Granted Patent B1

US 12,468,783 · App. 17/524,550 · Granted Nov 11, 2025

Inventors: Kenneth Jason Sanchez (San Francisco, CA); Gil Tamari (San Francisco, CA)

Assignee: QUANATA, LLC

G06F18/241G06F18/211G06F18/2155G06F18/22G06F18/2431G06N20/00G06Q40/08G06V10/751

Abstract

Claims (139)

1 . A computer-implemented method for classifying a vehicular trip, the method comprising:

obtaining a user classification associated with an unlabeled vehicular trip as for work or for personal use;

obtaining a first set of historic device interaction data associated with a first set of historic vehicular trips during which a vehicle operator operated a vehicle for work;

obtaining a second set of historic device interaction data associated with a second set of historic vehicular trips during which the vehicle operator operated the vehicle for personal use;

training a classification model based at least in part upon the first set of historic device interaction data and the second set of historic device interaction data such that:

upon receiving an input of device interaction data selected from the first set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for work; and

upon receiving an input of device interaction data selected from the second set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for personal use;

obtaining a set of unlabeled device interaction data associated with the unlabeled vehicular trip;

outputting a machine classification, using the classification model, the unlabeled vehicular trip as for work or for personal use by at least:

identifying, based at least in part upon the first set of historic device interaction data, a first set of baseline device interaction features associated with the first set of historic vehicular trips;

identifying, based at least in part upon the second set of historic device interaction data, a second set of baseline device interaction features associated with the second set of historic vehicular trips;

identifying, based at least in part upon the set of unlabeled device interaction data, a set of representative device interaction features associated with the unlabeled vehicular trip;

comparing the set of representative device interaction features against the first set of baseline device interaction features and the second set of baseline device interaction features;

obtaining a set of weights associated with the first set of baseline device interaction features, the second set of baseline device interaction features, and the set of representative device interaction features such that each baseline device interaction feature and an associated representative device interaction feature is assigned with a weight, wherein the set of weights comprise:

a first weight for a device interaction feature of content switching:

a second weight for a device interaction feature of entering text; and

a third weight for a device interaction feature of taking a phone call, wherein the first weight and the second weight are greater than the third weight; and

classifying the unlabeled vehicular trip by one of:

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features less than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for work; or

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features more than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for personal use; and

validating the user classification based at least in part upon the user classification and the machine classification made using the classification model.

2 . The computer-implemented method of claim 1 , wherein validating the user classification includes:

accepting the user classification upon determining that the user classification matches the machine classification made using the classification model; or

rejecting the user classification upon determining that the user classification does not match the machine classification made using the classification model.

3 . The computer-implemented method of claim 1 , wherein:

classifying the unlabeled vehicular trip includes generating a confidence level associated with the machine classification made using the classification model; and

validating the user classification includes accepting the user classification upon determining that the confidence level is less than a confidence threshold.

4 . The computer-implemented method of claim 1 , wherein validating the user classification includes:

obtaining a community classification for a set of similar vehicular trips traveled by one or more similar vehicle operators (a) having similar travel paths as the unlabeled vehicular trip, (b) sharing a common employer, (c) sharing a common work region, or (d) sharing a common work schedule with the vehicle operator; and

accepting the user classification upon determining that the user classification matches the machine classification made using the classification model and the community classification.

5 . The computer-implemented method of claim 1 , further comprising:

obtaining a work insurance claim submitted by the vehicle operator associated with the unlabeled vehicular trip; and

determining, based at least in part upon the machine classification, a claim decision by at least:

determining the claim decision as accept claim when the machine classification is for work; or

determining the claim decision as reject claim when the machine classification is for personal use.

6 . The computer-implemented method of claim 1 , further comprising:

obtaining a personal insurance claim submitted by the vehicle operator associated with the unlabeled vehicular trip; and

determining, based at least in part upon the machine classification, a claim decision by at least:

determining the claim decision as accept claim when the machine classification is for personal use; or

determining the claim decision as reject claim when the machine classification is for work.

7 . The computer-implemented method of claim 1 , further comprising:

upon classifying the unlabeled vehicular trip as for work, determining a set of policy modifications associated with a work insurance policy of the vehicle operator based at least in part upon the set of representative device interaction features; or

upon classifying the unlabeled vehicular trip as for personal use, determining a set of policy modifications associated with a personal insurance policy of the vehicle operator based at least in part upon the set of representative device interaction features.

8 . The computer-implemented method of claim 7 , wherein the one or more policy modifications includes modify policy premium, modify a risk score, modify an operator behavioral score, modify a policy incentive, issue a reimbursement, or modify a device interaction challenge.

9 . The computer-implemented method of claim 1 , further comprising:

obtaining a first set of historic path conditions associated with the first set of historic vehicular trips;

obtaining a second set of historic path conditions associated with the second set of historic vehicular trips; and

obtaining a set of unlabeled path conditions associated with the unlabeled vehicular trip, wherein:

the identifying the first set of baseline device interaction features includes calibrating the first set of baseline device interaction features based at least in part upon the first set of historic path conditions;

the identifying the second set of baseline device interaction features includes calibrating the second set of baseline device interaction features based at least in part upon the second set of historic path conditions; and

the identifying the set of representative device interaction features includes calibrating the set of representative device interaction features based at least in part upon the set of unlabeled path conditions.

10 . The computer-implemented method of claim 9 , wherein each of the first set of historic path conditions, the second set of historic path conditions, and the set of unlabeled path conditions includes path curvature, path speed limit, average speed by travelers on path, traffic condition, weather condition, or time of day.

11 . The computer-implemented method of claim 1 , wherein classifying the unlabeled vehicular trip further includes:

identifying, based at least in part upon the first set of baseline device interaction features, a first set of baseline feature vectors associated with the first set of historic vehicular trips;

identifying, based at least in part upon the second set of baseline device interaction features, a second set of baseline feature vectors associated with the second set of historic vehicular trips;

identifying, based at least in part upon the set of representative device interaction features, a set of representative feature vectors associated with the unlabeled vehicular trip;

mapping the first set of baseline feature vectors, the second set of baseline feature vectors, and the set of representative feature vectors on a feature space;

determining a first set of vector deviations between the set of representative feature vectors and the first set of baseline feature vectors in the feature space;

determining a second set of vector deviations between the set of representative feature vectors and the second set of baseline feature vectors in the feature space; and

classifying the unlabeled vehicular trip by at least:

upon determining that the first set of vector deviations is less than the second set of vector deviations, classifying the unlabeled vehicular trip as for work; or

upon determining that the first set of vector deviations is more than the second set of vector deviations, classifying the unlabeled vehicular trip as for personal use.

12 . The computer-implemented method of claim 11 , further comprising:

determining a first similarity metric based at least in part upon the first set of vector deviations;

determining a second similarity metric based at least in part upon the second set of vector deviations; and

classifying the unlabeled vehicular trip by at least:

upon determining that the first similarity metric is more than the second similarity metric, classifying the unlabeled vehicular trip as for work; or

upon determining that the first similarity metric is less than the second similarity metric, classifying the unlabeled vehicular trip as for personal use.

13 . The computer-implemented method of claim 12 , wherein determining the first similarity metric and the second similarity metric includes:

obtaining the set of weights;

determining, for each device interaction feature, a multiplication product by multiplying each vector deviation by an associated weight; and

determining the first similarity metric and the second similarity metric as weighted similarity metrics based at least in part upon a sum of the multiplication products for each of the first set of vector deviations and the second set of vector deviations.

14 . The computer-implemented method of claim 1 , wherein:

identifying the first set of baseline device interaction features includes identifying a first sequence of baseline device interaction features based at least in part upon the first set of historic device interaction data;

identifying the second set of baseline device interaction features includes identifying a second sequence of baseline device interaction features based at least in part upon the second set of historic device interaction data;

identifying the set of representative device interaction features includes identifying a sequence of representative device interaction features based at least in part upon the set of unlabeled device interaction data;

comparing the set of representative device interaction features against the first set of baseline device interaction features and the second set of baseline device interaction features includes comparing the sequence of representative device interaction features against the first sequence of baseline device interaction features and the second sequence of baseline device interaction features; and

classifying the unlabeled vehicular trip includes:

upon determining that the sequence of representative device interaction features deviates from the first sequence of baseline device interaction features less than from the second sequence of baseline device interaction features, classifying the unlabeled vehicular trip as for work; or

upon determining that the sequence of representative device interaction features deviates from the first sequence of baseline device interaction features more than from the second sequence of baseline device interaction features, classifying the unlabeled vehicular trip as for personal use.

15 . The computer-implemented method of claim 14 , wherein classifying the unlabeled vehicular trip further includes:

identifying, based at least in part upon the first sequence of baseline device interaction features, a first baseline vector pattern;

identifying, based at least in part upon the second sequence of baseline device interaction features, a second baseline vector pattern;

identifying, based at least in part upon the sequence of representative device interaction features, a representative vector pattern;

mapping the first baseline vector pattern, the second baseline vector pattern, and the representative vector pattern on a feature space;

determining a first vector pattern deviation between the first baseline vector pattern and the representative vector pattern in the feature space;

determining a second vector pattern deviation between the second baseline vector pattern and the representative vector pattern in the feature space; and

classifying the unlabeled vehicular trip by at least:

upon determining that the first vector pattern deviation is less than the second vector pattern deviation, classifying the unlabeled vehicular trip as for work; or

upon determining that the first vector pattern deviation is more than the second vector pattern deviation, classifying the unlabeled vehicular trip as for personal use.

16 . The computer-implemented method of claim 1 , wherein each of the first set of baseline device interaction features, the second set of baseline device interaction features, and the set of representative device interaction features includes taking a phone call, making a phone call, content switching, content interacting, entering text, interacting with a navigation application, interacting with a communication application, interacting with a vehicle monitoring application, interacting with a delivery application, interacting with a rideshare application, interacting with a finance application, interacting with an internet browsing application, interacting with a shopping application, or interacting with an entertainment application.

17 . The computer-implemented method of claim 16 , wherein each of the first set of baseline device interaction features, the second set of baseline device interaction features, and the set of representative device interaction features includes interaction frequency information, interaction duration information, interaction complexity information, or interaction method information.

18 . A computing system for classifying a vehicular trip, the computing system comprising:

one or more processors; and

a memory storing instructions that, upon execution by the one or more processors, cause the computing system to perform one or more processes including:

obtaining a user classification associated with an unlabeled vehicular trip as for work or for personal use;

obtaining a first set of historic device interaction data associated with a first set of historic vehicular trips during which a vehicle operator operated a vehicle for work;

obtaining a second set of historic device interaction data associated with a second set of historic vehicular trips during which the vehicle operator operated the vehicle for personal use;

training a classification model based at least in part upon the first set of historic device interaction data and the second set of historic device interaction data such that:

upon receiving an input of device interaction data selected from the first set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for work; and

upon receiving an input of device interaction data selected from the second set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for personal use;

obtaining a set of unlabeled device interaction data associated with the unlabeled vehicular trip;

outputting a machine classification, using the classification model, the unlabeled vehicular trip as for work or for personal use by at least:

identifying, based at least in part upon the first set of historic device interaction data, a first set of baseline device interaction features associated with the first set of historic vehicular trips;

identifying, based at least in part upon the second set of historic device interaction data, a second set of baseline device interaction features associated with the second set of historic vehicular trips;

identifying, based at least in part upon the set of unlabeled device interaction data, a set of representative device interaction features associated with the unlabeled vehicular trip;

comparing the set of representative device interaction features against the first set of baseline device interaction features and the second set of baseline device interaction features;

obtaining a set of weights associated with the first set of baseline device interaction features, the second set of baseline device interaction features, and the set of representative device interaction features such that each baseline device interaction feature and an associated representative device interaction feature is assigned with a weight, wherein the set of weights comprise:

 a first weight for a device interaction feature of content switching;

 a second weight for a device interaction feature of entering text; and

 a third weight for a device interaction feature of taking a phone call, wherein the first weight and the second weight are greater than the third weight; and

classifying the unlabeled vehicular trip by at least one of:

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features less than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for work; or

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features more than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for personal use; and

validating the user classification based at least in part upon the user classification and the machine classification made using the classification model.

19 . A non-transitory computer-readable medium storing instructions for classifying a vehicular trip, the instructions upon execution by one or more processors of a computing system, cause the computing system to perform one or more processes including:

obtaining a user classification associated with an unlabeled vehicular trip as for work or for personal use;

obtaining a first set of historic device interaction data associated with a first set of historic vehicular trips during which a vehicle operator operated a vehicle for work;

obtaining a second set of historic device interaction data associated with a second set of historic vehicular trips during which the vehicle operator operated the vehicle for personal use;

training a classification model based at least in part upon the first set of historic device interaction data and the second set of historic device interaction data such that:

upon receiving an input of device interaction data selected from the first set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for work; and

upon receiving an input of device interaction data selected from the second set of historic device interaction data, the classification model outputs a classification for an associated vehicular trip as for personal use;

obtaining a set of unlabeled device interaction data associated with the unlabeled vehicular trip;

outputting a machine classification, using the classification model, the unlabeled vehicular trip as for work or for personal use by at least:

identifying, based at least in part upon the first set of historic device interaction data, a first set of baseline device interaction features associated with the first set of historic vehicular trips;

identifying, based at least in part upon the second set of historic device interaction data, a second set of baseline device interaction features associated with the second set of historic vehicular trips;

identifying, based at least in part upon the set of unlabeled device interaction data, a set of representative device interaction features associated with the unlabeled vehicular trip;

comparing the set of representative device interaction features against the first set of baseline device interaction features and the second set of baseline device interaction features;

obtaining a set of weights associated with the first set of baseline device interaction features, the second set of baseline device interaction features, and the set of representative device interaction features such that each baseline device interaction feature and an associated representative device interaction feature is assigned with a weight, wherein the set of weights comprise:

a first weight for a device interaction feature of content switching;

a second weight for a device interaction feature of entering text; and

a third weight for a device interaction feature of taking a phone call, wherein the first weight and the second weight are greater than the third weight; and

classifying the unlabeled vehicular trip by one of:

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features less than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for work; or

upon determining that the set of representative device interaction features deviates from the first set of baseline device interaction features more than from the second set of baseline device interaction features, classifying the unlabeled vehicular trip as for personal use; and

validating the user classification based at least in part upon the user classification and the machine classification made using the classification model.

20 . The non-transitory computer-readable medium of claim 19 , wherein validating the user classification includes:

accepting the user classification upon determining that the user classification matches the machine classification made using the classification model; or

rejecting the user classification upon determining that the user classification does not match the machine classification made using the classification model.

Assignments (2)

Continuity (1)

References Cited (50)