Patent Issued for Systems and methods for classifying vehicle trips (USPTO 11162802): Allstate Insurance Company

2021 NOV 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11162802, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Kreig, Alex (Northbrook, IL, US), Link, Kelly (Chicago, IL, US), Recchia, Anthony (Chicago, IL, US), Tiwari, Chhavi (Chicago, IL, US), Yang, En-Chieh (Chicago, IL, US).

This patent was filed on October 28, 2019 and was published online on November 2, 2021.

From the background information supplied by the inventors, news correspondents obtained the following quote: “A transportation network company matches passengers with vehicles via websites and mobile apps. Drivers for transportation network companies typically own or lease their own vehicles when driving passengers. Accordingly, drivers can use their vehicle for both business purposes and personal purposes. However, due to the distributed nature of transportation network companies, it is difficult to assess when a specific driver is driving for a transportation network company as compared to personal use. Accordingly, there is a need to accurately determine if a driver is engaged in a trip for business or personal use.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “In light of the foregoing background, the following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. The following summary merely presents some concepts of the invention in a simplified form as a prelude to the more detailed description provided below.

“Systems and methods in accordance with embodiments of the invention can obtain and use a variety of telematics data to classify trips taken by a vehicle. Trip models can be generated based on telematics data captured during the operation of a vehicle. A variety of features of the trip model, such as the timing and/or location of stops made by the vehicle during one or more trips, can be used to classify the trip as a business trip or a personal trip. In several embodiments, machine classifiers are trained to classify features within the trip models based on historical trips that have been classified as business trips or personal trips. A number of trip models can be combined with other driver attributes to classify a particular vehicle and/or driver as engaged with a transportation network company.

“The arrangements described can also include other additional elements, steps, computer-executable instructions, or computer-readable data structures. In this regard, other embodiments are disclosed and claimed herein as well. The details of these and other embodiments of the present invention are set forth in the accompanying drawings and the description below. Other features and advantages of the invention will be apparent from the description, drawings, and claims.”

The claims supplied by the inventors are:

“1. A method, comprising: obtaining, by a classification server system, telematics data comprising a set of geographic locations and a set of times that a vehicle was at each geographic location; determining, by the classification server system, a set of trips based on the telematics data, wherein: each trip in the set of trips comprises a starting location, a starting time the vehicle was at the starting location, an ending location, and an ending time the vehicle was at the ending location; and the starting location and the ending location are indicated in the set of geographic locations; determining, by the classification server system, a set of features for each trip in the set of trips; and classifying, by the classification server system, each trip in the set of trips based on the set of features for each trip, wherein a classified trip comprises the set of features, a label indicating if the trip is a business trip or a personal trip, and a confidence metric indicating a probabilistic likelihood that the label correctly identifies a class of trip, wherein the business trip is defined as the vehicle being operated in a business capacity as part of a transportation network company and the personal trip is defined as the vehicle being operated for personal use.

“2. The method of claim 1, wherein the telematics data is obtained from a mobile device associated with a driver of the vehicle.

“3. The method of claim 1, wherein: the telematics data further comprises a driver identifier; and the method further comprising classifying, by the classification server system, a driver as a commercial driver or a personal driver based on the classified trips.

“4. The method of claim 1, wherein the set of features for a trip comprises at least one intermediate stop at a point of interest visited by the vehicle.

“5. The method of claim 4, further comprising: determining, by the classification server system, the ending location of a trip in the set of trips based on an ending threshold time; and determining, by the classification server system, the at least one intermediate stop based on an intermediate threshold time, wherein the intermediate threshold time is less than the ending threshold time.

“6. The method of claim 1, further comprising classifying a trip in the set of trips by: calculating, by the classification server system, a total trip duration based on the starting time and the ending time; calculating, by the classification server system, a trip driven distance based on a distance between each geographic location in the trip; calculating, by the classification server system, a ratio of the trip driven distance to an ideal distance driven between the starting location and the ending location of the trip; and generating, by the classification server system, the label based on the total trip duration, the trip driven distance, and the ratio of the trip driven distance to the ideal distance driven between the starting location and the ending location.

“7. The method of claim 1, further comprising: classifying each trip using a machine classifier; and retraining, by the classification server system, the machine classifier based on the classified trips.

“8. A computing device, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the computing device to: obtain telematics data comprising a set of geographic locations and a set of times that a vehicle was at each geographic location; determine a set of trips based on the telematics data, wherein: each trip in the set of trips comprises a starting location, a starting time the vehicle was at the starting location, an ending location, and an ending time the vehicle was at the ending location; and the starting location and the ending location are indicated in the set of geographic locations; determine a set of features for each trip in the set of trips; and classify each trip in the set of trips based on the set of features for each trip, wherein a classified trip comprises the set of features, a label indicating if the trip is a business trip or a personal trip, and a confidence metric indicating a probabilistic likelihood that the label correctly identifies a class of trip, wherein the business trip is defined as the vehicle being operated in a business capacity as part of a transportation network company and the personal trip is defined as the vehicle being operated for personal use.

“9. The computing device of claim 8, wherein the instructions, when executed by the one or more processors, further cause the computing device to obtain the telematics data from a mobile device associated with a driver of the vehicle.

“10. The computing device of claim 8, wherein: the telematics data further comprises a driver identifier; and the instructions, when executed by the one or more processors, further cause the computing device to classify a driver as a commercial driver or a personal driver based on the classified trips.

“11. The computing device of claim 8, wherein the set of features for a trip comprises at least one intermediate stop at a point of interest visited by the vehicle.

“12. The computing device of claim 11, wherein the instructions, when executed by the one or more processors, further cause the computing device to: determine the ending location of a trip in the set of trips based on an ending threshold time; and determine the at least one intermediate stop based on an intermediate threshold time, wherein the intermediate threshold time is less than the ending threshold time.

“13. The computing device of claim 8, wherein the instructions, when executed by the one or more processors, further cause the computing device to classify a trip in the set of trips by: calculating a total trip duration based on the starting time and the ending time; calculating a trip driven distance based on a distance between each geographic location in the trip; calculating a ratio of the trip driven distance to an ideal distance driven between the starting location and the ending location of the trip; and generating the label based on the total trip duration, the trip driven distance, and the ratio of the trip driven distance to the ideal distance driven between the starting location and the ending location.

“14. The computing device of claim 8, wherein the instructions, when executed by the one or more processors, further cause the computing device to: classify each trip using a machine classifier; and retrain the machine classifier based on the classified trips.

“15. A non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising: obtaining telematics data comprising a set of geographic locations and a set of times that a vehicle was at each geographic location; determining a set of trips based on the telematics data, wherein: each trip in the set of trips comprises a starting location, a starting time the vehicle was at the starting location, an ending location, and an ending time the vehicle was at the ending location; and the starting location and the ending location are indicated in the set of geographic locations; determining a set of features for each trip in the set of trips; and classifying each trip in the set of trips based on the set of features for each trip, wherein a classified trip comprises the set of features, a label indicating if the trip is a business trip or a personal trip, and a confidence metric indicating a probabilistic likelihood that the label correctly identifies a class of, wherein the business trip is defined as the vehicle being operated in a business capacity as part of a transportation network company and the personal trip is defined as the vehicle being operated for personal use.

“16. The non-transitory machine-readable medium of claim 15, wherein the instructions, when executed by one or more processors, further cause the one or more processors to perform steps comprising obtaining the telematics data from a mobile device associated with a driver of the vehicle.

“17. The non-transitory machine-readable medium of claim 15, wherein the instructions, when executed by one or more processors, further cause the one or more processors to perform steps comprising classifying a driver as a commercial driver or a personal driver based on the classified trips, wherein the driver is indicated in the telematics data.

“18. The non-transitory machine-readable medium of claim 15, wherein the set of features for a trip comprises at least one intermediate stop at a point of interest visited by the vehicle.

“19. The non-transitory machine-readable medium of claim 18, wherein the instructions, when executed by one or more processors, further cause the one or more processors to perform steps comprising: determining the ending location of a trip in the set of trips based on an ending threshold time; and determining the at least one intermediate stop based on an intermediate threshold time, wherein the intermediate threshold time is less than the ending threshold time.

“20. The non-transitory machine-readable medium of claim 15, wherein the instructions, when executed by one or more processors, further cause the one or more processors to classify a trip in the set of trips by: calculating a total trip duration based on the starting time and the ending time; calculating a trip driven distance based on a distance between each geographic location in the trip; calculating a ratio of the trip driven distance to an ideal distance driven between the starting location and the ending location of the trip; and generating the label based on the total trip duration, the trip driven distance, and the ratio of the trip driven distance to the ideal distance driven between the starting location and the ending location.”

For the URL and additional information on this patent, see: Kreig, Alex. Systems and methods for classifying vehicle trips. U.S. Patent Number 11162802, filed October 28, 2019, and published online on November 2, 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=11162802.PN.&OS=PN/11162802RS=PN/11162802

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