CTNF 18/437,001 CTNF 97689 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the request for continued examination filed on 04/29/2026, in which claims 1-20 are pending and addressed below. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/29/2026 has been entered. Information Disclosure Statement The information disclosure statement submitted on 04/20/2026 has been received and considered. Response to Arguments 07-37 AIA Applicant's arguments filed 04/29/2026 have been fully considered but they are not persuasive. With respect to the 35 U.S.C. 103 rejections: Applicant argues on pages 14-15 of the remarks that Wu does not execute checks that evaluate physiological plausibility. In response to applicant’s arguments that Wu fails to disclose checks that evaluate physiological plausibility, the examiner respectfully disagrees. Wu discloses that the orientation of body parts are analyzed to determine if the particular orientation is impossible or unlikely (Wu Col. 48, lines 42-45). Wu further discloses that a confidence level is decreased based on the orientation analysis determining an impossible or unlikely scenario, such as a left arm detected on the right side of the driver’s body (Wu Col. 48, lines 42-55). Therefore, Wu discloses checks that evaluate physiological plausibility by determining if the detected body part orientation is considered impossible or unlikely. Applicant’s arguments have been fully considered and have been found not persuasive. Applicant’s arguments with respect to Hoshina in view of Kumavat have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-6, 9-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, U.S. Patent No. 11951833 B1, in view of Kumavat et al., U.S. Patent Application Publication No. 2023/0192139 A1 (hereinafter Kumavat) . Regarding claim 1, Wu teaches an ego-machine comprising a plurality of processors (Wu Fig. 1 ) to: generate a representation of one or more detected human features based at least on executing at least a portion of an operator or occupant monitoring system of the ego-machine on a first set of the plurality of processors (see at least Wu Col. 11, lines 15-26: “The sensor fusion module 152 may be configured to analyze information from multiple sensors 114, capture devices 102a-102n and/or the database 174 for redundancy. By analyzing various data from disparate sources, the sensor fusion module 152 may be capable of making inferences about the data that may not be possible from one of the data sources alone. For example, the sensor fusion module 152 may analyze video data as well as radar, lidar, inertial, motion, V2X, location data (e.g., GPS, GNSS, ADAS, etc.), gaze direction, driver state, battery status and/or other sources to develop a model of a scenario to support decision making.” ; Col. 18, lines 47-51: “A camera (e.g., the lens 112a and the capture device 102a) is shown capturing an interior of the ego vehicle 50 (e.g., detecting the driver 202). A targeted view of the driver 202 (e.g., represented by a line 204a and a line 204b) is shown being captured by the capture device 102a.” ); generate a representation of one or more identified faults of the operator or occupant monitoring system based at least on executing one or more checks that evaluate physiological plausibility of the one or more detected human features on a second set of the plurality of processors (see at least Wu Col. 48, lines 42-55: “In addition to the proximity to the seats 454a- 454c, the processors 106a-106n may analyze the orientation of the body parts. Particular orientations may be impossible and/or unlikely. For example, if the detected left arm 512 of the passenger 502b reaches across the driver 202, the processors 106a-106n may determine that a left arm is detected on a right side of the body of the driver 202. The orientation of the fingers and/or hand may be used to determine that the detected left arm 512 is a left arm/hand. Detecting a left arm on the right side of the driver 202 may be an impossible and/or unlikely scenario (particularly if a left arm of the driver 202 is already detected on the left side of the driver 202), which may decrease a confidence level that the detected left arm 512 belongs to the driver 202.” ); and control, using the second set of the plurality of processors, one or more operations of the ego-machine based at least on representation of the one or more identified faults (see at least Wu Col. 58, lines 9-27: “In the step 786, the decision module 158 may determine a behavior of the user (e.g., the driver 202) based on the calculated confidence level. The confidence level may be determined based on an aggregate of multiple factors. For example, the decision module 158 may determine the cumulative result of the various increases or decreases performed in the steps 760-762, the steps 768-770, the steps 776-778, the steps 782-784 and/or other increase/decreases due to other factors analyzed. Based on the analysis of multiple factors (e.g., the behavior of various body parts), the decision module 158 may calculate an aggregate confidence level. The decision module 158 may use the aggregate confidence level to determine whether the behavior of the user is consistent with a person providing input to the infotainment unit 352. For example, the processors 106a-106n may generate the control signal VCTRL to enable/disable input to the infotainment unit 352 based on the aggregate confidence level determined by the decision module 158.” ; under broadest reasonable interpretation controlling operation includes enabling or disabling infotainment unit input). Wu fails to expressly disclose a second set of processors rated at a higher safety or reliability level than a first set of processors. However, Kumavat teaches the second set being rated at a higher safety or reliability level than the first set (see at least Kumavat [0084]: “In a first variation (e.g., as shown in FIG. 4), a watchdog controller (e.g., safety-critical, ASIL D-rated ECU, automotive grade ECU, etc.) with high reliability and availability (e.g., reliable communication, minimal processing tasks, etc.) is implemented as a separate ECU relative to the other ECUs and functions to perform safety-critical functions such as fault monitoring, fault management, diagnostics, executing fallback actions (MRCs), and guaranteeing operation of the AV to a safe state.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Wu with the higher safety or reliability level taught by Kumavat with reasonable expectation of success. Kumavat is directed towards the related field of addressing failure in an autonomous vehicle. Therefore, one of ordinary skill in the art would be motivated to modify Wu with Kumavat to improve safety (see at least Kumavat [0014]: “In a first variation, the technology confers the benefit of enabling an autonomous vehicle to be fail-operational in a way which is safe and optimally targeted to the particular failure, through a multi-layered redundant architecture for hardware and software subsystems of the vehicle.” ). Regarding claim 2, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein the one or more operations comprise controlling, using the second set of the plurality of processors, one or more autonomous driving features of the ego-machine based at least on the one or more checks that evaluate physiological plausibility (see at least Wu Col. 13, lines 33- 40: “The decision making module 158 may be configured to generate the signal VCTRL. The decision making module 158 maybe configured to use the information from the computer vision operations and/or the sensor fusion module 152 to determine which actions may be taken. For example, in an autonomous vehicle implementation, the decision making module 158 may determine which direction to turn.” ; Col. 58, lines 9-27: “In the step 786, the decision module 158 may determine a behavior of the user (e.g., the driver 202) based on the calculated confidence level…Based on the analysis of multiple factors (e.g., the behavior of various body parts), the decision module 158 may calculate an aggregate confidence level. The decision module 158 may use the aggregate confidence level to determine whether the behavior of the user is consistent with a person providing input to the infotainment unit 352. For example, the processors 106a-106n may generate the control signal VCTRL to enable/disable input to the infotainment unit 352 based on the aggregate confidence level determined by the decision module 158.” ). Regarding claim 3, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying a designated threshold range of motion to a detected head pose represented by the one or more detected human features (see at least Wu Col. 42, lines 40-53: “The processors 106a-106n may further analyze the head/face detection 472 to determine the behavior of the driver 202 (e.g., determine whether the driver 202 is attempting to provide input to the infotainment unit 352). The processors 106a-106n may analyze the direction of the head/face 472. In the example shown, the head/face 472 is shown facing straight ahead (e.g., towards the windshield 404). When the head/face 472 is directed straight ahead, the confidence level for interaction with the infotainment unit may be decreased. In an example, if the head/face 472 is determined to not be facing the windshield 404 (e.g., the head/face 472 is turned towards the location of the infotainment unit 352), then the confidence level for input from the driver 202 may be increased.” ; Col. 23, lines 17-20: “For example, the drowsiness and/or attentiveness of the driver 202 may be detected (e.g., recognizing that eyes are closing, recognizing that the head is drifting down, etc.).” ; Col. 48, lines 42-45: “In addition to the proximity to the seats 454a-454c, the processors 106a-106n may analyze the orientation of the body parts. Particular orientations may be impossible and/or unlikely.” ). Regarding claim 4, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying a designated threshold range of motion to a detected gaze direction represented by the one or more detected human features (see at least Wu Col. 42, line 54-Col. 43, line 12: “The processors 106a-106n may be configured to analyze the eyes 474a-474b to determine the behavior of the driver 202. The processors 106a-106n may be configured to determine the direction of the eyes 474a-474b…In the example shown, the eyes 474a-474b may be directed straight ahead (e.g., towards the windshield 404 and not directed towards the location of the infotainment unit 352), which may decrease the confidence level of detection of input by the driver 202. In another example, if the eyes 474a-474b were directed towards the location of the infotainment unit 352, then the decision module 158 may increase the confidence level of detection of input by the driver 202.” ; Col. 23, lines 16-20: “In some embodiments, the processors 106a-106n may be configured to approximate the gaze of the driver 202. For example, the drowsiness and/or attentiveness of the driver 202 may be detected (e.g., recognizing that eyes are closing, recognizing that the head is drifting down, etc.).” ; Col. 48, lines 42-45: “In addition to the proximity to the seats 454a-454c, the processors 106a-106n may analyze the orientation of the body parts. Particular orientations may be impossible and/or unlikely.” ). Regarding claim 5, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying a designated threshold on a change in a detected head pose represented by the one or more detected human features (see at least Wu Col. 42, lines 40-53: “The processors 106a-106n may further analyze the head/face detection 472 to determine the behavior of the driver 202 (e.g., determine whether the driver 202 is attempting to provide input to the infotainment unit 352). The processors 106a-106n may analyze the direction of the head/face 472. In the example shown, the head/face 472 is shown facing straight ahead (e.g., towards the windshield 404). When the head/face 472 is directed straight ahead, the confidence level for interaction with the infotainment unit may be decreased. In an example, if the head/face 472 is determined to not be facing the windshield 404 (e.g., the head/face 472 is turned towards the location of the infotainment unit 352), then the confidence level for input from the driver 202 may be increased.” ; Col. 23, lines 17-20: “For example, the drowsiness and/or attentiveness of the driver 202 may be detected (e.g., recognizing that eyes are closing, recognizing that the head is drifting down, etc.).” ; Col. 48, lines 42-45: “In addition to the proximity to the seats 454a-454c, the processors 106a-106n may analyze the orientation of the body parts. Particular orientations may be impossible and/or unlikely.” ). Regarding claim 6, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying a designated threshold on a change in a detected gaze direction represented by the one or more detected human features (see at least Wu Col. 42, line 54-Col. 43, line 12: “The processors 106a-106n may be configured to analyze the eyes 474a-474b to determine the behavior of the driver 202. The processors 106a-106n may be configured to determine the direction of the eyes 474a-474b…In the example shown, the eyes 474a-474b may be directed straight ahead (e.g., towards the windshield 404 and not directed towards the location of the infotainment unit 352), which may decrease the confidence level of detection of input by the driver 202. In another example, if the eyes 474a-474b were directed towards the location of the infotainment unit 352, then the decision module 158 may increase the confidence level of detection of input by the driver 202.” ; Col. 23, lines 16-20: “In some embodiments, the processors 106a-106n may be configured to approximate the gaze of the driver 202. For example, the drowsiness and/or attentiveness of the driver 202 may be detected (e.g., recognizing that eyes are closing, recognizing that the head is drifting down, etc.).” ; Col. 48, lines 42-45: “In addition to the proximity to the seats 454a-454c, the processors 106a-106n may analyze the orientation of the body parts. Particular orientations may be impossible and/or unlikely.” ). Regarding claim 9, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Kumavat further teaches wherein the one or more operations comprise using the second set of the plurality of processors to initiate, in response to identifying the one or more identified faults, one or more of disengagement of an autonomous driving feature, generation of a notification prior to the disengagement of the autonomous driving feature, or execution of one or more emergent driving maneuvers (see at least Kumavat [0161]: “Examples of failure responses can include, but are not limited to, any or all of: a warning transmitted within the system and/or outside the system (e.g., to a remote operator), the selection and/or activation of another component (e.g., implementation of a redundant component, etc.), transmitting of a notification (e.g., to a remote operator, to a human operator, etc.), implementation of an MRC (e.g., new trajectory, coming to a stop, slowing down, etc.), waiting and/or doing nothing (e.g., until further information is received, until a confidence value associated with the failure exceeds a predetermined threshold, until consensus is reached among a minimum number of subsystems, until the vehicle has completed a trip, etc.), and/or any other responses.” ; [0030]: “Based on the potential severity of such potential failures, an appropriate minimal risk condition (MRC) or other failure response (e.g., new trajectory) can be selected that minimizes the vehicle’s potential for harm (e.g., risk of collision).” ). Regarding claim 10, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu further teaches wherein one or more processors of the plurality of processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing deep learning operations; a system for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (see at least Wu Col. 7, lines 1-4: “The signal VCTRL and VCTRL′ may represent control instructions generated by the processors 106a-106n for the various vehicle actuators 116.” ; Col. 18, lines 29-31: “An automobile/vehicle 50 is shown. The apparatus 100 is shown as a component of the vehicle 50 (e.g., an ego vehicle).” ; Wu discloses at least a control system for an autonomous or semi-autonomous machine; examiner notes it is only required for the reference to disclose at least one limitation of the claim since the claim language uses “or” when listing claim limitations). Regarding claim 11, this claim recites a system of the ego machine of claim 1. Wu in view of Kumavat also teach a system of the ego machine of claim 1 as outlined in the rejection to claim 1 above. Therefore, claim 11 is rejected for the same rationale as claim 1. Regarding claim 12, this claim recites a system for the ego machine of claim 2 as explained above. Therefore, claim 12 is rejected for the same rationale as claim 2. Regarding claim 13, this claim recites a system for the ego machine of claim 3 as explained above. Therefore, claim 13 is rejected for the same rationale as claim 3. Regarding claim 14, this claim recites a system for the ego machine of claim 4 as explained above. Therefore, claim 14 is rejected for the same rationale as claim 4. Regarding claim 15 , this claim recites a system for the ego machine of claim 5 as explained above. Therefore, claim 15 is rejected for the same rationale as claim 5. Regarding claim 16, this claim recites a system for the ego machine of claim 6 as explained above. Therefore, claim 16 is rejected for the same rationale as claim 6. Regarding claim 18, this claim recites a system for the ego machine of claim 10 as explained above. Therefore, claim 18 is rejected for the same rationale as claim 10. Regarding claim 19, this claim recites a method performed by the ego machine of claim 1. Wu in view of Kumavat also teach a method performed by the ego machine of claim 1 as outlined in the rejection to claim 1 above. Therefore, claim 19 is rejected for the same rationale as claim 1. Regarding claim 20, this claim recites a method performed by the ego machine of claim 10 as explained above. Therefore, claim 20 is rejected for the same rationale as claim 10 . 07-21-aia AIA Claim s 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Kumavat, and further in view of Chiu, U.S. Patent Application Publication No. 2025/0206323 A1 . Regarding claim 7, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu in view of Kumavat fail to expressly disclose applying a maximum time or maximum number of frames between detected blinks. However, Chiu teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying at least one of a maximum time or a maximum number of frames between detected blinks (see at least Chiu [0035]: “In an embodiment, the physiological status is that a number of blinks is higher than the corresponding threshold. For example, blinking more than twice within 5 seconds; or blinking more than three times within 10 seconds. The above physiological status may be regarded as a slightly abnormal status.” ; wherein Wu Col. 48, lines 42-45 further discloses checking the physiological plausibility). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Wu in view of Kumavat with the blink detection taught by Chiu with reasonable expectation of success. Chiu is directed towards the related field of a driver monitoring method and driving risk evaluation system. Therefore, one of ordinary skill in the art would be motivated to modify Wu in view of Kumavat with Chiu to reduce accidents without frequently disturbing the driver (see at least Chiu [0005]: “The disclosure provides a driving monitoring method and a driving risk evaluation system, which may remind a driver in a timely manner to reduce the occurrence of accidents without disturbing the driver too frequently.” ). Regarding claim 17, this claim recites a system for the ego machine of claim 7 as explained above. Therefore, claim 17 is rejected for the same rationale as claim 7 . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Kumavat, and further in view of Liu, U.S. Patent Application Publication No. 2024/0336262 A1 . Regarding claim 8, Wu in view of Kumavat teach all elements of the ego-machine according to claim 1 as explained above. Wu in view of Kumavat fail to expressly disclose executing the one or more checks based at least on applying a designated threshold on a change in a detected measure of drowsiness. However, Liu teaches wherein the second set of the plurality of processors is further to execute the one or more checks that evaluate physiological plausibility based at least on applying a designated threshold on a change in a detected measure of drowsiness (see at least Liu [0165]: “When the driver state monitoring unit 200 is directly associated with the vehicle control unit 500, and the real-time sentiment identification result and/or the abnormal behavior monitoring result and/or the fatigue state monitoring result of the driver reaches or exceeds a danger threshold, the vehicle control unit 500 controls the vehicle to perform speed limit, deceleration, or emergency braking, and the alarm prompt unit sends an alarm prompt of a dangerous state or a dangerous behavior, and the analysis, identification or determination result and an action state of the vehicle control unit are uploaded to the remote management unit 600 through the on-board gateway unit 300.” ; wherein Wu Col. 48, lines 42-45 further discloses checking the physiological plausibility). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Wu in view of Kumavat with Liu with reasonable expectation of success. Liu is directed towards the related field of an active safety driver assistance system based on driver state monitoring. Therefore, one of ordinary skill in the art would be motivated to modify Wu in view of Kumavat with Liu to improve operating safety (see at least Liu [0010]: “Therefore, how to effectively monitor and actively process potential accident risks that may be caused by a driver, to effectively improve operating safety of all commercial vehicles such as vehicles included in the term “two types of passenger vehicles, one type of hazardous goods vehicles, one type of freight vehicles, and one type of school vehicles”, taxis, online vehicles and rental cars on various platforms is a problem urgently to be resolved in the art.” ) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Levin et al., U.S. Patent Application Publication No. 2013/0057671 A1, directed towards error classification of physiological data if the data is behaving in a way that is considered physically impossible. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH J SLOWIK whose telephone number is (571)270-5608. The examiner can normally be reached MON - FRI: 0900-1700. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANISS CHAD can be reached at (571)270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ELIZABETH J SLOWIK/Examiner, Art Unit 3662 /ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662 Application/Control Number: 18/437,001 Page 2 Art Unit: 3662 Application/Control Number: 18/437,001 Page 3 Art Unit: 3662 Application/Control Number: 18/437,001 Page 4 Art Unit: 3662 Application/Control Number: 18/437,001 Page 5 Art Unit: 3662 Application/Control Number: 18/437,001 Page 6 Art Unit: 3662 Application/Control Number: 18/437,001 Page 7 Art Unit: 3662 Application/Control Number: 18/437,001 Page 8 Art Unit: 3662 Application/Control Number: 18/437,001 Page 9 Art Unit: 3662 Application/Control Number: 18/437,001 Page 10 Art Unit: 3662 Application/Control Number: 18/437,001 Page 11 Art Unit: 3662 Application/Control Number: 18/437,001 Page 12 Art Unit: 3662 Application/Control Number: 18/437,001 Page 13 Art Unit: 3662 Application/Control Number: 18/437,001 Page 14 Art Unit: 3662 Application/Control Number: 18/437,001 Page 15 Art Unit: 3662 Application/Control Number: 18/437,001 Page 16 Art Unit: 3662
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