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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
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.
Claims 1-3, 12, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Niu et al. CN-112329566-A (hereinafter “Niu” – machine translation attached herewith), in view of Drozdov et al. U.S. Pre-Grant Application Publication 2022/0050521 (hereinafter “Drozdov”).
Regarding claim 1, Niu teaches a method for head posture judgment (see Niu Fig. 2 and para. 0041-46, as further detailed in para. 0052), comprising:
receiving a plurality of head images to obtain raw label data corresponding to the head images (see Niu’s head posture data set labeling unit 201 as described in para. 0047-48);
converting the raw label data into updated label data; wherein the updated label data comprise Euler angles marked in order of y-x-z directions (see Niu para. 0052 head posture algorithm unit 202);
inputting the updated label data of the head images into a deep learning network model, and using a loss function to train the deep learning network model to obtain a head posture detection model (see Niu para. 0038-39 for a description of the head pose estimation neural network unit 203 including the loss function); and
inputting calibration images and real-time images into the head posture detection model to determine head posture (Niu para. 0050 “The visual perception module 2031 is configured to collect visual image data of a driver during driving by using a single-channel visual perception device”).
Niu teaches that the continuous frame header action determining module 104 determines the type of head action by combining the continuous frame number value of the Euler angle of the head attitude output by the neural network (Niu para. 0052). This is understood to be different from the claimed “wherein the raw label data comprise Euler angles marked in order of x-y-z directions” since these are input to the deep learning network, while Niu provides Euler angles as the output of the deep learning network. However, Drozdov teaches using six degrees of freedom (yaw, pitch, and roll, along with 3D positions, z, x, and z) as input to a deep-learning based algorithm to estimate a user’s hear pose (see Drozdov para. 0027 and Figs. 2A and 2B). Niu and Drozdov are from a similar field of endeavor involving deep-learning models for head-pose estimation. It would have been obvious to one of ordinary skill at the time the invention was filed to combine Drozdov’s use of yaw, pitch, and roll within Niu’s head pose estimation neural network unit to achieve the known and expected uses and benefits of improved pose estimation.
Regarding claim 2 Drozdov further teaches that the Euler angles in the order of y-x-z directions are yaw, pitch, and roll (see Drozdov para. 0027 and Figs. 2A and 2B).
Regarding claim 3, Drozdov further teaches that the step of converting the raw label data into the updated label data comprises: generating a rotation matrix according to the Euler angles marked in the order of x-y-z directions in the raw label data; and generating the Euler angles in the order of y-x-z directions in the updated label data according to a magnitude relationship between a specific matrix element in the rotation matrix and a threshold (see Drozdov’s use of rotation matrix as described in para. 0086-87).
Regarding claim 12, Niu teaches the method for head posture judgment as claimed in claim 1, wherein a driver in the calibration images is looking straight ahead at a road in a normal driving posture (see Niu para. 0036 “the head pose data labeling unit 101 may construct a training data set for head pose estimation by acquiring facial images of different angles (-90 ° to 90 °, with the step length set to 30 °) in the horizontal and vertical directions during driving…”).
Claim 14 has been analyzed and is rejected for the reasons indicated re claim 1 above.
Claim 15 has been analyzed and is rejected for the reasons indicated re claim 2 above.
Regarding claim 16, Niu teaches The vehicle-mounted system as claimed in claim 15, wherein a positive direction of y is from the top to the bottom of a driver, a positive direction of x is from the right to the left of the driver, and the positive direction of z is from the front to the back of the driver (see Niu para. 0036 “the head pose data labeling unit 101 may construct a training data set for head pose estimation by acquiring facial images of different angles (-90 ° to 90 °, with the step length set to 30 °) in the horizontal and vertical directions during driving…”).
Allowable Subject Matter
Claims 4-11, 13, 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen R Koziol whose telephone number is (408)918-7630. The examiner can normally be reached M-F 8 AM - 4 PM Pacific Time.
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/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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