DETAILED ACTION
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 .
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 1-3, 5, 9-13, 15, 16, 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Pacala (WO 2021026241 A1) in view of Kulesh (US 2020/0233066 A1) and in view of Knuth (https://doi.org/10.1016/j.dsp.2019.102581).
Regarding Claim 1, Pacala discloses a LIDAR system ([0002]) comprising a light source for emitting signals ([0077]: " The Tx module 240 includes an emitter array 242, which can be a one-dimensional or two-dimensional array of emitters, and a Tx optical system 244, which when taken together can form an array of micro-optic emitter channels. Emitter array 242 or the individual emitters are examples of laser sources.), a detection unit ([0078]: “The Rx module 230 can include sensor array 236, which can be, e.g., a one dimensional or two-dimensional array of photosensors.”), and a controller ([0081]: “In some embodiments, the photon time series output from the ASIC are sent to the ranging system controller 250 for further processing”), the controller being configured to:
acquire, from the detection unit ([0079]: “In one embodiment, the sensor array 236 of the Rx module 230 is fabricated as part of a monolithic device on a single substrate (using, e.g., CMOS technology) that includes both an array of photon detectors and an ASIC 231 for signal processing the raw histograms from the individual photon detectors (or groups of detectors) in the array”), a plurality of data points representative of detected signals ([0081]: “In some embodiments, the photon time series output from the ASIC are sent to the ranging system controller 250 for further processing”);
perform a first iteration of an iterative process ([0223]: “After masking the initial peak, subsequent peaks can be detected recursively or iteratively by a peak detection circuit that is configured to identify a maximum peak in the histogram memory. Once a maximum peak is identified, it can be masked such that it is excluded from any subsequent executions of the peak detection circuit, thereby allowing new peaks to be identified with each execution.”; [0249]) for determining a distance of a first object from the LIDAR system based on the plurality of data points ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”), during the first iteration the controller being configured to:
determine a first number of bins based on the plurality of data points ([0113]: “But, as described in more detail below, the number of time bins can vary, e.g., based on properties of a particular object in an angle of incidence of the laser pulse.”):
organize the plurality of data points into the first plurality of bins, the first plurality of bins including the first number of bins, a given bin being associated with a respective count value ([0115]: “The histogram can accumulate the counts, with the count of a particular time bin corresponding to a sum of the measured data values all occurring in that particular time bin across multiple shots.”);
identify a target bin amongst the first plurality of bins, the target bin being associated with a largest count value amongst respective bins from the first plurality of bins ([0234]: “The peak detection circuit may be configured to cycle through the registers in the histogram memory and identify a maximum value.”):
determine the distance of the first object based on the target bin ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”): and
determine a reduced plurality of data points based on the plurality of data points, the reduced plurality of data points excluding data points associated with the target bin ([0238]: “the initial peak 2904 may be a known peak or one that was previously detected. Therefore, the masking circuit may provide a mask to the peak detection circuit to exclude the time bins in the mask interval 2906.”);
perform a second iteration of the iterative process for determining a distance of a second object from the LIDAR system based on the reduced plurality of data points ([0234]: “Some embodiments may use a method of recursive masking to exclude the initial peak 2904 and any peaks that were previously identified by the peak detection circuit.”; [0243]: “The masking process described above allows the peak detection circuit to be executed any number of times, and thus any number of peaks may be detected in the histogram memory.”).
Pacala does not teach and Kulesh does teach wherein the procedure is executed in response to the first number being above a pre-determined threshold ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. Kulesh notes in [0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Pacala does not teach and Knuth does teach, the first number maximizing a pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 2, which depends from rejected Claim 1, Pacala teaches wherein during the second iteration ([0239]: “When the initial peak 2904 is excluded from the execution of the peak detection circuit, the next-largest peak in the histogram memory can be identified.”) the controller is further configured to:
determine a second number of bins based on the reduced plurality of data points ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”)
organize the reduced plurality of data points into a second plurality of bins, the second plurality of bins including the second number of bins ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”);
identify a second target bin amongst the second plurality of bins, the second target bin being associated with a largest count value amongst the second plurality of bins ([0231]: “Additionally, the histogram memory may include a plurality of subsequent peaks 3002, 3004, 3006, 3008 representing photon counts resulting from reflections off of objects in the surrounding environment. To accurately calculate a distance, a peak detection circuit may cycle through the registers of the histogram memory to identify each peak while excluding previously detected peaks from being identified more than once.”);
determine the distance of the second object based on the second target bin ([0231], [0252]: “The processor 3308 may receive each of the stored peaks in the plurality of registers 3304 for each measurement and may perform distance and/or timing calculations to determine a distance between the optical measurement system and a reflecting object in the surrounding environment.”); and
determine another reduced plurality of data points based on the reduced plurality of data points, the other reduced plurality of data points excluding data points associated with the second target bin ([0238]: “the initial peak 2904 may be a known peak or one that was previously detected. Therefore, the masking circuit may provide a mask to the peak detection circuit to exclude the time bins in the mask interval 2906.”).
Pacala does not teach and Kulesh does teach conditionally executing the rest of the process in response to the second number being above the pre- determined threshold number of bins ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. Kulesh notes in [0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Pacala does not teach and Knuth does teach wherein the second number maximizing the pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 3, which depends from rejected Claim 1, Pacala does not teach and Kulesh does teach wherein in response to the second number being below the pre- determined threshold number of bins: stop the iterative process ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached, at which point the iteration stops).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. It is well-known in the art that iterative processes must have a stop condition, and a skilled worker would be able to implement one in the system of Claim 1 with predictable results.
Pacala does not teach and Knuth does teach wherein during the second iteration the controller is further configured to:
determine a second number of bins based on the reduced plurality of data points, the second number maximizing the pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 5, which depends from rejected Claim 1, Pacala further discloses wherein the detection unit comprises a Single- Photon Avalanche Diode (SPAD) detector ([0002], [0078]) and a Time to Digital (TDC) converter ([0104], [0118]).
Regarding Claim 9, which depends from rejected Claim 1, Pacala and Kulesh do not teach and Knuth does teach wherein to determine the first number of bins based on the plurality of data points the controller is configured to perform a Knuth technique, the controller is configured to use the pre-determined metric of the detected signals being indicative of interpretability of the detected signal (Page 1, Column 1: “Histograms are used extensively as non-parametric density estimators both to visualize data and to obtain summary quantities, such as the entropy, of the underlying density.” The procedure disclosed in Knuth maximizes the logarithm of the posterior probability which allows for optimal bin width estimation, and therefore enhanced interpretability of the signal.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Claim 1 with the further teaching of Knuth to use the pre-determined metric as an indicator of signal interpretability. A skilled worker in the art would be aware the maximizing the log posterior probability would result in optimal bin width which enhances signal interpretability, and would be able to implement this with predictable results.
Regarding Claim 10, which depends from rejected Claim 1, Pacala does not teach and Kulesh does teach wherein the controller is further configured to determine the pre-determined threshold number of bins based on at least one of (i) a maximum detection range of the LIDAR system and (ii) a detection resolution of the LIDAR system ([0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold based on the detection resolution of the system. Kulesh notes in [0033] that such a conditioning may be used “to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Regarding Claim 11, Pacala teaches a method for determining distance of objects from a LIDAR system ([0002]), the method executable by a controller ([0081]: “In some embodiments, the photon time series output from the ASIC are sent to the ranging system controller 250 for further processing”), the method comprising:
acquiring a plurality of data points ([0081]: “In some embodiments, the photon time series output from the ASIC are sent to the ranging system controller 250 for further processing”) representative of detected signals ([0079]: “In one embodiment, the sensor array 236 of the Rx module 230 is fabricated as part of a monolithic device on a single substrate (using, e.g., CMOS technology) that includes both an array of photon detectors and an ASIC 231 for signal processing the raw histograms from the individual photon detectors (or groups of detectors) in the array”);
performing a first iteration of an iterative process ([0223]: “After masking the initial peak, subsequent peaks can be detected recursively or iteratively by a peak detection circuit that is configured to identify a maximum peak in the histogram memory. Once a maximum peak is identified, it can be masked such that it is excluded from any subsequent executions of the peak detection circuit, thereby allowing new peaks to be identified with each execution.”; [0249]) for determining a distance of a first object from the LIDAR system based on the plurality of data points ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”), during the first iteration the method including:
determining a first number of bins based on the plurality of data points ([0113]: “But, as described in more detail below, the number of time bins can vary, e.g., based on properties of a particular object in an angle of incidence of the laser pulse.”):
organizing the plurality of data points into the first plurality of bins, the first plurality of bins including the first number of bins, a given bin being associated with a respective count value ([0115]: “The histogram can accumulate the counts, with the count of a particular time bin corresponding to a sum of the measured data values all occurring in that particular time bin across multiple shots.”);
identifying a target bin amongst the first plurality of bins, the target bin being associated with a largest count value amongst respective bins from the first plurality of bins ([0234]: “The peak detection circuit may be configured to cycle through the registers in the histogram memory and identify a maximum value.”);
determining the distance of the first object based on the target bin ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”);
and determining a reduced plurality of data points based on the plurality of data points, the reduced plurality of data points excluding data points associated with the target bin ([0238]: “the initial peak 2904 may be a known peak or one that was previously detected. Therefore, the masking circuit may provide a mask to the peak detection circuit to exclude the time bins in the mask interval 2906.”);
performing a second iteration of the iterative process for determining a distance of a second object from the LIDAR system based on the reduced plurality of data points ([0234]: “Some embodiments may use a method of recursive masking to exclude the initial peak 2904 and any peaks that were previously identified by the peak detection circuit.”; [0243]: “The masking process described above allows the peak detection circuit to be executed any number of times, and thus any number of peaks may be detected in the histogram memory.”).
Pacala does not teach and Kulesh does teach wherein the procedure is executed in response to the first number being above a pre-determined threshold ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. Kulesh notes in [0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Pacala does not teach and Knuth does teach, the first number maximizing a pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 12, which depends from rejected Claim 11, Pacala teaches wherein during the second iteration ([0239]: “When the initial peak 2904 is excluded from the execution of the peak detection circuit, the next-largest peak in the histogram memory can be identified.”) the method further comprises:
determining a second number of bins based on the reduced plurality of data points ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”)
organize the reduced plurality of data points into a second plurality of bins, the second plurality of bins including the second number of bins ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”);
identify a second target bin amongst the second plurality of bins, the second target bin being associated with a largest count value amongst the second plurality of bins ([0231]: “Additionally, the histogram memory may include a plurality of subsequent peaks 3002, 3004, 3006, 3008 representing photon counts resulting from reflections off of objects in the surrounding environment. To accurately calculate a distance, a peak detection circuit may cycle through the registers of the histogram memory to identify each peak while excluding previously detected peaks from being identified more than once.”);
determine the distance of the second object based on the second target bin ([0231], [0252]: “The processor 3308 may receive each of the stored peaks in the plurality of registers 3304 for each measurement and may perform distance and/or timing calculations to determine a distance between the optical measurement system and a reflecting object in the surrounding environment.”); and
determine another reduced plurality of data points based on the reduced plurality of data points, the other reduced plurality of data points excluding data points associated with the second target bin ([0238]: “the initial peak 2904 may be a known peak or one that was previously detected. Therefore, the masking circuit may provide a mask to the peak detection circuit to exclude the time bins in the mask interval 2906.”).
Pacala does not teach and Kulesh does teach conditionally executing the rest of the process in response to the second number being above the pre- determined threshold number of bins ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. Kulesh notes in [0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Pacala does not teach and Knuth does teach wherein the second number maximizing the pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 13, which depends from rejected Claim 11, Pacala does not teach and Kulesh does teach wherein the method further comprises in response to the second number being below the pre- determined threshold number of bins: stopping the iterative process ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached, at which point the iteration stops).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. It is well-known in the art that iterative processes must have a stop condition, and a skilled worker would be able to implement one in the system of Claim 11 with predictable results.
Pacala does not teach and Knuth does teach wherein during the second iteration the controller is further configured to:
determine a second number of bins based on the reduced plurality of data points, the second number maximizing the pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Regarding Claim 15, which depends from rejected Claim 11, Pacala further discloses wherein wherein the acquiring the plurality of data points comprises acquiring raw histogram data from a Time to Digital (TDC) converter of a LIDAR system ([0104], [0118]).
Regarding Claim 16, which depends from rejected Claim 11, Pacala further discloses wherein the method further comprises detecting the detected signals by at least one of a Single-Photon Avalanche Diode (SPAD) detector ([0002], [0078]) and a Silicon Photomultiplier (SiPM) detector, and digitizing the detected signals into the plurality of data points by a Time to Digital (TDC) converter ([0104], [0118]).
Regarding Claim 19, which depends from Claim 11, Pacala and Kulesh do not teach and Knuth does teach wherein to determine the first number of bins based on the plurality of data points the controller is configured to perform a Knuth technique, the controller is configured to use the pre-determined metric of the detected signals being indicative of interpretability of the detected signal (Page 1, Column 1: “Histograms are used extensively as non-parametric density estimators both to visualize data and to obtain summary quantities, such as the entropy, of the underlying density.” The procedure disclosed in Knuth maximizes the logarithm of the posterior probability which allows for optimal bin width estimation, and therefore enhanced interpretability of the signal.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Claim 1 with the further teaching of Knuth to use the pre-determined metric as an indicator of signal interpretability. A skilled worker in the art would be aware the maximizing the log posterior probability would result in optimal bin width which enhances signal interpretability, and would be able to implement this with predictable results.
Regarding Claim 20, which depends from rejected Claim 11, Pacala does not teach and Kulesh does teach wherein the method further comprises determining the pre-determined threshold number of bins based on at least one of (i) a maximum detection range of the LIDAR system and (ii) a detection resolution of the LIDAR system ([0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold based on the detection resolution of the system. Kulesh notes in [0033] that such a conditioning may be used “to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Regarding Claim 21, Pacala discloses a system for processing a digital signal, the system comprising a Time to Digital converter (TDC) ([0104], [0118]) and a controller ([0081]: “In some embodiments, the photon time series output from the ASIC are sent to the ranging system controller 250 for further processing”), being configured to:
acquire, from the TDC, a plurality of data points representative of the digital signal ([0104], [0118]);
perform a first iteration of an iterative process ([0223]: “After masking the initial peak, subsequent peaks can be detected recursively or iteratively by a peak detection circuit that is configured to identify a maximum peak in the histogram memory. Once a maximum peak is identified, it can be masked such that it is excluded from any subsequent executions of the peak detection circuit, thereby allowing new peaks to be identified with each execution.”; [0249]) for determining a feature of an artifact based on the plurality of data points ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”), during the first iteration the controller being configured to:
determine a first number of bins based on the plurality of data points ([0113]: “But, as described in more detail below, the number of time bins can vary, e.g., based on properties of a particular object in an angle of incidence of the laser pulse.”)
organize the plurality of data points into the first plurality of bins, the first plurality of bins including the first number of bins, a given bin being associated with a respective count value ([0115]: “The histogram can accumulate the counts, with the count of a particular time bin corresponding to a sum of the measured data values all occurring in that particular time bin across multiple shots.”);
identify a target bin amongst the first plurality of bins, the target bin being associated with a largest count value amongst respective bins from the first plurality of bins ([0234]: “The peak detection circuit may be configured to cycle through the registers in the histogram memory and identify a maximum value.”);
determine the feature of the artifact based on the target bin ([0236]: “Using any of these methods, the peak detection circuit may identify a number of time bins to represent the peak, and the value in these time bins may be sent to a processor to calculate a distance to the reflecting object.”; Under the broadest reasonable interpretations of the terms feature and artifact, finding the distance to a reflecting object reads on this limitation): and
determine a reduced plurality of data points based on the plurality of data points, the reduced plurality of data points excluding data points associated with the target bin ([0238]: “the initial peak 2904 may be a known peak or one that was previously detected. Therefore, the masking circuit may provide a mask to the peak detection circuit to exclude the time bins in the mask interval 2906.”);
perform a second iteration of the iterative process for determining a feature of another artifact based on the reduced plurality of data points ([0234]: “Some embodiments may use a method of recursive masking to exclude the initial peak 2904 and any peaks that were previously identified by the peak detection circuit.”; [0243]: “The masking process described above allows the peak detection circuit to be executed any number of times, and thus any number of peaks may be detected in the histogram memory.”).
Pacala does not teach and Knuth does teach, the first number maximizing a pre-determined metric of the detected signals (Page 4, Column 1: “In optimization problems, it is often easier to maximize the logarithm of the posterior…” see equation in text; Page 4, Column 2: Equation (32) allows one to easily identify the number of bins M which optimize the posterior. We call this the OPTBINS algorithm and provide the Matlab code in the Appendix”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Knuth to maximize the logarithm of the posterior. Knuth notes that it is easier to maximize the logarithm of the posterior, which can then be used to generate histograms with the optimal number of bins. This procedure can therefore be used to more quickly find the optimal number of bins, thus saving computation time and achieving more rapid results, which is very advantageous in LiDAR systems which often handle very large point cloud data sets.
Pacala does not teach and Kulesh does teach conditionally executing the rest of the process in response to the second number being above the pre- determined threshold number of bins ([0034]: “The at least one of the bin widths can be adjusted as an iterative step to improve the peak detection.” Adjusting the bin widths necessarily changes the number of bins over a fixed sampling interval, e.g. Figures 3B and 3C, [0064]. The iterative method of Kulesh can make bins coarser or finer [0033] so in the case of increasing coarseness, the bin size increases and bin number decreases until a threshold is reached)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Pacala with the teaching of Kulesh to condition the system’s operation off of reaching a threshold. Kulesh notes in [0033] that “As a specific example, the processing circuitry refines the at least one accuracy metric as a function of at least one of an indication of power or distance sensed in optically received signals by changing one or more of the bin widths of the plurality of different sub-histograms for a coarser detection to optimize or improve detection reliability of one or more distal ones of the physical objects, and by changing another one of the bin widths of another of the plurality of sub-histograms for a finer detection to optimize or improve resolution in terms of detecting one or more proximal ones of the physical objects.” Improving detection reliability or resolution are both desirable characteristics in a LiDAR system.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Kulesh and in view of Knuth as applied to Claim 1 above, and in view of Beer (DE 102017220774 A1).
Regarding Claim 4, which depends from rejected Claim 1, Pacala further teaches wherein during the second iteration the controller is further configured to:
determine a second number of bins based on the reduced plurality of data points ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”) the second number maximizing the pre-determined metric of the detected signals:
Pacala, Kulesh, and Knuth do not teach and Beer does teach wherein in response to the second number being below the pre- determined threshold number of bins: determine that the reduced plurality of data points represents a noise signal ([0020]: “If the number of bins in the third histogram falls below the defined limit (e.g., background event rate previously determined in the first runtime determination using the first histogram could be used.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pacala, Kulesh, and Knuth with the teaching of Beer to determine that if the number of bins is below a certain threshold, then the signal should be considered as noise or background. Beer notes in [0020] that if the number of bins is too low, then the variance can become large, and that the signal should therefore be considered as a background signal. This can therefore result in a better estimation of the noise component of the signal, and a better retrieval of distance overall.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Kulesh and in view of Knuth as applied to Claim 11 above, and in view of Beer (DE 102017220774 A1).
Regarding Claim 14, which depends from rejected Claim 11, Pacala further teaches wherein during the second iteration the method further comprises:
determining a second number of bins based on the reduced plurality of data points ([0240]: “During a second execution of the peak detection circuit, maximum values identified in mask interval 2906 and mask interval 3102 may be excluded from consideration. Alternatively, any time bins in mask interval 2906 or mask interval 3102 need not have their values examined for consideration as a maximum value and may be skipped by the peak detection circuit.”) the second number maximizing the pre-determined metric of the detected signals:
Pacala, Kulesh, and Knuth do not teach and Beer does teach wherein in response to the second number being below the pre- determined threshold number of bins: determine that the reduced plurality of data points represents a noise signal ([0020]: “If the number of bins in the third histogram falls below the defined limit (e.g., background event rate previously determined in the first runtime determination using the first histogram could be used.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pacala, Kulesh, and Knuth with the teaching of Beer to determine that if the number of bins is below a certain threshold, then the signal should be considered as noise or background. Beer notes in [0020] that if the number of bins is too low, then the variance can become large, and that the signal should therefore be considered as a background signal. This can therefore result in a better estimation of the noise component of the signal, and a better retrieval of distance overall.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Kulesh and in view of Knuth as applied to Claim 1 above, and in view of Won (US 2023/0168376 A1).
Regarding Claim 6, which depends from Claim 1, Pacala teaches where the detection unit comprises a Time to Digital (TDC) converter ([0104], [0118]).
Pacala in view of Kulesh and in view of Knuth does not teach and Won does teach wherein the detection unit comprises a Silicon Photomultiplier (SiPM) detector ([0478]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to follow the teaching of Won and incorporate a SiPM into the device of Claim 1. Won notes in [0489] that “the histogram by the SiPM 780 is advantageous in that the histogram may be quickly formed with only one laser beam emission.” More rapid histogram formation and lower power usage are both highly desirable characteristics in LiDAR systems.
Regarding Claim 7, which depends from rejected Claim 1, Pacala in view of Kulesh and in view of Knuth does not teach and Won does teach wherein the LiDAR system is a flash-type LiDAR system ([0517]: “The LiDAR may be implemented in various methods. For example, the LiDAR may be implemented using a flash method and a scanning method.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to follow the teaching of Won and implement the system of Claim 1 as a flash LiDAR system. Flash LiDAR devices are well-known in the art, and a skilled worker would be able to implement one in this system with predictable results.
Regarding Claim 8, which depends from rejected Claim 1, Pacala suggests ([0091]) but does not explicitly teach wherein the controller is further configured to generate a 3D point cloud at least partially based on the distance of the first object from the LiDAR system ([0795], [0804]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Won to generate a 3D point cloud into the device of Claim 1. Point clouds are well-known in the art and a skilled worker would be able to generate one in a LiDAR system with predictable results.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Pacala in view of Kulesh and in view of Knuth as applied to Claim 11 above, and in view of Won (US 2023/0168376 A1).
Regarding Claim 17, which depends from rejected Claim 11, Pacala in view of Kulesh and in view of Knuth does not teach and Won does teach wherein the LiDAR system is a flash-type LiDAR system ([0517]: “The LiDAR may be implemented in various methods. For example, the LiDAR may be implemented using a flash method and a scanning method.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to follow the teaching of Won and implement the method of Claim 11 as a flash LiDAR system. Flash LiDAR devices are well-known in the art, and a skilled worker would be able to implement one in this system with predictable results.
Regarding Claim 18, which depends from rejected Claim 11, Pacala suggests ([0091]) but does not explicitly teach wherein the method further comprises generating a 3D point cloud at least partially based on the distance of the first object from the LiDAR system ([0795], [0804]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Won to generate a 3D point cloud into the method of Claim 11. Point clouds are well-known in the art and a skilled worker would be able to generate one in a LiDAR system with predictable results.
Conclusion
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/B.W.C./ Examiner, Art Unit 3645
/ISAM A ALSOMIRI/ Supervisory Patent Examiner, Art Unit 3645