series models can use sensor measurements to extract long-term trends and seasonal patterns in order to predict future observations. Text prediction models extract information about the meaning of past sentences, grammaticality, and emotions in the text in order to predict the next word or phrase that you'll type.

This ebook, based on the latest ZDNet / TechRepublic special feature, explores how infrastructure around the world is being linked together via sensors, machine learning and analytics. As growing numbers of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data. Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI). Firms are increasingly feeding data collected by Internet of Things (IoT) sensors -- situated everywhere from farmers' fields to train tracks -- into machine-learning models and using the resulting information to improve their business processes, products and services. One of the most visible pioneers is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.

A non-comprehensive list of awesome things other people did in 2014 Last year I made a list off the top of my head of awesome things other people did. I loved doing it so much that I'm doing it again for 2014... The Current State of Machine Intelligence I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find -- my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then... The Things I Wish I Knew - Lessons Learned from Making Data Product Talk from DJ Patil, Greylock Partners as part of this seminar series featuring dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field A Data Analyst's Blog Is Transforming How New Yorkers See Their City It may have been the fire hydrants that certified Ben Wellington as the king of New York's "open data" movement.