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 Amendment
The amendment filed on June 5th, 2026 has been entered. Claims 1-11 and 13-20 remain pending in the application.
Response to Arguments/Remarks re. 35. U.S.C. § 112(d) Rejections
As stated by Applicant, the rejection under § 112(d) with respect to claim 12 has been rendered moot by the cancellation of claim 12. With respect to claim 19, the language as amended overcomes the rejection, and the rejection is accordingly withdrawn.
Response to Arguments/Remarks re. 35. U.S.C. § 103 Rejections
Applicant’s arguments with respect to the non-final rejection of claims under 35 U.S.C.
103 have been considered but are moot because the new grounds of rejection, as necessitated by
the amended claims, do not rely solely on the references applied in the prior rejection of record.
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.
Claims 1, 13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang et. al (US 20210217195 A1) (Hereinafter, “Hwang”) in view of Fanelli et. al Real Time Head Pose Estimation from Consumer Depth Cameras (Hereinafter, “Fanelli”) and Ren et. al (US 20230065491 A1) (Hereinafter, “Ren”)
With respect to claim 1, Hwang teaches:
One or more processors comprising processing circuitry to ([0014])
generate a model Fig. 8)
determine at least one three-dimensional (3D) measurement corresponding to a head pose of an occupant of a vehicle [0186]; Fig. 8; with the understanding that 3D sensors are configured to image as the vehicle is being operated (noting hands on steering wheel in Fig. 6, noting simultaneous operation of 2D and 3D image sensors under [0197], noting operation of 2D sensor within Fig. 6 under [0175] with further understanding that 3D sensor may be simultaneously executed under [0197] to acquire head pose information ([0179]-[0180]) and therefore expectedly operate within the scene of Fig. 6’s vehicle operation)
capture optical image data representing at least a portion of the occupant of the vehicle as the occupant of the vehicle operates the vehicle, wherein the optical image data is synchronized with the second depth image data (Fig. 5; Fig. 6, noting hands on steering wheel, wherein operation of the vehicle under BRI may be as simple as movement of the steering wheel; [0175] “Referring to FIG. 6, the 2D image sensor 124 may acquire a 2D image 601 of the head of the person. The processor 180 may detect at least one landmark point 602 from the 2D image 601. The processor 180 may map the 2D image 601 to the at least one landmark point 602 and may store the result in the memory 170”)
translate the at least one 3D measurement into a frame of reference of the optical image data to generate at least one 3D reference measurement ([0234]-[0236])
update a machine learning model to generate a prediction of a 3D pose of at least a portion of the occupant based at least on the optical image data and the at least one 3D reference measurement ([0234]-[0236]; Fig. 5)
Hwang does not explicitly teach:
generate a model customized for an occupant of a vehicle based at least on depth image data captured during a registration process in the vehicle
determine at least one three-dimensional (3D) measurement corresponding to a head pose of an occupant of a vehicle based at least on a deviation between a first 3D point cloud representation of the model customized for the occupant of the vehicle and a second 3D point cloud representation of at least a portion of the occupant of the vehicle based at least on second depth image data captured as the occupant of the vehicle operates the vehicle
However, Fanelli, in the same field of endeavor of 3D pose tracking, teaches:
One or more processors comprising processing circuitry to ([2])
generate a model customized [4]; Fig. 3)
determine at least one three-dimensional (3D) measurement corresponding to a head pose [4]; Fig. 3)
It would have been obvious to one of ordinary skill in the art as of the effective filing date
of the claimed invention, to modify Hwang to include the elements of depth-based 3D point
cloud modeling as taught by Fanelli. Doing so would provide a specific means of displaying the
3D information already collected by Hwang. Including this means would allow the system of
Hwang to better compare a 2D-estimated head pose model with a 3D head pose model – the
ultimate goal of Hwang. The method of Fanelli also allows for greater personalization of the estimation. This increases the accuracy of the system by accommodating user-specific features. As another system of 3D depth sensing, Hwang readily integrates the teachings of Fanelli with no compromise to underlying functionality.
Hwang and Fanelli do not explicitly teach:
captured during a registration process in the vehicle
However, Ren, in the same field of endeavor of pose estimation in vehicles, teaches:
captured during a registration process in the vehicle (Fig. 5A; [0063] “In one embodiment, when a subject enters a vehicle or is present in a monitored location, such as a driver's seat, a system can attempt to identify the person. As mentioned, this may include using facial recognition or biometrics to identify the person. If identified, the system can then attempt to determine whether that person or subject has an available profile, whether stored in non-transitory storage media in the vehicle or accessible over at least one network. If such profile is available and accessible, the system can utilize data in that profile to normalize data for that user and deploy an appropriate state estimation model. If the subject does not have an existing and accessible profile, an attempt can be made to generate such a profile. In some embodiments, this may include monitoring or capturing information related to that user over a period of time to attempt to determine baseline information. In some embodiments, the person may have an option to indicate whether the person would like to have a user calibration process performed, whereby the person can provide certain information, and have other information captured or obtained, that can be useful in generating one or more state baselines for user activity or behavior”)
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It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention, to modify Hwang and Fanelli to include the limitations of in-vehicle registration, as taught by Ren. Doing so would allow for the process to be streamlined within broader vehicle operations. The systems readily integrate, as a person of ordinary skill in the art would readily expect Hwang/Fanelli as combined to take place within a vehicle’s setting.
With respect to claim 13, Hwang, Fanelli, and Ren teach:
A system comprising one or more processors to (Hwang, [0014])
capture optical image data representing at least a portion of an occupant of a production vehicle (Hwang Fig. 5; noting that a “production vehicle” is without clear definition when read in light of the specification. A person of ordinary skill in the art would understand the teachings of Hwang to be applicable to vehicles generally, including the broad class of “production vehicle(s)” implicated by the claim)
using a machine learning model (Hwang [0241]-[0244]), generate a prediction of a three-dimensional (3D) pose corresponding to the occupant based at least on the optical image data, wherein the machine learning model is to infer the predicted 3D pose (Hwang, Fig. 5) based at least on
3D reference measurement data representing a deviation between a first 3D point cloud representation of a model customized for a training subject and a second 3D point cloud representation of at least a portion of the training subject based at least on depth image data captured as the training subject operates a vehicle (Fanelli, [4]; Fanelli, Fig. 3; Hwang, Fig. 5; Hwang, Fig. 6), wherein the model customized for the training subject is generated based at least on second depth image data captured during a registration process in the vehicle (Fanelli, [4]; Ren, Fig. 5A)
second optical image data representing at least a portion of the training subject captured as the training subject operates the vehicle, wherein the second optical image data is synchronized with the second depth image data (Hwang, Fig. 5)
A person of ordinary skill in the art would be motivated to combine these references for the reasons outlined in the rejection of claim 1.
With respect to claim 19, Hwang, Fanelli, and Ren teach:
The system of claim 13, wherein the system comprises at least one of:
a system implemented at least partially using cloud computing resources (Hwang, [0085]-[0086])
With respect to claim 20, Hwang, Fanelli, and Ren teach:
A method (Hwang, [0002]) comprising
generating a prediction of a three-dimensional (3D) pose of at least a portion of an occupant of a production vehicle based on a machine learning model trained (Hwang, [0241]- [0244]; Hwang, Fig. 5) using
one or more 3D reference measurements determined using a 3D point cloud representing a model customized for at least a portion of a training subject (Fanelli, [4]) that is generated based at least on depth image data captured during a registration process in a vehicle (Fanelli, [4]; Ren, Fig. 5A)
second depth image data captured as the training subject operates the vehicle (Hwang, Fig. 5)
and optical image data of at least a portion of the training subject synchronously captured with the second depth image data (Hwang, Fig. 5)
wherein the one or more 3D reference measurements are translated into a frame of reference of the optical image data (Hwang, [0234]-[0236])
A person of ordinary skill in the art would be motivated to combine these references for the reasons outlined in the rejection of claim 1.
Claims 2-9, 11, 14-15, and 17-18 as original claims are rejected in line with the Non-Final Rejection mailed 3/11/2026. To the extent “3D reference measurement” is recited instead of “3D ground truth measurement” in claims 10 and 16, it is without effect to their respective claim mapping as previously outlined, and accordingly claims 10 and 16 are also rejected. The addition of Ren as applied does not further render any claim depending from claims 1 or 13 non-obvious, as the teaching of Ren represents a minor distinction which otherwise readily integrates into the overarching systems to predictable success, for the reasons outlined above.
Conclusion
Applicant’s amendment necessitated the new grounds 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.
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/NOAH W BOYAR/
Examiner, Art Unit 2669
/IAN L LEMIEUX/Primary Examiner, Art Unit 2669
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