Patent Issued for Collision analysis platform using machine learning to reduce generation of false collision outputs (USPTO 11961339): Allstate Insurance Company

2024 MAY 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11961339, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Deram, Jeremy (Northbrook, IL, US), Schmitt, Kyle Patrick (Chicago, IL, US), Tammali, Venu Madhav (Chicago, IL, US).

This patent was filed on January 10, 2023 and was published online on April 16, 2024.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Aspects of the disclosure relate to enhanced processing systems for executing machine learning algorithms and automatically determining whether or not a collision occurred. Many organizations and individuals use sensor data to determine whether or not a collision occurred. In many instances, however, these determinations may result in false positive and/or false negative results. In addition to the inaccuracies caused by such results, unnecessary resources may be expended or deployed in response to a false positive determination. Similarly, resources may be wrongfully conserved or withheld in response to a false negative determination.”

Supplementing the background information on this patent, NewsRx reporters 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 real time on the edge automated collision determinations. In accordance with one or more arrangements discussed herein, a computing platform having at least one processor, a communication interface, and memory may receive sensor data. By applying one or more machine learning algorithms to the sensor data, the computing platform may generate an event and an associated confidence of whether or not the event is a collision event. In addition to generating the event and a confidence of whether a collision occurred, the computing platform may: 1) identify the location where the event is triggered, and 2) determine whether or not the location is within a predetermined radius of a false positive collection location. In response to determining that the data collection location is within the predetermined radius, the computing platform may modify the event confidence to indicate that a collision did not occur. In response to determining that the data collection location is not within the predetermined radius, the computing platform may: 1) analyze telematics data included in the sensor data to modulate the confidence of collision score, and 2) compare confidence of collision score to a predetermined collision threshold. In response to determining that the confidence of collision score does not exceed the predetermined collision threshold; the computing platform may modify the collision output to indicate that a collision did not occur.

“In response to determining that the confidence of collision score exceeds the predetermined collision threshold, in one or more instances, the computing platform may analyze angular velocity data included in the sensor data, by comparing the angular velocity data to one or more machine learning datasets corresponding to non-collision events in which a mobile device was dropped, to further modulate confidence of the collision score. The computing platform may compare the confidence of collision score to the predetermined collision threshold. In response to determining that the confidence of collision score does not exceed the predetermined collision threshold; the computing platform may modify the collision output to indicate that a collision did not occur. In response to determining that the confidence of collision score exceeds the predetermined collision threshold, in one or more instances, the computing platform may compute a time difference between trip start time and event time. In response to determining that the time difference is below a predetermined time threshold; the computing platform may modify the collision output to indicate that a collision did not occur.

“In one or more examples, the sensor data may be received from one or more of: a mobile device or vehicle based sensors. In one or more instances, the computing platform may analyze one or more of: a data collection location, telematics data, or angular velocity corresponding to a second mobile device to generate a second collision output, where the sensor data is provided to the computing platform, at least in part, by the second mobile device. The computing platform may determine whether the second collision output indicates that a collision did occur. In response to determining that the second collision output indicates that a collision did not occur, the computing platform may modify the collision output to indicate that a collision did not occur. In response to determining that the second collision output indicates that a collision did occur, the computing platform may affirm the collision output indicating that a collision did occur.

“In one or more examples, the computing platform may send a corroboration request to a mobile device corresponding to the sensor data. The computing platform may receive, from the mobile device, crash confirmation information indicating whether or not a collision occurred, which may be based on user input received at the mobile device indicating whether or not the collision occurred. In response to determining that the crash confirmation information indicates that the collision did not occur, the computing platform may modify the collision output to indicate that a collision did not occur. In response to determining that the crash confirmation information indicates that the collision did occur, the computing platform may affirm the collision output indicating that a collision did occur.

“In one or more instances, in response to generating the collision output indicating that a collision did not occur, the computing platform may compare barometric data included in the sensor data to a predetermined airbag deployment threshold. In response to determining that the barometric data exceeds the predetermined airbag deployment threshold, the computing platform may modify the collision output to indicate that a collision did occur. In one or more examples, in response to not generating a collision output indicating that a collision did not occur, the computing platform may: 1) compare one or more thresholds determined using the telematics data with the predetermined collision threshold, wherein the predetermined collision threshold is derived, using Machine Learning, from the dataset formed by merging historical telematics data and historical claims data; 2) in response to determining that the one or more thresholds are greater than the predetermined collision threshold, modify the collision output to indicate that a collision occurred; and 3) in response to determining that the one or more thresholds are lower than the predetermined collision threshold, affirm the collision output indicating that a collision did not occur.”

The claims supplied by the inventors are:

“1. A system comprising: a claim processing computing system configured to receive a request to initiate a claim from a mobile computing device; a dispatch computing system configured to receive one or more dispatch commands and dispatch personnel; a collision analysis computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: receive sensor data from the mobile computing device; generate, by applying one or more machine learning algorithms to the sensor data, a collision output indicating whether or not a collision occurred; in response to generating the collision output indicating that the collision occurred: identify a data collection location corresponding to the sensor data; determine whether or not the data collection location is within a predetermined radius of a false positive collection location; in response to determining that the data collection location is within the predetermined radius, modify the collision output to indicate that the collision did not occur; in response to determining that the data collection location is not within the predetermined radius; analyze telematics data included in the sensor data to compute a first likelihood of collision score; compare the first likelihood of collision score to a predetermined collision threshold; and in response to determining that the first likelihood of collision score does not exceed the predetermined collision threshold, modify the collision output to indicate that the collision did not occur; and in response to determining that the first likelihood of collision score exceeds the predetermined collision threshold, affirm the collision output indicating that the collision did occur; and send the one or more dispatch commands to the dispatch computing system, when the collision output indicates that the collision did occur, to cause the dispatch computing system to dispatch the personnel to a location of the collision.

“2. The system of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: analyze angular velocity data included in the sensor data to compute a second likelihood of collision score; compare the second likelihood of collision score to the predetermined collision threshold; in response to determining that the second likelihood of collision score does not exceed the predetermined collision threshold, modify the collision output to indicate that the collision did not occur; and in response to determining that the second likelihood of collision score exceeds the predetermined collision threshold, affirm the collision output indicating that the collision did occur.

“3. The system of claim 2, wherein analyzing the angular velocity data comprises comparing the angular velocity data to one or more machine learning datasets corresponding to non-collision events in which the mobile computing device was dropped.

“4. The system of claim 1, wherein the sensor data is collected from one or more vehicle based sensors.

“5. The system of claim 1, further comprising: a second mobile computing device configured to collect second mobile computing device data, the second mobile computing device data comprising one or more of the data collection location, telematics data, or angular velocity corresponding to the second mobile computing device, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: receive the second mobile computing device data from the second mobile computing device; generate a second collision output based on an analysis of the second mobile computing device data; determine whether the second collision output indicates that the collision did occur; in response to determining that the second collision output indicates that the collision did not occur, modify the collision output to indicate that the collision did not occur; and in response to determining that the second collision output indicates that the collision did occur, affirm the collision output indicating that the collision did occur.

“6. The system of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: send a corroboration request to the mobile computing device corresponding to the sensor data; receive, from the mobile computing device, crash confirmation information indicating whether or not the collision occurred, wherein the crash confirmation information is based on user input received by the mobile computing device indicating whether or not the collision occurred; in response to determining that the crash confirmation information indicates that the collision did not occur, modify the collision output to indicate that the collision did not occur; and in response to determining that the crash confirmation information indicates that the collision did occur, affirm the collision output indicating that the collision did occur.

“7. The system of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: in response to not generating the collision output indicating that the collision did not occur: compare barometric data included in the sensor data to a predetermined airbag deployment threshold; and in response to determining that the barometric data exceeds the predetermined airbag deployment threshold, modifying the collision output to indicate that the collision did occur.

“8. The system of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: in response to not generating the collision output indicating that the collision did not occur: compare one or more thresholds determined using the telematics data with the predetermined collision threshold, wherein the predetermined collision threshold is derived, using Machine Learning, from a dataset formed by merging historical telematics data and historical claims data; in response to determining that the one or more thresholds are greater than the predetermined collision threshold, modify the collision output to indicate that the collision occurred; and in response to determining that the one or more thresholds are lower than the predetermined collision threshold, affirm the collision output indicating that the collision did not occur.

“9. The system of claim 1, wherein the false positive collection location comprises one or more of: a ski resort, an amusement park, or a body of water.

“10. The system of claim 1, wherein analyzing the telematics data included to compute the first likelihood of collision score comprises comparing the telematics data to one or more machine learning datasets corresponding to a roller coast event, a ski event, or a boat event.

“11. The system of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the collision analysis computing platform to: update, after modifying the collision output, the one or more machine learning algorithms to indicate that a false positive collision determination was made by the one or more machine learning algorithms.

“12. A computer implemented method comprising: receiving, at a collision analysis computing platform, sensor data from a mobile computing device; generating, at the collision analysis computing platform, a collision output indicating whether or not a collision occurred by applying one or more machine learning algorithms to the sensor data; in response to generating the collision output indicating that the collision occurred: analyzing, at the collision analysis computing platform, telematics data included in the sensor data to compute a first likelihood of collision score; comparing, at the collision analysis computing platform, the first likelihood of collision score to a predetermined collision threshold; modifying, at the collision analysis computing platform, the collision output to indicate that the collision did not occur in response to determining that the first likelihood of collision score does not exceed the predetermined collision threshold, and in response to determining that the first likelihood of collision score exceeds the predetermined collision threshold: identifying, at the collision analysis computing platform, a data collection location corresponding to the sensor data, determining, at the collision analysis computing platform, whether or not the data collection location is within a predetermined radius of a false positive collection location, in response to determining that the data collection location is within the predetermined radius, modifying, at the collision analysis computing platform, the collision output to indicate that the collision did not occur, and in response to determining that the data collection location is not within the predetermined radius, affirming the collision output indicating that the collision did occur; and sending, at the collision analysis computing platform, one or more commands to a dispatch computing system to cause the dispatch computing system to dispatch personnel to a location of the collision based on a determination that the collision output indicates that the collision did occur.”

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

For the URL and additional information on this patent, see: Deram, Jeremy. Collision analysis platform using machine learning to reduce generation of false collision outputs. U.S. Patent Number 11961339, filed January 10, 2023, and published online on April 16, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11961339)&db=USPAT&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)