IP Library › Granted Patent US 12,373,853
Granted Patent B2
US 12,373,853 · App. 17/493,645 · Granted Jul 29, 2025

Systems and methods for managing vehicle operator profiles based on telematics inferences via an auction telematics marketplace with a bid profit predictive model

Inventor: Kenneth Jason Sanchez (San Francisco, CA)
Assignee: QUANATA, LLC
G06Q30/0202G06Q30/08G07C5/008
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Quick Facts
Patent No.
US 12,373,853
App. No.
17/493,645
Granted
Jul 29, 2025
Kind
B2
Abstract

Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets and a plurality of sensor data sets; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile, one or more telematics inferences, a data profile; and listing and continually updating the data profile onto a telematics marketplace; receiving a plurality of conditional bids for a target operator profile associated with a target data profile, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions; determining, for each conditional bid of the plurality of conditional bids, a predicted bid-generated profit or a predicted bid-generated revenue; determining a winning bid and an associated winning bidder; and transmitting the target operator profile to the winning bidder.

Claims (101)

1. A computer-implemented method for data management, the computer-implemented method comprising:

collecting a plurality of telematics data sets for a plurality of users associated with a plurality of marketplace participants via a software development kit (SDK) configured to be able to be installed on: (1) one or more mobile devices of the plurality of users, (2) one or more third-party applications on the one or more mobile devices of the plurality of users, and (3) one or more on-board computers of one or more automobiles of the plurality of users, wherein the plurality of users include a plurality of vehicle operators, wherein, when the SDK is installed on multiple third-party applications on a same mobile device, a single copy of a telematics data collection program is run while any other telematics collection programs are suspended to save processing power and reduce redundancy, and wherein the collecting of the plurality of telematics data sets for the plurality of users associated with the plurality of marketplace participants via the SDK comprises:

collecting continually a plurality of sensor data sets associated with the plurality of vehicle operators via one or more sensors when each of the one or more sensors has collected a predetermined threshold amount of respective sensor measurements, wherein the one or more sensors include at least one of a Global Positioning Systems (GPS) sensor, an accelerometer, or a gyroscope, the one or more sensors are configured to collect at least one of location sensor data, orientation sensor data, acceleration sensor data, or velocity sensor data;

for each respective vehicle operator of the plurality of vehicle operators:

producing, by the SDK, a uniformed output of a telematics data set of the plurality of telematics data sets in a standardized format, wherein the telematics data set in the standardized format includes a sensor data set of the plurality of sensor data sets in the standardized format;

determining and continually updating one or more telematics inferences based at least in part upon the sensor data set of the plurality of sensor data sets associated with a respective vehicle operator using one or more predictive models each having a respective first plurality of weights provided by a marketplace participant of the plurality of marketplace participants;

generating and continually updating a data profile including the one or more telematics inferences associated with the respective vehicle operator, wherein the data profile includes a universal operator score configured to be utilized for a plurality of uses by the plurality of marketplace participants, wherein a machine learning model generates the universal operator score by utilizing the sensor data set of the plurality of sensor data sets, wherein the machine learning model has a second plurality of weights that are configured to be trained by utilizing the plurality of sensor data sets associated with the vehicle operator obtained from the GPS sensor, the accelerometer, or the gyroscope, wherein the second plurality of weights include one or more weights that correspond to each of the one or more sensors, and wherein the machine learning model comprises a convolutional neural network;

receiving, from a plurality of bidders of the plurality of marketplace participants, a plurality of conditional bids for a target data profile selected from the data profiles of the plurality of vehicle operators, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions;

determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, a predicted winning bid; and

transmitting an operator profile associated with a winning bid to an associated winning bidder based at least in part upon the predicted winning bid.

2. The computer-implemented method of claim 1 ,

wherein the determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, the predicted winning bid further comprises:

assigning a weight modifier to each of the one or more payment conditions,

determining a likelihood of success of each of the one or more payment conditions,

identifying a respective one of the one or more conditional payments associated with each of the one or more payment conditions, and

multiplying the weight modifier, the likelihood of success, and the respective one of the one or more conditional payments associated with each of the one or more payment conditions;

wherein the computer-implemented method further comprises determining, based at least in part upon the predicted winning bid, the winning bid and the associated winning bidder; and

wherein the determining the predicted winning bid includes:

determining the winning bid as a bid of the plurality of conditional bids in which a predicted bid-generated profit for a marketplace entity is highest.

3. The computer-implemented method of claim 1 , wherein the determining the predicted winning bid includes:

determining a predicted bid-generated revenue and a predicted bid-generated costs; and

subtracting the predicted bid-generated revenue by the predicted bid-generated costs.

4. The computer-implemented method of claim 1 , wherein the determining the predicted winning bid includes:

determining a predicted user retention duration; and

determining the winning bid as a bid of the plurality of conditional bids in which a predicted long-term bid-generated profit for a full duration of the predicted user retention duration is highest.

5. The computer-implemented method of claim 1 , wherein the determining the predicted winning bid includes:

determining a predicted user retention duration; and

determining the winning bid as a bid of the plurality of conditional bids in which a predicted period-specific bid-generated profit for a period of interest predetermined by a marketplace entity is highest.

6. The computer-implemented method of claim 1 , wherein the determining the predicted winning bid includes:

determining, for each payment condition of the one or more payment conditions, a likelihood of condition fulfillment.

7. The computer-implemented method of claim 6 , wherein the determining the predicted winning bid includes:

multiplying, for each payment condition of the one or more payment conditions, the likelihood of condition fulfillment and an associated conditional payment of the one or more conditional payments.

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

the one or more conditional payments includes a first conditional payment and a second conditional payment;

the one or more payment conditions includes a first payment condition and a second payment condition;

the first conditional payment is withheld from completion at least until the first payment condition is satisfied; and

the second conditional payment is withheld from completion at least until the first payment condition and the second payment condition are satisfied.

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

the one or more sensors are used by a plurality of mobile applications; and

each of the plurality of vehicle operators uses at least one mobile application of the plurality of mobile applications.

10. A computing system, 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:

collecting a plurality of telematics data sets for a plurality of users associated with a plurality of marketplace participants via a software development kit (SDK) configured to be able to be installed on: (1) one or more mobile devices of the plurality of users, (2) one or more third-party applications on the one or more mobile devices of the plurality of users, and (3) one or more on-board computers of one or more automobiles of the plurality of users, wherein the plurality of users include a plurality of vehicle operators, wherein, when the SDK is installed on multiple third-party applications on a same mobile device, a single copy of a telematics data collection program is run while any other telematics collection programs are suspended to save processing power and reduce redundancy, and wherein the collecting of the plurality of telematics data sets for the plurality of users associated with the plurality of marketplace participants via the SDK comprises:

collecting continually a plurality of sensor data sets associated with the plurality of vehicle operators via one or more sensors when each of the one or more sensors has collected a predetermined threshold amount of respective sensor measurements, wherein the one or more sensors include at least one of a Global Positioning Systems (GPS) sensor, an accelerometer, or a gyroscope, the one or more sensors are configured to collect at least one of location sensor data, orientation sensor data, acceleration sensor data, or velocity sensor data;

for each respective vehicle operator of the plurality of vehicle operators:

producing, by the SDK, a uniformed output of a telematics data set of the plurality of telematics data sets in a standardized format, wherein the telematics data set in the standardized format includes a sensor data set of the plurality of sensor data sets in the standardized format;

determining and continually updating one or more telematics inferences based at least in part upon the sensor data set of the plurality of sensor data sets associated with a respective vehicle operator using one or more predictive models each having a respective first plurality of weights provided by a marketplace participant of the plurality of marketplace participants;

generating and continually updating a data profile including the one or more telematics inferences associated with the respective vehicle operator, wherein the data profile includes a universal operator score configured to be utilized for a plurality of uses by the plurality of marketplace participants, wherein a machine learning model generates the universal operator score by utilizing the sensor data set of the plurality of sensor data sets, wherein the machine learning model has a second plurality of weights that are configured to be trained by utilizing the plurality of sensor data sets associated with the vehicle operator obtained from the GPS sensor, the accelerometer, or the gyroscope, wherein the second plurality of weights include one or more weights that correspond to each of the one or more sensors, and wherein the machine learning model comprises a convolutional neural network;

receiving, from a plurality of bidders of the plurality of marketplace participants, a plurality of conditional bids for a target data profile selected from the data profiles of the plurality of vehicle operators, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions;

determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, a predicted winning bid; and

transmitting an operator profile, associated with a winning bid to an associated winning bidder based at least in part upon the predicted winning bid.

11. The computer system of claim 10 ,

wherein the determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, the predicted winning bid further comprises:

assigning a weight modifier to each of the one or more payment conditions,

determining a likelihood of success of each of the one or more payment conditions,

identifying a respective one of the one or more conditional payments associated with each of the one or more payment conditions, and

multiplying the weight modifier, the likelihood of success, and the respective one of the one or more conditional payments associated with each of the one or more payment conditions;

wherein the one or more processes further comprise determining, based at least in part upon the predicted winning bid, the winning bid and the associated winning bidder; and

wherein the determining the predicted winning bid includes:

determining the winning bid as a bid of the plurality of conditional bids in which a highest predicted bid-generated profit for a marketplace entity is highest.

12. The computer system of claim 10 , wherein the determining the predicted winning bid includes:

determining a predicted bid-generated revenue and a predicted bid-generated costs; and

subtracting the predicted bid-generated revenue by the predicted bid-generated costs.

13. The computer system of claim 10 , wherein the determining the predicted winning bid includes:

determining a predicted user retention duration; and

determining the winning bid as a bid of the plurality of conditional bids in which a predicted long-term bid-generated profit for a full duration of the predicted user retention duration is highest.

14. The computer system of claim 10 , wherein the determining the predicted winning bid includes:

determining a predicted user retention duration; and

determining the winning bid as a bid of the plurality of conditional bids in which a predicted period-specific bid-generated profit for a period of interest predetermined by a marketplace entity is highest.

15. The computer system of claim 10 , wherein the determining the predicted winning bid includes:

determining, for each payment condition of the one or more payment conditions, a likelihood of condition fulfillment.

16. The computer system of claim 15 , wherein the determining the predicted winning bid includes:

multiplying, for each payment condition of the one or more payment conditions, the likelihood of condition fulfillment and an associated conditional payment of the one or more conditional payments.

17. The computer system of claim 10 , wherein:

the one or more conditional payments includes a first conditional payment and a second conditional payment;

the one or more payment conditions includes a first payment condition and a second payment condition;

the first conditional payment is withheld from completion at least until the first payment condition is satisfied; and

the second conditional payment is withheld from completion at least until the first payment condition and the second payment condition are satisfied.

18. The computer system of claim 10 , wherein:

the one or more sensors are used by a plurality of mobile applications; and

each of the plurality of vehicle operators uses at least one mobile application of the plurality of mobile applications.

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

collecting a plurality of telematics data sets for a plurality of users associated with a plurality of marketplace participants via a software development kit (SDK) configured to be able to be installed on: (1) one or more mobile devices of the plurality of users, (2) one or more third-party applications on the one or more mobile devices of the plurality of users, and (3) one or more on-board computers of one or more automobiles of the plurality of users, wherein the plurality of users include a plurality of vehicle operators, wherein, when the SDK is installed on multiple third-party applications on a same mobile device, a single copy of a telematics data collection program is run while any other telematics collection programs are suspended to save processing power and reduce redundancy, and wherein the collecting of the plurality of telematics data sets for the plurality of users associated with the plurality of marketplace participants via the SDK comprises:

collecting continually a plurality of sensor data sets associated with the plurality of vehicle operators via one or more sensors when each of the one or more sensors has collected a predetermined threshold amount of respective sensor measurements, wherein the one or more sensors include at least one of a Global Positioning Systems (GPS) sensor, an accelerometer, or a gyroscope, the one or more sensors are configured to collect at least one of location sensor data, orientation sensor data, acceleration sensor data, or velocity sensor data;

for each respective vehicle operator of the plurality of vehicle operators:

producing, by the SDK, a uniformed output of a telematics data set of the plurality of telematics data sets in a standardized format, wherein the telematics data set in the standardized format includes a sensor data set of the plurality of sensor data sets in the standardized format;

determining and continually updating one or more telematics inferences based at least in part upon the sensor data set of the plurality of sensor data sets associated with a respective vehicle operator using one or more predictive models each having a respective first plurality of weights provided by a marketplace participant of the plurality of marketplace participants;

generating and continually updating a data profile including the one or more telematics inferences associated with the respective vehicle operator, wherein the data profile includes a universal operator score configured to be utilized for a plurality of uses by the plurality of marketplace participants, wherein a machine learning model is configured to generate the universal operator score by utilizing the sensor data set of the plurality of sensor data sets, wherein the machine learning model has a second plurality of weights that are configured to be trained by utilizing the plurality of sensor data sets associated with the vehicle operator obtained from the GPS sensor, the accelerometer, or the gyroscope, wherein the second plurality of weights include one or more weights that correspond to each of the one or more sensors, and wherein the machine learning model comprises a convolutional neural network;

receiving, from a plurality of bidders of the plurality of marketplace participants, a plurality of conditional bids for a target data profile selected from the data profiles of the plurality of vehicle operators, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions;

determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, a predicted winning bid; and

transmitting an operator profile associated with a winning bid to an associated winning bidder based at least in part upon the predicted winning bid.

20. The non-transitory computer-readable medium of claim 19 ,

wherein the determining, for each conditional bid of the plurality of conditional bids and based upon at least the one or more conditional payments and the one or more payment conditions, the predicted winning bid further comprises:

assigning a weight modifier to each of the one or more payment conditions,

determining a likelihood of success of each of the one or more payment conditions,

identifying a respective one of the one or more conditional payments associated with each of the one or more payment conditions, and

multiplying the weight modifier, the likelihood of success, and the respective one of the one or more conditional payments associated with each of the one or more payment conditions;

the one or more processes further comprise determining, based at least in part upon the predicted winning bid, the winning bid and the associated winning bidder; and

wherein the determining the predicted winning bid includes:

determining the winning bid as a bid of the plurality of conditional bids in which a predicted bid-generated profit for a marketplace entity is highest.

Assignments (2)
CHANGE OF NAME Recorded May 29, 2024
From: BLUEOWL, LLC
To: QUANATA, LLC
Reel/Frame 067558/0600 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 21, 2023
From: SANCHEZ, KENNETH JASON
To: BLUEOWL, LLC
Reel/Frame 065301/0280 →
Continuity (1)
Related Publication 20230146426A1 · May 11, 2023
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