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 .
Response to Amendment
The Amendment filed January 16th, 2026 has been entered. Claims 14-26 remain pending in the application.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-16, 19-22, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over O'Keeffe (United States Patent Application Publication 20180059248 A1), hereinafter O’Keeffe in view of Zhou et al.(United States Patent Application Publication 20200081105 A1), hereinafter Zhou.
Regarding claim 14, O’Keeffe teaches a method for determining a maximum range of a LIDAR sensor(FIG. 1A illustrates a laser range finder system 105 (e.g. a LIDAR)), comprising the following steps:
receiving measured values of the LIDAR sensor ([0080] Laser range finder 110 can further comprise a laser detector 122 to detect reflections from laser pulses.), the measured values being organized in a point cloud ([0010] The scan could initially be divided into 10 sweeps of the FOV, where each sweep is a 100 point grid and the sweeps are offset to generate the final point cloud), and each measured value of the measured values including a piece of directional information and a piece of radial distance information relative to the LIDAR sensor and representing a laser beam reflected from a particular direction and at a particular radial distance ([0087] For the purpose of this disclosure the FOV of laser range finder 110 can be defined as the set of all directions (e.g. combinations of elevation and azimuthal angles) in which the laser range finder can perform laser ranging measurements.);
assigning the measured values of the point cloud based on the pieces of directional information and the pieces of radial distance information to areas of interest of a field of view of the LIDAR sensor, each of the areas of interest being defined by a directional range and a radial distance range ([0109] In the embodiment of FIG. 7 the set of reflection data is simplified to be 0 for reflections that are outside the boundary of vehicle 170 and 1 for laser pulses with a TOF indicating placement within the boundary 220);
O’Keeffe fails to teach the method of determining a variance of a point distribution of measured values of an area of interest of the areas of interest; ascertaining a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and providing a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor.
However, Zhou teaches determining a variance of a point distribution of measured values of an area of interest of the areas of interest; ascertaining a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and providing a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor ([0064] In one aspect, after the near-distance plane is obtained by fitting the near-distance point cloud data, the distance from each point of the near-distance point cloud data to the near-distance plane is calculated. The thickness of the near-distance plane may be determined based on at least one of a distance mean, a variance or a mean function... It is also possible to determine a maximum distance among each distance as the thickness of the near-distance plane.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the ascertained information, analyzed via the point cloud variance, similar to Zhou, with a reasonable expectation of success. This would have the predictable result of limiting the required power of a lidar device to the encompass the maximum range needed for object detection.
Regarding claim 15, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein an object situated in an area of interest of the LIDAR sensor is represented by measured values of a point distribution assigned to the area of interest, the area of interest having a variance greater than or equal to the predetermined limiting value ([0134] FIGS. 13, 14A, 14B, 15 and 16 illustrate several embodiments of a progressive boundary localization (PBL) method. The PBL method can dynamically steer a laser range finder based in part on range data indicating regions with time-of-flight differences (e.g. indicating object boundaries); [0139] The PBL method enables the intervening laser pulses 1510a-e to be located in parts of the FOV 1310 estimated to contain an object boundary (i.e. that have TOF differences greater than the TOF threshold.).
Regarding claim 16, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the assignment of the measured values of the point cloud to areas of interest includes:
assigning the measured values of the point cloud to directional ranges; and assigning the measured values of the directional ranges to radial distance ranges ([0087] For the purpose of this disclosure the FOV of laser range finder 110 can be defined as the set of all directions (e.g. combinations of elevation and azimuthal angles) in which the laser range finder can perform laser ranging measurements; [0100] Laser range finder 407 can further comprise a 3D location calculator 464 to calculate a 3D location associated with a laser reflection 445.).
Regarding claim 19, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the variance includes a radial variance along a radial direction and/or a concentric variance along a concentric direction oriented perpendicularly to the radial direction ([0134] FIGS. 13, 14A, 14B, 15 and 16 illustrate several embodiments of a progressive boundary localization (PBL) method. The PBL method can dynamically steer a laser range finder based in part on range data indicating regions with time-of-flight differences (e.g. indicating object boundaries); [0139] The PBL method enables the intervening laser pulses 1510a-e to be located in parts of the FOV 1310 estimated to contain an object boundary (i.e. that have TOF differences greater than the TOF threshold.).
Regarding claim 20, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the predetermined limiting value of the variance is determined by an artificial intelligence, and the artificial intelligence is trained on a relationship between objects present in the areas of interest and variances of the point distributions of measured values of each of the areas of interest ([0273] The best way to select test region 4260c may be through unsupervised machine learning of those points in the FOV that have the best correlation to changes in larger regions. In one example laser range finder 110 can identify a correlation between test location 4260c and scan region 4250 using unsupervised learning and can subsequently perform a scan of the FOV where the density in scan region 4250 is based at least in part on the result of a rule applied to test region 4260c.).
Regarding claim 21, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the predetermined limiting value of the variance is experimentally ascertained ([0096] Conversely, when the location of the boundary of an object is known with some degree of accuracy a set of laser steering parameters can be selected to instruct or configure the steerable laser to generate a more complex dense scan region (e.g. 310b) that is smaller, yet still contains the boundary. For example, the boundary 190 of vehicle 170 in FIG. 1B can be estimated from a previous laser scan data to lie between an outer perimeter 320 and an inner perimeter 330.).
Regarding claim 22, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the predetermined limiting value of the variance is a function of the radial distance, and different predetermined limiting values are determined for the areas of interest of different radial distance ([0087] For the purpose of this disclosure the FOV of laser range finder 110 can be defined as the set of all directions (e.g. combinations of elevation and azimuthal angles) in which the laser range finder can perform laser ranging measurements; [0096] For example, the boundary 190 of vehicle 170 in FIG. 1B can be estimated from a previous laser scan data to lie between an outer perimeter 320 and an inner perimeter 330.).
Regarding claim 24, O’Keeffe, as modified, teaches the method as recited in claim 14, wherein the method for range determination of the LIDAR sensor is carried out during a time-of-flight of the LIDAR sensor ([0109] For example, if laser ranging data indicates a distinct change in TOF consistent with an object (e.g. vehicle 170) a boundary region 710a can be calculated that encompasses all locations in which the boundary of the object can be.).
Regarding claim 25, O’Keeffe teaches a computing unit configured to determine a maximum range of a LIDAR sensor, ([0104] a processing subassembly 520) the computing unit configured to:
receive measured values of the LIDAR sensor,([0080] Laser range finder 110 can further comprise a laser detector 122 to detect reflections from laser pulses.), the measured values being organized in a point cloud ([0010] The scan could initially be divided into 10 sweeps of the FOV, where each sweep is a 100 point grid and the sweeps are offset to generate the final point cloud), and each measured value of the measured values including a piece of directional information and a piece of radial distance information relative to the LIDAR sensor and representing a laser beam reflected from a particular direction and at a particular radial distance ([0087] For the purpose of this disclosure the FOV of laser range finder 110 can be defined as the set of all directions (e.g. combinations of elevation and azimuthal angles) in which the laser range finder can perform laser ranging measurements);
assign the measured values of the point cloud based on the pieces of directional information and the pieces of radial distance information to areas of interest of a field of view of the LIDAR sensor, each of the areas of interest being defined by a directional range and a radial distance range ([0109] In the embodiment of FIG. 7 the set of reflection data is simplified to be 0 for reflections that are outside the boundary of vehicle 170 and 1 for laser pulses with a TOF indicating placement within the boundary 220);
O’Keeffe fails to teach the computing unit configured to determine a variance of a point distribution of measured values of an area of interest of the areas of interests; ascertain a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and provide a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor.
However, Zhou teaches the computing unit configured to determine a variance of a point distribution of measured values of an area of interest of the areas of interests; ascertain a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and provide a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor ([0064] In one aspect, after the near-distance plane is obtained by fitting the near-distance point cloud data, the distance from each point of the near-distance point cloud data to the near-distance plane is calculated. The thickness of the near-distance plane may be determined based on at least one of a distance mean, a variance or a mean function... It is also possible to determine a maximum distance among each distance as the thickness of the near-distance plane.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the ascertained information, analyzed via the point cloud variance, similar to Zhou, with a reasonable expectation of success. This would have the predictable result of limiting the required power of a lidar device to the encompass the maximum range needed for object detection.
Regarding claim 26, O’Keeffe teaches a non-transitory computer-readable medium on which is stored a computer program including commands for determining a maximum range of a LIDAR sensor([0311] Any of the methods (including user interfaces) described herein may be implemented as software, hardware or firmware, and may be described as a non-transitory computer-readable storage medium storing a set of instructions capable of being executed by a processor), the computer program, when executed by a data processing unit, causing the data processing unit to perform the following steps:
receiving measured values of the LIDAR sensor, ([0080] Laser range finder 110 can further comprise a laser detector 122 to detect reflections from laser pulses), the measured values being organized in a point cloud ([0010] The scan could initially be divided into 10 sweeps of the FOV, where each sweep is a 100 point grid and the sweeps are offset to generate the final point cloud), and each measured value of the measured values including a piece of directional information and a piece of radial distance information relative to the LIDAR sensor and representing a laser beam reflected from a particular direction and at a particular radial distance ([0087] For the purpose of this disclosure the FOV of laser range finder 110 can be defined as the set of all directions (e.g. combinations of elevation and azimuthal angles) in which the laser range finder can perform laser ranging measurements);
assigning the measured values of the point cloud based on the pieces of directional information and the pieces of radial distance information to areas of interest of a field of view of the LIDAR sensor, each of the areas of interest being defined by a directional range and a radial distance range ([0109] In the embodiment of FIG. 7 the set of reflection data is simplified to be 0 for reflections that are outside the boundary of vehicle 170 and 1 for laser pulses with a TOF indicating placement within the boundary 220);
O’Keeffe fails to teach the computer programmed for determining a variance of a point distribution of measured values of an area of interest of the areas of interest; ascertaining a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and providing a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor.
However, Fischer teaches the computer programmed for determining a variance of a point distribution of measured values of an area of interest of the areas of interest; ascertaining a maximum distance range to the LIDAR sensor as the area of interest as a function of the variance reaching or exceeding a predetermined limiting value; and providing a value of a radial distance of the maximum distance range to the LIDAR sensor as the maximum range of the LIDAR sensor. ([0064] In one aspect, after the near-distance plane is obtained by fitting the near-distance point cloud data, the distance from each point of the near-distance point cloud data to the near-distance plane is calculated. The thickness of the near-distance plane may be determined based on at least one of a distance mean, a variance or a mean function... It is also possible to determine a maximum distance among each distance as the thickness of the near-distance plane.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the ascertained information similar to Fischer, with a reasonable expectation of success. This would have the predictable result of limiting the required power of a lidar device to the encompass the maximum range needed for object detection.
Claims 17-18, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over O'Keeffe, in view of Zhou, further in view of Fischer (United States Patent No. 11579295 B2), hereinafter Fischer.
Regarding claim 17, O’Keeffe, as modified, teaches the method as recited in claim 16,
O’Keeffe fails to teach the method wherein the ascertainment of the maximum distance range further includes: determining variances of point distributions of the areas of interest of a directional range in a sequence including a descending radial distance of the areas of interest from the LIDAR sensor, the maximum distance range of a particular directional range being given by a first area of interest in the sequence which has a determined variance reaching or exceeding the predetermined limiting value.
However, Fischer teaches the method wherein the ascertainment of the maximum distance range further includes: determining variances of point distributions of the areas of interest of a directional range in a sequence including a descending radial distance of the areas of interest from the LIDAR sensor, the maximum distance range of a particular directional range being given by a first area of interest in the sequence which has a determined variance reaching or exceeding the predetermined limiting value. ([Col.5, lines 14-21] In the present case, the LIDAR sensor 2 is operated at full power in the normal mode, that is to say at maximum transmission power and with maximum reception sensitivity. In this way, there is the greatest maximum range of the LIDAR sensor 2, which, as depicted in FIG. 2, is sufficient to detect all of the objects 11, 12, 13 and 14 that are in the scanned surroundings of the LIDAR sensor 2.; [Col. 5, lines 26-33] the normal power for sending the LIDAR signal is reduced in the test mode schematically depicted in FIG. 3 to a test power that is only 30% of the normal power. The reception sensitivity remains the same in this case, that is to say does not differ from the normal sensitivity. This has the associated circumstance that it is no longer possible for all objects 11, 12, 13 and 14 to be detected by the LIDAR sensor.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the descending radial distance of the area of interest similar to Fischer, with a reasonable expectation of success. This would have the predictable result of ensuring a true maximum range of the LiDAR device by means of sequentially reducing ranges.
Regarding claim 18, O’Keeffe, as modified, teaches the method as recited in claim 15,
O’Keeffe fails to teach the method wherein, for each directional range, a maximum distance range and a corresponding maximum range are provided.
However, Fischer teaches the method wherein, for each directional range, a maximum distance range and a corresponding maximum range are provided ([Col. 5, lines 17-21] In this way, there is the greatest maximum range of the LIDAR sensor 2, which, as depicted in FIG. 2, is sufficient to detect all of the objects 11, 12, 13 and 14 that are in the scanned surroundings of the LIDAR sensor 2.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the ascertained radial distance information similar to Fischer, with a reasonable expectation of success. This would have the predictable result of limiting the required power of a lidar device to the encompass the maximum range needed for object detection.
Regarding claim 23, O’Keeffe teaches the method as recited in claim 14,
O’Keeffe fails to teach the method wherein the providing of the maximum range includes: ascertaining a mean radial distance of the point distribution of the measured values of the maximum distance range as the radial distance of the maximum distance range.
However, Fischer teaches the method wherein the providing of the maximum range includes: ascertaining a mean radial distance of the point distribution of the measured values of the maximum distance range as the radial distance of the maximum distance range ([Col. 5, lines 47-56] it is thus now possible to ascertain how great the maximum range of the LIDAR sensor actually is in the normal mode if the objects 13 and 14 at their distances from the LIDAR sensor 2 ascertained in the normal mode can no longer be detected in the test mode. Specifically, the distance of the object 12, as ascertained in the normal mode, which could only just be detected in the test mode, permits the determination of a lower limit for the maximum range of the LIDAR sensor in the normal mode).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of this invention to modify the invention of O’Keeffe to comprise the determination of a maximum distance using the mean radial distance information similar to Fischer, with a reasonable expectation of success. This would have the predictable result of using a statistical model to determine a new maximum range without additional processing.
Response to Arguments
Applicant's arguments filed January 16th, 2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the prior art combination of Fischer and O’Keefe fails to teach the limitations of claim 14, it is noted that this combination has been reconsidered by the examiner under the newly amended claim limitations. Under the new round of examination and consideration, it has been decided that the prior art of record alone is insufficient to read on the newly amended claim limitations, as presented currently. However, with consideration of the prior art of Zhou, which demonstrates a LIDAR system designed to determine the maximum range allowed in an environment by first determining a variance, is obvious to combine with the prior art of record to one of ordinary skill in the art, for the above mentioned reasons. Seen as a whole, this prior art combination now teaches the same limitations of the immediate application, and as such the rejection is maintained in this Non-Final Office Action.
Conclusion
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/ROBERT W VASQUEZ/Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645