DETAILED ACTION
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/30/25 and 08/07/25 comply with provisions of 37 CFR 1.97. Accordingly, the examiner considered the information disclosure statements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Leung et al. (US 20170273552).
Regarding claim 1, Leung teaches a method for assessing the impact of environmental factors on vision, the method (abstract) comprising, displaying a virtual environment on screens of a virtual reality (VR) headset (110, 210) worn by a patient (fig. 1, HMD 110; fig. 2, HMD unit 210); fig. 7, step 702; ¶24, HMD 110 can be used to project three-dimensional virtual reality (VR) environments with virtual objects for the user wearing HMD 110); displaying a visual task in the virtual environment with an environmental factor (fig. 5 and 6 – daylight / nighttime environmental conditions); fig. 8-11 – task environments; ¶5, ¶3 teaches generating VR simulations that include daily life tasks (navigating streets, locating objects, climbing stairs) within VR environments with varying brightness and contrast level, these are environmental factors applied to the visual task. Figures 5 and 6 show daylight and nighttime environmental conditions applied to the same navigation task); prompting the patient to complete the visual task while the virtual environment includes the environmental factor (fig. 7, step 702-704, claims 1 and 8; claim 8, ¶3 and ¶5, teaches the user must complete the VR simulation (e.g., navigate through obstacles, locate objects) while the environmental factors (brightness, contrast) are active. The performance scores based on duration to complete the task and collisions.); and monitoring a patient’s eyes to collect an input as the patient attempts the visual task (fig. 2, sensor system 220; fig. 7, step 704; claim 5; ¶31, ¶7, fig. 7, step 704, teaches the monitoring voluntary and involuntary (including eye dilation) via sensor system 220 during the VR simulation. The sensor system includes motion sensors and physiological monitors.).
Regarding claim 4, Leung teaches the method of Claim 1, wherein displaying the visual task in the virtual environment with the environmental factor comprises displaying the visual task in the virtual environment with fog (brightness level and contrast level applied scene wide by computing device 230 via processor 232 and program instructions 234 displayed on display 214; fig. 5 (daylight), fig. 6 (nighttime); ¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions. Note: the VR simulation parameters include adjustable “brightness level” and “contrast level” applied uniformly across the VR environment and visual task. Fog is defined in VR rendering as a uniform decrease in contrast and increase in brightness across the entire scene. The brightness/contrast controls when applied scene wide directly produce fog as a rendering effect.).
Regarding claim 5, Leung teaches the method of Claim 4, wherein displaying the visual task in the virtual environment with fog comprises decreasing contrast and increasing brightness across the virtual environment and the visual task (¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a
patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions; ¶29, The contrast level of the virtual reality objects or brightness level of the virtual reality simulations
can also be adjusted to simulate different lighting conditions.).
Regarding claim 6, Leung teaches the method of Claim 1, wherein displaying the visual task in the virtual environment with the environmental factor comprises displaying the visual task in the virtual environment with smoke (brightness level and contrast level applied scene wide by computing device 230 via processor 232 and program instructions 234 displayed on display 214; fig. 5 (daylight), fig. 6 (nighttime); ¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions. Note: the VR simulation parameters include adjustable “brightness level” and “contrast level” applied uniformly across the VR environment and visual task. Smoke is defined in VR rendering as a uniform decrease in contrast and increase in brightness across the entire scene. The brightness/contrast controls when applied scene wide directly produce fog as a rendering effect.).
Regarding claim 7, Leung teaches the method of Claim 6, wherein displaying the visual task in the virtual environment with smoke comprises decreasing contrast and brightness across virtual environment and the visual task and displaying dark particles across the virtual environment and the virtual task (¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions; ¶29, The contrast level of the virtual reality objects or brightness level of the virtual reality simulations can also be adjusted to simulate different lighting conditions.).
Regarding claim 8, Leung teaches the method of Claim 1, wherein displaying the visual task in the virtual environment with the environmental factor comprises displaying the visual task in the virtual environment with wind (fig. 10A to 10C; ¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions. Fig. 10A, 10B, and 10C illustrate a tested subject navigating in a city area packed with pedestrians and vehicles while being required to avoid bumping into obstacles, other pedestrians and cross a road without being hit by the vehicles in the traffic. Note: disclose moving objects (pedestrians, vehicles, randomly appearing obstacles shown in fig. 10A to 10C), wind is defined as moving air that causes objects and particles to move through a scene. The VR environment in fig. 10A to 10C discloses VR scenes in which objects (pedestrians, vehicles, randomly appearing obstacles) move throughout the virtual environment while the visual task (navigation) is being completed. This moving object environment inherently constitutes an environmental condition in which objects move through the scene, which is the functional and perceptual equivalent of a wind environmental condition.).
Regarding claim 9, Leung teaches the method of Claim 8, wherein displaying the visual task in the virtual environment with wind comprises displaying particles and/or objects moving throughout the virtual environment and the visual task (fig. 10A to 10C; ¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions. Fig. 10A, 10B, and 10C illustrate a tested subject navigating in a city area packed with pedestrians and vehicles while being required to avoid bumping into obstacles, other pedestrians and cross a road without being hit by the vehicles in the traffic. Note: disclose moving objects (pedestrians, vehicles, randomly appearing obstacles shown in fig. 10A to 10C), wind is defined as moving air that causes objects and particles to move through a scene. The VR environment in fig. 10A to 10C discloses VR scenes in which objects (pedestrians, vehicles, randomly appearing obstacles) move throughout the virtual environment while the visual task (navigation) is being completed. This moving object environment inherently constitutes an environmental condition in which objects move through the scene, which is the functional and perceptual equivalent of a wind environmental condition.).
Regarding claim 10, Leung teaches the method of Claim 1, wherein monitoring the patient’s eyes to collect the input comprises monitoring the patient’s eyes in real-time as the patient attempts the visual task (fig. 7, step 704; claim 1, monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation; note: monitoring “during” the simulation is realtime monitoring concurrent with task performance).
Regarding claim 11, Leung teaches the method of Claim 1, wherein monitoring the patient’s eyes to collect the input comprises monitoring a gaze direction of the patient (fig. 2, sensor system 220; claim 5, wherein the sensor system includes motion sensors to sense the voluntary responses of the user, and biometric sensors to sense the involuntary responses of the user; note: the oculomotor responses as a tracked voluntary response collected by the sensor system therefore, the gaze direction tracking is inherent to and included in oculomotor response monitoring).
Regarding claim 12, Leung teaches the method of Claim 1, wherein monitoring the patient’s eyes (sensor system 220 includes optical / infrared sensor) to collect the input (¶7, The process may further include displaying the virtual reality simulation on a head-mounted display, and monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation; ¶31, Sensor
system 220 may also include biometric sensors to sense the involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size. Note: the reference expressly discloses monitoring the patient’s eyes in real time via sensor system 220, including an infrared optical sensor collecting oculomotor responses during the VR task) comprises monitoring a fixation duration (inherent, sensor system 220 (optical / infrared sensor). Shown in fig. 3 the voluntary responses with temporal processing using the processor 232 in computing device 230. Note: the sensor system 220 includes an optical sensor (e.g., infrared sensor) that detects oculomotor responses in real time during the VR simulation (fig. 3 and ¶26). Oculomotor responses collected by an infrared optical sensor operating in real time include, gaze direction, gaze position, and the temporal duration for which each faze position is maintained, i.e., fixation duration. A fixation is defined as the period during which the eye remains stationary and focused on a point. An infrared sensor tracking eye position in real time necessarily records both the spatial position of the eye and the temporal extend of that position, which together constitute fixation duration. Fixation duration is the temporal dimension of gaze tracking and gaze tracking is what sensor system 220 performs) of the patient (¶31, Sensor system 220 may also include biometric sensors to sense the involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size; ¶7, monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation.).
Regarding claim 13, Leung teaches the method of Claim 1, wherein monitoring the patient’s eyes to collect the input comprises monitoring a visual acuity of the patient. (¶6, the virtual reality (VR) platform can integrate the testing of different components of visual function (e.g., visual acuity, visual field, contrast sensitivity …)
Regarding claim 14, Leung teaches a system for assessing (visual disability detection system 200) the impact of environmental factors on vision, the system (fig. 2 and 3; ¶5, Embodiments of the present invention provide techniques for a visual disability detection system that employs virtual reality to assess visual performance of a patient based on activities of daily living. Embodiments of the present invention is designed to evaluate and measure the performance of a person in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions.) comprising, a virtual reality (VR) headset (HMD unit 210; 110 in fig. 1) worn by a patient (¶25, Visual disability detection system 200 may include a head-mounted display (HMD) unit 210, a sensor system 220, and a computing device 230 communicatively coupled to each another; ¶24, The virtual reality platform may include a head-mounted display (HMD) unit 110 worn by a user), the VR headset comprising screens (display 214 rendered by GPU 212; ¶26, HMD 210 can be a stereoscopic head-mounted display such as HMD 110, and may include one or more graphics processing units 212 or graphic converting units, and a display 214 that displays and renders virtual reality simulations in a virtual reality environment), one or more eye-tracking sensors (sensor system 220; fig. 2, sensor system 220 and fig. 3 oculomotor responses; ¶31, Sensor system 220 may also include biometric sensors to sense the involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size), and one or more eye-tracking cameras (¶31, optical sensor / infrared sensor with sensor system 220; ¶31, a sensor embedded in the HMD 210 can be used as a reference for the viewing direction.), the one or more eye-tracking sensors and cameras being configured to collect eye data (sensor embedded in HMD 210; ¶7, The process may further include displaying the virtual reality simulation on a head-mounted display, and monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation); and a computing device (computing device 230) in electronic communication (¶25, communicatively coupled between 210, 220, and 230; ¶25, Visual disability detection system 200 may include a head-mounted display (HMD) unit 210, a sensor system 220, and a computing device 230 communicatively coupled to each another.) with the VR headset, the computing device (230; ¶32, Computing device 230 may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation) being configured to cause a virtual environment with one or more environmental factors (brightness and contrast levels controlled by computing device 230; computing device 210 and processor 232; ¶29, designed to evaluate and measure the performance of a user in completing daily tasks in different VR environments with different brightness and contrast levels) to be displayed on the screens (display 214 of HMD unit 210), to cause a visual task to be displayed (program instructions 234 in computing device 230 generating VR simulation on display 214) in the virtual environment (¶7, a process for visual disability detection may include generating a virtual reality simulation in a virtual reality environment with virtual reality objects, in which the virtual reality simulation simulates a real life activity that tests visual responses of the user; ¶23, The daily activities simulated may include navigating on a busy street, walking up or down flights of stairs, driving a vehicle, and locating objects of interest in an environment such as objects on a shelf), to adjust the one or more environmental factors (computing device 230 adjusting brightness / contrast levels via processor 232 and program instructions 234; ¶29, designed to evaluate and measure the performance of a user in completing daily tasks in different VR environments with different brightness and contrast levels, simulating activities of daily living in a variety of light conditions; note: the computing device 230 adjusts brightness and contrast levels, the environmental factors, across VR environments (daylight, nighttime, varying contrast). These are adjusted by the program stored in memory of computing device 230), and to process the eye data (processor 232 in computing device 230 executing program instructions 234 using sensor readings from sensor system 220; ¶32, Computing device 230 (e.g., computer, smartphone, tablet, gaming console, etc.) may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation and sensor readings from sensor system 220), wherein the eye data comprises gaze direction (oculomotor responses collected by optical / infrared sensor within sensor system 220; ¶31, biometric sensors may include … optical sensor (e.g., infrared sensor) to detect oculomotor responses; note: fig. 3 workflow expressly lists “oculomotor responses” as collected voluntary response data. Gaze direction is the primary component of oculomotor response data collected by any infrared optical sensor eye tracking system), fixation duration (oculomotor / voluntary responses from sensor system 220 processed by processor 232; note: oculomotor response monitoring (fig. 3; ¶26) and voluntary response tracking data including performance data based on time metrics. Fixation duration is a standard component of oculomotor response data inherently collected by the infrared optical sensor system), and visual acuity (visual acuity testing integrated into visual disability detection system 200 via computing device 230 and sensor system 220; ¶6, The virtual reality (VR) platform can integrate the testing of different components of visual function (e.g., visual acuity, visual field, contrast sensitivity, color vision, stereopsis, etc.),) of the patient while the patient completes the visual task.
Regarding claim 15, Leung teaches the system of Claim 14, wherein the one or more eye-tracking sensors and cameras (sensor system 220 (optical/infrared sensor); ¶31) are configured to monitor eye movements (220; ¶31, oculomotor responses) of the patient (fig. 3, voluntary responses; ¶31, Sensor system 220 may also include biometric sensors to sense the involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size; ¶7, monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation.).
Regarding claim 16, Leung teaches the system of Claim 14, wherein the one or more eye-tracking sensors and cameras (sensor system 220 (optical/infrared sensor); ¶31) are configured to monitor fixation patterns (note: the reference sensor system comprises an infrared optical sensor detecting oculomotor responses (¶31), in necessarily and inherently configured to monitor fixation patterned because in ¶31 states “Sensor system 220 may also include biometric sensors to sense the
involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size)”, an infrared optical sensor whose expressed purpose is detecting oculomotor responses is, by its physical configuration and design purpose, a sensor configured to monitor fixation patterns because detecting oculomotor responses the function that generates fixation pattern data. The configuration of the sensor (infrared optics directed at the eye, collecting real time position data) is the configuration that produces fixation patterns.) of the patient (fig. 3, voluntary responses; ¶31, Sensor system 220 may also include biometric sensors to sense the involuntary responses of the user. Examples of such biometric sensors may include blood pressure sensor, heart rate sensor, optical sensor (e.g., infrared sensor) to detect oculomotor responses and changes in dilation or pupil size; ¶7, monitoring voluntary and involuntary responses of the user via a sensor system during the virtual reality simulation.).
Regarding claim 17, Leung teaches the system of Claim 14, wherein the computing device comprises an algorithm (computing device 230 comprising program instructions 234 executed by processor 232; ¶32, Computing device 230 may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation and sensor readings from sensor system 220 relayed to computing device 230; note: computing device 230 expressly contains program instructions 234, an algorithm, stored in memory and executed by processor 232 to process sensor data and compute performance scores and visual disability metrics) that processes the eye data (program instructions 234 in computing device 230 processing sensor readings from sensor system 220; ¶32, Computing device 230 may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation and sensor readings from sensor system 220 relayed to computing device 230; note: the algorithm processes sensor readings from sensor system, which includes the eye data (oculomotor responses, gaze direction) collected during the VR simulation, relayed to computing device 230 for analysis) to evaluate a reaction time (time required in completing a task / duration required to complete the task computed by program instructions 234 output as performance scores 234 stored in computing device 230; ¶5, Performance data such as the time required in completing a task, the number of collisions with the VR objects in the VR environments, the angle and the speed of collision, the size, color and contrast levels of the collided VR objects, etc. are recorded to compute performance scores. The performance scores can then be
used to quantify the visual performance for assessment, grading and monitoring of visual disability of a person. note: the performance score is computed based on parameters including “a duration required to complete the task” which is a direct measure of patient reaction time, i.e., the time elapsed from stimulus presentation to patient task response / completion) of the patient.
Regarding claim 20, Leung teaches the system of Claim 14, wherein the computing device (230) is further configured to generate a report (visual disability metrics 236 generated by program instructions 234 in computing device 230; fig. 7 step 708; ¶5, performance scores can then be used to quantify the visual performance for assessment, grading and monitoring), which is accessible at a user interface (¶27, visual disability detection system 200 may include one or more input devices (e.g., keyboard, gamepad, mouse, pointer, etc.) communicatively coupled to the computing device 230 and HMD 210) of computing device (230; ¶32, The performance scores can be displayed on a computing device such as a computer, a mobile computing device, or a smart phone, etc. The server can remotely send the results to the clinicians for monitoring the visual disability progression of the user. Clinicians or eye care providers can monitor the visual performance scores of the user remotely via internet access to the cloud server and devise any change of treatment approach accordingly.).
Claims 2 and 3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Leung et al. (US 20170273552) as evidence by Butler (US 8,550,631).
Regarding claim 2, Leung teaches the method of Claim 1, wherein displaying the visual task in
the virtual environment with the environmental factor comprises displaying the visual task in the virtual environment with glare (claim 8, fig. 5 and 6 – brightness variation; ¶5, VR environments with varying brightness and contrast levels as environmental factors. Glare as a specific rendering technique (increased brightness at target points and decreased surrounding contract and obscuring) is not expressly described, but the reference explicitly teaches manipulating brightness and contrast across the VR scene, the same underlying parameters As evidence by Butler’s (US 8,550,631) reference abstract and claim 1 to render glare using brightness/contrast adjustments to simulate a common real-world impairment factor)
Regarding claim 3, Leung teaches the method of Claim 2, wherein displaying the visual task in the virtual environment with glare comprises increasing brightness of the virtual environment and the visual task at one or more target points, decreasing contrast of the virtual environment and the visual task in areas surrounding the one or more target points, and obscuring the virtual environment and the visual task at the one or more target points (claim 8 and ¶5, the reference teaches brightness level and contrast level as adjustable parameters in the VR simulation (claim 8). The specific glare rendering techniques is not expressly disclosed but it is evident that are disclosed in discussion in claim 2, the Butler’s (US 8,550,631) reference abstract and claim 1 discloses the having the parameters to render glare using brightness/contrast adjustments).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Leung et al. (US 20170273552) as applied to claim 14 above, and further in view of Krueger (US 20180008141).
Regarding claim 18, Leung further teaches the computing device (computing device 230)
comprises an algorithm (computing device 230 comprising program instructions 234 executed by processor 232; ¶32, Computing device 230 may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation and sensor readings from sensor system 220 relayed to computing device 230; note: computing device 230 expressly contains program instructions 234, an algorithm, stored in memory and executed by processor 232 to process sensor data and compute performance scores and visual disability metrics) that processes the eye data (note: the algorithm processes sensor readings from sensor system 220, which are the eye data (oculomotor responses, gaze direction) collected during the VR task). Leung does not specifically teach to evaluate an eye movement accuracy of the patient. However, in a similar field of endeavor, Krueger teaches the system, wherein to evaluate an eye movement accuracy (¶15, Accuracy, amplitude, latency and velocity can be measured with oculomotor eye movements, most commonly with saccades, vergence, smooth pursuit, and vestibulo-ocular movements. Saccades can be elicited voluntarily, but occur reflexively whenever the eyes are open, even when fixated on a target. They serve as a mechanism for fixation, rapid eye movement, and the fast phase of optokinetic nystagmus. The rapid eye movements that occur during an important phase of sleep are also saccades. After the onset of a target appearance for a saccade, it takes about 200 ms for eye movement to begin. During this delay, the position of the target with respect to the fovea is computed (that is, how far the eye has to move), and the difference between the initial and intended position, or “motor error” ) of the patient. It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the system of Leung with to evaluate an eye movement accuracy of the patient of Krueger, for the purpose of providing continuous and accurate measurement (¶320).
Regarding claim 19, Leung further teaches the computing device comprises an algorithm that processes the eye data (computing device 230 comprising program instructions 234 executed by processor 232; ¶32, Computing device 230 may include one or more processors 232 and a memory storing program instructions to compute performance scores 234 and determine visual disability metrics 236 based on the voluntary and/or involuntary responses of the user to the virtual reality simulation and sensor readings from sensor system 220 relayed to computing device 230; note: computing device 230 expressly contains program instructions 234, an algorithm, stored in memory and executed by processor 232 to process sensor readings(eye data) from sensory system 220). Leung does not specifically teach to evaluate an eye movement stability of the patient. However, in a similar field of endeavor, Krueger teaches the system, wherein to evaluate an eye movement stability of the patient (¶37, Foveal Fixation Stability (FFS) refers to the ability to maintain an image on the fovea, which is crucial for the visual extraction of spatial detail. If the target image moves 1° from foveal center, or if random movement of the image on the fovea exceeds 2°/sec, visual acuity degrades substantially; ¶2, human ocular performance measurements that can be measured using YR/ AR/synthetic 3D include vestibulo-ocular reflex, saccades, visual pursuit tracking, nystagmus, vergence, eye-lid closure, dynamic visual acuity, kinetic visual acuity, retinal image stability, foveal fixation stability, and focused position of the eyes; ¶36, Dynamic visual stability (DYS) and retinal image stability (RIS) can be used interchangeably. In this document, DYS will be used to describe the ability to visualize objects accurately, with foveal fixation, while actively moving the head. note: the reference discloses three specific eye movement stability measurements as core VR clinical ocular performance metrics: foveal fixation stability, ability to maintain image on fovea; (2) retinal image stability (RIS/DVS), ability to maintain stable retinal image during head movement; and (3) nystagmus, involuntary oscillatory eye movement directly measuring instability. All three are algorithms that process oculomotor eye data to evaluate eye movement stability). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the system of Leung with to evaluate an eye movement stability of the patient of Krueger, for the purpose of providing a detection, assessment, or management of patient health(¶78).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY DUONG whose telephone number is (571)270-0534. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM.
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/HENRY DUONG/Primary Patent Examiner, Art Unit 2872 06/26/26
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