Patent Issued for Distributed data processing systems for processing remotely captured sensor data (USPTO 11067408)

2021 AUG 05 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Harish, Pratheek M. (Ontario, CA), Herrmann, Alexander (Chicago, IL, US), Karmarkar, Dipti (Hoffman Estates, IL, US), Schmitt, Kyle Patrick (Chicago, IL, US), Yeomans, Benjamin Robertson (Greensboro, NC, US), filed on July 6, 2018, was published online on July 20, 2021.

The assignee for this patent, patent number 11067408, is Allstate Insurance Company (Northbrook, Illinois, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Processing relatively large datasets may require a relatively large amount of processing power. In some instances, deploying, configuring, and implementing a system that can effectively process such large datasets while also efficiently using computing resources, such as processing power and network bandwidth, may be difficult and present various technical challenges. Aspects of the disclosure provide technical solutions that overcome these and/or other technical challenges, particularly in instances in a which a computer system is configured to process large datasets comprised of sensor data that is remotely captured by various mobile computing devices.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with processing remotely captured sensor data. For instance, one or more aspects of the disclosure relate to distributed data processing systems that are configured to receive sensor data that is remotely captured by various mobile computing devices and subsequently analyze the sensor data to derive various characteristics, such as detecting whether a user of a particular mobile computing device has taken an automobile trip and/or other features of such an automobile trip that may be identified based on the captured sensor data.

“In accordance with one or more embodiments, a computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a user computing device, sensor data captured by the user computing device using one or more sensors built into the user computing device. Subsequently, the computing platform may analyze the sensor data received from the user computing device by executing one or more data processing modules. Then, the computing platform may generate trip record data based on analyzing the sensor data received from the user computing device and may store the trip record data in a trip record database. In addition, the computing platform may generate user record data based on analyzing the sensor data received from the user computing device and may store the user record data in a user record database.

“In some embodiments, receiving the sensor data captured by the user computing device using the one or more sensors built into the user computing device may include receiving data captured by the user computing device using one or more of: an accelerometer, a gyroscope, a magnetometer, a barometer, a gravitometer, a proximity sensor, an ambient light sensor, an ambient temperature sensor, an orientation sensor, a pedometer, an altimeter, a satellite positioning sensor, or an activity recognition sensor.

“In some embodiments, analyzing the sensor data received from the user computing device by executing the one or more data processing modules may include executing one or more of a trip detection module, an axis alignment module, a driver detection module, a trip anomaly detection module, an exit point detection module, a left-right exit detection module, a front-rear detection module, an event detection module, a vehicle mode detection module, a places of interest determination module, a destination prediction module, a route prediction module, a customer insights module, or a car tracking module.

“In one or more additional or alternative embodiments, a computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a first user computing device, sensor data captured by the first user computing device using one or more sensors built into the first user computing device. The computing platform may analyze the sensor data received from the first user computing device to determine whether a first trip recorded in the sensor data received from the first user computing device was taken using a vehicle mode of transport or a non-vehicle mode of transport. Based on determining that the first trip recorded in the sensor data received from the first user computing device was taken using the vehicle mode of transport, the computing platform may generate first vehicular trip record data indicating that the first trip recorded in the sensor data received from the first user computing device was taken using the vehicle mode of transport. In addition, the computing platform may store the first vehicular trip record data in a driver detection database. Alternatively, based on determining that the first trip recorded in the sensor data received from the first user computing device was taken using the non-vehicle mode of transport, the computing platform may generate first non-vehicular trip record data indicating that the first trip recorded in the sensor data received from the first user computing device was taken using the non-vehicle mode of transport. In addition, the computing platform may store the first non-vehicular trip record data in the driver detection database.”

The claims supplied by the inventors are:

“1. A computing platform, comprising: at least one processor; a communication interface; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, via the communication interface, from a first user computing device, first sensor data captured by the first user computing device using one or more sensors built into the first user computing device, wherein the first sensor data is captured by the first user computing device during a first trip in a vehicle; analyze the first sensor data received from the first user computing device to determine a point in time at which a user of the first user computing device exited the vehicle; based on determining the point in time at which the user of the first user computing device exited the vehicle, generate exit-point-detection data relating the point in time at which the user of the first user computing device exited the vehicle to the first sensor data received from the first user computing device; store, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the exit-point-detection data relating the point in time at which the user of the first user computing device exited the vehicle to the first sensor data received from the first user computing device; analyze the first sensor data received from the first user computing device to identify an end location of the first trip in the vehicle; based on analyzing the first sensor data received from the first user computing device to identify the end location of the first trip in the vehicle, update a user-specific listing of trip-end locations; after updating the user-specific listing of trip-end locations, receive, via the communication interface, from the first user computing device, second sensor data captured by the first user computing device using the one or more sensors built into the first user computing device, wherein the second sensor data is captured by the first user computing device during a second trip in a vehicle; determine a distance between the end location of the first trip and a start location of the second trip; based on the distance between the end location of the first trip and the start location of the second trip, generate driver-detection data indicative of whether the user of the first user computing device is a driver during the second trip or a passenger during the second trip; and store, in the at least one database maintained by the computing platform and accessible to the one or more data analysis modules associated with the computing platform, the driver-detection data indicative of whether the user of the first user computing device is a driver during the second trip or a passenger during the second trip, wherein generating the driver-detection data indicative of whether the user of the first user computing device is a driver during the second trip or a passenger during the second trip comprises: generating data indicating that the user of the first user computing device is a driver during the second trip when the distance between the end location of the first trip and the start location of the second trip does not exceed a user-specific distance threshold; and generating data indicating that the user of the first user computing device is a passenger during the second trip when the distance between the end location of the first trip and the start location of the second trip exceeds the user-specific distance threshold, and wherein the user-specific distance threshold is learned by the computing platform based on information indicating end points of multiple previous trips taken by the user of the first user computing device and information indicating how closely to each end point of each previous trip of the multiple previous trips a subsequent car trip started.

“2. The computing platform of claim 1, wherein receiving the first sensor data captured by the first user computing device using the one or more sensors built into the first user computing device comprises receiving data captured by one or more of an accelerometer, a gyroscope, a magnetometer, a barometer, a gravitometer, a proximity sensor, an ambient light sensor, an ambient temperature sensor, an orientation sensor, a pedometer, an altimeter, a satellite positioning sensor, or an activity recognition sensor built into the first user computing device.

“3. The computing platform of claim 1, wherein analyzing the first sensor data received from the first user computing device to determine the point in time at which the user of the first user computing device exited the vehicle comprises using a sliding window approach to determine the point in time at which the user of the first user computing device exited the vehicle.

“4. The computing platform of claim 3, wherein using the sliding window approach to determine the point in time at which the user of the first user computing device exited the vehicle comprises: creating a plurality of partially overlapping window frames based on the first sensor data received from the first user computing device; calculating one or more statistical features, peak-based features, or spectral features of each window frame of the plurality of partially overlapping window frames; correlating peak-based features identified in the plurality of partially overlapping window frames with peak profiles associated with known car-exit events; and based on correlating the peak-based features with the peak profiles associated with the known car-exit events, identifying an onset of walking noise in the first sensor data received from the first user computing device to determine an exit point.

“5. The computing platform of claim 1, wherein analyzing the first sensor data received from the first user computing device to determine the point in time at which the user of the first user computing device exited the vehicle comprises identifying a time window in which the user of the first user computing device exited the vehicle.

“6. The computing platform of claim 1, wherein analyzing the first sensor data received from the first user computing device to determine the point in time at which the user of the first user computing device exited the vehicle comprises assigning probability values to a plurality of time windows associated with possible times at which the user of the first user computing device exited the vehicle.

“7. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: based on analyzing the first sensor data received from the first user computing device to determine the point in time at which the user of the first user computing device exited the vehicle, generate a notification indicating the point in time at which the user of the first user computing device exited the vehicle; and send, via the communication interface, to the first user computing device, the notification indicating the point in time at which the user of the first user computing device exited the vehicle, wherein sending the notification indicating the point in time at which the user of the first user computing device exited the vehicle to the first user computing device causes the first user computing device to prompt the first user associated with the first user computing device to confirm the point in time at which the user of the first user computing device exited the vehicle.

“8. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: based on analyzing the first sensor data received from the first user computing device to determine the point in time at which the user of the first user computing device exited the vehicle, send, to a data analyst console computing device, the exit-point-detection data relating the point in time at which the user of the first user computing device exited the vehicle to the first sensor data received from the first user computing device, wherein sending the exit-point-detection data to the data analyst console computing device causes the data analyst console computing device to wake and display exit-point information corresponding to the exit-point-detection data relating the point in time at which the user of the first user computing device exited the vehicle to the first sensor data received from the first user computing device.”

There are additional claims. Please visit full patent to read further.

For more information, see this patent: Harish, Pratheek M. Distributed data processing systems for processing remotely captured sensor data. U.S. Patent Number 11067408, filed July 6, 2018, and published online on July 20, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11067408.PN.&OS=PN/11067408RS=PN/11067408

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