Collision analysis platform using machine learning to reduce generation of false collision outputs

Patent metadata

Field	Value
Publication number	US-11961339-B2
Application number	US-202318095052-A
Country	US
Kind code	B2
Filing date	Jan 10, 2023
Priority date	Jun 26, 2020
Publication date	Apr 16, 2024
Grant date	Apr 16, 2024

Abstract

Aspects of the disclosure relate to computing platforms that utilize machine learning to reduce false positive/negative collision output generation. A computing platform may apply machine learning algorithms on received data to generate a collision output. In response to generating the collision output indicating a collision, the computing platform may identify a data collection location. If the data collection location is within a predetermined radius of a false positive collection location, the computing platform may modify the collision output to indicate a non-collision. If the data collection location is not within the predetermined radius, the computing platform may compute a score using telematics data and compare the score to a predetermined threshold. If the score does not exceed the predetermined threshold, the computing platform may modify the collision output to indicate a non-collision. If the score exceeds the predetermined threshold, the computing platform may affirm the collision output indicating a collision.

First claim

What is claimed is:

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 on

Assignees

• Allstate Insurance Co

Inventors

• Tammali Venu Madhav
• Schmitt Kyle Patrick
• Deram Jeremy

Classifications

• G07C5/008 Primary

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

• B60R21/00 Primary

  Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks (safety belts or body harnesses in vehicles B60R22/00; seats constructed to protect the occupant from the effect of abnormal g-forces, e.g. crash or safety seats, B60N2/42; energy-absorbing arrangements for hand wheels for steering vehicles B62D1/11; energy-absorbing arrangements for vehicle steering columns B62D1/19) · CPC title

• G06N20/00

  Machine learning · CPC title

• G08G1/202

  Dispatching vehicles on the basis of a location, e.g. taxi dispatching · CPC title

• B60R2021/0027

  Post collision measures, e.g. notifying emergency services · CPC title