IBM filed patents for airport security profiling technology

a bicycle, an engine running, a baby crying, or any other event.

Sensory data processing categorizes the events… For example, a type of event may include a pace of walking, a companion of the cohort, a time of day a cohort eats a meal, a brand of soda purchased by the cohort, a pet purchased by the cohort, a type of medication taken by the cohort, or any other event.

In terms of the sensors themselves, the system uses lots of diverse data-gatherers. From the patent application: “Multimodal sensors comprises at least one of a set of global positioning satellite receivers, a set of infrared sensors, a set of microphones, a set of motion detectors, a set of chemical sensors, a set of biometric sensors, a set of pressure sensors, a set of temperature sensors, a set of metal detectors, a set of radar detectors, a set of photosensors, a set of seismographs, and a set of anemometers.”

Angell told Wolfe that the system can even use olfactory sensors, which means they will smell the environment. The patent application also variously mentions license plate recognition technology, face recognition software, and retina scanners. Data captured from video streams from airport cameras is also analyzed. How does one computer process all this data fast enough to deliver a threat assessment quickly enough to airport security officials? Remember, the idea is to do the analysis in real time, as passengers are streaming through the terminal to board their flights. For a single box, this would be a processing challenge. The inventors, however, envision using a small grid of computers connected over a network. This would deliver ample power to do the real-time data crunching.

“Computers aren’t fast enough to do real-time modeling unless the paradigm shifts,” Angell told Wolfe. “That’s why this inference engine is a pretty big deal.”

That shift is embedded in how inference engine is formulated. It uses rule sets, designed by Angell, Friedlander, and Kraemer, which enable it to fairly efficiently query five million or ten million data cohorts, in a very short period of time.

Analyzing eye movements

There is another patent application in the group which takes the analysis of potential passenger threats to another level. It is entitled “Detecting Behavioral Deviations By Measuring Eye Movements” (patent application number 2009232357, filed September 2009; Friedlander is not involved in this patent; it is Angell and Kraemer only) From the filing: “The ocular metadata [patterns of eye movement] is analyzed…. In response to the patterns of ocular movements indicating behavioral deviations in the member of the cohort group, the member of the cohort group is identified as a person of interest.”

Specifically, eye movement characteristics which are monitored and analyzed include: change in pupil size (dilation); direction of gaze; visual line of gaze (where someone is looking); and rate of blinking; and furtive glances.

Profiling is specifically addressed in this patent application, as follows:

The profiled past comprises data that may be used, in whole or in part, for identifying the person, determining whether to monitor the person, and/or determining whether the person is a person of interest. Global profile data may be retrieved from a file, database, data warehouse, or any other data storage device. Multiple storage devices and software may also be used to store identification data 506. Some or all of the data may be retrieved from the point of contact device, as well. The profiled past may comprise an imposed profile, global profile, individual profile, and demographic profile. The profiles may be combined or layered to define the customer for specific promotions and marketing offers.

Analysis of eye movements, however, are not the final word in identifying passengers with potential ill intent. Patent application targets targets “Detecting Behavioral Deviations by Measuring Respiratory Patterns in Cohort Groups.”