CTNF 18/223,661 CTNF 79480 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. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 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. 07-21-aia AIA Claim (s) 1-15 are is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0122329 (Prinz et al. hereinafter D1) in view of US 2022/0137227 (Armstrong et al. hereinafter D2) . Referring to claim 1, A device for detecting objects in a monitored zone comprising at least one LiDAR sensor (D1, [0013], [0031], [0041], claims 8–10) for transmitting transmitted light beams into the monitored zone for scanning a plurality of measurement points and for generating measurement data from transmitted light remitted or reflected by the measurement points, with the measurement data comprising radial speeds of the measurement points (D1, [0013], [0018], [0024]); and a control and evaluation unit for evaluating the measurement data, wherein the LiDAR sensor is configured to detect polarization dependent intensities of the transmitted light remitted or reflected by the measurement points (D1, [0014], [0021], [0041]), the measurement data comprise the polarization dependent intensities, and Segmenting objects based on movement and polarization (D1, [0020], [0027], [0029]). D1 does not expressly disclose: 1) The use of an FMCW LiDAR sensor specifically (rather, it generally references LiDAR and radar) and 2) The explicit segmentation of measurement points into objects/object segments using both radial speed and polarization-dependent intensities. D2 teaches coherent LiDAR sensors (specifically FMCW LiDAR) that acquire both spatial coordinates and Doppler-derived (radial) velocity for each point in a point cloud (D2, [0019], [0022], claims 2–3). D2 explicitly teaches the segmentation process using both position and velocity data to cluster measurement points into objects/object segments (D2, [0022], [0060], [0041], [0050], [0059], claim 7). D2 describes clustering methods (e.g., K-means) and hypothesis testing for segmenting points, and further suggests that other attributes (such as intensity or polarization) may be included in the feature space for segmentation ([0060]: “Additional dimensions...can include intensity values, lateral velocities...and so on.”). It would have been obvious to one of ordinary skill to use the FMCW LiDAR and segmentation methods of D2 in the system of D1 to improve real-time segmentation and object identification, yielding predictable results. Re claim 2. The device in accordance with claim 1, wherein the control and evaluation unit is configured to filter the measurement points using the polarization dependent intensities of the transmitted light remitted or reflected by the measurement points and/or the radial speed of the measurement points (D1 [0018, 0061]). Alternatively, D2 also teaches filtering measurement points based on velocity or other attributes to focus processing on relevant points and reduce noise (see D2, [0025], [0066]). Filtering by speed or polarization is a routine step in signal processing to improve segmentation accuracy and computational efficiency Re claim 3. The device in accordance with claim 1, wherein the control and evaluation unit is configured to determine radial speeds of the objects and/or object segments and to extract features of the objects and/or object segments using the radial speeds of the objects and/or object segments (D1 [0023-24]). Re claim 4. The device in accordance with claim 3, wherein the control and evaluation unit is configured to classify the objects and/or object segments using the radial speeds of the objects and/or object segments (D1 [0024, 0027]). Re claim 5. The device in accordance with claim 1, wherein the control and evaluation unit is configured to discard measurement points having a radial speed under a predefined threshold value for the evaluation (D1 [0025, 0031]). 6. The device in accordance with claim 1, wherein the FMCW LiDAR sensor is stationary (D1 figure 1 and [0031, 0043, 0046). 7. The device in accordance with claim 6, wherein the device has at least one further FMCW LiDAR sensor having a further monitored zone and the monitored zone at least partly overlaps the further monitored zone (D1 0046]). 8. The device in accordance with claim 1, wherein the FMCW LiDAR sensor is movable. (D1 teaches mounting sensors on moving elements (robot arms) [0031, 0034]) 9. The device in accordance with claim 8, wherein the FMCW LiDAR sensor is fastened to a robot arm. (D1 teaches mounting sensors on moving elements (robot arms) [0031, 0034]) 10. The device in accordance with claim 9, wherein the FMCW LiDAR sensor is fastened to a driverless transport vehicle. (D1 teaches the sensor is mounted on a moving machine [see Abstract] which can be read on driverless transport vehicle). Alternatively, D2 is teaches mounting the sensors on vehicles ([0027, 0031, Fig 1A). It would have been obvious to mount the sensor on a driverless transport vehicles to provides robust velocity and spatial information, improving the reliability of object detection and segmentation, enabling real-time perception of the vehicle’s surroundings, which is essential for safe navigation and collision avoidance. Regarding claims 11-15, see rejections of claims 1-10 above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAM ALSOMIRI whose telephone number is (571)272-6970. The examiner can normally be reached 9-5:30 M-F. 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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. /ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645 Application/Control Number: 18/223,661 Page 2 Art Unit: 3645 Application/Control Number: 18/223,661 Page 3 Art Unit: 3645 Application/Control Number: 18/223,661 Page 4 Art Unit: 3645 Application/Control Number: 18/223,661 Page 5 Art Unit: 3645 Application/Control Number: 18/223,661 Page 6 Art Unit: 3645