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 .
Response to Election/Restriction
This action is in response to the Applicant’s response to Election/Restriction requirement dated 3/10/2026, the Applicant has elected species I (corresponding claims1-6 and 15-20) without traverse. The Election is made FINAL.
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 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 1-3, 5, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zandi et al. (US 2019007409).
Regarding claims 1 and 15 Zandi discloses a method and system for detecting psychophysiological states from eye movement data (Zandi Figs. 1 and 4, paragraphs 0022, 0047 and 0041 discloses “this section, the results of the proposed eye-tracking-based machine learning method (i.e., binary classification using eye tracking features introduced in FIG. 4) for non-intrusive assessment of the state of vigilance in drivers are presented. FIG. 1 depicts a schematic of the methodology presented”. This obviously corresponds to a method and system for detecting psychophysiological states from eye movement data), comprising:
camera capture a sequence of eye gaze vectors and eyelid openness levels over time (Zandi, Fig. 4, paragraph 0022 discloses “The SmartEye Pro 6.0 eye tracking system with two infrared cameras was mounted on the driving simulator to capture eye gaze, eye position, eyelid, blinking and pupilometry data of drivers, while the system was calibrated at the beginning of each driving session. The eye tracking system frame rate was 60 Hz, and the system delivered fixation and saccade labels for the eye gaze, measured at the accuracy of 0.5 degrees. In this experiment, the EEG was recorded simultaneously with the eye tracking data using the Emotiv EPOC+headset with 14 channels at the sampling frequency of 128 Hz, while the EPIC sensor by Plessey was utilized to acquire one-lead ECG data at the rate of 500 Hz. This obviously corresponds to camera capture a sequence of eye gaze vectors and eyelid openness levels over time);
a non-transitory memory storing instructions, and a machine learning model and a processor communicably coupled to the camera and the non-transitory memory the processor, when executing the instructions to (Zandi Figs. 1 and 4, paragraphs 0022, 0047 and 0041 discloses “this section, the results of the proposed eye-tracking-based machine learning method (i.e., binary classification using eye tracking features introduced in FIG. 4) for non-intrusive assessment of the state of vigilance in drivers are presented. FIG. 1 depicts a schematic of the methodology presented and paragraph 0035 discloses “in this study, a non-linear SVM and an RF (machine learning model) classifier have been used for binary identification of the state of vigilance, i.e. “alert” and “drowsy”, based on features extracted from the eye tracking data”. The system of Zandi obviously includes a non-transitory memory storing instructions i.e. memory, and a machine learning model and a processor communicably coupled to the camera and the non-transitory memory the processor, when executing the instructions to implement the system),
determine a plurality of second order eye movement metrics from the eye gaze vectors and eyelid openness levels, the plurality of second order eye movement metrics are indicative of discrete eye behaviors (Zandi , Fig. 4, paragraphs 0022 and 0047, note: Fig. 4, Blink-Duration, Frequency, Percentage, Fixation-Duration, Frequency. This obviously corresponds to determine a plurality of second order eye movement metrics from the eye gaze vectors and eyelid openness levels, the plurality of second order eye movement metrics are indicative of discrete eye behaviors );
transform the plurality of second order eye movement metrics into a machine- readable representation of the plurality of second order eye movement metrics within a pre-determined time window (Zandi, paragraph 0026 disclose eye tracking system used in this study collected multidimensional data, including various eye measurements, from drivers in each driving session at the rate of 60 Hz. The eye tracking data were then segmented into 10-sec epochs with 5-sec overlap, and 34 distinct features were extracted from each epoch. FIG. 4 presents the full list of these features, extracted from four main categories of the acquired eye tracking data: eye gaze, blink, pupil, and eyelid. The eye gaze consisted of a two-dimensional angular vector: heading (left/right) and pitch (up/down) angles in radian, Zandi in paragraph 0034 disclose “For each epoch of the eye gaze data, a similarity index was calculated based on correlation sum measure (40) to assess how concentrated the gaze was during that epoch and equation 6 disclose similarity index calculation based on gaze vector, Zandi in paragraph 0035 disclose In this study, a non-linear SVM and an RF classifier have been used for binary identification of the state of vigilance, i.e. “alert” and “drowsy”, based on features extracted from the eye tracking data and also note: paragraph 0036).
predict one or more psychophysiological states using the machine learning model based on the machine-readable representation of the plurality of second order eye movement metrics (Zandi, Figs. 1 & 4 and in paragraph 0034 Zandi disclose “For each epoch of the eye gaze data, a similarity index was calculated based on correlation sum measure (40) to assess how concentrated the gaze was during that epoch and equation 6 disclose similarity index calculation based on gaze vectors, .Zandi in paragraph 0035 disclose In this study, a non-linear SVM and an RF classifier have been used for binary identification of the state of vigilance, i.e. “alert” and “drowsy”, based on features extracted from the eye tracking data (Fig. 4) and also note: paragraph 0036). This obviously corresponds predict one or more psychophysiological states using the machine learning model based on the machine-readable representation of the plurality of second order eye movement metrics).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to capture a sequence of eye gaze vectors and eyelid openness levels over time using camera, determine a plurality of second order eye movement metrics from the eye gaze vectors and eyelid openness levels, transform the plurality of second order eye movement metrics into a machine- readable representation of the plurality of second order eye movement metrics within a pre-determined time window and predict one or more psychophysiological states using the machine learning model based on the machine-readable representation of the plurality of second order eye movement metrics as shown by Zandi because such a system and process provides automated system to measure non-intrusively fatigue and drowsiness in drivers eye as stated in ABSTRACT and paragraph 0005.
Regarding claims 2 and 16 Zandi disclose determining the plurality of second order eye movement metrics comprises, converting the eye gaze vectors to a sequence of eye gaze coordinates by determining points of intersection between each of the eye gaze vectors and a reference plane ().
Regarding claims 3 Zandi disclose transforming the plurality of second order eye movement metrics into the machine- readable representation includes calculating a number of blinks, a number of fixations, and an overall fixation time within the pre- determined time window (Zand, paragraphs 0034-0035 and paragraphs 0026-0028 and figure 4 shows percentage, duration and frequency of blinks and fixations therefore it is obvious that system of Zandi include transforming the plurality of second order eye movement metrics into the machine- readable representation includes calculating a number of blinks, a number of fixations, and an overall fixation time within the pre- determined time window).
Regarding claim 5 Zandi disclose determining the plurality of second order eye movement metrics further includes identifying blinks based on the eyelid openness levels, wherein a blink comprises a contiguous portion of frames from the sequence of eyelid openness levels satisfying a blink criterion based on calculated differences between adjacent eyelid openness levels for each frame of the contiguous portion of frames (Zandi paragraphs 0026-0028 and Fig.4 shows determining second order eye includes movement metrics further includes identifying blinks based on the eyelid openness levels and paragraphs 0026-0028 and Fig. 4 disclose duration, frequency and percentage therefore it is obvious that a blink comprises a contiguous portion of frames from the sequence of eyelid openness levels satisfying a blink criterion based on calculated differences between adjacent eyelid openness levels for each frame of the contiguous portion of frames).
Regarding claim 17 Zandi disclose label a contiguous portion of the sequence of eye gaze coordinates as a saccade or fixation based on changes in eye gaze direction and duration within pre-determined thresholds (Zandi Fig. 4 paragraphs 0026-0028, 0031 and Fig. 4 disclose eye gaze vectors saccade or fixation and disclose duration, percentage, velocity, pitch and roll therefore the system of Zandi disclose label a contiguous portion of the sequence of eye gaze coordinates as a saccade or fixation based on changes in eye gaze direction and duration within pre-determined thresholds).
Regarding claim 20 Zandi disclose identify blinks and long closures in the sequence of eyelid openness levels by applying pre- determined thresholds for eyelid openness and duration, and to calculate metainformation associated with each identified blink and long closure, including at least blink duration, eyelid close speed, and eyelid open speed (Zandi Fig. 4, paragraph 0026-0028 and 0031 disclose blink, fixation and saccade duration, frequency and percentage and velocity therefore it is obvious that the system of Zandi includes identify blinks and long closures in the sequence of eyelid openness levels by applying pre- determined thresholds for eyelid openness and duration, and to calculate metainformation associated with each identified blink and long closure, including at least blink duration, eyelid close speed, and eyelid open speed).
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Allowable Subject Matter Claims 4, 6 and 18-19 are objected as being dependent on rejected claim but would be allowable if rewritten in the independent form including limitations of the base claim and any intervening claims.
Communication Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISHRAT I SHERALI whose telephone number is (571)272-7398. The examiner can normally be reached Monday-Friday 8:00AM -5:00 PM.
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ISHRAT I. SHERALI
Examiner
Art Unit 2667
/ISHRAT I SHERALI/Primary Examiner, Art Unit 2667
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