# US 12,178,509 B2 · Method, system and apparatus for diagnostic assessment and screening of binocular dysfunctions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,178,509 B2 - Document ID: US12178509B2 - Status: Granted - Kind: B2 - Application number: 17/253,300 - Filed: 2019-06-18 - Published: 2021-09-09 - Granted: 2024-12-31 - Earliest claimed: 2018-06-18 - Assignees: New Jersey Institute of Technology - Applicants: New Jersey Institute of Technology - Inventors: Tara Lynn Alvarez; Chang Yaramothu; John Vito d'Antonio-Bertagnolli; Mitchell Scheiman - Web page: https://patentgrep.com/patent/US12178509B2 ## CPC - A61B 3/08: for testing binocular or stereoscopic vision, e.g. strabismus - A61B 3/005: {Constructional features of the display} - A61B 3/032: Devices for presenting test symbols or characters, e.g. test chart projectors (A61B3/036 takes precedence) - A61B 3/113: for determining or recording eye movement - A61B 5/7267: {involving training the classification device} - G02B 27/017: {Head mounted} - G02B 30/36: using refractive optical elements, e.g. prisms, in the optical path between the images and the observer - A61H 5/005: {Exercisers for training the stereoscopic view} - A61H 2201/1604: Head - A61H 2201/5007: computer controlled - A61H 2201/5043: Displays - A61H 2205/024: Eyes - G02B 2027/0134: {of stereoscopic type} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS - A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N) ## Abstract Exemplary embodiments relate to systems, methods, and apparatus to objectively assess binocular dysfunction for screening, diagnoses, and evaluation of vision/oculomotor function before, during, and after various forms of therapeutic interventions. Systems and methods diagnose and assess binocular dysfunction objectively and automatically, can render a visual stimulus on one or more displays, and can control accommodative and proximal vergence stimulation of a user's eyes. ## Claims ### Claim 1 A system for performing an assessment of oculomotor function of a user, the system comprising: a head mounted display configured to generate a stereoscopic effect viewable by the user; a computing system in communication with the head mounted display, the computing system comprising: a memory storing computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations comprising: rendering visual stimulus with the head mounted display; adjusting the visual stimulus to elicit a response from a visual system of the user; objectively measuring at least a subset of a plurality of oculomotor characteristics of the user based on the response; based on the objective measurement of the at least the subset of the plurality of oculomotor characteristics of the user, dynamically adjusting the rendering of the visual stimulus to a non-linear program sequence, using a trained machine learning model, to reduce at least one of a duration of an oculomotor function assessment and resources required to complete the oculomotor function assessment; and generating the oculomotor function assessment by combining the at least the subset of the plurality of oculomotor characteristics; wherein the dynamic adjustment of the rendering of the visual stimulus using the trained machine learning model does not reduce the sensitivity of the oculomotor function assessment despite the reduced assessment duration and/or the resources required to complete the oculomotor function assessment. ### Claim 2 (depends on 1) The system of claim 1, wherein the head mounted display is configured to measure movement of the user's eyes and transmit the measure of movement to the computing system. ### Claim 3 (depends on 2) The system of claim 2, wherein adjusting the visual stimulus is based on the measure of eye movement. ### Claim 4 (depends on 2) The system of claim 2, wherein objectively measuring the at least the subset of the plurality of oculomotor characteristics is based on the measure of eye movement. ### Claim 5 (depends on 1) The system of claim 1, wherein the plurality of oculomotor characteristics includes at least one of a near point of convergence, dissociated and associated phoria, fixation disparity saccades, smooth pursuit, binocular endurance, eye movement adaptation, fusional range, or vergence facility. ### Claim 6 (depends on 1) The system of claim 1, wherein the plurality of oculomotor characteristics include at least one of vergence peak velocity, accuracy, response amplitude, latency, time to peak velocity, time constant, error, variance, asymmetry between left and right eye movement, ramp respond speed, dissection of eye movement to assess magnitude height and width of fusion initiating component, or measure ratio of convergence to divergence peak velocity at different initial vergence angles. ### Claim 7 (depends on 1) The system of claim 1, wherein the plurality of oculomotor characteristics is weighted. ### Claim 8 (depends on 1) The system of claim 1, wherein the trained machine learning model is a first trained machine learning model, wherein a second trained machine learning module is used to render the visual stimulus with the head mounted display or to adjust the visual stimulus to elicit the response from the visual system of the user. ### Claim 9 A method for performing an assessment of oculomotor function of a user, the method comprising: rendering visual stimulus with a head mounted display using a stereoscopic effect; adjusting, by a computing system, the visual stimulus to elicit a response from a visual system of the user wearing the head mounted display; objectively measuring at least a subset of a plurality of oculomotor characteristics of the user based at least on the response; based on the objective measurement of the at least the subset of the plurality of oculomotor characteristics of the user, dynamically adjusting the rendering of the visual stimulus to a non-linear program sequence, using a trained machine learning model, to reduce at least one of a duration of an oculomotor function assessment and resources required to complete the oculomotor function assessment; and generating the oculomotor function assessment by combining the at least the subset of the plurality of oculomotor characteristics; wherein the dynamic adjustment of the rendering of the visual stimulus using the trained machine learning model does not reduce the sensitivity of the oculomotor function assessment despite the reduced assessment duration and/or the resources required to complete the oculomotor function assessment. ### Claim 10 (depends on 9) The method of claim 9, further comprising: measuring movement of the user's eyes via the head mounted display; and transmitting a measure of movement of the user's eyes to the computing system. ### Claim 11 (depends on 10) The method of claim 10, wherein adjusting the visual stimulus is based on the measure of movement. ### Claim 12 (depends on 10) The method of claim 10, wherein objectively measuring the at least the subset of the plurality of oculomotor characteristics is based on the measure of movement. ### Claim 13 (depends on 9) The method of claim 9, wherein the plurality of oculomotor characteristics includes at least one of a near point of convergence, dissociated and associated phoria, fixation disparity, saccades, smooth pursuit, binocular endurance, eye movement adaptation, fusional range, or vergence facility. ### Claim 14 (depends on 9) The method of claim 9, wherein the plurality of oculomotor characteristics include at least one of vergence peak velocity, accuracy, response amplitude, latency, time to peak velocity, time constant, error, variance, asymmetry between left and right eye movement, ramp respond speed, dissection of movement to assess magnitude height and width of fusion initiating component, or measure ratio of convergence to divergence peak velocity at different initial vergence angles. ### Claim 15 (depends on 9) The method of claim 9, wherein the plurality of oculomotor characteristics is weighted. ### Claim 16 (depends on 9) The method of claim 9, wherein the trained machine learning model is a first trained machine learning model, wherein a second trained machine learning module is used to render the visual stimulus with the head mounted display or to adjust the visual stimulus to elicit the response from the visual system of the user. ### Claim 17 A non-transitory computer-readable medium storing instructions that when executed by a processing device causes the processing device to implement a method for performing an assessment of oculomotor function of a user, the method comprising: rendering visual stimulus with a head mounted display using a stereoscopic effect; adjusting the visual stimulus to elicit a response from a visual system of the user wearing the head mounted display; objectively measuring at least a subset of a plurality of oculomotor characteristics of the user based at least on the response; based on the objective measurement of the at least the subset of the plurality of oculomotor characteristics of the user, dynamically adjusting the rendering of the visual stimulus to a non-linear program sequence, using a trained machine learning model, to reduce at least one of a duration of an oculomotor function assessment and resources required to complete the oculomotor function assessment; and generating the oculomotor function assessment by combining the at least the subset of the plurality of oculomotor characteristics; wherein the dynamic adjustment of the rendering of the visual stimulus using the trained machine learning model does not reduce the sensitivity of the oculomotor function assessment despite the reduced assessment duration and/or the resources required to complete the oculomotor function assessment. ### Claim 18 (depends on 17) The medium of claim 17, wherein the method further comprises: measuring movement of the user's eyes via the head mounted display; and transmitting a measure of movement of the user's eyes to the processing device. ### Claim 19 (depends on 18) The medium of claim 18, wherein adjusting the visual stimulus is based on the measure of movement. ### Claim 20 (depends on 18) The medium of claim 18, wherein objectively measuring the at least the subset of the plurality of oculomotor characteristics is based on the measure of movement. ### Claim 21 (depends on 17) The medium of claim 17, wherein the plurality of oculomotor characteristics includes at least one of a near point of convergence, dissociated and associated phoria, fixation disparity, saccades, smooth pursuit, binocular endurance, fusional range, or vergence facility. ### Claim 22 (depends on 17) The medium of claim 17, wherein the plurality of oculomotor characteristics include at least one of vergence peak velocity, accuracy, response amplitude, latency, time to peak velocity, time constant, error, variance, asymmetry between left and right eye movement, ramp respond speed, dissection of movement to assess magnitude height and width of fusion initiating component, or measure ratio of convergence to divergence peak velocity at different initial vergence angles. ### Claim 23 (depends on 17) The medium of claim 17, wherein the plurality of oculomotor characteristics is weighted. ### Claim 24 (depends on 17) The medium of claim 17, wherein the trained machine learning model is a first trained machine learning model, wherein a second trained machine learning module is used to render the visual stimulus with the head mounted display or to adjust the visual stimulus to elicit the response from the visual system of the user.