# US 12,213,734 B2 · Eye tracking fixation monitoring systems and methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,213,734 B2 - Document ID: US12213734B2 - Status: Granted - Kind: B2 - Application number: 18/336,747 - Filed: 2023-06-16 - Published: 2023-10-19 - Granted: 2025-02-04 - Earliest claimed: 2019-08-14 - Assignees: Alcon Inc. - Applicants: Alcon Inc. - Inventors: Sascha Birkner; Martin Gründig - Web page: https://patentgrep.com/patent/US12213734B2 ## CPC - A61B 3/113: for determining or recording eye movement - A61B 3/152: {for aligning} - A61B 3/0025: {characterised by electronic signal processing, e.g. eye models} - A61B 3/102: {for optical coherence tomography [OCT]} - A61B 3/1225: {using coherent radiation} - A61B 3/14: Arrangements specially adapted for eye photography - A61B 3/18: Arrangement of plural eye-testing or -examining apparatus - G06T 7/0016: {involving temporal comparison} - G06T 7/248: {involving reference images or patches} - G06T 7/74: {involving reference images or patches} - G06V 40/19: Sensors therefor - G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT] - G06T 2207/30041: Eye; Retina; Ophthalmic - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods for tracking eye movement during a diagnostic procedure include an eye tracker capturing images of an eye, and a control processor configured to detect an eye position and orientation in each of the images, determine an eye fixation position and orientation relative to an optical axis of the eye tracker, estimate eye fixation parameters based at least in part on the determined eye fixation position and orientation, and track the eye position and orientation by analyzing the images to determine the eye position and orientation relative to the eye fixation parameters. The eye fixation parameters may comprise a reference position and orientation of the eye when fixated. A histogram is constructed of detected eye positions and orientations and analyzed to determine an eye fixation position and orientation. ## Claims ### Claim 1 A system comprising: an eye tracker configured to capture a first plurality of images of a current eye; and a control processor configured to: detect an eye position and orientation in each of the first plurality of images; determine an eye fixation position and orientation relative to an optical axis of the eye tracker by inputting the eye position and orientation of the eye in the first plurality of images into a neural network trained using a set of historical data to determine when a historical eye was fixating and to output a determination of the current eye fixation position and orientation relative to the optical axis, wherein the neural network is trained by constructing and analyzing one or more histograms of detected historical eye positions and orientations; estimate eye fixation parameters based at least in part on the determined eye fixation position and orientation; and track the eye position and orientation by analyzing one or more images from the first plurality of images to determine the eye position and orientation relative to the eye fixation parameters. ### Claim 2 (depends on 1) The system of claim 1, wherein the eye fixation parameters comprise a reference position and orientation of the eye when fixated. ### Claim 3 (depends on 1) The system of claim 1, wherein analyzing the histogram further comprises determining a relative maximum value and determining whether coordinates of the relative maximum value comprise a fixation position and orientation. ### Claim 4 (depends on 3) The system of claim 3, wherein determining whether coordinates of the relative maximum value comprise a fixation position and orientation further comprise comparing the relative maximum value with a threshold and/or an average coordinate value of the histogram. ### Claim 5 (depends on 1) The system of claim 1, further comprising: a retina imaging system comprising an optical coherence tomography (OCT) scanner and/or a fundus camera configured to perform a retinal scan. ### Claim 6 (depends on 5) The system of claim 5, wherein the retina imaging system is configured to capture a second plurality of images of an eye; detect whether a fovea is present in one or more of the second plurality of images; identify a first image from the second plurality of images having the detected fovea; determine a second image from the first plurality of images having a temporal proximity to the first image; and analyze the second image to determine eye fixation parameters. ### Claim 7 (depends on 1) The system of claim 1, wherein the control processor is configured to track the eye position and orientation and calculate an offset from the eye fixation parameters and determine if the offset is less than a threshold value; wherein when the offset is less than the threshold value the eye is determined to be fixated and the control processor generates an indication of fixation; and wherein when the offset is greater than the threshold value the eye is determined to be out of alignment and the control processor generates an indication of no fixation. ### Claim 8 (depends on 1) The system of claim 1, wherein the control processor is further configured to perform an eye diagnostic procedure, and track eye position using the eye tracker during the eye diagnostic procedure. ### Claim 9 (depends on 1) The system of claim 1, further comprising a diagnostic device configured to perform an eye diagnostic procedure while tracking the position and orientation of the eye using an image capture device; wherein the diagnostic device is further configured to receive data representative of fixation and eye position during the eye diagnostic procedure based, at least in part, on the data representative of fixation and eye position. ### Claim 10 A method comprising: capturing a first plurality of images of an eye; detecting an eye position and orientation in each of the first plurality of images; determining an eye fixation position and orientation relative to an optical axis of the eye tracker by inputting the eye position and orientation of the eye in the first plurality of images into a neural network trained using a set of historical data to determine when a historical eye was fixating and to output a determination of the current eye fixation position and orientation relative to the optical axis, wherein the neural network is trained by constructing and analyzing one or more histograms of detected historical eye positions and orientations; estimating eye fixation parameters based at least in part on the determined eye fixation position and orientation; and tracking the eye position and orientation by analyzing one or more images from the first plurality of images to determine the eye position and orientation relative to the eye fixation parameters. ### Claim 11 (depends on 10) The method of claim 10, further comprising training a neural network to receive the first plurality of images and output a determination of an eye position. ### Claim 12 (depends on 10) The method of claim 10, wherein the eye fixation parameters comprise a reference position and orientation of the eye when fixated. ### Claim 13 (depends on 10) The method of claim 10, wherein analyzing the histogram further comprises determining a relative maximum value. ### Claim 14 (depends on 10) The method of claim 10, further comprising: performing a retina imaging scan of the eye using an optical coherence tomography (OCT) scanner. ### Claim 15 (depends on 14) The method of claim 14, further comprising: capturing a second plurality of images of an eye from the retina imaging scan; detecting whether a fovea is present in one or more of the second plurality of images; identifying a first image from the second plurality of images having the detected fovea; determining a second image from the first plurality of images having a temporal proximity to the first image; and analyzing the second image to determine eye fixation parameters. ### Claim 16 (depends on 10) The method of claim 10, further comprising tracking the eye position and orientation and calculating an offset from the eye fixation parameters and determine if the offset is less than a threshold value; wherein when the offset is less than the threshold value the eye is determined to be fixated and the control processor generates an indication of fixation; and wherein when the offset is greater than the threshold value the eye is determined to be out of alignment and the control processor generates an indication of no fixation. ### Claim 17 (depends on 10) The method of claim 10, further comprising performing an eye diagnostic procedure, and tracking eye position using an eye tracker during the eye diagnostic procedure. ### Claim 18 (depends on 10) The method of claim 10, further comprising performing an eye diagnostic procedure while tracking the position and orientation of the eye using an image capture device; and modifying the eye diagnostic procedure based, at least in part, on data representative of eye fixation parameters and a tracked eye position.