# US 12,014,829 B2 · Image processing and presentation techniques for enhanced proctoring sessions

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,014,829 B2
- Document ID: US12014829B2
- Status: Granted
- Kind: B2
- Application number: 17/661,533
- Filed: 2022-04-29
- Published: 2023-03-02
- Granted: 2024-06-18
- Earliest claimed: 2021-09-01
- Assignees: Emed Labs, LLC
- Applicants: Emed Labs, LLC
- Inventors: Nicholas Atkinson Kramer; Chistopher T. Larkin; Christopher Richard Williams; John Andrew Sands; Colman Thomas Bryant; Glen Crampton McKnight; Randall Eugene Hand
- Web page: https://patentgrep.com/patent/US12014829B2

## CPC

- G06T 5/94: based on local image properties, e.g. for local contrast enhancement
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06T 5/40: using histogram techniques
- G06T 5/50: using two or more images, e.g. averaging or subtraction
- G06T 5/92: based on global image properties
- G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI]
- G06V 10/60: relating to illumination properties, e.g. using a reflectance or lighting model
- G06V 10/751: {Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching}
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)}
- G06V 30/41: Analysis of document content (recognition of printed characters based on code marks G06V30/224)
- G06V 40/172: {Classification, e.g. identification}
- G16H 40/67: for remote operation
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- H04N 5/272: Means for inserting a foreground image in a background image, i.e. inlay, outlay
- G06T 2207/20212: Image combination
- G06T 2207/30004: Biomedical image processing
- G06T 2207/30201: Face
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

Systems and methods for providing remotely proctored at-home health testing and diagnostics are provided herein. In particular, systems and methods for improving proctor performance by increasing accuracy and reducing proctor time are disclosed. In some embodiments, images and/or video of a patient may be enhanced to aid the proctor. In some embodiments, proctor performance may be evaluated. In some embodiments, proctor time per patient may be reduced by automating one or more steps in a proctored testing process.

## Claims

### Claim 1

A computer-implemented method for a remote diagnostic testing platform, the computer-implemented method comprising:
receiving, by a computing system, a video feed from a user device of a user engaging in an on-demand test session;
analyzing, by the computing system, the video feed to automatically determine that a particular step in the on-demand test session has been reached by the user;
based on detection of the particular step, storing, by the computing system, a plurality of subsequently-received image frames of the video feed to a first buffer;
evaluating, by the computing system, the plurality of image frames stored in the first buffer against a set of criteria, wherein the evaluation comprises evaluating motion blur, a difference between a frame of the plurality of image frames and an adjacent image frame of the plurality of image frames, and a closeness of a camera exposure level to a predetermined exposure level;
selecting, by the computing system, a subset of the image frames stored in the first buffer based at least in part on the evaluation;
storing, by the computing system, the selected subset of image frames to a second buffer;
processing, by the computing system, the subset of image frames stored in the second buffer to generate a composite image; and
performing, by the computing system, one or more operations using the composite image.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, further comprising detecting, by the computing system, that the particular step in the on-demand test session has ended.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein performing comprises presenting the composite image to a proctor.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein performing comprises creating or modifying a training data set, the training data set to be used for training a machine learning model.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein performing comprises:
extracting, by the computing system, information from the composite image; and
querying, by the computing system, a database using the extracted information.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein processing comprises:
extracting, by the computing system, a region of interest from each image frame stored in the second buffer;
using, by the computing system, template-matching to overlay image data extracted from the frames stored in the second buffer;
processing, by the computing system, the overlaid image data to enhance the image; and
combining, by the computing system, the processed overlaid image data to form a composite image.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein enhancing the image comprises any combination of one or more of suppressing noise, normalizing illumination, rejecting motion blur, enhancing resolution, rotating, keystone correcting, or increasing image size.

### Claim 8 (depends on 7)

The computer-implemented method of claim 7, wherein normalizing illumination comprises:
determining, by the computing system, a size of the region of interest in at least one dimension;
accessing, by the computing system, a kernel for normalizing illumination levels in images;
dynamically adjusting, by the computing system, a size of the kernel in at least one dimension based at least in part on the determined size of the region of interest; and
applying, by the computing system, the adjusted kernel to one or more patches of the region of interest to normalize one or more levels of illumination within the region of interest.

### Claim 9 (depends on 6)

The computer-implemented method of claim 6, further comprising:
providing, to a proctor computing device, the composite image.