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 .
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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 17 December 2025 has been entered.
Response to Amendment
Claims 1, 6, 8, 13, 14, and 19 have been amended. Claims 5, 12 and 18 have been canceled by applicant’s amendments received 17 November 2025.
Claims 1-3, 6-11, 13-16 and 19-20 are currently pending.
Response to Arguments
Applicant's arguments filed 17 November 2025 regarding the rejection of claims 1-3, 5-8, 10-16 and 18-20 under 35 USC § 101 have been fully considered but they are not persuasive.
Applicant has added additional claim limitations to independent claims 1, 8 and 14 which state “wherein the processor is configured to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1.”, which is a limitation priorly presented as claim 5, and asserts (Remarks, pgs. 6, section ‘Rejections under 35 U.S.C. § 101’) the addition amounts to significantly more than the abstract idea, as well as is a practical application of the presently claimed invention. Applicant points out that the claimed invention “directly applies the calculated average value in a technical procedure for verifying and confirming the estimated signal of the current frame, which is implemented only within a specific technical environment of LiDAR signal processing.” (Remarks, pg. 6). While the examiner agrees that this is a technical procedure for image analysis which is utilized in systems such as LiDAR, image analysis which includes frame analysis for object detection is also known in systems such as time-of-flight cameras, and video analysis. There are no presented limitations, or further explanations, to explain exactly how this analysis requires a specific LiDAR system to complete or offers more than the insignificant extra-solution activity or invoking a generic processor within a generic LiDAR system.
Additionally, it is noted that this is ultimately used to confirm signals for distance determination. The distance determination, as described in specification paragraphs [0060] to [0066], can be classified as both a mental process and mathematical concept as range is determined by
d
=
c
t
2
which is derived from the standard equations of motion. While this is separate from the mathematical concept of determining similarity (or dissimilarity) between two frames, it does not overcome the previous rejection under 35 U.S.C. § 101 as this is mostly incidental within the field of lidar object detection and ranging and only further describes another mathematical concept used in the process as described.
As claims 2-3, 6-7, 10-11, 13, 15-16 and 19-20 are dependent upon independent claims 1, 8 and 14, and have not been substantially further amended, the rejections under 35 U.S.C. § 101 are also not overcome.
Applicant's arguments filed 17 November 2025 regarding the rejection of claims 1-3, 8, 10, 11, 14-16 under 35 USC 103 have been fully considered but they are not persuasive.
Applicant has amended independent claims 1, 8 and 14 to incorporate the limitation ‘to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1’, previously presented in claim 5. It was previously noted that that the presently claimed invention applies not a simple average, but an average of deviations calculated across previous frames to determine a target receive signal within the current frame. While this present configuration is explained in the specification, the scope of the argument is narrower than the claim limitation. The broadest reasonable interpretation of the limitation ‘from an estimated target signal based on deviations of previous frames’ does not limit deviations to be between consecutive frames, or to be between a previous frame and the current frame, which is taught by Li as a system which creates an optical flow map of pixels indicative of a change in an object’s location (based on pixel data) in consecutive frames, and therefore reads on this limitation as written. Additionally, as noted in the prior office action, Li calculates an average of the optical flow between images, and optical flow is inherently a tracking of changes between frames collected by a system.
Applicant’s arguments dated 17 November 2025 (remarks, pg. 7) state that Xu only teaches "single frame based estimation" (N) or between "adjacent frames" (N and N-1) and therefore cannot use an updating average based on all prior frames 1 to N-1. However, Xu describes an embodiment where ([0017]) detection is based on multiple frames of data at adjacent acquisition times, and where the target information is determined by "previous frame or previous frames of data to be detected and the time difference between the previous frame and the next frame." ([0026], emphasis added), and later determines that "the adjacent frames of data to be detected may be multiple frames of data" where the multiple frames may be adjacent acquisition times, where time series fusion may occur on frames collected at different sampling times ([0088], [0096], [0120]). While the examiner agrees with the Applicant that Xu, at most, vaguely describes using more than T-1, T, T+1 and T+2 (for example) frames for object tracking purposes, and does not discuss using a running average of deviations of prior frames for object tracking purposes, Li as cited remedies this.
Further, the applicant states that neither Xu or Li teach on the specific ways a frame, or bounding box, may be manipulated for object tracking. The examiner again agrees with this, but notes this limitation was not priorly presented within claim 1, and that as cited below, Zhou et al. (US 20200250832 A1) teaches utilizing historical averages of deviations to adjust bounding boxes.
Claim Objections
Claim 8 is objected to because of the following informalities:
There is a duplication of limitations introduced by the amendments presented 17 November 2025, where the new limitation “wherein the LiDAR target signal selection apparatus is configured to determine the deviations of the previous frames 1 to N-1, to determine an average value of each of the deviations,” coincides with the previously presented “wherein the LiDAR target signal selection apparatus is further configured to determine the deviations of the previous frames 1 to N-1 , and to determine an average value of each of the deviations”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-8, 10-16 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim recites "... estimate a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals, and to determine the estimated target signal based on deviations of previous frames 1 to N-1;". This is a mathematical concept that, under the broadest reasonable interpretation in view of the specification, is described as finding the similarity (or dissimilarity) between two frames or sets of points (Fig. 4, step 403, paragraph [0069]). This describes a mathematical calculation, as explained in MPEP 2106.04(a)(2)(I)(C). Further, a person looking at two subsequent frames could determine a target signal, both from deviations between the frames as well as other criteria, thereby the determination of a target signal as claimed also can be considered a mental process which could practically be performed in the human mind, where the step claimed is a high level of generality (as described in MPEP 2106.04(a)(2)(III)). The additional limitation of “wherein the processor is configured to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1.” uses the generic processor to determine an average deviation and uses that deviation to set a bounding box around a determined target signal to estimate a target signal. This is also a mathematical concept which only further describes the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction). These limitations clearly recite a judicial exception as both a mental process and mathematical calculation, and thus claim 1 fails prong 1 of Step 2A.
Moving to prong 2 of Step 2A, claim 1 also teaches “a storage configured to store data and algorithms driven by the processor.” According to the specification, this storage is used to store data and/or algorithms for estimating and determining target data (paragraph [0055]). Based on the description given in the specification, the determination of a target signal is based on the similarity between signals in subsequent frames. Collection of the different frames of data falls under mere data gathering as outlined in MPEP 2160.05(g) and therefore is insignificant extra-solution activity. Per MPEP 2106.05(f), invoking a generic computer system as a tool to perform an existing process, such as comparing two frames of data, fails to integrate the judicial exception into a practical application and thus fails prong 2 of Step 2A. Further, the addition of the limitation ‘…to determine a distance to a target based on the target signal of the current frame based on…’ does not add limitations of use beyond generally linking the judicial exception to a standard field of use within lidar ranging.
Finally, to evaluate Step 2B, it must be determined if the non-judicial exception limitations discussed above for claim 1 includes elements that are sufficient to amount to significantly more than the judicial exception. As discussed for Step 2A prong 2 these limitations have already been determined to be insignificant extra-solution activity or invoking a generic computer system merely as a tool to perform an existing process, thus failing Step 2B. Further, claim 1 does not add unconventional steps that confine the claim to a particular useful application nor does it add meaningful limitations that amount to more than generally linking the use of the mathematical calculations to a LiDAR environment. Claim 1 recites a judicial exception, but fails to integrate that judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding the analysis of claims 2-3, and 6-7, the analysis is similar to that of claim 1. Claims 2 and 3 further use the generic processor for determining a Euclidean distance between a determined target signal and signals of a current frame for estimating a target signal. Claims 6-7 use the generic processor to use the average deviation to set a bounding box around a determined target signal to estimate a target signal. Both of these sets of claims are mathematical concepts which only further describe the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction), and organizing data (determining if a value is within a range of values) to find correlation between frames. Additionally, these claims are dependent on claim 1, which is primarily directed towards a calculation of frame similarity and these claims do not add significantly more than the judicial exception. These claims merely include instructions for the apparatus on how to implement the abstract idea on a processor, as discussed in MPEP § 2106.05(f), and therefore do not integrate the judicial exception into a practical application.
Regarding claim 8, the claim recites an apparatus which is configured to “estimate a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals received by the light-receiving signal processor, and to determine the estimated target signal based on deviations of previous frames 1 to N-1; ". This is a mathematical concept that, under the broadest reasonable interpretation in view of the specification, is described as finding the similarity (or dissimilarity) between two frames or sets of points (Fig. 4, step 403, paragraph [0069]). This describes a mathematical calculation, as explained in MPEP 2106.04(a)(2)(I)(C). Further, a person looking at two subsequent frames could determine a target signal, both from deviations between the frames as well as other criteria, thereby the determination of a target signal as claimed also can be considered a mental process which could practically be performed in the human mind, where the step claimed is a high level of generality (as described in MPEP 2106.04(a)(2)(III)). The additional limitation of “wherein the LiDAR target signal selection apparatus is configured to determine the deviations of the previous frames 1 to N-1, to determine an average value of each of the deviations, and to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1” uses the generic processor to determine an average deviation and uses that deviation to set a bounding box around a determined target signal to estimate a target signal. This is also a mathematical concept which only further describes the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction). These limitations clearly recite a judicial exception as both a mental process and mathematical calculation, and thus claim 1 fails prong 1 of Step 2A.
Moving to prong 2 of Step 2A, the claim also includes limitations teaching “a light-transmitting signal processor configured to transmit a laser to a target; a light-receiving signal processor configured to detect light reflected back from the target”. According to the specification (paragraphs [0053] – [0057], Fig. 1), these processors are configured to control the transmission of light into and receive light from the environment as a way to acquire distance and magnitude data (Fig. 4, step 401, [0072]). Sending, and receiving, signals in this manner are well-understood, routine, conventional activity in the art of LiDAR ranging detection, and even when combined do little more than facilitate the abstract idea. Further, claim 8 does not include essential parts such as a detector, for collection of electromagnetic signals which represent distance information and allow for the creation of a point cloud; it only includes a signal processor (light-receiving signal processor, Fig. 1 (300)) and the point cloud itself (Fig. 1, (500)). The inclusion of processors configured to operate a general light emission step and to process light signals (but not actually detect the signals) are not only known in LiDAR but are also incomplete. The “target signal selection apparatus” is a “black box”, which only claims input (frames) and output (target signals). These additional elements do not otherwise place a meaningful limitation on the application or use of the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment (in this case, LiDAR). The processor, per MPEP 2106.05(f), is only invoking a generic computer system as a tool to perform an existing process such as comparing two frames of data and fails to integrate the judicial exception into a practical application. Thus claim 8 fails prong 2 of Step 2A. As in claim 1, the addition of the limitation ‘…to determine a distance to a target based on the target signal of the current frame based on…’ does not add limitations of use beyond generally linking the judicial exception to a standard field of use within lidar ranging.
Finally, to evaluate Step 2B, it must be determined if the non-judicial exception limitations discussed above for claim 8 includes elements that are sufficient to amount to significantly more than the judicial exception. As discussed for Step 2A prong 2 these limitations have already been determined to be insignificant extra-solution activity or invoking a generic computer system merely as a tool to perform an existing process, thus failing Step 2B. Claim 8’s inclusion of light-transmitting and light-receiving processors do not amount to an improvement to technology or the technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a), nor do they effect a transformation or reduction of a particular portion of the data to a different state or thing, as discussed in MPEP § 2106.05(c). Claim 8 does not add unconventional steps that confine the claim to a particular useful application nor does it add meaningful limitations that amount to more than generally linking the use of the mathematical calculations to a LiDAR environment. Claim 8 recites a judicial exception, but fails to integrate that judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding the analysis of claims 10 and 11-13, the analysis is similar to that of claim 8. Claims 10 and 11 specifies determining a Euclidean distance between a determined target signal and signals of a current frame for estimating a target signal by the ‘black box’ of the “LiDAR target signal selection apparatus” of claim 8. Claim 13 uses the deviation to set a bounding box around a determined target signal to estimate a target signal. Both of these sets of claims are mathematical concepts which only further describe the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction), and organizing data (determining if a value is within a range of values) to find correlation between frames and do not add significantly more than the judicial exception. These claims merely include instructions for the apparatus on how to implement the abstract idea on a processor, as discussed in MPEP § 2106.05(f), and therefore do not integrate the judicial exception into a practical application.
Regarding claim 14, the claim recites two method steps including "estimating, by a processor, a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals;” and “determining, by the processor, the estimated target signal based on deviations of previous frames 1 to N-1”. Both limitations are a mathematical concept that, under the broadest reasonable interpretation in view of the specification, is described as finding the similarity (or dissimilarity) between two frames or sets of points (Fig. 4, step 403, paragraph [0069]). This describes a mathematical calculation, as explained in MPEP 2106.04(a)(2)(I)(C). Further, a person looking at two subsequent frames could determine a target signal, both from deviations between the frames as well as other criteria, thereby the determination of a target signal as claimed also can be considered a mental process which could practically be performed in the human mind, where the step claimed is a high level of generality (as described in MPEP 2106.04(a)(2)(III)). The additional limitation of “and wherein the determining of the estimated target signal includes setting a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1.” uses the generic processor to determine an average deviation and uses that deviation to set a bounding box around a determined target signal to estimate a target signal. This is also a mathematical concept which only further describes the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction). These limitations clearly recite a judicial exception as both a mental process and mathematical calculation, and thus claim 1 fails prong 1 of Step 2A.
Moving to prong 2 of Step 2A, claim 14 also teaches steps of “transmitting a laser signal to a target” and “detecting a signal reflected back from the target”. According to the specification, these steps occur as a way to acquire distance and magnitude data (Fig. 4, step 401, [0072]). Collection of the different frames of data falls under mere data gathering as outlined in MPEP 2160.05(g), as it is necessary data gathering and outputting for object detection in LiDAR. Because these limitations are well known in LiDAR and do not impose meaningful limits on the claim, they are therefore insignificant extra-solution activity. Per MPEP 2106.05(f), invoking a generic computer system as a tool to perform an existing process, such as comparing two frames of data, fails to integrate the judicial exception into a practical application and thus fails prong 2 of Step 2A. As in claim 1, the addition of the limitation ‘…to determine a distance to a target based on the target signal of the current frame based on…’ does not add limitations of use beyond generally linking the judicial exception to a standard field of use within lidar ranging.
Finally, to evaluate Step 2B, it must be determined if the non-judicial exception limitations discussed above for claim 14 includes elements that are sufficient to amount to significantly more than the judicial exception. As discussed for Step 2A prong 2 these limitations have already been determined to be insignificant extra-solution activity or invoking a generic computer system merely as a tool to perform an existing process, thus failing Step 2B. Further, claim 14 does not add unconventional steps that confine the claim to a particular useful application nor does it add meaningful limitations that amount to more than generally linking the use of the mathematical calculations to a LiDAR environment. Claim 14 recites a judicial exception, but fails to integrate that judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding the analysis of claims 15, 16 and 19-20, the analysis is similar to that of claim 14. Claims 15 and 16 further use the generic processor for determining a Euclidean distance between a determined target signal and signals of a current frame for estimating a target signal. Claims 19-20 use the generic processor to determine whether a target signal is within a bounding box to estimate a target signal. Both of these sets of claims are mathematical concepts which only further describe the generic mathematical steps taken to manipulate the data using a mathematical function (averaging, addition and subtraction), and organizing data (determining if a value is within a range of values) to find correlation between frames. Additionally, these claims are dependent on claim 14, which is primarily directed towards a method of calculation of frame similarity and these claims do not add significantly more than the judicial exception. These claims merely include additional steps in a method which is based on a judicial exception implemented by a generic processor, as discussed in MPEP § 2106.05(f), and therefore do not integrate the judicial exception into a practical application.
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) 1-3, 6-8, 10, 11, 13-16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (CN 112154444A) and in view of Li (US 20200250832 A1), and further in view of Zhou (US 20190130191).
Regarding claim 1, Xu teaches a Light Detection and Ranging (LiDAR) target signal selection apparatus comprising:
a processor ([0045], [0173]; Fig. 8, processor (84)) configured to estimate a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals ([0108]), to determine a target signal of the current frame N ([0010], [0108], where target detection of a next frame can be used as the current frame to determine target) from the estimated target signal based on deviations of the previous frames 1 to N-1,
and to determine a distance to a target based on the target signal of the current frame ([0018], [0140], where each frame includes data indicative of distance between object and detection device, and the target inspection result can be corrected by detection results),
wherein the processor is further configured to determine the deviations of the previous frames 1 to N-1 ([0109]),
and a storage configured to store data and algorithms driven by the processor ([0045], [0174]; Fig. 8, memory (82)).
Xu fails to teach a processor which determines an average value of the deviations, or a processor setting a boundary range around a previous signal based on the average value of the deviations between the current frame and prior frames.
Li teaches a processor which is configured to determine the deviations of the previous frames 1 to N-1 ([0016] - [0017]), and to determine an average value of each of the deviations ([0023], temporal information may include an average value of an optical flow associated with each of the detected plurality of moving regions).
Zhou teaches a system wherein the processor is configured to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1 ([0153] - [0159], [0170]; where a bounding box around an object can be set based on historical average rate of movement of object/bounding box, the size of the bounding box can also be set based on a weighted average and where weight is set to a suitable value ).
Xu teaches determining deviations of a current frame from prior frames ([0108]), and Li describes that averages of deviations are useful in utilizing prediction filters for determining optical flow, and therefore tracking, of objects in a plurality of moving objects ([0048]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to use the historical average of both size and location of a bounding box around an object for tracking purposes, as taught by Zhou, to modify the weighted value as the average deviations of object location as taught by Li, to further modify the apparatus taught by Xu with a reasonable expectation of success. Introducing an averaging of deviations between frames would have a predictable result of mitigating unpredictable motion of objects where traditional tracking/detection schemes are inefficient in predicting erratic movement, which Li notes can plague traditional systems of object motion prediction ([0003] – [0004]). Li describes that the use of optical flow maps, or maps which detail how locations of objects change in time between frames, can help with tracking multiple objects. This would have predictable results of improving models (such as using historic information as taught by Zhou) used by systems to be optimized to reduce uncertainty (Li, [0117]).
Regarding claim 2, Xu teaches the LiDAR target signal selection apparatus of claim 1, wherein
the processor is configured to determine a Euclidean distance between the determined target signal of the previous frame N-1 and the signals of the current frame N ([0120]).
Regarding claim 3, Xu teaches the LiDAR target signal selection apparatus of claim 2, wherein
the processor is configured to estimate a signal having a lowest Euclidean distance among the signals of the current frame N as the target signal ([0123], where if the distance is below a certain threshold two signals are determined to be the same target).
Regarding claim 6, Xu as modified above teaches the LiDAR target signal selection apparatus of claim 1.
Xu fails to teach a processor determining whether a signal is within a boundary range.
Li teaches a processor is configured to determine whether the estimated target signal of the current frame N is within the deviation average boundary range ([0049], where an image processing apparatus may apply a threshold deviation between the predicted location and the actual location of an object).
Xu teaches a processor which establishes bounding boxes around target objects ([0078]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Xu to incorporate the teachings of Li as a method of expanding bounding boxes around target signals to assist in the detection and tracking of objects with a reasonable expectation of success.
Regarding claim 7, Xu as modified above teaches the LiDAR target signal selection apparatus of claim 6.
Xu fails to teach a processor determining whether a signal is within a boundary range and, if so, conclude the signal is the target signal.
Li teaches a processor which is configured to determine the estimated target signal of the current frame N when the processor concludes that the estimated target signal of the current frame N is within the deviation average boundary range ([0049], For example, for very small differences, the object may be identified as the same object that suffered occlusion and differences greater than threshold deviation, the object that reappears post the occlusion at a different location may be identified as a new object.).
Xu teaches a processor which establishes bounding boxes around target objects ([0078]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Xu to incorporate the teachings of Li as a method of expanding bounding boxes around target signals to assist in the detection and tracking of objects with a reasonable expectation of success. Use of bounding boxes would have a predictable result in reducing computational power needed in the system of Xu by limiting data analyzed to specific regions of interest.
Regarding claim 8, Xu teaches a LiDAR system comprising:
a light-transmitting signal processor ([0045], [0173]; Fig. 8, processor (84)) configured to transmit a laser to a target ([0077]);
a light-receiving signal processor configured to detect light reflected back from the target ([0078]);
a LiDAR target signal selection apparatus configured to estimate a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals received by the light-receiving signal processor ([0080]-[0083]; Fig. 2 steps (204) and (206), where target tracking and prediction are performed based on the information of adjacent frames), to determine a target signal of the current frame N ([0010], [0108], where target detection of a next frame can be used as the current frame to determine target) from the estimated target signal based on deviations of previous frames 1 to N-1 ([0109]),
and to determine a distance to a target based on the target signal of the current frame ([0018], [0140], where each frame includes data indicative of distance between object and detection device, and the target inspection result can be corrected by detection results), wherein the LiDAR target signal selection apparatus is further configured to determine the deviations of the of the previous frames 1 to N-1 ([0109]),
and a point cloud configured to output a distance value of the target signal determined by the LiDAR target signal selection apparatus in 3D graphics ([0077]).
Xu fails to teach a processor which determines an average value of the deviations, or a processor setting a boundary range around a previous signal based on the average value of the deviations between the current frame and prior frames.
Li teaches a processor which is configured to determine the deviations of the previous frames 1 to N-1 ([0016] - [0017]), and to determine an average value of each of the deviations ([0023], temporal information may include an average value of an optical flow associated with each of the detected plurality of moving regions).
Zhou teaches a system wherein the processor is configured to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1 ([0153] - [0159], [0170]; where a bounding box around an object can be set based on historical average rate of movement of object/bounding box, the size of the bounding box can also be set based on a weighted average and where weight is set to a suitable value ).
Xu teaches determining deviations of a current frame from prior frames ([0108]), and Li describes that averages of deviations are useful in utilizing prediction filters for determining optical flow, and therefore tracking, of objects in a plurality of moving objects ([0048]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to use the historical average of both size and location of a bounding box around an object for tracking purposes, as taught by Zhou, to modify the weighted value as the average deviations of object location as taught by Li, to further modify the apparatus taught by Xu with a reasonable expectation of success. Introducing an averaging of deviations between frames would have a predictable result of mitigating unpredictable motion of objects where traditional tracking/detection schemes are inefficient in predicting erratic movement, which Li notes can plague traditional systems of object motion prediction ([0003] – [0004]). Li describes that the use of optical flow maps, or maps which detail how locations of objects change in time between frames, can help with tracking multiple objects. This would have predictable results of improving models (such as using historic information as taught by Zhou) used by systems to be optimized to reduce uncertainty (Li, [0117]).
Regarding claim 10, Xu teaches the LiDAR system of claim 8, wherein
the LiDAR target signal selection apparatus is configured to determine an Euclidean distance between the determined target signal of the previous frame N-1 and the signals of the current frame N ([0120]).
Regarding claim 11, Xu teaches the LiDAR system of claim 10, wherein
the LiDAR target signal selection apparatus is configured to estimate a signal having a lowest Euclidean distance among the signals of the current frame N as the target signal ([0123], where if the distance is below a certain threshold two signals are determined to be the same target).
Regarding claim 13, Xu as modified above teaches the LiDAR system of claim 8.
Xu fails to teach a processor determining whether a signal is within a boundary range and, if so, conclude the signal is the target signal.
Li teaches a target signal selection apparatus is configured to determine whether the estimated target signal of the current frame N is within the deviation average boundary range, and to determine the estimated target signal of the current frame N when a processor of the LiDAR target signal selection apparatus concludes that the estimated target signal of the current frame N is within the deviation average boundary range ([0049]).
Xu teaches establishing a system which sets bounding boxes around target objects ([0078]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Xu to incorporate the teachings of Li as a method of expanding bounding boxes around target signals to assist in the detection and tracking of objects with a reasonable expectation of success. Use of bounding boxes would have a predictable result in reducing computational power needed in the system of Xu by limiting data analyzed to specific regions of interest.
Regarding claim 14, Xu teaches a Light Detection and Ranging (LiDAR) target signal selection method comprising:
transmitting a laser signal to a target ([0077]);
detecting a signal reflected back from the target ([0078]);
estimating, by a processor ([0045], [0173]; Fig. 8, processor (84)), a target signal among signals of a current frame N by use of a determined target signal of a previous frame N-1 among N LiDAR receiving signals ([0108]);
determining a target signal of the current frame N ([0010], [0108], where target detection of a next frame can be used as the current frame to determine target), by the processor, from the estimated target signal based on deviations of previous frames 1 to N-1 ([0109]), and determining a distance to a target based on the target signal of the current frame ([0018], [0140], where each frame includes data indicative of distance between object and detection device, and the target inspection result can be corrected by detection results),
wherein the determining of the estimated target signal includes determining the deviations of the previous frames 1 to N-1 ([0109]).
Xu fails to teach a processor which determines an average value of the deviations, or a processor setting a boundary range around a previous signal based on the average value of the deviations between the current frame and prior frames.
Li teaches a processor which is configured to determine the deviations of the previous frames 1 to N-1 ([0016] - [0017]), and to determine an average value of each of the deviations ([0023], temporal information may include an average value of an optical flow associated with each of the detected plurality of moving regions).
Zhou teaches a system wherein the processor is configured to set a deviation average boundary range by extending the previous frame N-1 in a (+) direction and a (-) direction by the average value of the deviations based on the determined target signal of the previous frame N-1 ([0153] - [0159], [0170]; where a bounding box around an object can be set based on historical average rate of movement of object/bounding box, the size of the bounding box can also be set based on a weighted average and where weight is set to a suitable value ).
Xu teaches determining deviations of a current frame from prior frames ([0108]), and Li describes that averages of deviations are useful in utilizing prediction filters for determining optical flow, and therefore tracking, of objects in a plurality of moving objects ([0048]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to use the historical average of both size and location of a bounding box around an object for tracking purposes, as taught by Zhou, to modify the weighted value as the average deviations of object location as taught by Li, to further modify the apparatus taught by Xu with a reasonable expectation of success. Introducing an averaging of deviations between frames would have a predictable result of mitigating unpredictable motion of objects where traditional tracking/detection schemes are inefficient in predicting erratic movement, which Li notes can plague traditional systems of object motion prediction ([0003] – [0004]). Li describes that the use of optical flow maps, or maps which detail how locations of objects change in time between frames, can help with tracking multiple objects. This would have predictable results of improving models (such as using historic information as taught by Zhou) used by systems to be optimized to reduce uncertainty (Li, [0117]).
Regarding claim 15, Xu teaches the LiDAR target signal selection method of claim 14, wherein
the estimating of the target signal includes determining an Euclidean distance between the determined target signal of the previous frame N-1 and the signals of the current frame N ([0120]).
Regarding claim 16, Xu teaches the LiDAR target signal selection method of claim 15, wherein
the estimating of the target signal includes estimating a signal having a lowest Euclidean distance among the signals of the current frame N ([0123], where if the distance is below a certain threshold two signals are determined to be the same target).
Regarding claim 19, Xu as modified above teaches the LiDAR target signal selection method of claim 14.
Xu fails to teach a method of determining whether a signal is within a boundary range.
Li teaches a method where the determining of the estimated target signal includes determining whether the estimated target signal of the current frame N is within the deviation average boundary range ([0049]).
Xu teaches a method of establishing bounding boxes around target objects ([0078]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Xu to incorporate the teachings of Li as a method of expanding bounding boxes around target signals to assist in the detection and tracking of objects with a reasonable expectation of success.
Regarding claim 20, Xu as modified above teaches the LiDAR target signal selection method of claim 19.
Xu fails to teach method for determining whether a signal is within a boundary range and, if so, conclude the signal is the target signal.
Li teaches a selection method where the determining of the estimated target signal includes determining the estimated target signal of the current frame N when the processor concludes that the estimated target signal of the current frame N is within the deviation average boundary range ([0049]).
Xu teaches a method which includes establishing bounding boxes around target objects ([0078]). Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Xu to incorporate the teachings of Li as a method of expanding bounding boxes around target signals to assist in the detection and tracking of objects with a reasonable expectation of success. Use of bounding boxes would have a predictable result in reducing computational power needed in the system of Xu by limiting data analyzed to specific regions of interest.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( CN 112154444A) in view of Li (US 20200250832 A1) and Zhou ( US 20190130191), as applied to Claim 8 above, and further in view of Shin (US 20180149753 A1).
Regarding claim 9, Xu as modified above teaches the LiDAR system of claim 8.
Xu and Li fail to teach the optical components of the LiDAR system, which may include a scan motor.
Shin teaches a scan motor configured to transmit the laser to various angles of view ([0110], [0139]; Fig. 14, second mirror (130) is movable and may adopt MEMS technology to steer).
To one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Xu to incorporate the teachings of Shin, where a motor and/or motorized mirror, such as a MEMS mirror, is incorporated into a LiDAR system to transmit the laser to various angles within the environment with a reasonable expectation of success. Transmitting electromagnetic signals, such as laser light, into an environment around a ranging system is well-known, and would have predictable results to one of ordinary skill in the art of being used in the collection of ranging and/or velocity information about objects.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Takahama et al (JP 2004151922 A) teaches a system which detects relative positions of an object, such as in lidar, where differential images of regions of interest are determined by changing the size of a bounding box of a region of interest, which can include an average value.
Korb et al. (US20150371431A1) teaches a process where imaging sensor data of a current frame is variably compressed based on information such as a difference in changes of areas of interest, and an average of the changes of the areas of interest, which yields information on the variability of the areas within an image ([0040]).
Murugavel (“Multiple Object Tracking Algorithms and Object Tracking”, 2019) discloses a known process and code for tracking multiple objects through multiple frames of images, where the objects are tracked by Euclidean distances between subsequent frames.
Grancharov et al .(US 20210264619 A1) teaches on systems where object tracking is done on frames of video, where objects have bounding boxes for object detection, and object location is based on the locations in previous frames of the video.
KUROKAWA T (JP 6280020 B2) teaches a system for monitoring multiple moving objects, where position determination is weighted higher for more similar locations of relative positional locations of objects between images.
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/K.M.R./Examiner, Art Unit 3645
/JAMES R HULKA/Primary Examiner, Art Unit 3645