CTNF 18/764,932 CTNF 86527 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. 07-30-03-h AIA Claim Interpretation The claim term “ego machine” in claim 11 has been interpreted to have the special definition: “an autonomous or semi-autonomous vehicle or machine” (based on the Spec at par. [0005]). See MPEP 2111.01(IV)-(V). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-3, 5-8, 11-15, and 17-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Deegan (U.S. Pub. 2021/0287035) . Regarding claims 1, 13, and 19, Deegan discloses (Figs. 1-5) one or more processors comprising processing circuitry (see pars. [0049]-[0052]) to: determine a measure of misalignment [0027] between a calibrated state (at 100 percent: [0027]) and a subsequent state of a sensor (where the alignment is 80 percent, for example: [0027]) based at least on a reference frame (any one of an image, bounding box, or pointcloud: see pars. [0012]-[0013], [0015]-[0016]; Figs. 2A/B) of sensor data generated using the sensor in the calibrated state (see pars. [0022] and [0024]) and a test frame of sensor data (any one of a subsequent an image, bounding box, or pointcloud: see pars. [0012]-[0013], [0015]-[0016]; Figs. 2A/B) generated using the sensor in a subsequent state (i.e. when the LiDAR becomes mis-aligned: see pars. [0015], [0022], and [0024]); and based at least on the measure of misalignment exceeding a tolerance (i.e. an alignment error exists, threshold amout: [0027]-[0028]), generate a calibration adjustment (removal of erroneous data points: [0031]) based at least on the test frame of sensor data (based on the mis-aligned LiDAR image: [0027]-[0031]). The processor/system of Deegan, as applied above in the rejection of claims 1 and 13, would perform the method and meet the limitations of claim 19. Regarding claim 2, Deegan discloses (Figs. 1-5) to determine the measure of misalignment [0027], the processing circuitry is further to determine a measure of difference or similarity between the reference frame and the test frame [0027]. Regarding claim 3, Deegan discloses (Figs. 1-5) the processing circuitry is further to determine a measure of difference or similarity between one or more static regions (could be stationary objects, such as road signs: [0011], especially if the vehicle is also stationary) of the reference frame and the test frame [0027]. Regarding claim 5, Deegan discloses (Figs. 1-5) the tolerance is associated with a characteristic of a neural network (the generation of bounding boxes using a NN leads to the evaluation of mis-alignment: see pars. [0012]-[0013] and [0027]). Regarding claim 6, Deegan discloses (Figs. 1-5) an input of the neural network [0013] includes sensor data generated using the sensor (see pars. [0022]-[0023] and [0031]). Regarding claims 7 and 14, Deegan discloses (Figs. 1-5) the processing circuitry is further to convert a measure of difference or similarity between the reference frame and an indexed frame (see pars. [0012] and [0018]-[0019]) to the calibration adjustment (determines average alignment: [0018]-[0019], which then is applied to the calibration: [0026]-[0028]). Regarding claim 8, Deegan discloses (Figs. 1-5) the processing circuitry is further to generate the calibration adjustment based at least on processing the reference frame [0022]-[0023] of sensor data using a neural network (see pars. [0012]-[0013] and [0022]-[0023]). Regarding claims 11 and 17, Deegan discloses (Figs. 1-5) the sensor is a sensor of an ego-machine (i.e. autonomous vehicle: [0011] and [0015]), and the processing circuitry is further to automatically apply the calibration adjustment to a calibration of the sensor of the ego-machine [0031]. Regarding claims 12, 18, and 20, Deegan discloses (Figs. 1-5) the processing circuitry is comprised in at least one of: a control system for an autonomous or semi-autonomous machine (see pars. [0011] and [0015]); a perception system for an autonomous or semi-autonomous machine (see pars. [0011]-[0012] and [0015]); a system for performing remote operations (see pars. [0049]-[0050]); a system for generating synthetic data (such as the bounding box and pointcloud: see pars. [0015]-[0016]); a system for generating synthetic data using AI (machine learning, NNs: [0012]-[0013]); a system implemented at least partially in a data center [0050]; or a system implemented at least partially using cloud computing resources [0050] . 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Deegan (U.S. Pub. 2021/0287035) in view of Lau (U.S. Pub. 2020/0363501) . Regarding claim 4, Deegan is applied as above, but does not disclose the processing circuitry is further to apply high pass filtering to the one or more static regions of the reference frame and test frame. Lau discloses the processing circuitry is further to apply high pass filtering to the one or more static regions of the reference frame and test frame (see pars. [0120] and [0122]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Deegan’s device so that the processing circuitry is further to apply high pass filtering to the one or more static regions of the reference frame and test frame, as taught by Lau. Such a modification would reduce noise and clutter and smooth the images (see Lau: pars. [0120] and [0122]) . 07-21-aia AIA Claim s 9-10 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Deegan (U.S. Pub. 2021/0287035) in view of Arnold et al. ("A Survey on 3D Object Detection Methods for Autonomous Driving Applications," - Oct. 2019) . Regarding claims 9-10 and 15-16, Deegan is applied as above, and discloses the processing circuitry is further to generate an initial calibration adjustment [0031] based at least on a measure of difference or similarity between the reference frame and an indexed frame (see pars. [0027]-[0028] and [0031]). Deegan does not disclose the processing circuitry is further to generate the calibration adjustment based at least on iteratively refining a calibration adjustment predicted using a neural network; and to generate the calibration adjustment based at least on processing a new frame using a neural network, the new frame being generated using the sensor and based on the initial calibration adjustment. Arnold discloses the processing circuitry is further to generate the calibration adjustment based at least on iteratively refining a calibration adjustment (such as in a fusion method: see Section IV C. “Fusion Based Methods”) predicted using a neural network (see Section IV B. “Point Cloud Based Methods” – the bounding boxes and pointclouds are predicted); and to generate the calibration adjustment based at least on processing a new frame using a neural network (see Section IV C. “Fusion Based Methods”), the new frame being generated using the sensor and based on the initial calibration adjustment (see Section IV B. “Point Cloud Based Methods” – based on the captured 2D image). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Deegan’s device so that the processing circuitry is further to generate the calibration adjustment based at least on iteratively refining a calibration adjustment predicted using a neural network; and to generate the calibration adjustment based at least on processing a new frame using a neural network, the new frame being generated using the sensor and based on the initial calibration adjustment, as taught by Arnold. Such a modification would use complimentary information to enhance performance – see Arnold Section IV C. “Fusion Based Methods.” Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Stoschek et al. (U.S. Pub. 2021/0089058) discloses a vehicular monitoring system with a plurality of sensors, where a calibrated sensor is used to calibrate an uncalibrated sensor (see Fig. 7, par. [0052]). Itu et al. “A Self-Calibrating Probabilistic Framework for 3D Environment Perception Using Monocular Vision.” Sensors (Basel), Feb 2020. Discloses self-calibrating cameras/sensors for autonomous vehicles. Yan et al. “Sensors-to-car calibration for autonomous driving in road scenarios.” ArXiv, May 2023. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Benjamin Schmitt, whose telephone number is (571) 270-7930. The examiner can normally be reached M-F | 8:30-5:00. 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, Walter Lindsay can be reached at (571) 272-1674. 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. /BENJAMIN R SCHMITT/Primary Examiner, Art Unit 2852 Application/Control Number: 18/764,932 Page 2 Art Unit: 2852 Application/Control Number: 18/764,932 Page 3 Art Unit: 2852 Application/Control Number: 18/764,932 Page 4 Art Unit: 2852 Application/Control Number: 18/764,932 Page 5 Art Unit: 2852 Application/Control Number: 18/764,932 Page 6 Art Unit: 2852 Application/Control Number: 18/764,932 Page 7 Art Unit: 2852 Application/Control Number: 18/764,932 Page 8 Art Unit: 2852 Application/Control Number: 18/764,932 Page 9 Art Unit: 2852
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