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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/28/2024 and 01/23/2025 have been entered and considered. Initialed copies of the PTO-1449 by the Examiner are attached.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 1 and 8 recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
· Claim 1; recites the limitation, “an acquisition module, configured to ….” [Line 9].
· Claim 1; recites the limitation, “a partitioning module, configured to ….” [Line 12,].
· Claim 1; recites the limitation, “an augmentation module, configured to ….” [Line 12].
· Claim 1; recites the limitation, “a classification module, configured to ….” [Line 17].
Claim 1; recites the limitation, “a classification module, configured to ….” [Line 17].
Claim 1; recites the limitation, “an aggregation module, configured to ….” [Line 21].
Claim 8; recites the limitation, “a sampling module, configured to ….” [Line 1].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1 and 8:
(i) “an acquisition module” (Fig. 1, #151. Paragraph [0034][0047]- an acquisition module 151 of a classification subsystem 150 to allow for segmentation by a classification module 155 thereof. Segmentation can include semantic segmentation, wherein each point is classified in a given class 149 from a number of possible classes, instance segmentation, wherein objects are identified and delineated in the point cloud, and panoptic segmentation, wherein objects are identified and delineated and points not in objects are classified. The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the acquisition module do have sufficient structure associated with it.).
(ii) “a partitioning module” (Fig. 1, #152. Paragraph [0034]- Partitioning and/or sampling makes it possible to achieve good performance of the segmentation on LiDAR data with different points densities, while nonetheless increasing the computational efficiency by using less computational resources such as processor time and memory space. In some embodiments, to further improve computational efficiency, tiles can be further divided in columns by the partitioning module 152, as will be explained in more details below with respect to method step 250. In some of these embodiments, to obtain an optimal tradeoff between computational performance and result quality, additional tiles and/or columns can be created through augmentation approaches by the augmentation module 153, as will be explained in more details below with respect to method step 240. The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the partitioning module do have sufficient structure associated with it.).
(iii) “an augmentation module” (Fig. 1, #153. Paragraph [0034][0059]- a step 240 is performed to augment the input data by creating additional, transformed tiles. Each transformed tile can be fed to the trained model, thereby providing multiple inferences yielding a multiple segmentation, i.e., a plurality of classifications or class probabilities for each point, which can be aggregated at a later point, improving classification accuracy and providing smoother object surfaces. Transformations can include for instance any three-dimensional coordinate transformations, e.g., translation, rigid, similarity, affine and/or projective transforms The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the augmentation module does have sufficient structure associated with it.).
(iv) “a classification module” (Fig. 1, #155 Paragraph [0034][0047]- the classification module 155 can include a trained model taking point data 140 from a point cloud as input and predicting a class 149 for each point as output. As an example only, classes can indicate that a point is predicted to correspond to ground, vegetation (possibly low vegetation, medium vegetation or high vegetation), a building, water, rail, a road surface, a wire, a transmission tower, a wire-structure connector, a bridge deck or an overhead structure The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the classification module does have sufficient structure associated with it.).
(v) “an aggregation module” (Fig. 2, #156. Paragraph [[0018]0039]- aggregating a plurality of segmented transformed tiles output by the neural network to create a final semantic segmentation result defining a segmented point cloud including a class for each point of the LiDAR point cloud. The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the aggregation module does have sufficient structure associated with it.).
. (vi) “a sampling module” (Fig. 1, #154. Paragraph [0039][0061]- Partitioning and/or sampling makes it possible to achieve good performance of the segmentation on LiDAR data with different points densities, while nonetheless increasing the computational efficiency by using less computational resources such as processor time and memory space. The steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors. (Wherein the sampling module does have sufficient structure associated with it.).
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 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) in view of Rekow et al (US Patent No.: 20220326763).
Regarding independent claim 1, Jannat teaches a system for performing a semantic segmentation of a LiDAR point cloud (a deep learning model developed to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data – see abstract), the system comprising:
an aerial collection vehicle equipped with an LiDAR transceiver (aerial lidar data – see title),
and a classification subsystem (U-NET model – see section III), comprising at least:
an acquisition module (input layer – see section III, subsection C), configured to receive the LiDAR point cloud, a partitioning module (input layer – see section, subsection C), configured to partition the LiDAR point cloud in tiles, each tile representing a sampled area of the LiDAR point cloud (raw LiDAR data set, it was decomposed into tiles - see section III),
an augmentation module, configured to create a plurality of transformed tiles associated with each tile ([d]ata augmentation was performed via two image sampling approaches: (1) random background sampling and (2) random rotations and translations of each training label within the tile perceptual field - see section, subsection C) using at least one of:
rotating the corresponding tile about an axis by a plurality of random angles (random rotations and translations of each training label within the tile perceptual field - see section III, subsection C), and
dividing the corresponding tile using a specific column size randomly chosen from a predefined range of optimal column sizes;
a classification module (U-NET decoder - see section III, subsection B)), configured to implement multiple semantic segmentation (classifications to take into consideration image values from a larger perceptual field and also reRekow ces adverse impacts associated with labels that lie on the boundary of the image - see section III, subsection E), the classification module comprising a neural network trained to input one of the plurality of transformed tiles and output a corresponding segmented transformed tile ([w]e refer to this approach as a sliding-window classification output - see section III, subsection E)); and
an aggregation module (U-NET decoder - see section III, subsection B), configured to aggregate a plurality of segmented transformed tiles output by the neural network and create a final semantic segmentation result defining a segmented point cloud including a class for each point of the LiDAR point cloud ([t]he results
are combined by choosing the class having the highest joint probability across all tile classifications - see section III, subsection E).
Jannat does not explicitly teach the LiDAR transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.
However, Rekow explicitly teaches the LiDAR transceiver comprising at least a transmitter (transmitter 104 (e.g., laser) – see [p][0034] and Fig 1) configured to send a laser beam towards an object ((e.g., laser) that transmits an emitted light signal 110 – see [p][0034]) and a receiver (a receiver 106 (e.g., photodiode) -see [p][0034] and Fig 1) configured to detect a reflection of the laser beam on the object (a receiver 106 (e.g., photodiode) that detects a return light signal 114, -see [p][0034]), the optical transceiver configured to create point data based on the reflection of the beam (see [p][0034]), the point data corresponding to the LiDAR point cloud (see [p][0034]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Rekow of the LiDAR transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.
Wherein having Jannat the LiDAR transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by capturing one or more events occurring within the environment. since both Jannat and Rekow are methods for aggregating LIDAR data. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Rekow captures one or more events occurring within the environment. (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) see Abstract and Rekow et al (Pub No.: US20230213633A1), see Abstract).
Regarding claim 2, Jannat in view of Rekow teach the system of claim 1, Jannet does not explicitly teach further comprising one or more additional aerial collection vehicles, each equipped with an additional LiDAR transceiver, wherein the point data is a combination of the detections of the aerial collection vehicle and of the one or more additional aerial collection vehicles.
However, Rekow explicitly teaches further comprising one or more additional aerial collection vehicles, each equipped with an additional LiDAR transceiver, wherein the point data is a combination of the detections of the aerial collection vehicle and of the one or more additional aerial collection vehicles (see [p][003][0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Rekow of further comprising one or more additional aerial collection vehicles, each equipped with an additional LiDAR transceiver, wherein the point data is a combination of the detections of the aerial collection vehicle and of the one or more additional aerial collection vehicles.
Wherein having Jannat further comprising one or more additional aerial collection vehicles, each equipped with an additional LiDAR transceiver, wherein the point data is a combination of the detections of the aerial collection vehicle and of the one or more additional aerial collection vehicles.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by capturing one or more events occurring within the environment. since both Jannat and Rekow are methods for aggregating LIDAR data. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Rekow captures one or more events occurring within the environment. (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) see Abstract and Rekow et al (Pub No.: US20230213633A1), see Abstract).
Claims 3-4, 8-11, 14 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) in view of Uljanovs et al (Pub No.: US20230419659A1).
Regarding independent claim 3, Jannat teaches a system for performing a semantic segmentation of a LiDAR point cloud (a deep learning model developed to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data – see abstract), the system comprising:
at least one processor (GPU – see section V, subsection B);
an acquisition module, configured to receive tiles, each representing a sampled area of the LiDAR point cloud (d]ata augmentation was performed via two image sampling approaches: (1) random background sampling and (2) random rotations and translations of each training label within the tile perceptual field - see section, subsection C);
an augmentation module, configured to create a plurality of transformed tiles associated with each tile ([d]ata augmentation was performed via two image sampling approaches: (1) random background sampling and (2) random rotations and translations of each training label within the tile perceptual field - see section, subsection C) using at least one of:
rotating the corresponding tile about an axis by a plurality of random angles (random rotations and translations of each training label within the tile perceptual field - see section III, subsection C), and
dividing the corresponding tile using a specific column size randomly chosen from a predefined range of optimal column sizes;
a classification module (U-NET decoder - see section III, subsection B)), configured to implement multiple semantic segmentation (classifications to take into consideration image values from a larger perceptual field and also adverse impacts associated with labels that lie on the boundary of the image - see section III, subsection E), the classification module comprising a neural network trained to input one of the plurality of transformed tiles and output a corresponding segmented transformed tile ([w]e refer to this approach as a sliding-window classification output - see section III, subsection E)); and
an aggregation module (U-NET decoder - see section III, subsection B), configured to aggregate a plurality of segmented transformed tiles output by the neural network and create a final semantic segmentation result defining a segmented point cloud including a class for each point of the LiDAR point cloud ([t]he results
are combined by choosing the class having the highest joint probability across all tile classifications - see section III, subsection E).
Janet does not explicitly teach memory; and a predefined range of optimal column sizes for dividing the tiles at least one.
However, Uljanovs explicitly teaches memory (see [p][0047]); and a predefined range of optimal column sizes for dividing the tiles at least one (segment the point-cloud data in the training dataset to define a plurality of point-cloud data tiles, with each of the plurality of point-cloud data tiles comprising the point-cloud data corresponding to an area of a predetermined size of the geographical region point-cloud data tiles -see [p][0047]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Uljanovs of memory; and a predefined range of optimal column sizes for dividing the tiles at least one.
Wherein having Jannat memory; and a predefined range of optimal column sizes for dividing the tiles at least one.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by processing a point-cloud data, generated using reflected pulses based on a remote sensing techniques since both Jannat and Uljanovs are methods for aggregating LIDAR data. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Uljanovs processes a point-cloud data, generated using reflected pulses based on a remote sensing technique (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) see Abstract and Uljanovs et al (Pub No.: US20230419659A1), see Abstract).
Regarding claim 4, Jannat in view of Uljanovs teach the system of claim 3, Jannat explicitly teaches wherein the point data comprise, for each of a plurality of points, at least one of: coordinates (the LiDAR source data equivalent to the input layer size of our network were extracted with varying positions and orientations – see section III, subsection C and subsection E), a return number, a number of returns, an intensity and a scan angle.
Regarding claim 8, Jannat in view of Uljanovs teach the system of claim 3, Jannat explicitly teaches comprising a sampling module, configured to sample the plurality of transformed tiles before input in the neural network (see abstract).
Regarding claim 9, Jannat in view of Uljanovs teach the system of claim 3, Jannat explicitly teaches wherein the neural network is trained to classify points corresponding to different layers of a same object in different classes (train the model to be able to segment every pixel in any input image into corresponding class labels or backgrounds – see section I, [p][003]).
Regarding claim 10, Jannat in view of Uljanovs teach the system of claim 3, Jannat explicitly teaches further configured to create a digital terrain map, a building footprint (see Fig 9) and/or a vegetation map.
Regarding independent claim 11, Jannat teaches a method for performing a semantic segmentation of a LiDAR point cloud (a deep learning model developed to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data – see abstract), the method comprising:
receiving tiles, each representing a sampled area of the LiDAR point cloud (d]ata augmentation was performed via two image sampling approaches: (1) random background sampling and (2) random rotations and translations of each training label within the tile perceptual field - see section, subsection C);
creating a plurality of transformed tiles associated with each tile ([d]ata augmentation was performed via two image sampling approaches: (1) random background sampling and (2) random rotations and translations of each training label within the tile perceptual field - see section, subsection C) using at least one of:
rotating the corresponding tile about an axis by a plurality of random angles (random rotations and translations of each training label within the tile perceptual field - see section III, subsection C), and
dividing the corresponding tile using a specific column size randomly chosen from a predefined range of optimal column sizes;
performing multiple semantic segmentation (classifications to take into consideration image values from a larger perceptual field and also adverse impacts associated with labels that lie on the boundary of the image - see section III, subsection E), the classification module comprising a neural network trained to input one of the plurality of transformed tiles and output a corresponding segmented transformed tile ([w]e refer to this approach as a sliding-window classification output - see section III, subsection E)); and
aggregating a plurality of segmented transformed tiles output by the neural network and create a final semantic segmentation result defining a segmented point cloud including a class for each point of the LiDAR point cloud ([t]he results
are combined by choosing the class having the highest joint probability across all tile classifications - see section III, subsection E).
Janet does not explicitly teach a predefined range of optimal column sizes for dividing the tiles at least one.
However, Uljanovs explicitly teaches a predefined range of optimal column sizes for dividing the tiles at least one (segment the point-cloud data in the training dataset to define a plurality of point-cloud data tiles, with each of the plurality of point-cloud data tiles comprising the point-cloud data corresponding to an area of a predetermined size of the geographical region point-cloud data tiles -see [p][0047]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Uljanovs a predefined range of optimal column sizes for dividing the tiles at least one.
Wherein having Jannat and a predefined range of optimal column sizes for dividing the tiles at least one.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by processing a point-cloud data, generated using reflected pulses based on a remote sensing techniques since both Jannat and Uljanovs are methods for aggregating LIDAR data. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Uljanovs processes a point-cloud data, generated using reflected pulses based on a remote sensing technique (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) see Abstract and Uljanovs et al (Pub No.: US20230419659A1), see Abstract).
Regarding claim 14, which corresponds to claims 4 except for reciting a different statutory category of method. Therefore, the rejection analysis of claim 4 are fully applicable to claim 14.
Regarding claim 18, which corresponds to claims 8 except for reciting a different statutory category of method. Therefore, the rejection analysis of claim 8 are fully applicable to claim 18
Regarding claim 19, which corresponds to claims 9 except for reciting a different statutory category of method. Therefore, the rejection analysis of claim 9 are fully applicable to claim 19
Janna Regarding claim 20, Jannat in view of Uljanovs teach the method of claim 11, Jannat explicitly teaches further comprising creating a digital terrain map, a building footprint (see Fig 9) and/or a vegetation map.
Claim 5, 12-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) in view of Uljanovs et al (Pub No.: US20230419659A1) as applied to claims 3 and 11 further in view of Rekow et al (US Patent No.: 20220326763). Regarding claim 5, Jannat in view of Uljanovs teach the system of claim 4, however, Jannat in view of Uljanovs fail to explicitly teach wherein the input of the neural network comprises, for each point, at least the scan angle of the point.
Rekow discloses wherein the input of the neural network comprises, for each point, at least the scan angle of the point. (check the scanning angle and thus directed point in the field at any time point of the scanning process for a LiDAR scanner -see [p][0125]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat in view of Uljanovs of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Rekow of wherein the input of the neural network comprises, for each point, at least the scan angle of the point.
Wherein having Jannat wherein the input of the neural network comprises, for each point, at least the scan angle of the point.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by selecting a portion of the points of data and aggregating the selected portion to provide aggregated data since both Jannat and Rekow are methods for aggregating LIDAR. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Rekow capture one or more events occurring within the environment (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]), see Abstract and Rekow et al (US Patent No.: 20220326763)).
Regarding claim 12, Jannat in view of Uljanovs teach the method of claim 11, however, Jannat in view of Uljanovs fail to explicitly teach, comprising the step of acquiring the point cloud by an aerial collection vehicle equipped with a LiDAR transceiver, the optical transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the LiDAR optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.
However, Rekow explicitly teaches comprising the step of acquiring the point cloud by an aerial collection vehicle equipped with a LiDAR transceiver (a receiver 106 (e.g., photodiode) -see [p][0034] and Fig 1) the optical transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the LiDAR optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud. (a receiver 106 (e.g., photodiode) that detects a return light signal 114, -see [p][0034]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat in view of Uljanovs of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Rekow comprising the step of acquiring the point cloud by an aerial collection vehicle equipped with a LiDAR transceiver, the optical transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the LiDAR optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.
Wherein having Jannat comprising the step of acquiring the point cloud by an aerial collection vehicle equipped with a LiDAR transceiver, the optical transceiver comprising at least a transmitter configured to send a laser beam towards an object and a receiver configured to detect a reflection of the laser beam on the object, the LiDAR optical transceiver configured to create point data based on the reflection of the beam, the point data corresponding to the LiDAR point cloud.t.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by selecting a portion of the points of data and aggregating the selected portion to provide aggregated data since both Jannat and Rekow are methods for aggregating LIDAR. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Rekow capture one or more events occurring within the environment (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]), see Abstract and Rekow et al (US Patent No.: 20220326763), see Abstract).
Regarding claim 13, Jannat in view of Uljanovs and Rekow teach the method of claim 12, however, Jannat in view Rekow of fails to explicitly teach, comprising the steps of: acquiring additional point data by one or more additional collection vehicles, each equipped with an additional optical transceiver; and combining the point data and the additional point data to form the point cloud.
Uljanovs explicitly teaches acquiring additional point data by one or more additional collection vehicles, each equipped with an additional optical transceiver (see [p][0033]); and combining the point data and the additional point data to form the point cloud (combination with the tiling of the point-cloud datasets, the choice of sampling algorithm further improves the efficiency of the method -see [p][0060]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Uljanovs acquiring additional point data by one or more additional collection vehicles, each equipped with an additional optical transceiver; and combining the point data and the additional point data to form the point cloud.
Wherein having Jannat acquiring additional point data by one or more additional collection vehicles, each equipped with an additional optical transceiver; and combining the point data and the additional point data to form the point cloud.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by processing a point-cloud data, generated using reflected pulses based on a remote sensing techniques since both Jannat and Rekow are methods for aggregating LIDAR data. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Uljanovs processes a point-cloud data, generated using reflected pulses based on a remote sensing technique (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) see Abstract and Uljanovs et al (Pub No.: US20230419659A1), see Abstract).
Regarding claim 15, which corresponds to claims 5 except for reciting a different statutory category of method. Therefore, the rejection analysis of claim 5 are fully applicable to claim 15.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]) in view of Uljanovs et al (Pub No.: US20230419659A1) as applied to claims 3 and 11 in view of Du et al (US Patent No.: US20230213633A1).
Regarding claim 6, Jannat in view of Uljanovs teach system of claim 3, Jannat in view of Uljanovs teach fails to explicitly teach wherein the tiles are provided with an overlap buffer and wherein points of the segmented transformed tiles output corresponding to the overlap buffer are discarded.
Du explicitly teaches wherein the tiles are provided with an overlap buffer (222, target data buffer – see [p][0061]) and wherein points of the segmented transformed tiles output corresponding to the overlap buffer are discarded (perform a continuity check to determine which of the points of data to select and which of the points of data to discard – see [p][0010]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Jannat in view of Uljanovs of having a system for performing a semantic segmentation of a LiDAR point cloud, the system comprising: an aerial collection vehicle equipped with an LiDAR transceiver with the teachings of Rekow of wherein the tiles are provided with an overlap buffer and wherein points of the segmented transformed tiles output corresponding to the overlap buffer are discarded.
Wherein having Jannat wherein the tiles are provided with an overlap buffer and wherein points of the segmented transformed tiles output corresponding to the overlap buffer are discarded.
The motivation behind the modification would have been to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data by selecting a portion of the points of data and aggregating the selected portion to provide aggregated data since both Jannat and Du are methods for aggregating LIDAR. Wherein Jannat segments, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data while Du selects a portion of the points of data and aggregating the selected portion to provide aggregated data (Please see Jannat et al (NPL titled: Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning [cited in IDS]), see Abstract Du et al (Pub No.: US20230213633A1)).
Regarding claim 17, which corresponds to claims 6 except for reciting a different statutory category of method. Therefore, the rejection analysis of claim 6 are fully applicable to claim 17.
Allowable Subject Matter
Claims 7 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jiang et al (Pub No.: 20260105766) discloses a method and system provide the ability to segment a first point cloud. The first point cloud is rendered into multiple two-dimensional (2D) images. The images are segmented to generate a semantic segmentation mask. The images are then backprojected into a 3D classified point cloud. The classified point cloud is segmented into geometric segments and voting is performed for each segment to determine the majority classification and reassign minority classifications. A final point cloud is then exported as a segmented classified point cloud.
Sheu et al (Pub No.: 20220027675) discloses using 3D point cloud data such as that captured by a LiDAR as ground truth data for training an instance segmentation deep learning model. 3D point cloud data captured by a LiDAR can be projected on a 2D image captured by a camera and provided as input to a 2D instance segmentation model. 2D sparse instance segmentation masks may be generated from the 2D image with the projected 3D data points. These 2D sparse masks can be used to propagate loss during training of the model. Generation and use of the 2D image data with the projected 3D data points as well as the 2D sparse instance segmentation masks for training the instance segmentation model obviates the need to generate and use actual instance segmentation data for training, thereby providing an improved technique for training an instance segmentation model.
Nguyen et al (Pub No.: US20240404064) discloses systems and methods for performing semantic segmentation of LiDAR point clouds are provided. LiDAR point data are generated using aerial vehicles equipped with LiDAR transceivers. Tiles representing sampled areas of the point cloud are augmented by rotation or division, allowing for multiple semantic segmentation by a neural network, resulting in a smoother segmentation with an increased quality. To increase the quality further, buffers can be used around tiles, the scan angle of each LiDAR point can be used as input to the neural network, and points of an object can be segmented into different classes. The resulting segmentations are then aggregated to provide a final semantic segmentation, which can be used to create a digital terrain map, a building footprint or a vegetation map.
Ho et al (US Patent No.: 10936908) discloses a systems and methods for semantic labeling of point clouds using images. Some implementations may include obtaining a point cloud that is based on lidar data reflecting one or more objects in a space; obtaining an image that includes a view of at least one of the one or more objects in the space; determining a projection of points from the point cloud onto the image; generating, using the projection, an augmented image that includes one or more channels of data from the point cloud and one or more channels of data from the image; inputting the augmented image to a two dimensional convolutional neural network to obtain a semantic labeled image wherein elements of the semantic labeled image include respective predictions; and mapping, by reversing the projection, predictions of the semantic labeled image to respective points of the point cloud to obtain a semantic labeled point cloud.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
May 28, 2026
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