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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CHINA 202310614915.0, filed on May 26, 2023.
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.
Claim(s) 1 – 6, 9 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Armenyan et al (US 11,609,334 B1) (herein after Armenyan) in view of Guo (US 2018/0341022 A1) (herein after Guo).
Regarding Claim 1, Armenyan discloses, 1. A sampling data processing method for a LiDAR (Fig. 1, The LIDAR system 100), wherein the LiDAR comprises a sampling module (Fig. 1, signal processing unit 112, image processing system 114), at least two intermediate processing modules (Fig. 10. Data storage device 1018, main memory 1004; “Col. 23. Ln. 58 FIG. 10 is a block diagram of an example computing device 1000 that may perform one or more of the operations described herein“), a digital center module (Fig. 10, processing device 1002), — and wherein the method comprises: sampling an echo (Fig. 3. return signal 313 from reflections; “Col. 11. Ln. 18 FIG. 3 is a block diagram illustrating an example LIDAR system 300 according to some embodiments of the present disclosure”) through the sampling module to obtain sampling data (Fig. 3, data points 340); processing the sampling data through the at least two intermediate processing modules in sequence (Fig. 8. Col. 20. Ln. 45 plurality of signal processing operations 410 configured to execute sequentially) to obtain point cloud data (Fig. 3. point cloud 350), wherein each intermediate processing module performs data processing on data from an upstream module (Fig. 8. signal processing operations 410; “Col. 20. Ln. 39 FIG. 8 is a block diagram illustrating an example of operating a signal processing system 303”) to obtain intermediate data and outputs the intermediate data to a downstream module (Fig. 8, signal processing operations 410); inputting the point cloud data into a cache (Fig. 3, memory 330) of the digital center module for storage; — and generating at least one frame (Fig. 3. Col. 13. Ln. 3 a frame may represent a digital "snapshot" of scenes) of point cloud data based on the point cloud data; when a module to be analyzed is determined (Fig. 4. Col. 13. Ln. 51 an input to the signal processing operation 410; “Col.13. Ln. 33 FIG. 4 is a schematic diagram illustrating an example signal processing system 303”) from the at least two intermediate processing modules, fetching data from the module to be analyzed (Fig. 4. Col. 13. Ln. 61 access the memory 330 to retrieve the data associated with the first point 340A to be processed) to obtain fetched data; switching the digital center module to input the fetched data into a cache (Fig. 3, memory 330) of the digital center module for storage; —.
Armenyan fails to disclose, — and a framing module, — reading the point cloud data from the cache through the framing module, — and reading the fetched data from the cache through the framing module, and performing fault analysis on the module to be analyzed based on the fetched data.
In analogous art, Guo discloses, — and a framing module (Fig. 6, The local coordinate data frame calculating module 63), — reading the point cloud data from the cache through the framing module (Fig. 7, ¶ 134 memory 71, local coordinate data frame calculating module 63), — and reading the fetched data from the cache through the framing module (Fig. 7, ¶ 134 memory 71, local coordinate data frame calculating module 63), and performing fault analysis on the module to be analyzed based on the fetched data (Fig. 2, ¶ 76 each side has a certain error. A norm is defined for these errors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Armenyan by combining the method performed by a lidar disclosed by Armenyan with a method performed by a lidar comprising: a framing module, reading point cloud data from a cache through the framing module, and reading fetched data from the cache through the framing module, and performing fault analysis on a module to be analyzed based on the fetched data; disclosed by Guo for the benefit of processing lidar data with high accuracy even in locations where GPS is not needed. [Guo: ¶ 22: The method improves the registration accuracy and accuracy. Moreover, GPS is not needed during the mapping process, so the method is applicable to multiple indoor and outdoor application scenarios].
Regarding Claim 2, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 2. The method according to claim 1, wherein when the module to be analyzed is determined from the at least two intermediate processing modules, the method further comprises: controlling an intermediate processing module downstream of the module to be analyzed (Fig. 8. Col. 13. Ln. 25 point 340 may undergo a number of different types of analyses) to enter a bypass mode (Fig. 8. Col. 23. Ln. 20 operation are performed without modifying data); and directly passing the fetched data to the digital center module through the intermediate processing module (Fig. 8. Col. 23. Ln. 17 different memory addresses;) having entered the bypass mode.
Regarding Claim 3, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 3. The method according to claim 1, wherein the at least two intermediate processing modules are connected to the digital center module through a group of buses (Fig. 10, bus 1030) respectively; and wherein the fetched data is inputted into the digital center module through a bus (Fig. 10. Col. 24. Ln. 10 processing device 1002, a main memory 1004 which may communicate with each other via a bus 1030) between the module to be analyzed and the digital center module.
Regarding Claim 4, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 4. The method according to claim 1, wherein the LiDAR has a working mode (Fig. 8. Col. 20. Ln. 45 plurality of signal processing operations 410 configured to execute sequentially) and a detection mode (Fig. 8. Col. 14. Ln. 30 signal processing operation 410 may analyze the other points 340); wherein in the working mode, the method further comprises processing the sampling data through the at least two intermediate processing modules in sequence (Fig. 8. Col. 20. Ln. 45 plurality of signal processing operations 410 configured to execute sequentially), to obtain point cloud data, and inputting the point cloud data into a cache of the digital center module for storage (Fig. 3, memory 330); and wherein in the detection mode, the method further comprises determining the module to be analyzed (Fig. 8. Col. 14. Ln. 30 signal processing operation 410 may analyze the other points 340) from the at least two intermediate processing modules (Fig. 8. Col. 14. Ln. 32 determine if the first point 340A could be modified).
Regarding Claim 5, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 5. The method according to claim 1, wherein the sampling module comprises a plurality of pixels (Fig. 1. Col. 10. Ln. 10 overlay a 3D point cloud data with the image data), and the sampling data comprises pixel data (Fig. 1. Col. 10. Ln. 10 overlay a 3D point cloud data with the image data) obtained through at least some of the pixels for sampling; and wherein when the module to be analyzed is determined (Fig. 8. Col. 14. Ln. 32 determine if the first point 340A could be modified) from the at least two intermediate processing modules, before fetching the data from the module to be analyzed (Fig. 8. Col. 16. Ln. 25 a first index 440A and a second index 440B may be generated) to obtain the fetched data (Fig. 8. Col. 16. Ln. 35 view and/or analyze the metadata 345), the method further comprises: reading pixel data in different regions (Fig. 8. Col. 16. Ln. 19 Indices 440 may be generated to reference respective ones of the points 340) of the sampling module for a plurality of times (Fig. 8. Col. 16. Ln. 47 copy one or more portions 345B_1 of the second point 340B), wherein the pixel data read in one time is processed by the module to be analyzed (Fig. 8. Col. 16. Ln. 35 view and/or analyze the metadata 345), and after the fetched data of the module to be analyzed is stored in the cache (Fig. 8. Col. 16. Ln. 43 copying one or more portions 345A_1, 345A_2) of the digital center module, a next reading starts (Fig. 8. Col. 16. Ln. 43 form the new point 340N by copying one or more portions the new point 340N).
Regarding Claim 6, Armenyan in view of Guo disclose the limitations of claim 5, which this claim depends on.
Armenyan further discloses, 6. The method according to claim 5, wherein when the fetched data corresponding to the pixel data read in the one time is stored in the cache (Fig. 8. Col. 16. Ln. 43 copying one or more portions 345A_1, 345A_2) of the digital center module, the fetched data corresponding to the pixel data read in a last time is covered (Fig. 8. Col. 17. Ln. 29 writes to memory 330 may be delayed until all, or most, of the operations are 30 complete) in the cache.
Regarding Claim 9, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 9. The method according to claim 1, wherein before the module to be analyzed is determined (Fig. 8. Col. 14. Ln. 30 signal processing operation 410 may analyze the other points 340) from the at least two intermediate processing modules (Fig. 8. Col. 14. Ln. 32 determine if the first point 340A could be modified), the cache of the digital center module is released and divided into at least two regions (Fig. 8. Col. 16. Ln. 19 Indices 440 may be generated to reference respective ones of the points 340); and wherein the switching the digital center module to input the fetched data into a cache (Fig. 3, memory 330) of the digital center module for storage comprises: storing the fetched data in at least two regions of the cache of the digital center module through a ping-pong operation (Fig. 8. Col. 25. Ln. 32 distinct operations may be performed in an intermittent or alternating manner).
Regarding Claim 10, Armenyan discloses, 10. A sampling data processing device (Fig. 1, The LIDAR system 100), comprising a sampling module (Fig. 1, signal processing unit 112, image processing system 114), at least two intermediate processing modules (Fig. 10. Data storage device 1018, main memory 1004; “Col. 23. Ln. 58 FIG. 10 is a block diagram of an example computing device 1000 that may perform one or more of the operations described herein“), a digital center module (Fig. 10, processing device 1002), — and a fetching module (Fig. 8, signal processing operations 410), wherein: the sampling module is configured to sample an echo (Fig. 3. return signal 313 from reflections; “Col. 11. Ln. 18 FIG. 3 is a block diagram illustrating an example LIDAR system 300 according to some embodiments of the present disclosure”) to obtain sampling data (Fig. 3, data points 340); the at least two intermediate processing modules are configured to process the sampling data in sequence (Fig. 8. Col. 20. Ln. 45 plurality of signal processing operations 410 configured to execute sequentially) to obtain point cloud data (Fig. 3. point cloud 350), wherein each intermediate processing module is configured to perform data processing on data from an upstream module (Fig. 8. signal processing operations 410; “Col. 20. Ln. 39 FIG. 8 is a block diagram illustrating an example of operating a signal processing system 303”) to obtain intermediate data and then output the intermediate data to a downstream module (Fig. 8. signal processing operations 410); the digital center module is configured to input the point cloud data into a cache (Fig. 3, memory 330) of the digital center module for storage; — the fetching module is configured to, when the module to be analyzed (Fig. 4. Col. 13. Ln. 51 an input to the signal processing operation 410; “Col.13. Ln. 33 FIG. 4 is a schematic diagram illustrating an example signal processing system 303”) is determined from the at least two intermediate processing modules, fetch data from the module to be analyzed to obtain fetched data; the digital center module is further configured to switch to input the fetched data into a cache (Fig. 3, memory 330) of the digital center module for storage; —.
Armenyan fails to disclose, — a framing module, — the framing module is configured to read the point cloud data from the cache and generate at least one frame of point cloud data based on the point cloud data; — and the framing module is further configured to read the fetched data from the cache and perform fault analysis on the module to be analyzed based on the fetched data.
In analogous art, Guo discloses, — a framing module (Fig. 6, The local coordinate data frame calculating module 63), — the framing module is configured to read the point cloud data from the cache (Fig. 7, ¶ 134 memory 71, local coordinate data frame calculating module 63) and generate at least one frame of point cloud data based on the point cloud data (Fig. 7, ¶ 134 memory 71, local coordinate data frame calculating module 63); — and the framing module is further configured to read the fetched data from the cache and perform fault analysis on the module to be analyzed based on the fetched data (Fig. 2, ¶ 76 each side has a certain error. A norm is defined for these errors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Armenyan by combining the sampling data processing device disclosed by Armenyan with a sampling data processing device comprising: a framing module, the framing module is configured to read the point cloud data from the cache and generate at least one frame of point cloud data based on the point cloud data; and the framing module is further configured to read the fetched data from the cache and perform fault analysis on the module to be analyzed based on the fetched data; disclosed by Guo for the benefit of processing lidar data with high accuracy even in locations where GPS is not needed. [Guo: ¶ 22: The method improves the registration accuracy and accuracy. Moreover, GPS is not needed during the mapping process, so the method is applicable to multiple indoor and outdoor application scenarios].
Regarding Claim 11, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 11. A LiDAR, comprising: a processor (Fig. 10, processing device 1002); and a storage medium storing executable code (Fig. 10 main memory 1004), wherein when the executable code is executed by the processor, the processor performs the method according to claim 1 (Fig. 10. Col. 24, Ln. 48 point analysis 1066 for carrying out the operations described herein).
Claim(s) 7, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Armenyan et al (US 11,609,334 B1) (herein after Armenyan) in view of Guo (US 2018/0341022 A1) (herein after Guo), and further in view of PACALA (US 2018/0329066 A1) (herein after Pacala).
Regarding Claim 7, Armenyan in view of Guo disclose the limitations of claim 1, which this claim depends on.
Armenyan further discloses, 7. The method according to claim 1, wherein the inputting the point cloud data into the cache of the digital center module for storage comprises: sequentially storing point cloud data (Fig. 8. Col. 20. Ln. 45 plurality of signal processing operations 410 configured to execute sequentially) corresponding to the sampling data obtained by sampling through the sampling module in the cache of the digital center module in an order of sampling (Fig. 8. Col. 21. Ln. 23 series of first indices 440A from a first buffer 810A) by the sampling module, wherein point cloud data on a same row (Fig. 8. Col. 21. Ln. 14 the buffer 810 may be a first-in, first-out (FIFO) buffer) in one frame of point cloud data is stored at adjacent storage addresses (Fig. 8. Col. 21. Ln. 23 series of first indices 440A from a first buffer 810A) in the cache, so that the framing module can read the point cloud data in the cache in an order of rows (Fig. 8. Col. 21. Ln. 18 analyzed from the buffer 810), —
Armenyan fails to disclose, — and wherein a maximum pitch angle and a minimum pitch angle of point cloud data on the same row in the one frame of point cloud data differ by less than a first preset angle.
In analogous art, Pacala discloses, — and wherein a maximum pitch angle and a minimum pitch angle (Fig. 7, ¶ 84 other methods of combination can be used such as max value, min value) of point cloud data on the same row in the one frame of point cloud data differ by less than a first preset angle (Fig. 7, ¶ 84 group of color pixels whose values can be combined, associated with a single LIDAR pixel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Armenyan in view of Guo by combining the method performed by a lidar disclosed by Armenyan in view of Guo with a method performed by a lidar wherein, a maximum pitch angle and a minimum pitch angle of point cloud data on the same row in the one frame of point cloud data differ by less than a first preset angle; disclosed by Pacala for the benefit of processing lidar data with increased reliability and reduced complexity [Pacala: ¶ 40 a separate module for rotary actuation is no longer needed, thereby increasing reliability, decreasing complexity, and helping to simplify the assembly process.].
Regarding Claim 8, Armenyan in view of Guo in view of Pacala disclose the limitations of claim 7, which this claim depends on.
Armenyan further discloses, 8. The method according to claim 7, wherein the point cloud data comprises distance data (Fig. 8. Col. 10. Ln. 11 distance of objects in the surrounding area.) and reflectivity data (Fig. 8. Col. 11. Ln. 23 return signal 313 from reflections); wherein the cache of the digital center module partitions and stores the distance data and the reflectivity data separately (Fig. 8. Col. 23. Ln. 17 different memory addresses); and wherein the distance data on the same row (Fig. 8. Col. 21. Ln. 14 the buffer 810 may be a first-in, first-out (FIFO) buffer) in the one frame of point cloud data is stored at adjacent storage addresses (Fig. 8. Col. 21. Ln. 23 series of first indices 440A from a first buffer 810A) in the cache, and the reflectivity data on the same row (Fig. 8. Col. 21. Ln. 14 the buffer 810 may be a first-in, first-out (FIFO) buffer) in the one frame of point cloud data is stored at adjacent storage addresses (Fig. 8. Col. 21. Ln. 23 series of first indices 440A from a first buffer 810A) in the cache.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lietz et al. (US 2023/0213634 A1) teaches, a sampling data processing method for a LiDAR (Fig. 1, ¶ 38 FIG. 1 schematically shows a representation of an embodiment of a lidar system 1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH O. NYAMOGO whose telephone number is (469)295-9276. The examiner can normally be reached 9:00 A to 5:00 P CT.
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/JOSEPH O. NYAMOGO/
Examiner
Art Unit 2858
/FARHANA A HOQUE/Primary Examiner, Art Unit 2858
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