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 .
Claim Status
Claims 2, 4-12 and 14-22 are pending for examination.
Response to Arguments / Remarks
Applicant's arguments filed 5/5/2026 have been fully considered.
Applicant’s arguments with respect to claims 2, 4-12 and 14-21 have been fully considered and are persuasive. The rejections have been withdrawn.
In response to the new claim 22, the claim is rejected by Grube (Pub. No.: US 2016/0090097 A1) in view of Alasry (Pat. No.: US 9,925,872 B1).
Grube teaches fatigue detection system is configured to use camera to monitor the blink rate and eyelid closure of the user but fail to expressly teach the camera is an infrared camera.
However, in the same field of drowsiness / fatigue detection, Alasry teaches the use of infrared camera to determine eye movements. See Col. 3, line 58 – Col. 4, line 4, “The DSM 60 can include any suitable number of sensors 62. The sensors 62 can be any suitable sensors to monitor the driver 22, such as the driver's eye position and movement, head position and movement, facial expressions, etc. For example, the sensors 62 can include a camera, infrared light-emitting diodes (LEDs) and infrared sensors. The DSM 60 includes a control module configured to receive facial data sensed by the sensors 62, including images of the driver's face, head, and/or eyes. The control module then analyzes the facial data to determine the face angle, head position, and/or eye position of the driver 22, as well as detect long-duration eye closure of the driver 22, and to estimate an alertness level of the driver 22.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Grube’s camera with an infrared camera improve detection accuracy.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Grube (Pub. No.: US 2016/0090097 A1) in view of Alasry (Pat. No.: US 9,925,872 B1).
Regarding claim 22, Grube teaches an in-flight risk management system (Abstract, Fig. 1, fatigue detection for airline pilot 100) comprising:
an eye tracking sensor incorporated into a control panel of an aircraft
(Fig. 1, biometric sensors 108, para [0010], “The biometric sensors 108 can also include one or more digital cameras that can detect information about the eyes of the vehicle operator.”),
one or more in-flight risk management (IRM) computers in communication with the eye tracking sensor (Fig. 1, processor 110); and
one or more data stores in communication with the one or more IRM computers and storing instructions that, when executed, cause the one or more IRM computers to perform operations Fig. 1, memory 102 stores fatigue algorithm 106) comprising:
receiving sensor data from the eye tracking sensor (Fig. 3A, step 302, para [0017], “In block 302, during vehicle operation, the processor 110 can constantly or nearly-continuously monitor the biometric sensors 108 to detect biometric data related to the vehicle operator.”);
processing the sensor data to determine fatigue for a flight crew member (Fig. 3A, steps 304-308), wherein processing comprises:
analyzing one or more of:
number and duration of eyelid closures;
gaze fixation duration on specific cockpit instruments;
gaze scanning patterns across multiple instruments; and
pupil dilation (para [0010], “For example, the digital cameras can detect various eye metrics, such as eye blink rate (i.e., how often the vehicle operator blinks), eye movement (i.e., how much the vehicle operator is looking around rather than staring in one direction), and/or eye closure amount (e.g., how much of the vehicle operator's eyes are covered by his eye lids) of the vehicle operator. Deviations in blink rate and/or a change in blink rate from a vehicle operator's baseline measurements may indicate fatigue. Additional examples of fatigue indicators can include eyes fixing on one spot (e.g., not scanning the environment) more or less than a baseline amount and partially-closed eye lids more or less than a baseline amount.”. The system determines the eye blink rate and eye closure amount obtained from the camera.);
identifying, based on the analyzing, fatigue for the flight crew member;
comparing the analyzed sensor data to predetermined fatigue-indicating profiles (Fig. 3A, and para [0010], “For example, the digital cameras can detect various eye metrics, such as eye blink rate (i.e., how often the vehicle operator blinks ), eye movement (i.e., how much the vehicle operator is looking around rather than staring in one direction), and/or eye closure amount (e.g., how much of the vehicle operator’s eyes are covered by his eye lids) of the vehicle operator. Deviations in blink rate and/or a change in blink rate from a vehicle operator's baseline measurements may indicate fatigue.”. The system determines whether the eye movements deviate from the baseline); and
calculating a fatigue measurement based on the comparison (Fig. 3A, step 306, the system calculates the fatigue level);
generating, based on the fatigue measurement, a real-time fatigue assessment for the flight crew member (The system determines the pilot is fatigue or not based on the fatigue level); and
updating a predicted fatigue profile for the flight crew member based on the real- time fatigue assessment (Fig. 2, Fig. 3B, para [0014], “
Data points can be gathered in an initial step to create a statistical model and additional data points can be gathered periodically to update the vehicle operator's statistical model. For example, a pilot may perform an initial series of “flights” in a flight simulator, as discussed above, to create a statistical model. Periodically, the pilot may return to the simulator to update the model (e.g., by adding new data points and/or by replacing the original data points with new data points).” and para [0016], “In block 206, the gathered biometric data (from blocks 202a-n) and the determined fatigue levels (from blocks 204a-n) from the multiple operations can be statistically analyzed. For example, a linear regression analysis may be performed to determine which biometric data provide the maximum discriminatory capability between fatigue levels. As other examples, a non-linear regression analysis, a machine learning analysis, neural networks trained on known fatigue/non-fatigued instances from training data, or other statistical models can be used. In block 208, the method 200 can output the resulting statistical model for the vehicle operator to the vehicle operator profile 104.”. The system updates and tracks the pilot fatigue level over a period of time.).
Grube fails to teach the camera is an infrared camera.
However, in the same field of drowsiness / fatigue detection, Alasry teaches the use of infrared camera to determine eye movements. See Col. 3, line 58 – Col. 4, line 4, “The DSM 60 can include any suitable number of sensors 62. The sensors 62 can be any suitable sensors to monitor the driver 22, such as the driver's eye position and movement, head position and movement, facial expressions, etc. For example, the sensors 62 can include a camera, infrared light-emitting diodes (LEDs) and infrared sensors. The DSM 60 includes a control module configured to receive facial data sensed by the sensors 62, including images of the driver's face, head, and/or eyes. The control module then analyzes the facial data to determine the face angle, head position, and/or eye position of the driver 22, as well as detect long-duration eye closure of the driver 22, and to estimate an alertness level of the driver 22.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Grube’s camera with an infrared camera improve detection accuracy.
Allowable Subject Matter
Claims 2, 4-12 and 14-21 are allowed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685
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