Detection, classification, and prediction of bacteria colony growth in vehicle passenger cabin

Patent metadata

Field	Value
Publication number	US-12080080-B2
Application number	US-202117373050-A
Country	US
Kind code	B2
Filing date	Jul 12, 2021
Priority date	Jul 12, 2021
Publication date	Sep 3, 2024
Grant date	Sep 3, 2024

Abstract

Systems, methods, and computer program products that are configured to identify or otherwise detect the presence of bacteria, classify the identified or detected bacteria, and also predict the growth of the classified bacteria on various touchable surfaces within a vehicle passenger cabin or compartment. Such systems, methods, and computer program products are configured to identify/detect, classify, and predict the presence and/or growth of bacteria, and transmit one or more alerts, warnings, and/or reports to vehicle owners, service providers, and/or occupants based on the identification/detection, classification, and prediction.

First claim

What is claimed is:

1. A computer system, comprising:
a computing server having one or more processors; and
a non-transitory memory operatively coupled to the one or more processors comprising a set of instructions executable by the one or more processors to cause the one or more processors to:
receive, from a client system, captured image data relating to one or more images of one or more target surfaces in a vehicle passenger cabin;
detect a presence of bacteria in the vehicle passenger cabin by applying a first machine learning algorithm to conduct differential image analysis of the captured image data; and
predict growth of classified bacteria by applying a second machine learning algorithm to conduct time-lapsed differential image analysis of the captured image data.

2. The computer system of claim 1 , wherein, prior to applying the first machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to detect bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

3. The computer system of claim 1 , wherein the one or more processors are to execute the set of instructions to classify the detected bacteria by applying a third machine learning algorithm to conduct classification analysis of the captured image data.

4. The computer system of claim 3 , wherein, prior to applying the third machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to classify the detected bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

5. The computer system of claim 1 , wherein, prior to applying the second machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to predict growth of the classified bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

6. The computer system of claim 1 , wherein the one or more processors are to execute the set of instructions to, in response to the detection, send one or more of an audio alert, a visual alert, and a haptic alert to the client system.

7. A computer program product including at least one non-transitory computer readable medium comprising a set of instructions, which when executed by one or more processors of a computing server, cause the computing server to:
receive, from a client system, captured image data relating to one or more images of one or more target surfaces in a vehicle passenger cabin;
detect a presence of bacteria in the vehicle passenger cabin by applying a first machine learning algorithm to conduct differential image analysis of the captured image data; and
predict the growth of classified bacteria by applying a second machine learning algorithm to conduct time-lapsed differential image analysis of the captured image data.

8. The computer program product of claim 7 , wherein, prior to applying the first machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to detect bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

9. The computer program product of claim 7 , wherein the one or more processors are to execute the set of instructions to classify the detected bacteria by applying a third machine learning algorithm to conduct classification analysis of the captured image data.

10. The computer program product of claim 9 , wherein, prior to applying the third machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to classify the detected bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

11. The computer program product of claim 7 , wherein, prior to applying the second machine learning algorithm, the one or more processors are to execute the set of instructions to train a convolutional neural network to predict growth of the classified bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

12. The computer program product of claim 7 , wherein the one or more processors are to execute the set of instructions to, in response to the detection, send one or more of an audio alert, a visual alert, and a haptic alert to the client system.

13. A method of detecting bacteria in a vehicle passenger cabin, comprising:
receiving, from a first client system, captured image data relating to one or more images of one or more target surfaces in a vehicle passenger cabin;
detecting a presence of bacteria in the vehicle passenger cabin by applying a first machine learning algorithm to conduct differential image analysis of the captured image data; and
predicting the growth of classified bacteria by applying a second machine learning algorithm to conduct time-lapsed differential image analysis of the captured image data.

14. The method of claim 13 , further comprising, prior to applying the first machine learning algorithm:
training a convolutional neural network to detect bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

15. The method of claim 13 , further comprising classifying the detected bacteria by applying a third machine learning algorithm to conduct classification analysis of the captured image data.

16. The method of claim 15 , further comprising, prior to applying the third machine learning algorithm:
training a convolutional neural network to classify the detected bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

17. The method of claim 13 , further comprising, prior to applying the second machine learning algorithm:
training a convolutional neural network to predict growth of the classified bacteria based on one or more of stored bacteria image data and wireless network bacteria image data.

Assignees

• Toyota Eng & Mfg North America

Inventors

• Gupta Rohit
• Wang Ziran
• Wang Yanbing
• Han Kyungtae
• Tiwari Prashant

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06F18/2413

  based on distances to training or reference patterns · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V20/698 Primary

  Matching; Classification · CPC title

• G06N3/08

  Learning methods · CPC title