# US 10,769,571 B2 · Security and content protection by test environment analysis

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- Number: US 10,769,571 B2
- Document ID: US10769571B2
- Status: Granted
- Kind: B2
- Application number: 15/939,052
- Filed: 2018-03-28
- Published: 2019-06-27
- Granted: 2020-09-08
- Earliest claimed: 2017-12-27
- Assignees: Pearson Education, Inc.
- Applicants: Pearson Education, Inc.
- Inventors: Gregory Anderson; Wayne Bailey; Colleen M. Dolan; Jide Fajobi; Darrick Jensen; Ronald D. Lancaster Jr.; Matthew Maloney; Michael Nealis; Steven C. Nordberg; Peter Pascale; Andrew Stockinger
- Web page: https://patentgrep.com/patent/US10769571B2

## CPC

- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06F 9/546: {Message passing systems or structures, e.g. queues}
- G06F 11/3438: {monitoring of user actions (tracking the activity of the user H04L67/535)}
- G06F 21/31: User authentication
- G06F 21/32: using biometric data, e.g. fingerprints, iris scans or voiceprints
- G06F 21/577: {Assessing vulnerabilities and evaluating computer system security}
- G06F 21/604: {Tools and structures for managing or administering access control systems}
- G06Q 10/063118: {Staff planning in a project environment}
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 30/0283: Price estimation or determination
- G06Q 50/20: Education
- G06Q 50/205: {Education administration or guidance}
- G06V 20/46: {Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames}
- G09B 7/00: Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F)
- G09B 7/02: of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B 7/07: providing for individual presentation of questions to a plurality of student stations
- G10L 25/03: characterised by the type of extracted parameters
- H04L 63/06: {for supporting key management in a packet data network (cryptographic mechanisms or cryptographic arrangements for key management H04L9/08)}
- H04L 63/08: {for authentication of entities (cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32)}
- H04L 63/102: {Entity profiles}
- H04L 63/107: {wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals}
- H04L 63/1408: {by monitoring network traffic (monitoring network traffic per se H04L43/00)}
- H04L 65/1069: Session establishment or de-establishment
- H04L 67/306: User profiles
- H04L 67/52: specially adapted for the location of the user terminal
- H04L 67/60: 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)
- H04N 7/15: Conference systems
- H04N 7/18: Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- G06F 2221/2139: Recurrent verification
- G10L 25/51: for comparison or discrimination
- H04L 63/302: {gathering intelligence information for situation awareness or reconnaissance}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## 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.

## Claims

### Claim 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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 1)

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.

### Claim 4 (depends on 1)

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.

### Claim 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.

### Claim 6 (depends on 5)

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.

### Claim 7 (depends on 5)

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.

### Claim 8 (depends on 5)

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.

### Claim 9 (depends on 5)

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.

### Claim 10 (depends on 5)

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.

### Claim 11 (depends on 10)

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.

### Claim 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.

### Claim 13 (depends on 12)

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.

### Claim 14 (depends on 13)

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.

### Claim 15 (depends on 12)

The method of claim 12, wherein the data from the environment of the candidate client device includes a sequence of images and further comprising extracting the first attribute from the data by determining a luminance value of at least one image in the sequence of images and determining 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.

### Claim 16 (depends on 12)

The method of claim 12, wherein the data from the environment of the candidate client device includes a sequence of images and further comprising extracting the first attribute from the data by determining a contrast ratio value of at least one image in the sequence of images and determining 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.

### Claim 17 (depends on 12)

The method of claim 12, wherein the data from the environment of the candidate client device includes audio data and further comprising extracting the first attribute from the data by determining an average volume level of the audio data and determining that the first attribute violates the first condition by determining the average volume level exceeds a volume level threshold indicating an unsuitable testing environment.

### Claim 18 (depends on 17)

The method of claim 17, wherein determining the average volume level of the audio data further comprises 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.