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 .
DETAILED ACTION
This office action is in regard to application # 18/843,561 that was filed on 09/03/2024. Claims 1,7-17, 31, 33-34, 39, and 42-47 are currently pending and are under examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 39 and 43-46 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 39 recites the limitation "the at least one processor" in in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claims 43 and 44 recites the limitation "said optimization" in in line 1. There is insufficient antecedent basis for this limitation in the claim. Claim 43 and 44 seem to depend from claim 8. For the purpose of examination, the examiner assumes claims 43 and 44 depend from claim 42. Appropriate correction/dependency required.
Claim 45 recites the limitation "said least one processor" in in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 46 recites the limitation "said at least one processor" in in line 3. There is insufficient antecedent basis for this limitation in the claim.
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) 1,7, 9-17, 31, 33-34, 39, and 42-47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Keilaf et al. (US 2020/0386872) in view of Irish et al. (US 2017/0301716).
Regarding Claim 1, Keilaf discloses a LIDAR systems comprising: at least one light source (112, Fig. 1A) configured for scanning a selected scene (abstract FOV 120, Fig. 1A, Fig. 2A); at least one light sensor (106, Fig. 1A) comprising at least one pixel, said at least one pixel comprises a plurality of sub-pixels, each of said plurality of sub-pixels is configured to generate an output indicative of light collected by said respective sub-pixel (abstract, Claim 1; “…receive from at least one sensor having a plurality of detection elements reflections signals indicative of light reflected from objects in the field of view…”); and a processing unit configured and operable for: receiving a plurality of outputs from said plurality of sub-pixels (claim 1 and abstract. i.e. processor receives reflection signals from the plurality of detection elements), determining a subset of sub-pixels of said plurality of sub-pixels (abstract, claim 1, “…dynamically allocate a first subset of the plurality of detection elements to constitute a first pixel; dynamically allocate a second subset…. following processing of the first pixel and the second pixel, dynamically allocate a third subset of the plurality of detection elements to constitute a third pixel, the third subset verlapping with at least one of the first subset and the second subset, and differing from each of the first subset and the second subset…”, i.e. the dynamic allocation of which detection elements(sub-pixels) form a given pixel directly where is the readout of the pixel by selecting the contributing subset).
Keilaf does not explicitly disclose that the subset determination is based on said output and one or more signals to noise ratio(SNR) criteria and the explicit purpose of improving signal to noise ratio (SNR).
Irish teaches a LiDAR receiver with an array of SPADS (detection elements/sub-pixels), a processing unit that selects and activates only a subset of SPADs (rows and columns) expected to receive the reflected laser spot, while leaving the others inactive, explicitly to improve SNR by eliminating noise from the ambient light on non-illuminated elements (abstract, para. [0003]-[0008], i.e. Detailed description of row/column transistors end processing unit determining the active subset based on predicted spot location/size)). Irish receives the outputs of the SPADs and uses processing logic to choose the subset for higher SNR.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the dynamic subset allocation framework disclosed in Keilaf with SNR driven selective activation criterion taught in Irish with a reasonable expectation of success because both references are direct to the problem of optimizing multi-element detector readout in a scanning lidar and the combination yields a predictable result of higher Signal-to-noise ratio (SNR).
Regarding Claim 7, modified Keilaf discloses dynamically selecting different subsets (arrangement and effective number of detection elements) that constitutes each pixel (Keilaf, claim 1). Irish select both location/arrangement and number of active elements explicitly for SNR (Irish, row/column selection). Combining the dynamic allocation of Keilaf with the SNR criteria of Irish yields the claim limitation.
Regarding Claims 9-10, Keilaf discloses a processor that dynamically allocates successive subsets of a plurality of detection elements to constitute successive pixels, including subsets that overlap with and differ from prior subsets(abstract; claim 1). Because the detection elements form a two-dimensional array, any allocated subset inherently defines special regions with the overall pixel area. The processor receives and processes the reflection signals from those elements after each allocation.
Keilaf does not explicitly state that the pixel is divided into first and second readout regions(top/bottom or left/right) and that a relation between the readout data of those regions is determined.
Irish teaches a two-dimensional SPAD array with independent row-select and column-select transistors that allow activation of arbitrary rectangular (or near rectangular) portions of the array (abstract; detailed description of selecting subsets of rows and columns corresponding to expected laser spot location and size). Selecting a subset of rows necessarily creates upper and lower (top bottom) regions; selecting a subset of columns creates left and right regions. Comparing or relating the signals obtained from different special portions of the active area is a routine step in determining which portion yields the best return and therefore have the highest SNR.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Keilaf’s dynamic subset allocation by incorporating the special partitioning and signal relation analysis taught by Iris with great expectation of success. Iris already operates on two-dimensional array and select active regions according to spatial distribution of the return. Dividing a pixel into top/bottom or left/right readout regions and determining a relation between those regions is a predictable refinement that helps center the active surface on the return and further improves SNR. There is no unexpected.
Regarding Claim 11, modified Keilaf discloses a LIDAR system explicitly allocates multiple(first, second, third, fourth) pixels from overlapping/ differing subsets of the same detection element RA and processes them sequentially(claim 1). Relating data between neighboring pixels to refine subset is inherent).
Regarding Claim 12, Keilaf discloses a LIDAR system that employs a sensor comprising a plurality of detection elements arranged in an array (abstract, claim 1). Neighboring detection elements that are not part of the currently allocated subset function as additional detectors located next to the active cell pixels. Irish teaches that the location and size of the reflected spot are Known or estimated, and the surrounding detectors can be monitored to refine the active area (Irish: determination of expected spot location and size) (non-selected) detectors to map the intensity distribution and thereby refine the subset selection in the direct obvious extension of those teachings.
Regarding Claim 13-15, Keilaf processes reflection signals obtained from successively allocated special subsets detection elements (abstract, claim 1). Those subsets form special clusters whose collective outputs constitute a readout distribution. Irish operate on special distribution of the return across the detector array. Once a special cluster of active elements has been selected standard LiDAR post processing include: computing the intensity weighted centroid (center of mass) of the cluster to refine angular position; measuring special extent of the cluster to estimate object dimension or spot size; and using the amplitude of the return to estimate reflectivity. These calculations are routine once a higher SNR special cluster have been obtained by the selective activation technique of Iris. Applying them to the cluster already formed by Keilaf’s dynamic allocation is therefore obvious and yields only expected improvement in object parameter accuracy.
Regarding Claim 16, Keilaf discloses a processor dynamically allocates subset “following processing of the first pixel and the second pixel” and continues to do so for subsequent pixels while the system is scanning the field of view (claim 1). This is exactly determination of the subset during typical ongoing operation. Irish also perform selective activation continuously throughout scanning (Irish : activation synchronized with each laser pulse).
Regarding Claim 17, Claim 17 is rejected on the same grounds as the rejection of claim 1 above. Keilaf discloses the corresponding method steps of controlling a light source,, receiving output from a detection element and dynamically determining/ are locating subsets that vary the effective pixel readout(Keilaf, claim 1, abstract). The SNR-based determination is satisfied by the Irish reference for the same reasons given for claim 1.
Regarding Claim 31, Claim 31 is rejected on the same grounds as the rejection of claim 1 and 17 above. Keilaf and Irish Each processing units/ circuit that implement the subset allocation and readout variation. Implementing the logic steps/processes as a computer readable instruction on a medium is also disclosed/taught (Keilaf, para. [0018], [0351]; Irish, para. [0042]). Therefore, Implementing the of claim 1 and method of claim 17 Add the computer readable instructions on a usable medium would have been obvious for a person of ordinary skill in the art.
Regarding Claim 33, modified Keilaf discloses the claimed invention except one or more SNR criteria are selected out of: obtaining a maximal SNR, providing a maximal SNR under a certain situation, and/or providing a maximal SNR under certain misalignments. It would have been an obvious matter of design choice to select one or more SNR criteria out of: obtaining a maximal SNR, providing a maximal SNR under a certain situation, and/or providing a maximal SNR under certain misalignments., since applicant has not disclosed that selecting one or more SNR criteria out of: obtaining a maximal SNR, providing a maximal SNR under a certain situation, and/or providing a maximal SNR under certain misalignments solves any stated problem or is for any particular purpose and it appears that the invention would perform equally as well with SNR criteria used in modified Keilaf. Irish targets maximum SNR. Accounting for situational factors or misalignment is a very reason adaptive subset selection is performed.
Regarding Claim 34, broadly interpreted, conditioning subset selection on environmental or object related factors that affect ambient noise and return strength is a routine adaptation of the SNR optimization technique taught in Irish ((Irish, abstract, para. [0003]-[0008], i.e. Detailed description of row/column transistors end processing unit determining the active subset based on predicted spot location/size; any factor that affects spot location and size would be associated with the SNR criterion), applied to the dynamic allocation framework of Keilaf.
Regarding Claim 39, modified Keilaf discloses comprising multiple aligned pixels each associated with respective light beam emitted by the at least one light source and sharing a common optical path, the at least one processor is configured to select a similar subset of sub-pixels from the plurality of sub-pixels of each of the multiple pixels (Keilaf operate on multi-element sensor array with common optics and allocates multiple pixels; applying correspondingly similar or shifted subset across pixel illuminated by related beams is inherent).
Regarding Claim 42, Keilaf does not explicitly state that allocation is performed by optimizing among a priority of candidate subsets according to one or more SNR criteria. Irish SPAD array Lidar receiver in which the processing unit selects and activates only a subset of SPADs that are expected to receive the reflect later return while deactivating the remainder, expressly to improve SNR by excluding ambient light noise (abstract, Detailed description of determining the active portion of the array based on predicted spot location and size). Selecting among possible row/column combinations(i.e. evaluating different candidate active areas) in order to maximize SNR is inherent implementation of the selective active logic taught by Irish. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Keilaf’s dynamic subset allocation framework by incorporating the SNR-based evaluation and selection of candidate subset taught by Irish with great expectation of success in order to choose one active subset over the other. Evaluating a plurality of candidate succeeds against an SNR metric is predictable And produces only expected result of higher SNR pixel readout with no unexpected results.
Regarding Claim 43-44, Keilaf already processed this reflection signals associated with objects located at various distances and in different portions of the field of view (abstract, Claim 1, description of receiving reflections from objects in the field of view).
Irish teaches that the subset selection is performed with the knowledge of expected location (and therefore range and angular region) of the reflected laser return (Irish, Predicted spot size and range). Evaluating the signals originating from a particular distance bin or spatial region when deciding among candidate subsets is therefore a direct and obvious application of the SNR-optimization logic already present in the references to multi-scan, multi-object data already handled by Keilaf.
Regarding Claim 45, Keilaf discloses continuous/ongoing determination. The processor dynamically allocates a new subset "following processing of the first pixel and the second pixel” and continues to do so for subsequent pixels(claim 1). This is inherently periodic(tied to each processing cycle) and continual during scanning operation. Irish also operate the selective activation throughout the scanning process (Irish: Activation synchronized with each laser transmission). Combining the continuous allocation of Keilaf With SNR criteria of Irish the claimed periodic or continual determination.
Regarding Claim 46-47, Keilaf’s dynamic allocation already occurs after processing of prior results and can be performed on the basis of data collected during initial or intermediate acquisition cycle (claim 1). Irish teachers that the active subset is chosen on the basis of expected or measured illumination condition, which is in practice requires a period of observation or calibration(i.e. learning) during which signal, and noise statistics are evaluated.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective fighting date of the invasion to recognize that the duration of which learning/calibration interval is a matter of ordinary design choice. Short intervals(a single acquisition frame less than a minute or less than a second) are conventional for real time adaptive LiDAR systems that updates subset selection on the per-frame or per-scan basis. Longer intervals are equally conventional for initial system calibration or for slowly varying environmental conditions. Selecting any of the recited durations produces only the predictable result of obtaining sufficient statistics for SNR-based decision and does not yield unexpected results.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over modified Keilaf et al. (US 2020/0386872) in view of Levesque et al. (US 7,917,320).
Regarding claim 8, modified Keilaf lacks To explicitly disclose that the SNR criterion is on average SNR computed within one or more scans from the relation between object reflected signals.
Levesque explicitly teaches the SNR used for processing reflected optical signals in a ranging system is routinely obtained by averaging multiple independent waveforms (each corresponding to a pulse or scan acquisition). The averaged SNR is defined the relation between the signal amplitude associated with light reflected from object and a noise amplitude, and the improvement scales (col. 3, lines 15-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the SNR within one or more scans of modified Keilaf by averaging SNR as taught in Levesque with a reasonable expectation of success in order to improve overall SNR measurement quality by reducing random background noise and making hidden signal patterns easier to see. Doing so requires no change in principle and produces only the expected improvement in metric reliability.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Severini et al. (“SPAD array for LiDAR with region-of-interest selection and smart TDC routing”) discloses at least one light source configured for scanning a selected scene (a pulsed laser used in a direct time of flight (dToF) single point rangefinder/LiDAR system that illuminates a target/scene. The laser spot falls on only a portion of the detector array); at least one light sensor comprising at least one pixel that comprises a plurality of sub-pixels, each configured to generate an output indicative of light collected by the respective sub-pixel (a 10x40 SPAD array. Individual SPADs function as a sub-pixels. Each SPAD produces a digital trigger/output pulse when it detect a photon (or dark count); a processing unit that receives a plurality of outputs from sub-pixels; determining a subset of the sub-pixels; performs readout of the pixel/array according to the determined subset.
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Assres H. Woldemaryam
Primary Examiner (Aeronautics and Astronautics)
Art Unit 3642
/ASSRES H WOLDEMARYAM/Primary Examiner, Art Unit 3642
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