Patent Issued for Controlling vehicles using contextual driver and/or rider data based on automatic passenger detection and mobility status (USPTO 11928621): Allstate Insurance Company

2024 MAR 29 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11928621, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Allen, Rachel Edelshteyn (Chicago, IL, US), Coleman, II, Neal Edgar (Evanston, IL, US), Irey, Grady (Des Plaines, IL, US).

This patent was filed on July 31, 2018 and was published online on March 12, 2024.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Aspects of the disclosure relate to controlling the operation of one or more vehicles of a shared mobility service, such as a rideshare service, car-share service, carpool service, peer to peer pickup service, and the like. Shared mobility services are becoming increasingly common as shared mobility services and applications continue to improve. Autonomous vehicles may further increase the utility and appeal of shared mobility services. Despite advances in various technologies, however, it may be difficult to effectively match riders with other riders and/or drivers (e.g., for shared mobility services that do not rely on autonomous vehicles). Such effective matching is hampered by lack of data about riders, drivers, and vehicles, especially when a new rider, driver, or vehicle first joins a shared mobility service.”

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 operating shared mobility services safely and effectively. Collecting and creating data that may be used to incentivize safe operation and reward safe drivers may further unlock opportunities to use analytics strategically to provide flexible products (e.g., insurance products) to shared mobility services.

“In accordance with one or more embodiments, a computing platform having at least one processor, a memory, and a communication interface may determine, based on one or more of information about a driver and information about a vehicle, an initial safety prediction for the driver; receive, from a mobile device associated with the driver, sensor data collected during operation of the vehicle; determine a subset of the sensor data related to one or more shared mobility statuses; determine, based on the subset of the sensor data, a safety score for the driver; and send instructions, based on the initial safety prediction and the determined safety score, to a shared mobility service, wherein the instructions are configured to modify an operation of the shared mobility service.

“In some embodiments, the computing platform may determine the subset of the sensor data by analyzing a recorded audio signal to detect a voice of at least one passenger of the vehicle. The computing platform may analyze the recorded audio signal to detect the voice of the at least one passenger by determining, based on one or more acoustic properties of the recorded audio signal, whether the recorded audio signal contains a media program; and detecting the voice of the at least one passenger responsive to determining that the recorded audio signal does not contain a media program. Additionally or alternatively, the computing platform may analyze the recorded audio signal to detect the voice of the at least one passenger by generating, from the recorded audio signal, a first voice print; comparing the first voice print to a second voice print associated with the driver; and determining, based on the comparison, that the first voice print does not match the second voice print.

“In some embodiments, the computing platform may determine the subset of the sensor data by analyzing a wireless signal to determine a number of mobile devices associated with at least one passenger of the vehicle.

“In some embodiments, the computing platform may be configured to provide an incentive to the driver or the shared mobility service. The computing platform may provide the incentive by determining, based on the initial safety prediction, a safety cost for the driver; subtracting the determined safety score from the safety cost to yield a balance; and causing a transaction with the shared mobility service, wherein the transaction is based on the balance, wherein the transaction is the provided incentive.”

The claims supplied by the inventors are:

“1. A method comprising: receiving sensor data wirelessly collected during operation of a vehicle, wherein the sensor data is associated with a plurality of timestamps associated with recorded audio signals to detect a voice of at least one passenger of the vehicle; assigning, based on the timestamps, a first subset of the sensor data to one or more first periods during which a driver was transporting passengers associated with a shared mobility service, and a second subset of the sensor data to one or more second periods during which the driver was not transporting passengers associated with the shared mobility service; training a first machine learning model to determine a first safety score indicating a performance of the driver during the one or more first periods based on the first subset of the sensor data, wherein the first machine learning model is trained by trip data that correlates to a passenger mobility status associated with carrying passengers; training a second machine learning model to determine a second safety score indicating a performance of the driver during the one or more second periods based on the second subset of the sensor data, wherein the second machine learning model is trained by trip data that correlates to a no passenger mobility status associated with not carrying passengers; determining a portion of a trip fare to provide to the driver and driver-specific feedback based on the first safety score and the second safety score, wherein the driver-specific feedback is associated with a condition of the one or more first periods or the one or more second periods; providing real-time driver-specific feedback including the driver-specific feedback in a display of a mobile system associated with the driver in real-time upon a next detection of the condition; retraining the first machine learning model and the second machine learning model based on additional real-time driving data collected after providing the driver-specific feedback; and providing updated real-time driver-specific feedback based on updated safety scores calculated by the retrained first machine learning model and the retrained second machine learning model.

“2. The method of claim 1, wherein determining the one or more first periods comprises analyzing a recorded audio signal of the recorded audio signals to detect a voice of at least one passenger of the vehicle.

“3. The method of claim 2, wherein analyzing the recorded audio signal to detect the voice of the at least one passenger comprises: determining, based on one or more acoustic properties of the recorded audio signal, whether the recorded audio signal contains a media program; and detecting the voice of the at least one passenger responsive to determining that the recorded audio signal does not contain a media program.

“4. The method of claim 2, wherein analyzing the recorded audio signal to detect the voice of the at least one passenger comprises: generating, from the recorded audio signal, a first voice print; comparing the first voice print to a second voice print associated with the driver; and determining, based on the comparison, that the first voice print does not match the second voice print.

“5. The method of claim 1, wherein determining the one or more first periods comprises analyzing a wireless signal to determine a number of mobile devices present in the vehicle.

“6. The method of claim 1, further comprising: determining an estimated safety cost for the driver; determining an actual safety cost based on the first safety score and the second safety score; subtracting the actual safety cost from the estimated safety cost to yield a balance; and causing a transaction with the shared mobility service, wherein the transaction is based on the balance.

“7. The method of claim 1, further comprising determining, based on the sensor data, a number of passengers in the vehicle during the one or more first periods.

“8. The method of claim 1, further comprising transmitting, to a mobile device associated with the driver of the vehicle, an indication of the first safety score and a recommendation for improving the first safety score.

“9. A 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 computing platform to: receive sensor data collected during operation of a vehicle, wherein the sensor data is associated with a plurality of timestamps associated with recorded audio signals to detect a voice of at least one passenger of the vehicle; assign, based on the timestamps, a first subset of the sensor data to one or more first periods during which a driver was transporting passengers associated with a shared mobility service, and a second subset of the sensor data to one or more second periods during which the driver was not transporting passengers associated with the shared mobility service; train a first machine learning model to determine a first safety score indicating a performance of the driver during the one or more first periods based on the first subset of the sensor data, wherein the first machine learning model is trained by trip data that correlates to a passenger mobility status associated with carrying passengers; train a second machine learning model to determine a second safety score indicating a performance of the driver during the one or more second periods based on the second subset of the sensor data, wherein the second machine learning model is trained by trip data that correlates to a no passenger mobility status associated with not carrying passengers; determine a portion of a trip fare and driver-specific feedback to provide to the driver based on the first safety score and the second safety score, wherein the driver-specific feedback is associated with a condition of the one or more first periods or the one or more second periods; provide real-time driver-specific feedback including the driver-specific feedback in a display of a mobile system associated with the driver in real-time upon a next detection of the condition; retrain the first machine learning model and the second machine learning model based on additional real-time driving data collected after providing the driver-specific feedback; and provide updated real-time driver-specific feedback based on updated safety scores calculated by the retrained first machine learning model and the retrained second machine learning model.

“10. The computing platform of claim 9, wherein the instructions, when executed by the at least one processor, cause the computing platform to determine the one or more first periods by analyzing a recorded audio signal of the recorded audio signals to detect a voice of at least one passenger of the vehicle.

“11. The computing platform of claim 9, wherein the instructions, when executed by the at least one processor, cause the computing platform to determine the one or more first periods by analyzing a wireless signal to determine a number of mobile devices present in the vehicle.

“12. The computing platform of claim 9, wherein the instructions, when executed by the at least one processor, further cause the computing platform to: determine an estimated safety cost for the driver; determine an actual safety cost based on the first safety score and the second safety score; subtract the actual safety cost from the estimated safety cost to yield a balance; and cause a transaction with the shared mobility service, wherein the transaction is based on the balance.”

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

For the URL and additional information on this patent, see: Allen, Rachel Edelshteyn. Controlling vehicles using contextual driver and/or rider data based on automatic passenger detection and mobility status. U.S. Patent Number 11928621, filed July 31, 2018, and published online on March 12, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11928621)&db=USPAT&type=ids

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