Security and content protection by test environment analysis

Patent metadata

Field	Value
Publication number	US-10769571-B2
Application number	US-201815939052-A
Country	US
Kind code	B2
Filing date	Mar 28, 2018
Priority date	Dec 27, 2017
Publication date	Sep 8, 2020
Grant date	Sep 8, 2020

Abstract

A system includes a candidate client device including a processor, a camera, and a microphone. The system includes one or more server hardware computing devices communicatively coupled to a network and in communication with the candidate client device. Each of the one or more server hardware computing devices includes at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the one or more server hardware computing devices to receive the sequence of images and the audio data from the candidate client device, extract a first video attribute from the sequence of images, extract a first audio attribute from the audio data, determine that at least one of the first video attribute violates a first video condition and the first audio attribute violates a first audio condition, and prevent the candidate client device from performing a candidate evaluation function using candidate evaluation software application.

First claim

The invention claimed is:

1. A system, comprising: a candidate client device including:
a processor configured to implement a candidate evaluation software application,
a camera configured to record a sequence of images of an environment of the candidate client device, and
a microphone configured to record audio data in the environment of the candidate client device; and
one or more server hardware computing devices communicatively coupled to a network and in communication, via the network, with the candidate client device, and each comprising at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the one or more server hardware computing devices to:
receive the sequence of images and the audio data from the candidate client device,
extract a first video attribute from the sequence of images,
extract a first audio attribute from the audio data, implement a machine learning engine to determine that the first video attribute or the first audio attribute violates a first condition by determining, based on a feature vector including a plurality of features that are extracted from the sequence of images and the audio data, that the candidate client device is in an unsuitable testing environment, and
prevent the candidate client device from performing a candidate evaluation function using the candidate evaluation software application.

2. The system of claim 1 , wherein the one or more server hardware computing devices is further configured to:
access historical candidate data to determine a risk factor for a candidate associated with the candidate client device; and
before determining that at least one of the first video attribute violates a first video condition and the first audio attribute violates a first audio condition, determine that the risk factor exceeds a threshold.

3. The system of claim 1 , wherein the first audio attribute is an average volume level of the audio data in a frequency range that does not include frequencies less than 50 Hertz and greater than 300 Hertz and wherein the at least one processor is configured to determine that the first audio attribute violates the first audio condition by determining the average volume level exceeds a volume level threshold indicating an unsuitable testing environment.

4. The system of claim 1 , wherein the first video attribute is a luminance value or contrast ratio of at least one image in the sequence of images and wherein the at least one processor is configured to determine that the first video attribute violates the first video condition by determining the luminance value falls below a first luminance threshold value indicating an unsuitable testing environment or the contrast ratio falls below a first contrast ratio threshold value indicating an unsuitable testing environment.

5. A system, comprising:
a server computer communicatively coupled to a network and in communication, via the network, with a candidate client device, the candidate client device including a data capture device configured to record data from an environment of the candidate client device, the server computer being configured to:
receive, via the network, the data from the environment of the candidate client device, extract a first attribute from the data,
implement a machine learning engine to determine that the first attribute violates a first condition by determining, based on a feature vector including a plurality of features that are extracted from the data from the environment of the candidate client device that the candidate client device is in an unsuitable testing environment, and
generate an alert indicating that the candidate client device is in an unsuitable testing environment.

6. The system of claim 5 , wherein the server computer is configured to:
access historical candidate data to determine a trust score for a candidate associated with the candidate device; and
before determining that the first attribute violates a first condition, determine that the trust score falls below a threshold.

7. The system of claim 5 , wherein the data from the environment of the candidate client device includes a sequence of images and audio data and the plurality of features includes at least a luminance value of at least one image from the sequence of images, a contrast ratio value of at least one image in the sequence of images, and an average volume level of the audio data.

8. The system of claim 5 , wherein the data from the environment of the candidate client device includes a sequence of images and the server computer is configured to extract a first attribute from the data by determining a luminance value of at least one image in the sequence of images and determine that the first attribute violates the first condition by determining the luminance value exceeds a first luminance value indicating an unsuitable testing environment or falls below a second luminance value indicating the unsuitable testing environment.

9. The system of claim 5 , wherein the data from the environment of the candidate client device includes a sequence of images and the server computer is configured to extract a first attribute from the data by determining a contrast ratio value of at least one image in the sequence of images and determine that the first attribute violates the first condition by determining the contrast ratio value exceeds a first contrast ratio value indicating an unsuitable testing environment or falls below a second contrast ratio value indicating the unsuitable testing environment.

10. The system of claim 5 , wherein the data from the environment of the candidate client device includes audio data and the server computer is configured to extract the first attribute from the data by determining an average volume level of the audio data and determine that the first attribute violates the first condition by determining the average volume level exceeds a volume level threshold indicating an unsuitable testing environment.

11. The system of claim 10 , wherein the server computer is configured to determine the average volume level of the audio data by determining an average volume level of the audio data in a frequency range that does not include frequencies less than 50 Hertz and greater than 300 Hertz.

12. A method, comprising:
receiving, via a network, data captured from an environment of a candidate client device, the candidate client device including a data capture device configured to record the data from the environment of the candidate client device;
extracting a first attribute from the data; implementing a machine learning engine to determine that the first attribute violates a first condition; and
generating an alert indicating that the candidate client device is in an unsuitable testing environment.

13. The method of claim 12 , wherein implementing the machine learning engine to determine that the first attribute violates the first condition further comprises determining, based on a feature vector including a plurality of features that are extracted from the data from the environment of the candidate client device, whether the candidate client device is in an unsuitable testing environment.

14. The method of claim 13 , wherein the data from the environment of the candidate client device includes a sequence of images and audio data and the plurality of features includes at least a luminance value of at least one image from the sequence of images, a contrast ratio value of at least one image in the sequence of images, and an average volume level of the audio data.

15. The method of claim 12 , wherein the data from

Assignees

• Pearson Education Inc

Inventors

• Anderson Gregory
• Bailey Wayne
• Dolan Colleen M
• Fajobi Jide
• Jensen Darrick
• Lancaster Jr Ronald D
• Maloney Matthew
• Nealis Michael
• Nordberg Steven C
• Pascale Peter
• Stockinger Andrew

Classifications

• H04L67/306

  User profiles · CPC title

• H04L67/60

  Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources (admission control or resource allocation H04L47/70) · CPC title

• H04L67/52

  specially adapted for the location of the user terminal · CPC title

• H04L67/01

  Protocols · CPC title

• G06V20/46

  Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames · CPC title