Tyco Patent Award

I made a tool that improved data quality of an ‘edge IOT’ application at work (which was not coincidentally the product I was hired to help maintain, extend, and refactor 2015 – 2017).

It was submitted as a patent, but JCI / SensorMatic vowed to file a trade secret so as not to make method public.

In broad strokes, it was essentially an interactive data visualization and statistical tool that served to tune new installations with a data transformation pipeline for ‘data mining’ wifi packet scans. (In aggregate! extreme care taken (salt + hash) to maintain individual anonymity.)

Tools: custom embedded linux kernel, jupyterlab, python

Official press release

Straight from the abstract:

“A tool to improve current calibration techniques for customer smartphone tracking in a retail setting, by automatically collecting, analyzing, and classifying wireless data. The inventive algorithm for the analysis of how a customer travels through the layout of a retail store provides retailers with greater understanding of how customers are reacting and interacting with their products. The collected metrics (including draw rate, dwell time, and engagement) can aid a retailer in creating and cultivating the customer’s retail experience.”

Thanks to Michael Kuo, Kevin Weigel, Brandon Peters for their hard work in the platform (hopefully made a bit easier with this tool), Dan Mueller for air cover, JCI and Sensormatic for having a healthy patent and IP pipeline, and Adrian Collins for exemplary support during the process.