Patent Issued for Distributed data processing systems for processing remotely captured sensor data (USPTO 11187550): Allstate Insurance Company

2021 DEC 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Harish, Pratheek M. (Ontario, CA), Herrmann, Alexander (Chicago, IL, US), Schmitt, Kyle Patrick (Chicago, IL, US), Yeomans, Benjamin Robertson (Greensboro, NC, US), filed on July 6, 2018, was published online on November 30, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11187550 is assigned to Allstate Insurance Company (Northbrook, Illinois, United States).

The following quote was obtained by the news editors 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 the background information obtained for this patent, NewsRx journalists 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, sensor data captured by the first user computing device using one or more sensors built into the first user computing device during a trip in a vehicle; analyze the sensor data received from the first user computing device to align at least one axis of a reference frame of the first user computing device with at least one axis of a reference frame of the vehicle, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises: using barometer data obtained from a barometer sensor of the first user computing device to estimate a slope traveled by the vehicle; and aligning a vertical axis of the reference frame of the first user computing device with a vertical axis of the reference frame of the vehicle based on the slope estimated from the barometer data; based on aligning the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle, generate alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle; and 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 alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle.

“2. The computing platform of claim 1, wherein receiving the 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 sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using gravity measurement data and principal component analysis to perform vertical axis alignment.

“4. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using gravity measurement data and principal component analysis to perform full axis alignment.

“5. The computing platform of claim 4, wherein using gravity measurement data and principal component analysis to perform the full axis alignment comprises using a Butterworth low-pass filter to perform the full axis alignment.

“6. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using quaternion time series data obtained from an orientation sensor and satellite course measurement data to perform vertical axis alignment.

“7. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using quaternion time series data obtained from an orientation sensor of the first user computing device and satellite course measurement data to perform full axis alignment.

“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: send, to a driver detection module, the alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle.

“9. 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 storing the alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle, send the alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle to a data analyst console computing device, wherein sending the alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle to the data analyst console computing device causes the data analyst console computing device to wake and display axis alignment information corresponding to the alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle.

“10. A method, comprising: at a computing platform comprising at least one processor, a communication interface, and memory: receiving, by the at least one processor, 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 during a trip in a vehicle; analyzing, by the at least one processor, the sensor data received from the first user computing device to align at least one axis of a reference frame of the first user computing device with at least one axis of a reference frame of the vehicle, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises: using barometer data obtained from a barometer sensor of the first user computing device to estimate a slope traveled by the vehicle; and aligning a vertical axis of the reference frame of the first user computing device with a vertical axis of the reference frame of the vehicle based on the slope estimated from the barometer data; based on aligning the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle, generating, by the at least one processor, alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle; and storing, by the at least one processor, 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 alignment data relating the sensor data received from the first user computing device to the reference frame of the vehicle.

“11. The method of claim 10, wherein receiving the 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.

“12. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using gravity measurement data and principal component analysis to perform vertical axis alignment.

“13. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using gravity measurement data and principal component analysis to perform full axis alignment.

“14. The method of claim 13, wherein using gravity measurement data and principal component analysis to perform the full axis alignment comprises using a Butterworth low-pass filter to perform the full axis alignment.

“15. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using quaternion time series data obtained from an orientation sensor and satellite course measurement data to perform vertical axis alignment.

“16. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to align the at least one axis of the reference frame of the first user computing device with the at least one axis of the reference frame of the vehicle comprises using quaternion time series data obtained from an orientation sensor of the first user computing device and satellite course measurement data to perform full axis alignment.”

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

URL and more information on this patent, see: Harish, Pratheek M. Distributed data processing systems for processing remotely captured sensor data. U.S. Patent Number 11187550, filed July 6, 2018, and published online on November 30, 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=11187550.PN.&OS=PN/11187550RS=PN/11187550

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