Granted Patent B2

US 12,749,500 · App. 18/741,121 · Granted Sep 29, 2026

Inventors: Philip Schneider (Amherst, NY); Michael Pokora (Tonawanda, NY); Livio Forte, III (Lloyd Harbor, NY); Justas Birgiolas (Milton, VT); Dennis Christopher Fedorishin (Amherst, NY)

Assignee: ACV Auctions Inc.

G10L25/78G01M13/028G01M15/02G01M15/12G10L25/30

Abstract

Claims (78)

1 . A method for using a trained machine learning (ML) model to detect presence of vehicle defects from audio and vibration acquired at least in part during operation of an engine of a vehicle, the method comprising:

using at least one computer hardware processor to perform:

obtaining, via at least one communication network,

a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine, and

a first vibration signal that was acquired, using at least one vibration sensor, at least in part during operation of the engine; and

processing both the first audio recording and the first vibration signal using the trained ML model to detect presence of at least one vehicle defect, wherein the trained ML model has been trained to detect presence of one or more defects of vehicle from audio and vibration features about the vehicle, the processing comprising:

generating audio features from the first audio recording,

generating vibration features from the first vibration signal, and

processing both the audio features and the vibration features using the trained ML model, by providing both the audio features and the vibration features as inputs to the trained ML model for processing, to obtain output indicative of presence or absence of the at least one vehicle defect.

2 . The method of claim 1 , wherein generating the audio features from the first audio signal comprises:

generating an audio waveform from the first audio recording; and

generating a two-dimensional (2D) representation of the audio waveform.

3 . The method of claim 2 , wherein the audio recording comprises at least a first waveform for at least a first audio channel, and wherein generating the audio waveform from the first audio recording comprises:

resampling the first waveform to a target frequency to obtain a resampled waveform;

normalizing the resampled waveform by subtracting its mean and dividing by its standard deviation to obtain a normalized waveform; and

clipping the normalized waveform to a target maximum to obtain the audio waveform.

4 . The method of claim 3 , wherein generating the two-dimensional (2D) representation of the audio waveform comprises generating a time-frequency representation of the audio waveform.

5 . The method of claim 4 , wherein generating the time-frequency representation of the audio waveform comprises generating a Mel-scale log spectrogram from the audio waveform.

6 . The method of claim 1 ,

wherein generating the audio features from the first audio signal comprises:

generating an audio waveform from the first audio recording, and

generating a two-dimensional (2D) representation of the audio waveform; and

wherein generating the vibration features from the first vibration signal comprises:

generating a vibration waveform from the first vibration signal, and

generating a two-dimensional (2D) representation of the vibration waveform.

7 . The method of claim 6 ,

wherein generating the 2D representation of the audio waveform comprises generating a Mel-scale log spectrogram of the audio waveform, and

wherein generating the 2D representation of the vibration waveform comprises generating a log-linear scale spectrogram of the vibration waveform.

8 . The method of claim 6 ,

wherein the audio waveform has a sampling rate between 8 and 45 kHz; and

wherein the vibration waveform has a sampling rate between 10 and 200 Hz.

9 . The method of claim 1 , further comprising:

obtaining, via the at least one communication network, metadata indicating one or more properties of the vehicle,

wherein using the trained ML model to detect the presence of the at least one vehicle defect further comprises generating metadata features from the metadata,

wherein processing the audio features and the vibration features further comprises processing the audio features, the vibration features and the metadata features using the trained ML model to obtain the output indicative of the presence or absence of the at least one vehicle defect, and

wherein the properties of the vehicle are selected from the group consisting of: a reading of an odometer of the vehicle, a model of the vehicle, a make of the vehicle, an age of the vehicle, a type of drivetrain in the vehicle, a type of transmission in the vehicle, a measure of displacement of the engine, a fuel type for the vehicle, an indication of whether on-board diagnostics (OBD) codes could be obtained from the vehicle, a number of incomplete readiness monitors reported by an OBD scanner, one or more BlackBook-reported engine properties, a list of one or more OBD codes, location of the vehicle, information about weather at the location of the vehicle, and information about a seller of the vehicle.

10 . The method of claim 9 , wherein the metadata comprises text indicating at least one of the one or more properties, and generating the metadata features from the metadata comprises generating a numeric representation of the text.

11 . The method of claim 1 , wherein the output is indicative of the presence or absence of internal engine noise, timing chain noise, engine accessory noise, and/or exhaust noise.

12 . The method of claim 1 ,

wherein the trained ML model is a deep neural network model having at least one million parameters, and

wherein processing the first audio recording and the first vibration signal using the trained ML model to detect the presence of the at least one vehicle defect comprises computing the output using values of the at least one million parameters, the audio features and the vibration features.

13 . The method of claim 12 , wherein the trained ML model comprises:

a first neural network portion comprising a first plurality of convolutional layers configured to process the audio features;

a second neural network portion comprising a second plurality of layers configured to process the vibration features; and

a fusion neural network portion comprising one or more fully connected layers configured to combine outputs produced by the first neural network portion and the second neural network portion to obtain the output indicative of the presence or absence of the at least one vehicle defect.

14 . The method of claim 13 ,

wherein the audio features comprise a 1D audio waveform and a 2D representation of the audio waveform, and the first plurality of convolutional layers comprises 1D convolutional layers configured to process the 1D audio waveform and 2D convolutional layers configured to process the 2D representation of the audio waveform, and

wherein the vibration features comprise a 1D vibration waveform and a 2D representation of the vibration waveform, and the second plurality of convolutional layers comprises 1D convolutional layers configured to process the 1D vibration waveform and 2D convolutional layers configured to process the 2D representation of the vibration waveform.

15 . The method of claim 13 ,

wherein the trained ML model further comprises a third neural network portion comprising one or more fully connected layers configured to process metadata features generated from metadata indicating one or more properties of the vehicle, and

wherein the one or more fully connected layers of the fusion neural network are configured to combine outputs produced by the first neural network portion, the second neural network portion, and the third neural network portion to obtain the output indicative of the presence or absence of the at least one vehicle defect.

16 . The method of claim 1 , further comprising:

acquiring, using the at least one acoustic sensor, the first audio recording at least in part during operation of the engine; and

acquiring, using the at least one vibration sensor, the first vibration signal at least in part during operation of the engine.

17 . The method of claim 1 , further comprising:

determining, based on the output, that the at least one vehicle defect was detected using the first audio recording and the first vibration signal, and

generating an electronic vehicle condition report indicating that the at least one vehicle defect was detected using the first audio recording and the first vibration signal and a measure of confidence in that detection.

18 . The method of claim 17 , further comprising:

transmitting the electronic vehicle condition report, via the at least one communication network, to one or more reviewers; and

upon review and approval of the electronic vehicle condition report, initiating an online vehicle auction to auction the vehicle.

19 . A system, comprising:

at least one computer hardware processor; and

at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for using a trained machine learning (ML) model to detect presence of vehicle defects from audio and vibration acquired at least in part during operation of an engine of a vehicle, the method comprising:

obtaining, via at least one communication network,

a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine, and

a first vibration signal that was acquired, using at least one vibration sensor, at least in part during operation of the engine; and

processing both the first audio recording and the first vibration signal using the trained ML model to detect presence of at least one vehicle defect, wherein the trained ML model has been trained to detect presence of one or more defects of vehicle from audio and vibration features about the vehicle, the processing comprising:

generating audio features from the first audio recording,

generating vibration features from the first vibration signal, and

processing both the audio features and the vibration features using the trained ML model, by providing both the audio features and the vibration features as inputs to the trained ML model for processing, to obtain output indicative of presence or absence of the at least one vehicle defect.

20 . At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for using a trained machine learning (ML) model to detect presence of vehicle defects from audio and vibration acquired at least in part during operation of an engine of a vehicle, the method comprising:

obtaining, via at least one communication network,

a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine, and

a first vibration signal that was acquired, using at least one vibration sensor, at least in part during operation of the engine; and

processing both the first audio recording and the first vibration signal using the trained ML model to detect presence of at least one vehicle defect, wherein the trained ML model has been trained to detect presence of one or more defects of vehicle from audio and vibration features about the vehicle, the processing comprising:

generating audio features from the first audio recording,

generating vibration features from the first vibration signal, and

processing both the audio features and the vibration features using the trained ML model, by providing both the audio features and the vibration features as inputs to the trained ML model for processing, to obtain output indicative of presence or absence of the at least one vehicle defect.

Assignments (2)

Continuity (5)

References Cited (95)