DETAILED ACTION
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.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Page 43, paragraph [0120], line 2, “a first cluster 1310…” where 1310 in the spec is not shown in the drawing.
Page 44, paragraph [0122], line 11, “the cluster blockage level of cluster 1310 is 3…” where 1310 in the spec is not shown in the drawing.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (a low of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, According to the Alice/Mayo test step 1, claim 1 falls under one of the four statutory categories of patent eligible subject matter as defined by 35 U.S.C. 101: a process, machine, manufacture, or composition of matter. In particular, claim 1 is directed to a system, which is considered as a machine.
Step 2A Prong One: The claim recites mental processes, which are abstract ideas. The limitation of cluster projected locations on the window … is a data selection that can be made by a human. Furthermore, the claim recites the limitations of determine an edge of a shape…, and analyze signal properties of one or more of the received pulses of light…. These determinations, under the broadest reasonable interpretation in light of the disclosure, are merely judgments that can be made by a person. A human can select a cluster of data, determine an edge of a shape encompassing the cluster of data and analyze signal properties of the data to see if the emitted pulses of light that are associated with projected locations on the window within a threshold distance from the edge of the shape. See Fig. 10-12 to select cluster, define the edge and analyze the signal properties which can be done by a person. This is analogous to the example of described by MPEP section 2106.04(a)(2).III.A, where a claim to “collecting and comparing known information”, which are steps that can be practically performed in the human mind.
Step 2A Prong Two: the claim recites the additional limitation of a light source…, a scanner…, a receiver to detect…received pulses of light corresponding to scattered reflection returns of …, and a processor configured to determine…. This does not integrate the mental processes into a practical application because this limitation simply provides a generic method to collect data about the environment, to the field of use of lidar technology (any generic LIDAR system can collect data). See MPEP 2106.05(h). The collected data then can be performed by a human to select a cluster of data, determine an edge of a shape encompassing the cluster of data and analyze signal properties of the data. This additional limitation does not integrate the mental processes into a practical application.
Step 2B: The above analysis in Step 2A prong two applies. The additional limitation of a system comprising a light source…, a scanner…, a receiver to detect…received pulses of light corresponding to scattered reflection returns of …, and a processor configured to determine…does not amount to significantly more than the mental process of cluster the data and determine an edge of shape and analyze the signal properties of the received pulses of light. Using a LIDAR device to carry out the mental process of observing an environment and collecting data from an environment only provide data for analysis, simply amounts to an insignificant extra-solution activity. This is analogous to the example listed in MPEP § 2106.05(g), which explains that taking food orders from only table-based customers or drive-through customers, is an insignificant application.
For these reasons, claim 1 does not amount to significantly more than the abstract idea.
Regarding claim 2, it provides more detail of the technology field. As combined with all additional elements of claim 1, it is not sufficient to integrate the abstract idea of image evaluation into practical application. For the same reason, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 3, it provides more detail of the technology field. As combined with all additional elements of claim 1, it is not sufficient to integrate the abstract idea of image evaluation into practical application. For the same reason, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 4, it provides more detail of the technology field. As combined with all additional elements of claim 1, it is not sufficient to integrate the abstract idea of image evaluation into practical application. For the same reason, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 5, includes the limitation “further comprising outputting a recommendation associated with the projected location corresponding to the blockage on the window”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 5 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 6, includes the limitation “further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by free space loss”, which can determine by a person. Thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 6 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 7, includes the limitation “further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by a factor in an environment independent of the blockage on the window”, which can be determine by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 7 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 8, includes the limitation of “using density-based spatial clustering of applications with noise (DBSCAN) to identify clusters” is merely using a mathematical algorithm to cluster the data that can be performed in the human mind. As combined with all the abstract idea in claim 1, claim 8 is still considered to recite an abstract idea because it is using recited algorithm to perform the data processing for image evaluation. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 9, it provides more detail of the technology field. As combined with all additional elements of claim 1, it is not sufficient to integrate the abstract idea of image evaluation into practical application. For the same reason, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 10, it provides more detail of the technology field. As combined with all additional elements of claim 1, it is not sufficient to integrate the abstract idea of image evaluation into practical application. For the same reason, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claims 11, includes the limitation of “determining a corresponding blockage level…, determining an average…, assigning a cluster…, and in response to a determination … determining the cluster is associated with the blockage on the window” is merely judgments that can be made by a person. A human can determine a corresponding blockage level, determining an average, assigning a cluster blockage level and in response to the result, determine that the cluster is associated with the blockage on the window. As combined with all the abstract idea in claim 1, claim 11 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claims 12, includes the limitation of “determining a corresponding blockage level…, determining an average…, assigning a cluster…, and in response to a determination … determining the cluster is not associated with the blockage on the window” is merely judgments that can be made by a person. A human can determine a corresponding blockage level, determining an average, assigning a cluster blockage level and in response to the result, determine that the cluster is not associated with the blockage on the window. As combined with all the abstract idea in claim 1, claim 12 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 13, includes the limitation of “populate a K-Dimensional (K-D) tree with a subset of the one or more of the received pulses of light… and the K-D tree is searchable to determine neighbors of a search point” is merely using a mathematical algorithm to cluster and select the data that can be performed in the human mind. As combined with all the abstract idea in claim 12, claim 13 is still considered to recite an abstract idea because it is using recited algorithm to perform the data processing for image evaluation. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 14, includes the limitation of “querying the K-D tree to find neighbors within the threshold distance” is merely using a mathematical algorithm to select the data that can be performed in the human mind. As combined with all the abstract idea in claim 13, claim 14 is still considered to recite an abstract idea because it is using recited algorithm to perform the data processing for image evaluation. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 15, includes the limitation “further configured to output an indication of the blockage on the window along with a confidence level”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 15 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 16, includes the limitation “further configured to output an indication of a benign blockage on the window”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 16 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 17, includes the limitation “further configured to output un-clustered noise point”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 17 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 18, includes the limitation “wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a plurality of frames”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 18 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 19, includes the limitation “wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a persistence of the projected locations across the plurality of frames”, which can be performed by a person and thus can be classified as a mental process. As combined with all the abstract idea in claim 1, claim 19 is still considered to recite an abstract idea which can be performed by a human. Therefore, it is also not sufficient to take the claim out of the abstract idea for lacking significant more.
Regarding claim 20, is the method claim possesses nearly identical limitation to those of claim 1 and is thus rejected for the same reasoning.
Regarding claim 21, According to the Alice/Mayo test step 1, claim 21 falls under one of the four statutory categories of patent eligible subject matter as defined by 35 U.S.C. 101: a process, machine, manufacture, or composition of matter. In particular, claim 21 is directed to a computer program product embodied in a non-transitory computer readable medium, which is considered as a machine.
Step 2A Prong One: The claim recites mental processes, which are abstract ideas. The limitation of cluster projected locations on the window … is a data selection that can be made by a human. Furthermore, the claim recites the limitations of determine an edge of a shape…, and analyze signal properties of one or more of the received pulses of light…. These determinations, under the broadest reasonable interpretation in light of the disclosure, are merely judgments that can be made by a person. A human can select a cluster of data, determine an edge of a shape encompassing the cluster of data and analyze signal properties of the data to see if the emitted pulses of light that are associated with projected locations on the window within a threshold distance from the edge of the shape. See Fig. 10-12 to select cluster, define the edge and analyze the signal properties which can be done by a person. This is analogous to the example of described by MPEP section 2106.04(a)(2).III.A, where a claim to “collecting and comparing known information”, which are steps that can be practically performed in the human mind.
Step 2A Prong Two: the claim recites the additional limitation of emitting an output beam…, scanning the output beam…, detecting received pulse …are below a detection threshold…. This does not integrate the mental processes into a practical application because this limitation simply provides a generic method to collect data about the environment, to the field of use of lidar technology (any generic LIDAR system can collect data). See MPEP 2106.05(h). The collected data then can be performed by a human to select a cluster of data, determine an edge of a shape encompassing the cluster of data and analyze signal properties of the data. This additional limitation does not integrate the mental processes into a practical application.
Step 2B: The above analysis in Step 2A prong two applies. The additional limitation of emitting an output beam…, scanning the output beam…, detecting received pulse …are below a detection threshold…does not amount to significantly more than the mental process of cluster the data and determine an edge of shape and analyze the signal properties of the received pulses of light. Using a LIDAR device to carry out the mental process of observing an environment and collecting data from an environment only provide data for analysis, simply amounts to an insignificant extra-solution activity. This is analogous to the example listed in MPEP § 2106.05(g), which explains that taking food orders from only table-based customers or drive-through customers, is an insignificant application.
For these reasons, claim 21 does not amount to significantly more than the abstract idea.
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) 1-2, 8, 17 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks et al. (US 20200018854 A1, hereinafter “Hicks”), modified in view of Ting et al. (US 12405382 B1, hereinafter “Ting”).
Regarding claim 1, Hicks teaches a system, comprising:
a light source configured to emit an output beam comprising pulses of light through a window (Hicks; Fig. 1, [0037], light source 110 emits an output beam of light 125 through window 157 [0042]);
a scanner configured to scan the output beam across a field of regard of the system (Hicks; Fig. 1, [0047], scanner 120 scans across a field of regard of the lidar system 100);
a receiver configured to detect, through the window, received pulses of light corresponding to scattered reflection returns of a first part of the emitted pulses of light (Hicks; Fig. 1, [0040], the receiver 140 received returned signal 135; [0042], the housing 155 includes a window 157 through which the beams 125 and 135 pass), wherein scattered reflection returns, if any, of a second part of the emitted pulses of light are below a detection threshold of the receiver (Hicks; Fig. 10b, [0090], rain 752 is the obscurant in the path 744 of the emitted pulse resulting in multiple returns 758A-D as light is scattered from drops of rain (equivalent to a 2nd part of the emitted pulses of light and it is smaller compared to target reflected signal 748). The returns 758A-D may be discard on the basis of one or more factors. One factor may include the characteristics of the pulses, such as magnitude or shape of the return. This implies there is a threshold (can refer to Fig. 8, threshold level 634 [0080]) to compare the magnitude of the return such that to determine whether to discard the return or not); and
a processor configured to determine whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window (Hicks; Fig. 10B, [0090], the machine vision system 10 may discard the returns 758A-D (scattered from drops of rain) on the basis of one or more factors (imagery captured by the camera or other factor include the characteristics of the pulses)) including by being configured to:
Hicks does not teach,
cluster projected locations on the window for the second part of the emitted pulses of light into one or more clusters;
determine an edge of a shape encompassing at least one of the clusters; and
analyze signal properties of one or more of the received pulses of light corresponding to one or more of the first part of the emitted pulses of light that are associated with projected locations on the window within a threshold distance from the edge of the shape.
Ting disclosed in Fig. 6, Fig. 7, column 14, line 1, step 706, process 700 comprise determining, as a set of split returns (equivalent to clusters) including the first split return one or more additional split returns associated with the first split return (602 in Fig. 6, column 13, line 44). Column 15, line 6, the ML model may include an edge detection model that determines an edge of the set of split return and determine a percentage of the split returns that are within a threshold distance of the edge. The ML model indicates that the set of split returns as forming a solid shape and therefore associated with particulate matter or as forming a hollow shape and therefore being associate with a solid surface. Furthermore, the split return (dark gray, equivalent to 2nd part of scattered returns) is associated with the single return (light gray, equivalent to 1st part of scattered returns) as can be seen in the Fig. 5, Fig. 6, the dark gray is associated to light gray area.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting with a reasonable expectation of success. The reasoning for this is first clustering of the reflected signal; determining an edge of the clustered signal and analyzing the signal properties such that to determine the reflected signal properties to prevent the false position indication of the existence of blockage on the sensor during Lidar detection (Ting; column 1, line 14-30; column 14, line 1; column 13, line 44; Column 15, line 6).
Regarding claim 2, Hicks as modified above teaches the system as recited in claim 1, wherein the blockage on the window includes an absorbing blockage that causes at least some backscatter (Hicks; Fig. 10B, [0090], rain 752 is the obscurant in the path 744 of the emitted pulse, resulting in multiple returns as light is scattered from drops of rain).
Regarding claim 8, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the clustering the projected locations on the window includes using density-based spatial clustering of applications with noise (DBSCAN) to identify clusters.
Ting disclosed in column 14, line 23, determining the set of split returns may be based at least in part on K-means clustering…, density-based spatial clustering of applications with noise (DBSCAN).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses; using DBSCN to identify clusters taught by Ting with a reasonable expectation of success. The reasoning for this is using DBSCN to identify clusters for clustering the reflected signal for further signal processing (Ting; column 1, line 14-30; column 14, line 1, line 23; column 13, line 44; Column 15, line 6).
Regarding claim 17, Hicks as modified above teaches the system as recited in claim 1, wherein the processor is further configured to output un-clustered noise points (Hicks; [0113] at block 908, a processor receives an indication of a location of an object based on the returned light. The indication may be timing information corresponding to the returns detected by the lidar system as illustrated by times t.sub.1-t.sub.6 in FIG. 8C. The indication may correspond to, for example, the emitted light scattering off a solid object (either directly or via specular reflection or multi-path) or an obscurant, or it may correspond to a range wrap event or a false-positive detection generated due to noise in the system).
Claim 20 is the method claim possesses nearly identical limitation to those of claim 1 and is thus rejected for the same reasoning.
Regarding claim 21, Hicks teaches a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for (Hicks; [0124], one or more computer programs instructions encoded or stored on a computer readable non-transitory storage medium):
emitting an output beam comprising pulses of light through a window (Hicks; Fig. 1, [0035], light source 110 emits an output beam of light 125 through window 157 [0042]);
scanning the output beam across a field of regard (Hicks; Fig. 1, [0047], scanner 120 scans across a field of regard of the lidar system 100);
detecting, through the window (Hicks; Fig. 1, [0040], the receiver 140 received returned signal 135; [0042], the housing 155 includes a window 157 through which the beams 125 and 135 pass), received pulses of light corresponding to scattered reflection returns of a first part of the emitted pulses of light, wherein scattered reflection returns, if any, of a second part of the emitted pulses of light are below a detection threshold (Hicks; Fig. 10b, [0090], rain 752 is the obscurant in the path 744 of the emitted pulse resulting in multiple returns 758A-D as light is scattered from drops of rain (equivalent to a 2nd part of the emitted pulses of light and it is smaller compared to target reflected signal 748). The returns 758A-D may be discard on the basis of one or more factors. One factor may include the characteristics of the pulses, such as magnitude or shape of the return. This implies there is a threshold (can refer to Fig. 8, threshold level 634 [0080]) to compare the magnitude of the return such that to determine whether to discard the return or not); and
determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window (Hicks; Fig. 10B, [0090], the machine vision system 10 may discard the returns 758A-D (scattered from drops of rain) on the basis of one or more factors (imagery captured by the camera or other factor include the characteristics of the pulses)) including by:
Hicks does not teach,
clustering projected locations on the window for the second part of the emitted pulses of light into one or more clusters;
determining an edge of a shape encompassing at least one of the clusters; and
analyzing signal properties of one or more of the received pulses of light corresponding to one or more of the first part of the emitted pulses of light that are associated with projected locations on the window within a threshold distance from the edge of the shape.
Ting disclosed in Fig. 6, Fig. 7, column 14, line 1, step 706, process 700 comprise determining, as a set of split returns (equivalent to clusters) including the first split return one or more additional split returns associated with the first split return (602 in Fig. 6, column 13, line 44). Column 15, line 6, the ML model may include an edge detection model that determines an edge of the set of split return and determine a percentage of the split returns that are within a threshold distance of the edge. The ML model indicates that the set of split returns as forming a solid shape and therefore associated with particulate matter or as forming a hollow shape and therefore being associate with a solid surface. Furthermore, the split return (dark gray, equivalent to 2nd part of scattered returns) is associated with the single return (light gray, equivalent to 1st part of scattered returns) as can be seen in the Fig. 5, Fig. 6, the dark gray is associated to light gray area.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the computer program product embodied in a non-transitory computer readable medium and comprising computer instructions taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting with a reasonable expectation of success. The reasoning for this is first clustering of the reflected signal; determining an edge of the clustered signal and analyzing the signal properties such that to determine the reflected signal properties to prevent the false position indication of the existence of blockage on the sensor during Lidar detection (Ting; column 1, line 14-30; column 14, line 1; column 13, line 44; Column 15, line 6).
Claim(s) 3-4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Terefe et al. (US 12235396 B1, hereinafter “Terefe”).
Regarding claim 3, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the blockage on the window causes a degradation of a point cloud associated with the received pulses of light and the point cloud compared with a previous point cloud prior to an existence of the blockage on the window.
Terefe disclosed in Fig. 4, column 12, line 46, lidar cloud data without an obstruction 404 and lidar cloud data with an obstruction 406. As can been seen with obstruction the point cloud is degradation results in error detection.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the blockage on the window causes a degradation of a point cloud associated with the received pulses of light and the point cloud compared with a previous point cloud prior to an existence of the blockage on the window taught by Terefe with a reasonable expectation of success. The reasoning for this is comparing the point cloud data before and after an obstruction occurred to identify the degradation of the point cloud causes by the obstruction (Terefe; column 12, line 46).
Regarding claim 4, Hicks as modified above teaches the system as recited in claim 3.
Hicks does not teach, wherein the blockage on the window decreases performance of a consumer of the point cloud.
Terefe disclosed in Fig. 4, column 12, line 46, lidar cloud data without an obstruction 404 and lidar cloud data with an obstruction 406. As can been seen with obstruction the point cloud is degradation predictably to decreases performance of a consumer of the point cloud. Column 7, line 56, disclosed the reason of using a cleaning operation to remove the obstruction 106 from the sensor 110 with the obstruction threshold based on an impact to operation of the lidar sensor 110 (e.g., a value at which the lidar sensor performance degrades below a pre-determined amount).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the blockage on the window causes a degradation of a point cloud associated with the received pulses of light and the point cloud compared with a previous point cloud prior to an existence of the blockage on the window; wherein the blockage on the window decreases performance of a consumer of the point cloud taught by Terefe with a reasonable expectation of success. The reasoning for this is comparing the point cloud data before and after an obstruction occurred to identify the degradation of the point cloud causes by the obstruction (Terefe; column 12, line 46). Furthermore, using a cleaning operation to remove the obstruction 106 from the sensor 110 with the obstruction threshold based on an impact to operation of the lidar sensor 110 (Terefe; column 7, line 56).
Regarding claim 18, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a plurality of frames.
Terefe disclosed in Fig. 4, column 12, line 46, lidar cloud data without an obstruction 404 and lidar cloud data with an obstruction 406. As can been seen with obstruction the point cloud is degradation results in error detection; Column 22, line 34, The lidar data can be received by the obstruction detection component 104 at substantially a same time that the lidar data is captured by the lidar sensor while in other examples the lidar data can be received after a period of time, or at pre-determined intervals (e.g., every two minutes, every threshold number of image frames, etc.). This implies the detection of the obstruction on the window is based on plurality of image frames.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a plurality of frames taught by Terefe with a reasonable expectation of success. The reasoning for this is using a plurality of the frames to determine the obstruction using the obstruction detection component predictably to increase the confidence level of the detection.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Wang et al. (US 20230194684 A1, hereinafter “Wang”).
Regarding claim 5, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, further comprising outputting a recommendation associated with the projected locations corresponding to the blockage on the window.
Wang disclosed in paragraph [0074], based on detecting a blockage, the Lidar device may output an alert (flag) to a user and/or to an external computing system include an indication that a blockage is present at the lidar device and/or that a user should remove the blockage.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include further comprising outputting a recommendation associated with the projected locations corresponding to the blockage on the window taught by Wang with a reasonable expectation of success. The reasoning for this is after determining blockage on the window, outputting an recommendation to a user to solve the blockage issue (Wang; [0074]).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Isaacson et al. (US 20250085429 A1, hereinafter “Isaacson”).
Regarding claim 6, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the processor is further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by free space loss.
Isaacson disclosed in Fig. 4A-4B, [0064], sky loss (equivalent to free space loss) can be determined by observing holes in measurements scans of a scene (scene 400) obtained via lidar sensor 108A. Any points that remain empty may reflect regions of the lidar scan where no return has been received. In response to a ray terminating at a lidar measurement point that includes a positive elevation angle in a global reference frame, this ray may be determined as pointing toward the sky of the scene based on the lidar sensor 108A being level with the ground. Reconstructed scene 450 can accurately depict sky 415 of scene 400.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the processor is further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by free space loss taught by Isaacson with a reasonable expectation of success. The reasoning for this is identifying the reflection signal is caused by free space loss such as sky loss and further reconstruction the area with free space loss as a sky area (Isaacson; [0064]).
Regarding claim 7, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the processor is further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by a factor in an environment independent of the blockage on the window.
Isaacson disclosed in Fig. 4A-4B, [0064], sky loss (equivalent to free space loss) can be determined by observing holes in measurements scans of a scene (scene 400) obtained via lidar sensor 108A. Any points that remain empty may reflect regions of the lidar scan where no return has been received. This ray may be determined as pointing toward the sky of the scene based on the lidar sensor 108A being level with the ground. Reconstructed scene 450 can accurately depict sky 415 of scene 400.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the processor is further configured to determine that at least a portion of the second part of the emitted pulses of light is caused by a factor in an environment independent of the blockage on the window taught by Isaacson with a reasonable expectation of success. The reasoning for this is identifying the reflection signal is caused by free space loss such as sky loss (equivalent to cause by a factor in an environment not from the blockage on the window) and further reconstruction the area with free space loss as a sky area (Isaacson; [0064]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Peterson (US 10930033 B1, hereinafter “Peterson”).
Regarding claim 9, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the threshold distance from the edge of the shape is based at least on scan density.
Peterson disclosed in column 15, line 62, the value of the edge distance threshold depends on the resolution of the image and the resolution of the display. The value of the edge distance threshold can be user adjustable. For example, a user can adjust the value of the edge distance threshold depending on the density of detected edges in the image.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the threshold distance from the edge of the shape is based at least on scan density taught by Peterson with a reasonable expectation of success. The reasoning for this is adjust the value of the edge distance threshold depending on the density of detected edges in the image predictably to increase the accuracy of the data analysis.
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Wan et al. (US 20230305124 A1, hereinafter “Wan”).
Regarding claim 10, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein analyzing the signal properties of the one or more of the received pulses of light includes excluding at least one of the one or more of the received pulses of light within the threshold distance having a blockage level below a threshold blockage level.
Wan in Fig. 8A-D, [0116]-[0117], disclosed an associated representative grid of the FOV scanned by the Lidar system having the partially blocked window 804. The segments of the grid having blockage state flag values that reach a value greater than a blockage threshold value are distinguished using a higher digital value. Larger values are present in the middle of the grid indicating that the obstructing object is present in the middle of the window. There is no specific discussion of exclusive the data which has a blockage value below a threshold blockage value, but as can be seen in the Fig. 8B, with the flag value of 0 (on the upper and lower edge of the edge area) will not be considered for blockage determination since this data present no weight on the blockage detection as expected.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein analyzing the signal properties of the one or more of the received pulses of light includes excluding at least one of the one or more of the received pulses of light within the threshold distance having a blockage level below a threshold blockage level taught by Wan with a reasonable expectation of success. The reasoning for this is exclusive the data within the threshold distance having a blockage level below a threshold blockage level from the data analysis predictably to increase the accuracy of the data analysis.
Regarding claim 11, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein analyzing the signal properties of the one or more of the received pulses of light corresponding to the one or more of the first part of the emitted pulses of light that are associated with the projected locations on the window within the threshold distance from the edge of the shape includes:
determining a corresponding blockage level for each edge point of a plurality of edge points associated with the edge of the shape;
determining an average of the corresponding blockage levels of the plurality of edge points;
assigning a cluster blockage level to a cluster associated with the edge of the shape; and
in response to a determination that the assigned cluster blockage level of the cluster is above a cluster blockage level threshold, determining that the cluster is associated with the blockage on the window.
Wan in Fig. 8A-D, [0116]-[0117], disclosed an associated representative grid of the FOV scanned by the Lidar system having the partially blocked window 804. The segments of the grid having blockage state flag values that reach a value greater than a blockage threshold value are distinguished using a higher digital value. As can be seen that the edge point of the shape has associated flag values and this value is determined by comparing with the blockage threshold value. Larger values are present in the middle of the grid indicating that the obstructing object is present in the middle of the window.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include determining a corresponding blockage level for each edge point of a plurality of edge points associated with the edge of the shape; determining an average of the corresponding blockage levels of the plurality of edge points; assigning a cluster blockage level to a cluster associated with the edge of the shape; and in response to a determination that the assigned cluster blockage level of the cluster is above a cluster blockage level threshold, determining that the cluster is associated with the blockage on the window taught by Wan with a reasonable expectation of success. The reasoning for this is using a blockage state flag values to determine the status of the blockage level compared with the blockage threshold value such that to identify the obstructing object is present in the window (Wan; [0116]-[0117]).
Regarding claim 12, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein analyzing the signal properties of the one or more of the received pulses of light corresponding to the one or more of the first part of the emitted pulses of light that are associated with the projected locations on the window within the threshold distance from the edge of the shape includes:
determining a corresponding blockage level for each edge point of a plurality of edge points associated with the edge of the shape;
determining an average of the corresponding blockage levels of the plurality of edge points;
assigning a cluster blockage level to the cluster associated with the edge of the shape; and
in response to a determination that the assigned cluster blockage level of the cluster is below a cluster blockage level threshold, determining that the cluster is not associated with the blockage on the window.
Wan in Fig. 8A-D, [0116]-[0117], disclosed an associated representative grid of the FOV scanned by the Lidar system having the partially blocked window 804. The segments of the grid having blockage state flag values that reach a value greater than a blockage threshold value are distinguished using a higher digital value. As can be seen that the edge point of the shape has associated flag values and this value is determined by comparing with the blockage threshold value. Larger values are present in the middle of the grid indicating that the obstructing object is present in the middle of the window.
Wan further disclosed in Fig. 9, [0118], step 920, the method 900 may further include determining a window state that identifies whether the window is blocked or not, based upon the received scattered light pulse and received reflected light pulse. In response to the window not being either fully or partially blocked, the method 900 may loop back to step 904. Combine with both Fig. 8A-D and Fig. 9, one of ordinary skill in the art can recognize that when the window blocked detection determine that the blockage state flag values that is less than a blockage threshold value indicating that the obstructing object is not present in the window as expected and then the method 900 may loop back to step 904.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include determining a corresponding blockage level for each edge point of a plurality of edge points associated with the edge of the shape; determining an average of the corresponding blockage levels of the plurality of edge points; assigning a cluster blockage level to the cluster associated with the edge of the shape; and in response to a determination that the assigned cluster blockage level of the cluster is below a cluster blockage level threshold, determining that the cluster is not associated with the blockage on the window taught by Wan with a reasonable expectation of success. The reasoning for this is using a blockage state flag values to determine the status of the blockage level compared with the blockage threshold value such that to identify whether the obstructing object is present in the window (Wan; [0116]-[0117]).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Wan, in view of Hieida et al. (US 20200202175 A1, hereinafter “Hieida”).
Regarding claim 13, Hicks as modified above teaches the system as recited in claim 12.
Hicks does not teach, wherein the processor is further configured to, prior to the clustering the projected locations on the window for the second part of the emitted pulses of light into one or more clusters:
populate a K-Dimensional (K-D) tree with a subset of the one or more of the received pulses of light, each member of the subset having a blockage level greater than a pre-defined threshold level; and
the K-D tree is searchable to determine neighbors of a search point.
Wan in Fig. 8A-D, [0116]-[0117], disclosed an associated representative grid of the FOV scanned by the Lidar system having the partially blocked window 804. The segments of the grid having blockage state flag values that reach a value greater than a blockage threshold value are distinguished using a higher digital value. Larger values are present in the middle of the grid indicating that the obstructing object is present in the middle of the window. There is no specific discussion of exclusive the data which has a blockage value below a threshold blockage value, but as can be seen in the Fig. 8B, with the flag value of 0 (on the upper and lower edge of the edge area) will not be considered for blockage determination since this data present no weight on the blockage detection as expected. Therefore, all the data used are expected having a blockage level greater than a pre-defined threshold level.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include each member of the subset having a blockage level greater than a pre-defined threshold level taught by Wan with a reasonable expectation of success. The reasoning for this is including the data within the threshold distance having a blockage level above a threshold blockage level from the data analysis predictably to increase the accuracy of the data analysis.
However, Hicks modified in view of Ting, in view of Wan still not teach,
populate a K-Dimensional (K-D) tree with a subset of the one or more of the received pulses of light, each member of the subset having a blockage level greater than a pre-defined threshold level; and
the K-D tree is searchable to determine neighbors of a search point.
Hieida disclosed in paragraph [0044], using K-dimensional trees or Locality-sensitive hashing to search for the neighboring points on the basis that these points are located within a distance ε1 from the given point. The distance ε1 is a threshold preset by a user.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include each member of the subset having a blockage level greater than a pre-defined threshold level taught by Wan, include using K-dimensional trees to determine the neighboring point taught by Hieida with a reasonable expectation of success. The reasoning for this is using K-dimensional trees to determine the neighboring point on the basis that these points are located within a distance ε1 from the given point (Hieida; [0044]).
Regarding claim 14, Hicks as modified above teaches the system as recited in claim 13.
Hicks does not teach, wherein analyzing the signal properties of the one or more of the received pulses of light corresponding to the one or more of the first part of the emitted pulses of light that are associated with the projected locations on the window within the threshold distance from the edge of the shape includes querying the K-D tree to find neighbors within the threshold distance.
Hieida disclosed in paragraph [0044], using K-dimensional trees or Locality-sensitive hashing to search for the neighboring points on the basis that these points are located within a distance ε1 from the given point. The distance ε1 is a threshold preset by a user.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include each member of the subset having a blockage level greater than a pre-defined threshold level taught by Wan, include using K-dimensional trees to determine the neighboring point taught by Hieida with a reasonable expectation of success. The reasoning for this is using K-dimensional trees to determine the neighboring point on the basis that these points are located within a distance ε1 from the given point (Hieida; [0044]). Predictably to increase the accuracy of the data analysis.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Mu et al. (US 10769454 B2, hereinafter “Mu”).
Regarding claim 15, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the processor is further configured to output an indication of the blockage on the window along with a confidence level.
Mu disclosed in Column 11, line 17, each region tracker may also contain a “blockage score” indicating a confidence evaluation (or a likelihood) of the tracked set of candidate blocked region being blocked. In other words, the blockage score may serve as the “age” of the candidate blocked regions being blocked.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the processor is further configured to output an indication of the blockage on the window along with a confidence level taught by Mu with a reasonable expectation of success. The reasoning for this is each region tracker may also contain a “blockage score” indicating a confidence evaluation (or a likelihood) of the tracked set of candidate blocked region being blocked predictably to show the confidence level of detected blockage status.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Kunz et al. (US 20200309942 A1, hereinafter “Kunz”).
Regarding claim 16, Hicks as modified above teaches the system as recited in claim 1.
Hicks does not teach, wherein the processor is further configured to output an indication of a benign blockage on the window.
Kunz disclosed in Fig. 6, [0136], determining the sensor occlusion (fog, or rain) is a false positive such that the region is really occlusion free of objects (equivalent to benign blockage); [0138], step 608 the vehicle may use the occlusion free-region to provide information or warnings to a human driver based on the operating conditions of the vehicle. For example, a warning may be provided if a turn is unsafe.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein the processor is further configured to output an indication of a benign blockage on the window taught by Kunz with a reasonable expectation of success. The reasoning for this is outputting an warning based on the occlusion free-region (equivalent to benign blockage on the window) to alert human driver (Kunz; [0136]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hicks, modified in view of Ting, in view of Terefe, in view of Mu.
Regarding claim 19, Hicks as modified above teaches the system as recited in claim 18.
Hicks does not teach, wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a persistence of the projected locations across the plurality of frames.
Mu disclosed in Column 11, line 17, each region tracker may also contain a “blockage score” indicating a confidence evaluation (or a likelihood) of the tracked set of candidate blocked region being blocked. In other words, the blockage score may serve as the “age” of the candidate blocked regions being blocked. A high blockage score may indicate that the tracked set of candidate blocked regions are more likely to be blocked for a long time and are therefore troublesome, while a low blockage score may show that the tracked set of candidate blocked regions may be unblocked or blocked for a short period of time and may not be alarming yet.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of this invention to modify the system taught by Hicks to include clustering the second part of the emitted pulses of light into one or more clusters; determine an edge of a shape of the clusters; analyze signal properties of one or more of the received pulses taught by Ting, include wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a plurality of frames taught by Terefe, include wherein determining whether at least a portion of the second part of the emitted pulses of light corresponds to a blockage on the window is based at least on a persistence of the projected locations across the plurality of frames taught by Mu with a reasonable expectation of success. The reasoning for this is using plurality of the frames to determine the blockage of a confidence evaluation (Mu; Column 11, line 17).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bacchus et al. (US 20190106085 A1) disclosed in Figs. 6A-6E, [0041], illustrate a non-limiting method for the contamination detectors to sense the presence of dirt on the sensor lens cover. By comparing successive video frames, it can be determined that the dirt particle 602N is present N frames later as shown by video frame 600N continuing to detect the presence of the dirt particle 602N. As will be appreciated, by applying threshold techniques to the video frames 6001 through 600N a contamination detector 208 can multiply and filter the threshold images 600’I through 600’N to produce the analyzed sensor cover frame 606 indicating the continued presence of dirt particle 602’ through N successive video frames. This would cause the decision function 204 to determine to initiate the cleaning process as discussed above in connection with Fig. 4.
Frieventh Cienfuegos et al. (US 20190250259 A1) disclosed in Fig. 5, [0055], the computer 110 may be programmed to determine at least three intensities of received optical signals based on data received from at least three of the photodiode sensors 270, and to identify dimensions of the dirty area 300 based on the at least three intensities of the received optical signal. Additionally or alternatively, the computer 110 may be programmed to identify a location of the dirty area 300 based on determined intensities or reflected optical signal form at least three of the plurality of the photodiode sensors.
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/CHIA-LING CHEN/Examiner, Art Unit 3645
/YUQING XIAO/Supervisory Patent Examiner, Art Unit 3645
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