DETAILED ACTION
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 statement (IDS) submitted on 11/19/2024, 03/11/2025 was filed and is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
The preliminary amendments filed on 11/19/2024 (which has title “AMENDMENTS TO THE CLAIMS”) has been entered. Claims 4-12, 17 were amended. Claims 1-20 remain pending in the application.
Claim Objections
Claims 1 and 16 objected to because of the following informalities:
Regarding claim 1, possible typo involving missing closing parenthesis that has a starting parenthesis in line 1, but appears to have no closing parenthesis. Additionally, the acronyms “VPT/OVPT” do not appear to be clearly defined in the claims, but the specification does provide the meaning of the acronyms of “Visual Photosensitivity Discomfort Threshold (VPT) objectively (OVPT)” ([0005]).
Regarding claim 16, possible typo that states “th econfiguration”, that may potentially be read as “the configuration”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claim 1 and 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “greater measurement repeatability” in claim 13 is a relative term which renders the claim indefinite. The term “greater measurement repeatability” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The “comparison” limitation would be affected by the indefiniteness of “greater measurement repeatability”. The specification does make reference to confidence (such as in [0086]). In the interest of compact prosecution, the phrase “greater measurement repeatability” will be interpreted as involving confidence score or patient endurance (as a patient becoming blind as a result of a bright light shining into their eyes would affect repeatability) until applicant amends the claims.
Regarding claim 1, the phrase "for example" (or stated as “e.g.”) renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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, 2, 4, 5, 7, 9, 11, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Verriotto (“New Methods for Quantification of Visual Photosensitivity Threshold and Symptoms”, 2017), in view of Karamitsos (“A Modified CNN Network for Automatic Pain Identification Using Facial Expressions”, 2021).
Regarding claims 1, 18, and 20, Verriotto teaches A method to measure of visual photosensitivity discomfort threshold (Verriotto, Abstract, Methods: “We designed and built the ocular photosensitivity analyzer (OPA), an automated instrument to determine light intensity visual photosensitivity threshold (VPT)”. “determine light intensity visual photosensitivity threshold” is being interpreted as “method to measure of visual photosensitivity discomfort threshold”) (e.g., objective visual photosensitivity discomfort threshold or presence of a condition associated therewith within a subject (Verriotto, Abstract, Purpose: “Visual photosensitivity is a common symptom difficult to measure and diagnose, and is found in many ocular and neurological disorders”), the method comprising:
obtaining, by one or more processors (Verriotto, see nearest image below, “computer graphical user interface” is being interpreted to involve one or more processors from the computer), a plurality of images of the subject (Verriotto, see nearest image below, “live video recording” of the “patient” is being interpreted as involving “plurality of images of the subject”), wherein each image includes a representation of i) at least one pupil (Verriotto, Figure 3, Section M image which shows the eyes, that includes pupil and palpebral fissure contour, of the subject) and ii) a corresponding palpebral fissure contour of the subject captured (Verriotto, Figure 3, Section L image which shows the eyes, that includes pupil and palpebral fissure contour, of the subject) while the at least one pupil and corresponding palpebral fissure contour was being subjected to a light stimulus (Verriotto, pg 6, Figure 3 tex, reproduced below:
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. “Illumination intensity” and Figure 3 Section M shows “at least one pupil and corresponding palpebral fissure contour” of the subject);
determining, by the one or more processors, executing instructions for a …. having been configured using a plurality of images (Verriotto, pg 5, column 1, lines 2-3: ”designed to record high resolution infrared video (50 fps)”. “Video” is being interpreted as involving a non-limiting example of “plurality of images”) of a plurality of subjects (Verriotto, pg 7, column 2, last paragraph, “measurements of the same patients”. “Patients” are being interpreted as involving “plurality of subjects”) captured at a plurality of different illumination levels (Verrioto, Figure 3 text, “Data graph with reversals”; Figure 3 section H, the recorded reversals shows “plurality of different illumination levels”), an output value (Verriotto, Figure 3 text: “reversals and mean indicator” are being interpreted as involving non-limiting examples of “output value”) corresponding to a measure of visual photosensitivity discomfort threshold (Verriotto, title, “Quantification of Visual Photosensitivity Threshold”, which is being interpreted as involving “visual photosensitivity discomfort threshold”) in which the visual photosensitivity discomfort threshold (Verriotto, title, “Quantification of Visual Photosensitivity Threshold”, which is being interpreted as involving “visual photosensitivity discomfort threshold”) is defined by an estimated illuminance of a retina of the subject (Verriotto, Figure 3 text, “(L) illumination intensity (Lux)”, which is being interpreted as involving “estimated illuminance”; Figure 3 Section L, which shows retina of the subject), and
outputting, by the one or more processors, the output value in a report for a stimulus illumination tool for VPT/OVPT measurement (Verriotto, pg 7, Figure 4 and text, reproduced below:
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. “Data graph” and Figure 4 are being interpreted as non-limiting examples of “output value in a report for a stimulus illumination tool for VPT/OVPT measurement”),
wherein the output value (Verrioto, Figure 3 text, “(K) Data graph with reversals and mean indicator”. “Mean indicator” is being interpreted as involving a non-limiting example of “output value”) is used to diagnose (Verrioto, Abstract, Purpose: “Visual photosensitivity is a common symptom difficult to measure and diagnose, and is found in many ocular and neurological disorders”) the subject for photophobia or light sensitivity condition (Verrioto, Abstract, Purpose: “Visual photosensitivity is a common symptom difficult to measure and diagnose, and is found in many ocular and neurological disorders”. “Visual photosensitivity” is being interpreted as involving “light sensitivity condition”. Examiner notes this is part of an “OR” phrase, so only one item will be considered); or used to evaluate an effectiveness or adjust a configuration of eyewear or optical instruments for reducing a subject's reaction or discomfort to light sensitivity-causing stimuli.
However, Verrioto does not appear to explicitly teach neural network.
Pertaining to the same field of endeavor, Karamitsos teaches
…neural network… (Karamitsos, Abstract: “In this paper, a convolutional neural network (CNN) model was built and trained to detect pain through patients’ facial expressions”)
Verrioto and Karamitsos are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain (as taught by Verrioto) to include automatic pain identification using facial expressions (as taught by Karamitsos) because the combination provides an improvement to patient care (Karamitsos, Abstract).
Regarding claim 2, Verriotto teaches The method of claim 1 further comprising:
Outputting (Verriotto, pg 6, Figure 3, the GUI is outputting), by the one or more processors, visualization (Verriotto, pg 6, Figure 3, bottom-right is showing a visualization of a face with pupils present) indicating an estimated area (Verriotto, pg 6, Figure 3, bottom-right is showing an estimated area of the face) of the at least one pupil (Verriotto, pg 6, Figure 3, bottom-right is showing at least one pupil) in at least one image (Verriotto, pg 6, Figure 3, bottom-right is showing at least one image), or a portion thereof, of the plurality of images of the subject (Verriotto, pg 5, column 1, lines 2-3: “designed to record high resolution infrared video”, which is being interpreted to be of the patient, or subject),
wherein the estimated area (Karamitsos, Figure 2, left-side, the face is being interpreted as involving a non-limiting example of the estimated area) was generated by the neural network (Karamitsos, Figure 2 text, “VGG16 architecture”, a non-limiting example would be once the face enters the neural network architecture, the estimated area is generated as an output of a non-limiting example of the first layer) and used by the neural network to determine the output value (Karamitsos, Figure 2, the non-limiting examples of output value of “No-Pain” and “Pain”).
Regarding claim 4, Verriotto teaches The method of claim 1, wherein the plurality of images are acquired from an ocular photosensitivity analyzer (Verriotto, Abstract, Methods: “We designed and built the ocular photosensitivity analyzer (OPA)”).
Regarding claim 5, Verriotto teaches The method of claim 1, wherein the plurality of images are acquired from a video camera system (Verriotto, pg 5, lines 2-3: “designed to record high resolution infrared video”) comprising illumination control (Verriotto, pg 2, column 2, 2nd to last paragraph: “The OPA produces light stimuli with a bi-cupola array panel configured with 210 light emitting diodes (LEDs) focused at 50 cm (Fig. 2a), with a power regulator controlled via computer (Fig. 2b).”).
Regarding claim 7, Verriotto teaches The method of claim 1, wherein the neural network was trained with a second plurality of images of the plurality of subjects (Karamitsos, pg 406, Section 4, first paragraph, “The data used to fulfill this study is the UNBC-McMaster Shoulder Pain Expression Archive Database”. It would be obvious to try another dataset where head has motion because PHOSITA would know that heads have motion) at different head motions (Karamitsos, pg 407, first full paragraph: “For each test, a video sequence was recorded with frequent changes in pose, as we can see in Figure 3”. “Frequent changes in pose” is being interpreted as a non-limiting example of “different head motions”).
Regarding claim 9, Verriotto teaches The method of claim 1, wherein the neural network (Karamitsos, Abstract: “In this paper, a convolutional neural network (CNN) model was built and trained to detect pain through patients’ facial expressions”) employs feature extracted (Karamitsos, pg 403, last line: “First, they used a CNN model to learn facial features”) from at least one of: (i) a developed eye model, (ii) an optical model of an ocular photosensitivity analyzer or camera system (Verriotto, Abstract, Methods: “We designed and built the ocular photosensitivity analyzer (OPA)”. Examiner notes the “at least one of” is being interpreted as an “OR” statement, so only one item will be considered), or (iii) a physical eye model comprising a variable associated with pupil diameter and/or refractive error.
Regarding claim 11, Verriotto teaches The method of claim 1, wherein the neural network (Karamitsos, Figure 2 text: “VGG16 architecture”) employs a feature extracted (Karamitsos, pg 405, 5th bulletpoint: “resulting feature maps”) from a physical facial model of the subject (Karamitsos, pg 405, Figure 2, left side of image, which shows physical facial model of the subject).
Regarding claim 13, Verriotto teaches The method of claim 1, wherein the output value defined by the estimated illuminance of the retina of the subject (Verriotto, Figure 3 text: “predicted threshold”) and as generated by the neural network (Karamitsos, Abstract: “In this paper, a convolutional neural network (CNN) model was built and trained to detect pain through patients’ facial expressions”), has a greater measurement repeatability as compared to the output value being defined by stimuli illuminance (Verriotto, pg 1, column 2, “light stimulation may lead to pain from stimulation of the trigeminal nerve”. Which shows for subjects where the light stimulation, or stimuli illuminance, leads to pain that is too much for the patient to handle, then the testing is over immediately, affecting “measurement repeatability”.).
Regarding claim 14, Verriotto teaches The method of claim 1, wherein the output value is used to diagnose the subject for photophobia or light sensitivity condition (Verriotto, Abstract, Purpose: “Visual photosensitivity is a common symptom difficult to measure and diagnose, and is found in many ocular and neurological disorders. We developed two novel reproducible quantitative assessments of visual photosensitivity”. “Quantitative assessment” is being interpreted as involving “output value”. “Visual photosensitivity” is being interpreted as involving “light sensitivity condition”).
Regarding claim 15, Verriotto teaches The method of claim 1, wherein the output value is used to evaluate the effectiveness of eyewear or optical instruments (Verriotto, pg 6, column 1, last paragraph: “The study subjects were instructed to answer the VLSQ-8 questions as if they were wearing usual correction (i.e., glasses or contact lenses, if any) and to choose one answer for each question”. It would be obvious to try the OPA with eyewear, or glasses, that the patient is wearing to evaluate the effectiveness of the eyewear).
Regarding claim 16, Verriotto teaches The method of claim 1, wherein the output value is used to adjust the configuration of eyewear or optical instruments for reducing a subject's reaction (Verriotto, Figure 4, which shows 10 response reversals, a non-limiting example of the output values going down shows reduction in a subject’s reaction to the light intensity. The OPA is being interpreted as a non-limiting example of an optical instrument. Examiner notes this limitation is part of an “Or” statement, so only one item needs to be considered) or discomfort to the light sensitivity-causing stimuli.
Regarding claim 17, Verriotto teaches The method of claim 1, wherein the one or more processors are located in a local computing device (Verriotto, pg 4, Figure 2 text: “(G) Laptop computer”) or a cloud platform.
Regarding claim 19, Verriotto teaches The system of claim 18, further comprising an ocular photosensitivity analyzer (Verriotto, see nearest image below, “OPA”), wherein the ocular photosensitivity analyzer comprises:
at least one hardware processor (Verriotto, see nearest image below, “computer” is being interpreted as involving at least one hardware processor);
a programmable light source comprising a plurality of multi-spectra light modules configured to emit light at a range of wavelengths (Verriotto, see nearest image below, “The OPA produces light stimuli with a bi-cupola array panel configured with 210 light-emitting diodes (LEDs) focused at 50 cm (Fig. 2a), with a power regulator controlled via computer (Fig. 2b).”);
a sensing system comprising one or more sensors (Verriotto, see nearest image below, “record light intensity data” is being interpreted as a non-limiting example of involving one or more sensors to record data); and
one or more software modules that are configured to (Verriotto, see nearest image below, “graphical user interface” is being interpreted as involving “one or more software modules”), when executed by the at least one hardware processor (Verriotto, see nearest image below, “computer”), receive an indication of a lighting condition comprising one or more wavelengths of light (Verriotto, see nearest image below, “record light intensity data”), configure the programmable light source to emit light according to the lighting condition (Verriotto, see nearest image below, “output light stimuli ranging from 0.1 to 32,000 lux”), and, for each of one or more iterations, activate the programmable light source to emit the light according to the lighting condition (Verriotto, see nearest image below, “The OPA produces light stimuli with a bi-cupola array panel”), and collect a response, by a subject, to the emitted light via the sensing system (Verriotto, pg 2-3, Section “Assessment of Visual Photosensitivity Light Threshold: OPA”, ¶1-2, reproduced below:
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. “record light intensity data” and “record subject responses” are non-limiting examples of “collect a response, by a subject, to the emitted light via the sensing system”).
Claim(s) 3, 6, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Verriotto, as modified by Karamitsos, in view of Lou (“Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery”, 2021).
Regarding claim 3, Verriotto teaches The method of claim 1 further comprising:
Outputting (Verriotto, pg 6, Figure 3, the GUI is outputting), by the one or more processors, visualization (Verriotto, pg 6, Figure 3, bottom-right is showing a visualization of a face with palpebral fissure contour present) indicating a palpebral fissure contour (Verriotto, pg 6, Figure 3, bottom-right is showing a visualization of a face with palpebral fissure contour present) in at least one image (Verriotto, pg 6, Figure 3, bottom-right is showing a visualization of a face that is interpreted as “at least one image”), or a portion thereof, of the plurality of images of the subject (Verriotto, pg 5, column 1, lines 2-3: “designed to record high resolution infrared video”, which is being interpreted to be of the patient, or subject),
However, Verriotto and Karamitsos does not appear to explicitly teach wherein the palpebral fissure contour was generated by the neural network and used by the neural network to determine the output value.
Pertaining to the same field of endeavor, Lou teaches
wherein the palpebral fissure contour (Lou, pg 2280, Figure 1, which shows “palpebral fissure contour” was segmented, or is a non-limiting example of a visualization) was generated by the neural network (Lou, pg 2279, “Deep learning with convolutional neural networks (CNN) has achieved state-of-the-art performance for automatic ophthalmological image segmentation”) and
used by the neural network to determine the output value (Lou, pg 2279, column 1, first full paragraph: “Therefore, the aims of this study were to propose a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery, as an objective evaluation of surgical effectiveness”. “Objective evaluation” is being interpreted to involve “output value” for the objective evaluation).
Verriotto, Karamitsos, and Lou are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain and modified to involve a neural network (as taught by Verrioto and Karamitsos) (as taught by Verriotto and Karamitsos) to include wherein the palpebral fissure contour was generated by the neural network and used by the neural network to determine the output value (as taught by Lou) because the combination provides an improvement to objective assessment of patient outcomes (Lou, Abstract).
Regarding claim 6, Verriotto teaches The method of claim 1,
However, Verriotto and Karamitsos does not appear to explicitly teach wherein the neural network had been trained with a second plurality of images of a second plurality of subjects quantified as having different eye sizes.
Pertaining to the same field of endeavor, Lou teaches
wherein the neural network had been trained with a second plurality of images of a second plurality of subjects (Lou, pg 2279, column 2, last paragraph, “Step 1: Regions of eyes were localized by an open-source project named Face Alignment [13]. Facial image of 2069 volunteers (4138 eyes) from the Second Affiliated Hospital of Zhejiang University were used to train the eye segmentation network”) quantified as having different eye sizes (Lou, pg 2279, column 2, last paragraph, “The eyelid and the corneal limbus were outlined by two ophthalmologists”, which is being interpreted as a non-limiting example of “quantified”; Figure 1, left and right side shows segmented eyes of different size).
Verriotto, Karamitsos, and Lou are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain and modified to involve a neural network (as taught by Verrioto and Karamitsos) (as taught by Verriotto and Karamitsos) to include wherein the neural network had been trained with a second plurality of images of a second plurality of subjects quantified as having different eye sizes (as taught by Lou) because the combination provides an improvement to objective assessment of patient outcomes (Lou, Abstract).
Regarding claim 12, Verriotto teaches The method of claim 1,
However, Verriotto and Karamitsos does not appear to explicitly teach “wherein the neural network comprises an image segmentation algorithm”.
Pertaining to the same field of endeavor, Lou teaches
wherein the neural network comprises an image segmentation algorithm (Lou, pg 2280, column 1, first full paragraph, “The Attention R2U-Net based model predicted eyelid and cornea segmentation mask”. Which involves a non-limiting example of a neural network; Figure 1, which shows the image segmentation results).
Verriotto, Karamitsos, and Lou are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain and modified to involve a neural network (as taught by Verrioto and Karamitsos) (as taught by Verriotto and Karamitsos) to include wherein the neural network comprises an image segmentation algorithm (as taught by Lou) because the combination provides an improvement to objective assessment of patient outcomes (Lou, Abstract).
Claim(s) 8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Verriotto, as modified by Karamitsos, in view of Aguilar (“Ocular Photosensitivity Analyzer: An Automated Instrument Designed to Determine Visual Photosensitivity Thresholds”, 2019).
Regarding claim 8, Verriotto teaches The method of claim 1,
wherein the obtained plurality of images (Karamitsos, pg 406, Section 4, ¶1: “The data used to fulfill this study is the UNBC-McMaster Shoulder Pain Expression Archive Database”) each includes a representation of a facial area of the subject (Karamitsos, pg 405, Figure 2, left side, input face image of the subject is being interpreted as a “representation of a facial area of the subject”),
wherein the neural network (Karamitsos, pg 405, Figure 2 text, “VGG16 architecture”) has been configured to classify facial expressions (Karamitsos, pg 405, Figure 2, “Pain” and “No Pain” are being interpreted as non-limiting examples of classification of “facial expressions”), and
However, Verriotto and Karamitsos does not appear to explicitly teach “changes thereof, selected from the group consisting of blinking rate, squinting, and frowning, and wherein parameters associated with the classified facial expressions are used to generate the determined output value”.
Pertaining to the same field of endeavor, Aguilar teaches
changes thereof, selected from the group consisting of blinking rate, squinting, and frowning (Aguilar, pg 1, Section 1.1, ¶1: “Visual photosensitivity varies considerably between individuals, manifesting as annoyance, squinting, distraction, blinking, tearing, and light aversion”), and
wherein parameters associated with the classified facial expressions are used to generate the determined output value (Aguilar, pg 109, ¶2, reproduced below:
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. “Facial muscle changes” is being interpreted as “classified facial expressions”. “Objective measure for VPT [visual photosensitivity threshold, as cited on page i]” is being interpreted as “determined output value”).
Verrioto, Karamitsos, and Aguilar are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain with neural network (as taught by Verrioto and Karamitsos) to include changes thereof, selected from the group consisting of blinking rate, squinting, and frowning, and wherein parameters associated with the classified facial expressions are used to generate the determined output value (as taught by Aguilar) because the combination provides an improvement to reliably quantify VPT and to provided comprehensive clinical outcome measures (Aguilar, pg ii). Further, it would be obvious to try adding in a second neural network as Aguilar already teaches training for determining output value without facial expression, then experiment to try adding in “facial muscle changes…as an object measure for VPT” using another neural network. PHOSITA would know a non-limiting example of a neural network such as CNN does feature extraction from images.
Regarding claim 10, Verriotto teaches The method of claim 1, wherein the obtained plurality of images each includes a representation of a facial area of the subject (Karamitsos, pg 405, 5th bulletpoint: “the resulting feature maps”, which is being interpreted as a non-limiting example of “a representation of a facial area of the subject”; Figure 2, left side, shows the facial area of the subject),
wherein a second neural network has been configured to generate a facial expression parameter (Karamitsos, pg 405, Figure 2, which shows the facial expression parameters outputs on the right side of “Pain” and “No-Pain”), and
However, Verriotto and Karamitsos does not appear to explicitly teach wherein the facial expression parameter is used to generate the determined output value.
Pertaining to the same field of endeavor, Aguilar teaches
wherein the facial expression parameter is used to generate the determined output value (Aguilar, pg 109, ¶2, reproduced below:
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. “Facial muscle changes” is being interpreted as “facial expression parameter”. “Objective measure for VPT [visual photosensitivity threshold, as cited on page i]” is being interpreted as “determined output value”).
Verrioto, Karamitsos, and Aguilar are considered to be analogous art because they are directed to medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for quantification of visual photosensitivity threshold where light stimulation may lead to pain with neural network (as taught by Verrioto and Karamitsos) to include wherein the facial expression parameter is used to generate the determined output value (as taught by Aguilar) because the combination provides an improvement to reliably quantify VPT and to provided comprehensive clinical outcome measures (Aguilar, pg ii). Further, it would be obvious to try adding in a second neural network as Aguilar already teaches training for determining output value without facial expression, then experiment to try adding in “facial muscle changes…as an object measure for VPT” using another neural network. PHOSITA would know a non-limiting example of a neural network such as CNN does feature extraction from images.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nankivil et al (US 20210244277 A1, 2021) discloses optical photosensitivity analyzer that tracks reversals (similar to instant application).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY B DUONG whose telephone number is (571)272-1358. The examiner can normally be reached Monday - Thursday 10a-9p (ET).
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/J.B.D./Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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