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 .
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.
Claim(s) 1-3, 11-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guzik et al US 2019/0008441 to Guzik et al. (“Guzik”) and US 20230337911 A1 to Ooi et al. (“Ooi”).
As to claim 1, 19 and 20 Guzik teaches a system for real-time visual health monitoring during extended use (fig. 1-6, abstract), comprising: a head-mounted display (¶00189, headset); eye-tracking sensors (Fig. 6, eye tracking); one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: Ooi teaches generating a VR user interface corresponding to a three-dimensional virtual environment (¶0021, ¶0023, ¶0039, gaze ray control or assessment generated from measured/tracked eye movements and fixation of a user while the user interacts with objects and environment within a three-dimensional computing environment); rendering the VR user interface on the VR headset (Fig. 1A, label 100a); continuously monitoring, using the eye-tracking sensors, user eye movements and behavior during extended VR sessions (¶0041, The head-mounted display VR system 102 includes a driver interface 109 that can provide outputs 111 from the eye origin tracking and gaze tracking modules); and dynamically adjusting the VR user interface based on detected visual health indicators (¶0003, ¶0005, ¶0018, ¶0031, ¶0040, ¶0042, ¶0052-0053, ¶0057, determining cognitive responses/function, processing speed and cogitative fatigue form monitoring user's response and tracking eye movement). In view of the teachings of Ooi, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik. The suggestion/motivation would be to employ gaze rays generated from measured/tracked eye movements and fixation for interaction with objects and environment within a three-dimensional computing environment.
As to claim 2, Guzik and Ooi teaches the invention substantially as claimed above, but
failed to explicitly teach The method of Claim 1, wherein the high-resolution VR headset has a resolution of at least 60 pixels per degree (PPD), a refresh rate of 90-120 Hz, and a field of view of 100-120 degrees, and wherein the eye-tracking sensors have an accuracy of 0.1-degree precision and a latency of less than 10 milliseconds. However, at the time the invention was made, it would have been an obvious matter of design choice to a person of ordinary skill in the art at the time the invention was made to design high-resolution VR headset has a resolution of at least 60 pixels per degree (PPD), a refresh rate of 90-120 Hz, and a field of view of 100-120 degrees, and wherein the eye-tracking sensors have an accuracy of 0.1-degree precision and a latency of less than 10 milliseconds because the Applicant has not disclosed that that these specific features provides an advantage, is used for a particular purpose, or solves a stated problem. One of ordinary skill in the art, furthermore, would have expected VR 110 of Guzik's invention and the Applicant's invention, to perform equally well with either the visual fidelity and the responsiveness latency taught by Guzik's invention or the claimed visual fidelity and the responsiveness latency because both VR headsets would perform the same function of being worn by a
user to display different tests and receives user's responses for cognitive evaluation.
As to claim 3, Guzik and Ooi teaches the invention substantially as claimed above, wherein the extended VR sessions comprise gaming sessions lasting 2-4 hours, educational sessions lasting 1-2 hours, or professional training simulations lasting 30 minutes to several hours. However, it would have been obvious to one having an ordinary skill in the art at the time the invention was made to present a series of interactive multitasking scenarios comprises simulating sessions ranging from 15 to 60 minutes, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or working ranges involves only routine skill in the art, In re Aller, 105 USPQ 233.
As to claim 11, Guzik and Ooi teaches the method of Claim 1, wherein dynamically adjusting the VR user interface comprises modifying display settings including brightness, contrast, or color temperature (Ooi, ¶0084, The object can be presented in the same location all the time or can be randomized (so the user would have to identify and search for them). In addition, to adjust the difficulty of the task and/or to assess at different levels of difficulty, the system can vary the contrast and/or luminance of the object or target. The system may also present the object or target in different sizes, e.g., in diminishing size. For purely diagnostic applications, the system would present a standard target or object so it could be normalized for the population).
As to claim 12, Guzik and Ooi teaches the method of Claim 11, wherein modifying display settings comprises reducing brightness by 10-30% Ooi, ¶0084, The object can be presented in the same location all the time or can be randomized (so the user would have to identify and search for them). In addition, to adjust the difficulty of the task and/or to assess at different levels of difficulty, the system can vary the contrast and/or luminance of the object or target. The system may also present the object or target in different sizes, e.g., in diminishing size. For purely diagnostic applications, the system would present a standard target or object so it could be normalized for the population)or increasing font size by 10-20% during prolonged reading tasks.
As to claim 13, Guzik and Ooi teaches the method of Claim 1, further comprising using machine learning algorithms to detect patterns of fatigue based on historical data (¶0045, FIG. 2c may involve statistical tests. For example, the Student's t-test may be used when the variances of both the current test response timing for one or more types of tests and previous test response timing are equal or the Welch's t-test when the variances are not equal. If the current test and previous test are identical in test type and test order, a paired t-test may be used to compare results and determine if differences exist in patient timing test responses to tests. It should be understood alternative or additional statistical tests may be used to compare results, as well as heuristics based on logical relationships, machine learning techniques, and the like may also be used to compare test responses).
As to claim 15, Guzik and Ooi teaches the method of Claim 1, further comprising generating a visual health report including visual strain indicators over time, recommended adjustments, and long-term trends (¶0040-0043 and ¶0057, Fig.2c, storing this information in a plurality of current patient test-response records, ¶0040 in fig.2a, recording correct/incorrect responses and correct/incorrect
response timing, ¶0039, fig.2b, storing current test results and previous test results for comparison).
As to claim 16, Guzik and Ooi teaches the method of Claim 1, further comprising providing a user interface for real-time feedback and recommendations related to visual health (¶0052).
As to claim 17, Guzik and Ooi teaches the method of Claim 1, further comprising calibrating the system using a control group of 20-50 individuals with diverse age and visual profiles (¶0004).
As to claim 18, Guzik and Ooi teaches the method of Claim 1, further comprising: establishing baseline visual health metrics for the user; comparing real-time eye tracking data to the baseline metrics; and initiating visual interface adjustments when deviations from the baseline exceed predetermined thresholds (Guzik, ¶0042).
Claim(s) 4-6 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guzik and Ooi as applied to claim 1 above, and further in view of US 20240148599 A1 to Altman et al. (“Altman”).
As to claim 4, Guzik and Ooi teaches the method of claim 1, Guzik and Ooi does not teach wherein monitoring user eye movements and behavior comprises tracking blink rate, blink duration, pupil dilation, and fixation stability. Altman teaches wherein monitoring user eye movements and behavior comprises tracking blink rate, blink duration, pupil dilation, and fixation stability (¶0011, eliciting a blink response in a patient/user. Generally, this blink response is caused by creating a temporary shade, haze, blur, or similar optical feature (referred herein after as haze for simplicity) in front of a user's eyes, which thereby induces the user to blink. When a tear film in the eye breaks, a blur is created. The brain detects the haze and triggers a blink, so that the tear film on the eye is rebuilt. The devices of the present invention seeks to mimic this haze to trigger the brain in eliciting a blink response at any given time (e.g., at regular intervals, irregular intervals, or based on sensor data). In view of the teachings of Altman, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik and Ooi. The suggestion/motivation would be used to treat various vision related conditions that may be addressed by increasing a blink rate of a user, such as including dry eye syndrome.
As to claim 5, Guzik, Ooi and Altman teaches the method of Claim 4, wherein tracking blink rate comprises measuring the number of blinks per minute, with 12-15 blinks per minute considered normal at rest (¶0005, Healthy individuals typically blink about 10-15 times per minute. With each blink, the eye's tear film is renewed, which protects and moisturizes the eye. The tear film consists of three sublayers: mucus, watery and oil layer at the top, which protects the eye from dryness (water evaporation)).
As to claim 6, Guzik, Ooi and Altman teaches the method of Claim 4, wherein tracking blink duration comprises measuring the length of each blink, with 100-150 milliseconds considered normal (¶0047, The blink inducing device (e.g., eyeglasses, monitor screen, or the like) may create haze in front of a user's eyes relatively quickly or may more slowly increase and/or decrease the haze level to more reliably induce a blink response. For example, the haze may be created and maintained for 0.1 second to 1 second for a generally quick optical obstruction that is created, or may be created and maintained in a relatively longer time frame, such as between 1 second and 10 seconds. In a more specific example, the haze may be maintained in about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 seconds (as well as increments in between)).
As to claim 9, Guzik and Ooi teaches the method of Claim 1, Guzik and Ooi does not teach wherein detecting visual health indicators comprises tracking blink rate, blink duration, pupil dilation and fixation stability, wherein increased blink rate and duration indicates fatigue, diminished fixation stability indicates strain, and persistent pupil dilation indicates excessive cognitive load or discomfort. Altman teaches wherein detecting visual health indicators comprises tracking blink rate, blink duration, pupil dilation and fixation stability, wherein increased blink rate and duration indicates fatigue, diminished fixation stability indicates strain, and persistent pupil dilation indicates excessive cognitive load or discomfort (¶0011, eliciting a blink response in a patient/user. Generally, this blink response is caused by creating a temporary shade, haze, blur, or similar optical feature (referred herein after as haze for simplicity) in front of a user's eyes, which thereby induces the user to blink. When a tear film in the eye breaks, a blur is created. The brain detects the haze and triggers a blink, so that the tear film on the eye is rebuilt. The devices of the present invention seeks to mimic this haze to trigger the brain in eliciting a blink response at any given time (e.g., at regular intervals, irregular intervals, or based on sensor data). In view of the teachings of Altman, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik and Ooi. The suggestion/motivation would be used to treat various vision related conditions that may be addressed by increasing a blink rate of a user, such as including dry eye syndrome.
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guzik ,Ooi and Altman as applied to claim 4 above, and further in view of US 20200121195 A1 to Bressler et al. (“Bressler”).
As to claim 7, Guzik, Ooi and Altman teaches the method of Claim 4, Guzik, Ooi and Altman does not teach wherein tracking pupil dilation comprises measuring pupil size, with 2-4 millimeters considered normal. Bressler teaches wherein tracking pupil dilation comprises measuring pupil size, with 2-4 millimeters considered normal (¶0072, information and data that may be useful for the pupil size and response test, along with the color sensitivity test. For example, the scene settings 176 refers to various characteristics of the scene displayed on the screen 108 of the VR headset, including but not limited to scene brightness and scene colors. The brightness in the scene settings 176 is changed for the pupil response test, and specific colors in the scene settings are changed for the color sensitivity test. For example, in the color sensitivity test, VR headset 102 is configured to observe whether the patient responds to yellow and/or blue colors. In this regard, yellow/blue color vision loss is rare and thus serves as an indicator of visual impairment that may be associated with some medical conditions. Left pupil size 178 refers to the size of the patient's left pupil, measured in millimeters by the eye tracking hardware 106 and software 120. Right pupil size 180 refers to the size of the patient's right pupil, measured in millimeters the eye tracking hardware 106 and software 120.). In view of the teachings of Bresler, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik, Ooi and Altman. The suggestion/motivation would be to create a virtual-reality (“VR”) environment that implements visual symptom tests, and which utilizes eye tracking technology to detect or indicate visual symptoms and associated medical conditions.
As to claim 8, Guzik, Ooi and Altman teaches the method of Claim 4, Guzik, Ooi and Altman does not teach wherein tracking fixation stability comprises measuring eye movement during fixation, with 0.5 degrees or less considered stable. Altman teaches wherein tracking fixation stability comprises measuring eye movement during fixation, with 0.5 degrees or less considered stable (¶0075, to eye movement in general, which may be useful for all the visual symptom tests. The eye position 194 refers to the X and Y coordinate position of each of the patient's pupils within the eye socket, measured by the tracking hardware 106 and software 120. The eye jitter 196 refers to the angle between each patient's eye's direction and the direction of each eye at the last sample, measured in degrees by the eye tracking hardware 106 and software 120. Eye position 194 and eye jitter 196 information may be particularly useful for the previously mentioned targeting test. The targeting test measures ability to detect the presence of an object that appears in a patient's field of view and the patient's ability to focus their gaze on that object. The targeting test is administered by making an object appear at several locations for a set amount of time in the patient's field of view. In some particular examples, an object appears in eight different locations in the patient's field of view for about 3 to 5 seconds, where each object location includes a different direction and distance metric. The patient is instructed to focus their gaze on the target object when detected, and the appropriate eye data is measured and recorded upon detection). In view of the teachings of Bresler, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik, Ooi and Altman. The suggestion/motivation would be to create a virtual-reality (“VR”) environment that implements visual symptom tests, and which utilizes eye tracking technology to detect or indicate visual.
Claim(s) 10 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guzik and Ooi as applied to claim 4 above, and further in view of US 20200405215 A1 to Tinjust.
As to claim 10, Guzik and Ooi teaches the method of Claim 1, Guzik and Ooi does not teach wherein dynamically adjusting the VR user interface comprises providing break recommendations based on cumulative strain metrics. Tinjust teaches wherein dynamically adjusting the VR user interface comprises providing break recommendations based on cumulative strain metrics (¶0132, The apparatus 100 may then signal the user of possible cognitive exertion at step 404. In some examples, the signal is a warning, heeding that the user stop immediately the cognitive task the user is carrying out. In other examples, the signal instead may request feedback from the user. For instance, when the ANS assessment module 203 begins to detect signs of cognitive exertion, the ANS assessment module 203 may request that the user provide feedback on his or her fatigue at step 405. The apparatus 100 may address, via display 106, a question to the user, e.g. “Are you feeling tired?” The user may respond by <YES> or <NO>. If the user selects <YES>, then the apparatus 100 may urge the user to immediately cease carrying out the cognitive task he or she is currently undertaking. If the user selects <NO>, then the apparatus 100 may simply output a warning to slow down or take a break. In some examples, the question may be phrased to ask the user when does the user, performing physical activity, start feeling signs of cognitive fatigue, or cognitive exertion. The apparatus may then record when the user provides input indicative of signs of cognitive exertion, and can record at what level of physical exertion (e.g. by recording the heart rate, or heart rate maintained at a level for a given time) that the signs of cognitive exertion present themselves. The apparatus 100 may then continue to monitor the physiological measurements of the user at step 402 for signs that the physiological measurements for that bodily function return to, or are close to, that of the user at rest. Steps 402 to 405 may then be repeated until user stops carrying out the cognitive task, either because the task is complete, or the user surpasses a recommended cognitive exertion level as assessed the ANS assessment module 203.). In view of the teachings of Tinjust, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik and Ooi. The suggestion/motivation would be to determine if a patient's cognitive function is improving.
As to claim 14, Guzik and Ooi teaches the method of Claim 1, Guzik and Ooi does not teach further comprising using predictive models to anticipate when fatigue will likely occur and preemptively adjust visual settings. Tinjust teaches further comprising using predictive models to anticipate when fatigue will likely occur and preemptively adjust visual settings ( ¶0148, testing cognitive function may have a main module that extracts, from an internal database (or in some examples, and/or an external database), cognitive exercises (e.g. image pairs for conducting cognitive tests). The main module may process the data through program code including, for instance, a machine learning algorithm or deep learning algorithm, and the result of the processing may yield an adaptive cognitive exercise (e.g. image pairs for conducting cognitive tests) executed by the perceptual generator to produce the images on the display. The information may also be sent to the clinical decision support system, so that the information may be shared with the clinician and patient support module. The cognitive tests can be generated by the generic tests generator. The tests may also be generated by the adaptive tests generator, where the adaptive test generator may communicate with a clinical decision support system (CDSS), where the tests are adapted as a function, for instance, of input received externally from a clinician and patient support module (CPSM) or telemedicine module, where, for example, a medical professional or medical practitioner may provide input (in some examples remotely) to tailor or to generate specific cognitive tests. The adaptive test generator may also communicate with an intelligence module that can send instructions to adapt the test as a function of previous results. In some examples, the cognitive testing apparatus may be used at, for example, the home of the patient, where the supervising medical practitioner may provide input remotely (e.g. at his or her office), such as suggesting certain cognitive exercises to be carried out by the patient, where the medical practitioner can then supervise the carrying out of the tests remotely). In view of the teachings of Tinjust, it would have been obvious before the effective filing date of the invention to modify the teachings of Guzik and Ooi. The suggestion/motivation would be to determine if a patient's cognitive function is improving.
Conclusion
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/CHRISTINE A KURIEN/Examiner, Art Unit 2421 /NATHAN J FLYNN/Supervisory Patent Examiner, Art Unit 2421
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