For the last quarter century, IBM has been the leading recipient of U.S. patents. In 2016, IBM inventors received a record-setting 8,088 patents. But records were made to be broken, and in 2017 IBM once again set the bar with 9,043 patents granted to its inventors.
The patents IBMers received last year range in areas and topics from artificial Intelligence to cloud computing to blockchain, cybersecurity, quantum computing, and more. A couple of IBM’s new patents that were granted to Neil Sahota, IBM Master Inventor and Watson Business Development Manager, should be of great interest to developers and data scientists.
Triggered Controlled Event Listener Learner, or T-CELL
In the medical world, a t-cell is “a type of white blood cell that is of key importance to the immune system and is at the core of adaptive immunity, the system that tailors the body’s immune response to specific pathogens” (medicinenet.com). What Neil and his team has done is invented something similar for software.
Neil’s patented T-CELL, or Triggered Controlled Event Listener Learners, are essentially blocks of code monitoring a system environment and can react to changes in that environment. If the T-CELLs see a new pattern or if there’s a virus attack they can respond accordingly to respond to the threat or take advantage of an opportunity.
“Similar to T-cells in our body that help our bodily system fight off infection or take beneficial healthy actions, Triggered Controlled Event Listener Learners are constantly looking at a computer system and adapting or adjusting to different situations,” said Sahota.
Machine Learning also could be applied to T-CELLs so they can be given a basic set of rules to know how to try and respond to changes under certain circumstances. Upon detecting a change to the system, a T-CELL would communicate with other T-CELLs and even create a new T-CELL to respond or adapt to the change without adversely affecting the rest the system.
A common, public misconception about data, computers, and artificial intelligence is that people think that a computer can tell you what data is meaningful and important, when in reality it’s the data scientist that does that.
Under the current workflow with data scientists, developers and AI, the data scientist determines what information or data is meaningful, and then the developers program the AI to look for these insights in what usually is large amounts of unstructured data. These decisions are typically made using the most available or evident sources.
What if, instead, the computer was able to ingest all of the unstructured data and present all of the potential important and useful data points to the data scientist?
That’s the idea behind an invention patented by Neil and his co-inventors during 2017. The concept behind the invention includes the creation of an Enterprise Intelligence framework under which a machine will actually help data scientists determine and identify what data is most meaningful and and enable developers to program AI to deliver insights that can help improve decisions about their business and markets.
“The goal with this invention is to enable the machine will actually make sense of the data and help you determine which data is actually meaningful and useful, as well as what the potential insights will be,” Sahota said.
These inventions are just a few of patents granted to more than 8,500 IBM researchers, engineers, scientists and designers in 47 different U.S. states and 47 countries in 2017.
Neil Sahota is a Business Development Leader and Master Inventor at IBM and was granted 4 U.S. patents in 2017.
For more information on IBM’s 25 consecutive years of patent leadership, please click here.