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 07/14/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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.
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 1-5, 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dolsma et al. (Pub. No.: US 2023/0105077) in view of Komogortsev (Pub. No.: US 2017/0135577, Applicant’s IDS filed 07/14/2025).
Consider claim 1, Dolsma a method for detecting substance abuse by a subject (paragraph [0028], detection of substance abuse in suspects), the method comprising:
capturing at least one of pupil gaze and pupil size data from at least one eye of the subject (paragraph [0028], [0040], capturing the subject’s pupils of one or both eyes and point-of-gaze);
filtering the data to remove noise (paragraph [0045], [0279], lowpass filter to remove noise); and
identifying a potential for substance abuse in the subject (paragraph [0028], captured images and/or videos are then analyzed for stalled eye movements, out-of-focus point-of-gaze, and pupil dilation detected during a test for substance abuse or DUI may indicate intoxication).
Dolsma further discloses a plurality of vectors (paragraph [0046], 3D vectors).
Dolsma does not specifically disclose dividing the data into a plurality of overlapping vectors;
transforming the plurality of overlapping vectors into frequency data; and
computing magnitudes of each frequency.
Komogortsev discloses dividing the data into a plurality of overlapping vectors (paragraph [0333], raw eye movement signal of overlapping fixations (i.e., feature vectors, see paragraph [0365]) is initially partitioned);
transforming the plurality of overlapping vectors into frequency data (paragraphs [0328], [0333], raw eye movement signal is initially partitioned into n equal-duration (Tint) nonoverlapping sequential segments); and
computing magnitudes of each frequency (paragraph [0434], For each kind of eye movement event (fixation/saccade/glissade), a number of features in are extracted order to describe its frequency-of-occurrence).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to replace the vectors as disclosed by Dolsma with the plurality of overlapping vectors as taught by Komogortsev to ensure that a robust sequence of fixations and saccades is captured without significant overlapping effects for the selected stimulus (Komogortsev, paragraph [0333]).
Consider claim 2, the combination of Dolsma and Komogortsev discloses wherein capturing at least one of pupil gaze and pupil size comprises capturing pupil gaze (paragraph [0039], capturing pupils (including pupil changes, see paragraph [0046]) of one or both eyes and point-of-gaze).
Consider claim 3, the combination of Dolsma and Komogortsev discloses wherein capturing at least one of pupil gaze and pupil size comprises capturing pupil size (paragraph [0039], capturing pupils (including pupil changes, see paragraph [0046]) of one or both eyes and point-of-gaze).
Consider claim 4, the combination of Dolsma and Komogortsev discloses wherein capturing at least one of pupil gaze and pupil size comprises capturing both pupil gaze and pupil size (paragraph [0039], capturing pupils (including pupil changes, see paragraph [0046]) of one or both eyes and point-of-gaze).
Consider claim 5, the combination of Dolsma and Komogortsev discloses wherein capturing at least one of pupil gaze and pupil size comprises capturing both pupil gaze and pupil size for both eyes of the subject (paragraph [0039], capturing pupils (including pupil changes, see paragraph [0046]) of one or both eyes and point-of-gaze).
Consider claim 9, the combination of Dolsma and Komogortsev discloses wherein transforming the overlapping vectors into frequency data comprises applying at least one of a low-pass filter, a band-pass filter, and a high-pass filter to the vectors (paragraph [0045], lowpass filter).
Consider claim 11, the combination of Dolsma and Komogortsev discloses further comprising the step of normalizing and adjusting the data for at least one of age or sex (Komogortsev, paragraph [0151], subjects (26 males/6 females), ages 18-40).
Claims 6-8, 12, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Dolsma and Komogortsev in view of Finkel (Pub. No.: US 2021/0045679).
Consider claims 6, 7, the combination of Dolsma and Komogortsev does not specifically disclose wherein dividing the data into a plurality of overlapping vectors comprises dividing the data into vectors of 500 frames.
Finkel discloses wherein dividing the data into a plurality of overlapping vectors comprises dividing the data into vectors of 500 frames (paragraph [0028], pupillary data of an isolated hippus captured over a 5-second period acquired at 100 frames per second).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to replace the division of data as disclosed by the combination of Dolsma and Komogortsev with the division of data as taught by Finkel to obtain the pupillary video sequences (Finkel, paragraph [0022]).
Consider claim 8, the combination of Dolsma and Komogortsev does not specifically disclose wherein transforming the overlapping vectors into frequency data comprises applying a Fourier transform to the overlapping vectors.
Finkel discloses wherein transforming the overlapping vectors into frequency data comprises applying a Fourier transform to the overlapping vectors (paragraph [0038], [0064], Fourier transform).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to replace the transformation of overlapping vectors as disclosed by the combination of Dolsma and Komogortsev with the transformation of overlapping vectors as taught by Finkel to provide a non-invasive approach for further informing this paradigm by indicating the presence of a substance (Finkel, paragraph [0064]).
Consider claim 12, the combination of Dolsma and Komogortsev does not specifically disclose the step of predicting substance abuse based on frequency-based metrics.
Finkel discloses the step of predicting substance abuse based on frequency-based metrics (paragraph [0046], comparative metric over a frequency range associated with methadone users).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method for detecting substance abuse as disclosed by the combination of Dolsma and Komogortsev with the method for detecting substance abuse as taught by Fenkel to determine that the patient has had an acute exposure to methadone (Fenkel, paragraph [0046]).
Consider claim 14, the combination of Dolsma, Komogortsev, and Fenkel discloses the step of predicting substance abuse based on time-based metrics (paragraph [0063], time-based moving average of subject’s answer scores to questions).
Consider claim 16, the combination of Dolsma, Komogortsev, and Fenkel discloses wherein the step of predicting substance abuse based on time-based metrics comprises using a machine learning model (paragraph [0063], machine learning model).
Allowable Subject Matter
Claims 10, 13, 15, and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 10, the prior art of record fails to disclose taking a square of a sum of squares of a real and an imaginary portion of each complex number in a Fourier transform result. Fenkel discloses applying a Fourier transform to the overlapping vectors (paragraph [0038], [0064], Fourier transform), but fails to further define taking a square of a sum of squares of a real and an imaginary portion of each complex number in a Fourier transform result.
Regarding claim 13, the prior art of record fails to disclose using a logistic regression. Fenkel discloses predicting substance abuse based on frequency-based metrics (paragraph [0046], comparative metric over a frequency range associated with methadone users), but fails to further define usage of logistic regression.
Regarding claim 15, the prior art of record fails to disclose using a neural network. The combination of Dolsma, Komogortsev, and Fenkel discloses the step of predicting substance abuse based on time-based metrics (Dolsma, paragraph [0063], time-based moving average of subject’s answer scores to questions), but fails to further define usage of a neural network.
Regarding claim 17, the prior art of record fails to disclose generating a composite index score based on both the frequency-based metrics and the time-based metrics. Fenkel discloses predicting substance abuse based on both time-based metrics and frequency-based metrics, but is silent on further discussion of generating a composite index score.
The remaining claims are objected to due to dependence on objective claims.
Conclusion
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/Gerald Johnson/
Primary Examiner, Art Unit 3797
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