# US 12,033,432 B2 · Determining digital markers indicative of a neurological condition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,033,432 B2 - Document ID: US12033432B2 - Status: Granted - Kind: B2 - Application number: 18/485,031 - Filed: 2023-10-11 - Published: 2024-02-01 - Granted: 2024-07-09 - Earliest claimed: 2021-05-03 - Assignees: NeuraLight Ltd. - Applicants: NeuraLight Ltd. - Inventors: Edmund Ben-Ami; Micha Yochanan Breakstone; Rotem Zvi Bar-Or; Vladimir Anisimov - Web page: https://patentgrep.com/patent/US12033432B2 ## CPC - G06V 40/18: Eye characteristics, e.g. of the iris - A61B 3/0025: {characterised by electronic signal processing, e.g. eye models} - A61B 3/113: for determining or recording eye movement - A61B 5/163: {by tracking eye movement, gaze, or pupil change} - G06F 3/013: {Eye tracking input arrangements (G06F3/015 takes precedence)} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/09: Supervised learning - G06T 7/0012: {Biomedical image inspection} - G06T 7/0016: {involving temporal comparison} - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06V 10/82: using neural networks - G06N 3/084: Backpropagation, e.g. using gradient descent - G06T 2207/10016: Video; Image sequence - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30041: Eye; Retina; Ophthalmic - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Disclosed are system, method, and computer program product embodiments for determining a digital marker indicative of a neurological condition. An embodiment operates by detecting a region of interest in an image frame of a face of a user. The region of interest includes an eye of the user and the image frame is obtained from a video stream. The embodiment then generates a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, performs deconvolution on the portion of the image frame specified by the region of interest using the PSF, determines a set of oculometric parameters of the eye of the user using the deconvolved image, and determines one or more digital markers of the user based on the set of oculometric parameters. The one or more digital markers are indicative of a neurological condition of the user. ## Claims ### Claim 1 A computer-implemented method for efficiently determining a digital marker indicative of a neurological condition, comprising: detecting, using one or more processors, a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream; generating a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, and oculometric parameters; performing deconvolution on the portion of the image frame specified by the region of interest using the PSF; determining a set of oculometric parameters of the eye of the user using the deconvolved image; and determining one or more digital markers of the user based on the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the oculometric parameters include a pupil center, iris data, and eyelid data. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the acquired data comprises stimulus data and device data; wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and wherein the device data includes orientation information of the device. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and wherein the generating the PSF comprises: generating the PSF based on the pre-set of oculometric parameters. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the determining the set of oculometric parameters comprises: detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the identifying comprises: identifying the quadratic curve based on a maximal likelihood estimation. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein determining the one or more digital markers comprises: determining a pupillary response function based on the set of oculometric parameters, wherein the pupillary response function is determined by performing iterative blind deconvolution to estimate two or more parameters that define the pupillary response function. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the oculometric parameters comprise eyelid data or a pupil center. ### Claim 10 A system for efficiently determining a digital marker indicative of a neurological condition, comprising: one or more memories; and at least one processor each coupled to at least one of the memories and configured to perform operations comprising: detecting a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream; generating a point spread function (PSF) based on acquired data, a portion of the image frame specified by the region of interest, and oculometric parameters; performing deconvolution on the portion of the image frame specified by the region of interest using the PSF; determining a set of oculometric parameters of the eye of the user using the deconvolved image; and determining one or more digital markers of the user based on at least the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user. ### Claim 11 (depends on 10) The system of claim 10, wherein the oculometric parameters include a pupil center, iris data, and eyelid data. ### Claim 12 (depends on 10) The system of claim 10, wherein the acquired data comprises stimulus data and device data; wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and wherein the device data includes orientation information of the device. ### Claim 13 (depends on 10) The system of claim 10, the operations further comprising: prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and wherein the generating the PSF comprises: generating the PSF based on the pre-set of oculometric parameters. ### Claim 14 (depends on 10) The system of claim 10, wherein the determining the set of oculometric parameters further comprises: detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries. ### Claim 15 (depends on 14) The system of claim 14, wherein the identifying comprises: identifying the quadratic curve based on a maximal likelihood estimation. ### Claim 16 (depends on 10) The system of claim 10, the operations further comprising: iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters. ### Claim 17 A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: detecting a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream; generating a point spread function (PSF) based on acquired data, a portion of the image frame specified by the region of interest, and oculometric parameters; performing deconvolution on the portion of the image frame specified by the region of interest using the PSF; determining a set of oculometric parameters of the eye of the user using the deconvolved image; and determining one or more digital markers of the user based on at least the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the oculometric parameters include a pupil center, iris data, and eyelid data. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the acquired data comprises stimulus data and device data; wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and wherein the device data includes orientation information of the device. ### Claim 20 (depends on 17) The non-transitory computer-readable medium of claim 17, the operations further comprising: prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and wherein the generating the PSF comprises: generating the PSF based on the pre-set of oculometric parameters. ### Claim 21 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the determining the set of oculometric parameters comprises: detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries. ### Claim 22 (depends on 17) The non-transitory computer-readable medium of claim 17, the operations further comprising: iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters.