DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
3. The amendment filed August 18, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome each and every objection.
Response to Arguments
4. Applicant's arguments filed August 18, 2026 have been fully considered but they are not persuasive.
5. Applicant argues that Bajpayee et al. (U.S. Patent Application Publication No. 2025/0022217 A1), hereinafter referred to as Bajpayee, “fails to disclose or suggest that the image quality threshold is used to identify one image, from among multiple camera images, to serve as a reference image against which space tensors of other images are corrected, as required by claim 1.” The Applicant argues that Bajpayee’s teaching of the image quality threshold only filters out low-quality images from further processing.
Examiner replies that Bajpayee’s teaching of filtering out low quality images using an image quality threshold in Paragraph 52 does teach identifying a reference image that serves as a reference image against which space tensors of other images are corrected. The Applicant has not defined the specific condition that the reference image is identified under. Thus, under broadest reasonable interpretation, the images that are not filtered out teach identifying reference images that satisfy the specified condition of being above a certain quality.
Bajpayee then teaches in Paragraph 66 that the sets of second space tensors which are output from the DNN are corrected by entering them into a loss function. There are also ground truth tensors from the reference image which teach the reference space tensors. The ground truth tensors come from the reference image because only images that passed the specified condition are used in the process. Bajpayee teaches the loss function takes the set of second space tensors and ground truth tensors to update the DNN which can then produce a new predicted tensor. This updated DNN will generate tensors again which teaches generating the sets of third space tensors which is based on the loss function or corrected sets of second space tensors. Correcting the second set of space tensors using a loss function that takes in the set of second space tensors and reference space tensors teaches that the reference image is used to correct space tensors.
Furthermore, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., "to serve as a reference image against which space tensors of other images are corrected") are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The claim does not specify that the space tensors to be corrected must come from other images aside from the reference image. Thus, the space tensors to be corrected can come from the reference image and not other images.
Thus, Bajpayee teaches “identify[ing] at least one object space tensor regarding a first specified type indicating a specified external object in each of the sets of second space tensors based on identifying a reference image according to a specified condition among the first images” and that the reference image is used for correction.
6. Applicant argues that Bajpayee’s ground-truth/loss-function training does not teach “generating third space tensors by correcting the second space tensors based on reference space tensors of the reference image” as recited in claim 1. The Applicant asserts that the claim 1 requires “correcting sets of second space tensors based on a set of reference space tensors included in the reference image,” and that “a representation of a corresponding object in the reference image is used to correct space tensors associated with the other images.” The Applicant argues that Bajpayee does not teach “correction of space tensors associated with different images based on the space tensors of a selected reference image.”
Examiner replies that in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., "a representation of a corresponding object in the reference image is used to correct space tensors associated with the other images" and “correction of space tensors associated with different images based on the space tensors of a selected reference image”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim 1 does not require a corresponding object in the reference image. Although claim 1, lines 17-18 claim identifying a space tensor corresponding to the object space tensor in each of the sets of second space tensors, it does not require that the objects be corresponding to each other. The claim only asserts that the object space tensor has a first specified type but does not require the reference space tensor to also have the same type. Bajpayee teachings in Paragraph 66-67 of a loss function that compares a ground truth tensor and predicted tensor teach that the two tensors correspond to each other.
Furthermore, Claim 1 does not require the “correction of space tensors associated with different images based on the space tensors of a selected reference image”. Claim 1 does not claim that the tensors should come from different images separate from the selected reference image. The tensors corrected can come from the same reference image.
Furthermore, Bajpayee teaches in Paragraph 66 that the sets of second space tensors which are output from the DNN are corrected by entering them into a loss function. There are also ground truth tensors from the reference image which teach the reference space tensors. Bajpayee teaches the loss function takes the set of second space tensors and ground truth tensors to update the DNN which can then produce a new predicted tensor. This updated DNN will generate tensors again which teaches generating the sets of third space tensors which is based on the loss function or corrected sets of second space tensors. Correcting the second set of space tensors using a loss function that takes in the set of second space tensors and reference space tensors teaches that the reference image is used to correct space tensors.
The claim does not define what correcting the set of second space tensors mean. Bajpayee teaches in Paragraph 66 that the network outputs space tensors. An initial output teaches a set of second space tensors and the next output after the network is updated teaches a set of third space tensors. Thus, the loss function which corrects the network’s output of space tensors teaches correcting the set of second space tensors. The network’s outputted correction teaches generating the set of third space tensors.
Thus, Bajpayee teaches generating third space tensors by correcting the second space tensors based on reference space tensors of the reference image as recited in claim 1.
7. Applicant argues that Bajpayee’s “disclosure that a tensor may include a predicted semantic classifier for an object does not disclose identifying, in each of multiple sets of second space tensors, an object space tensor regarding a first specified type based on identifying a reference image according to a specified condition.” The Applicant asserts that the mere assignment of semantic classifications to detected objects does not disclose the claimed use of an object of the first specified type in the inter-image correction process.
Examiner replies that the claim does not define what the first specified type should be. Thus, under broadest reasonable interpretation, the Examiner interprets the first specified type to be any assigned classification, group, or label. Bajpayee Paragraph 57 supports the idea that the semantic classifier can be an assigned classification like a light pole class, road sign class, and more. Thus, Bajpayee teaching in Paragraph 65 that the tensors have predicted semantic classifiers teaches identifying an object space tensor with a specified type since the semantic classifier classifies the object space tensor into a specified class.
8. Conclusion: The rejections set in the previous Office Action are shown to have been proper, and the claims are rejected below. Therefore, the present Office Action is made final.
Claim Rejections - 35 USC § 102
9. 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.
10. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
11. Claim(s) 1, 3, 6, 10, 12, 14, and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bajpayee et al. (U.S. Patent Application Publication No. 2025/0022217 A1), hereinafter referred to as Bajpayee.
12. Regarding claim 1, Bajpayee teaches a vehicle control apparatus comprising: a plurality of cameras; and a processor configured to, based on first images obtained through the plurality of cameras (Paragraphs 16-17 teach capturing multiple images using a camera or sensor; Figure 5B teaches a plurality of cameras and sensors; Paragraph 22 teaches the disclosed method can be performed by a processor) obtain (i) depth information indicating a distance between a vehicle and an external object included in each of the first images and (ii) semantic information including data associated with a type of the external object (Paragraph 18 teaches tensors are obtained that teach a depth information and semantic classifier for objects in a 2D image. The semantic classifier teaches the semantic information including the type of the external object is obtained. Paragraph 17 teaches the depth is a distance from a sensor device or camera. Paragraph 51 teaches the sensor is attached on a device. Thus, the distance teaches a distance between a vehicle and external object),
generate sets of first space tensors respectively corresponding to the first images representing at least a portion within a specified radius from the vehicle based on at least one of the depth information, the semantic information, or any combination thereof, that is included in each of the first images (Paragraph 60-63 teaches a tensor module creates a tensor that is based on the depth through their 3D coordinates or semantic class. This teaches a first space tensor based on depth or semantic information and based on the "image data relative to the sensor device."; Paragraph 26-27 teaches the sensor devices has sensor parameters affecting the image data. The sensor parameter can include a focal length and field of view which sets the radius from the vehicle that the images represent),
convert the sets of first space tensors to sets of second space tensors represented in a reference coordinate system including a first axis, a second axis, and a third axis (Paragraph 65 teaches tensors can be updated with predicted 3D characteristics which include the X, y, and Z coordinates as explained in Paragraph 64. The updated tensor teaches a converted set of second space tensors),
identify at least one object space tensor regarding a first specified type indicating a specified external object in each of the sets of second space tensors based on identifying a reference image according to a specified condition among the first images (Paragraph 65 teaches the updated tensor or second space tensors have information on individual objects depicted in the image. The information includes "semantic classifier" which are an object class. These teach object space tensors regarding a first specified type indicating a specified external object; Paragraph 52 teaches the process or method is only executed in images with a certain image quality and "In instances in which the image 109 and/or the image data 110 obtained using the sensor device 105 fails to satisfy the image quality threshold, the obtained image may not be used for additional processing, such as described herein". This teaches a reference image identified with a specific condition and only these images are used for processing like creating the object space tensor),
generate sets of third space tensors by correcting the sets of second space tensors based on a set of reference space tensors included in the reference image, based on identifying a space tensor corresponding to the object space tensor in each of the sets of second space tensors (Paragraph 66 teaches the tensor generation can be updated with ground truth tensors through loss functions. The ground truth tensors are reference space tensors from the image. The updated DNN will then generate sets of third space tensors based on the corrected sets of second space tensors or loss functions; Paragraph 67 teaches the loss or correction is based on identifying differences between predicted object labels and ground truth object labels. This teaches identifying space tensors corresponding to an object space tensor and comparing it to the ground truth tensor),
and output modeling information representing the first images in 3D space, based on applying a specified algorithm to the set of reference space tensors and the sets of third space tensors (Abstract teaches that tensors are used to generate 3D representations; Paragraph 70 teaches the updated tensors can provide an output 3D representation; Paragraph 92 teaches the 3D representation is created based on the tensors and can use Bezier curve fitting operations).
13. Regarding claim 3, Bajpayee teaches the limitations of claim 1. Bajpayee further teaches the apparatus wherein the processor is further configured to correct the sets of second space tensors based on a camera placed toward a front of the vehicle among the plurality of cameras (Paragraph 60 teaches data collected from various different sensors on the vehicle may be transformed to a reference sensor frame. Thus, the reference sensor or reference camera can be any camera on the vehicle; Figure 5B teaches there can be a camera places in the front of the car like 568, 570, 572, and 598. Paragraph 70 teaches the ground truth tensors can come from images from a reference camera. The reference camera can be selected as one of the front facing cameras shown in Figure 5B, support found in Paragraph 60. Thus, for each image that produces a ground truth tensor to correct the set of second space tensors as taught in Paragraph 66 can come from one of those front facing cameras).
14. Regarding claim 6, Bajpayee teaches the limitations of claim 1. Bajpayee further teaches the apparatus wherein the processor is further configured to convert the sets of first space tensors into the sets of second space tensors based on at least one of the sets of first space tensors, internal parameters associated with the plurality of cameras, or any combination thereof (Paragraph 32-33 teaches the 3D characteristics of the object like their X, Y, and Z coordinates can be associated or affected by the sensor parameters; Paragraph 65 teaches tensors can be updated with predicted 3D characteristics which include the X, Y, and Z coordinates as explained in Paragraph 64. The update of the tensor can be considered teaching converting a set of first space tensors to a set of second space tensors. This update is also based on at least one of the sets of first space tensors since it is updating the original first space tensor. Furthermore, the X, Y, Z coordinates being affected by the sensor parameters teaches the conversion based on internal parameters associated with the plurality of cameras).
15. Regarding claim 10, Bajpayee teaches the limitations of claim 1. Bajpayee further teaches further comprising: at least one of a Light Detection and Ranging (LiDAR), a RADAR, or any combination thereof (Paragraph 25 teaches sensor can be a LIDAR or RADAR sensor),
wherein the processor is further configured to identify a distance between the external object and the vehicle based on at least one of the LiDAR, the RADAR, or any combination thereof (Paragraph 17 teaches the depth is a distance from a sensor device or camera. The sensor can be a LIDAR or RADAR sensor as taught in Paragraph 25),
and apply the distance between the external object and the vehicle to at least one of the sets of first space tensors, the sets of second space tensors, the sets of third space tensors, or any combination thereof (Paragraph 18 teaches the tensor includes the depth data).
16. Regarding claim 12, claim 12 is the vehicle control method claim of apparatus claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
17. Regarding claim 14, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 3. Therefore, similar rationale as applied in the rejection of claim 3 applies herein.
18. Regarding claim 17, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 6. Therefore, similar rationale as applied in the rejection of claim 6 applies herein.
Claim Rejections - 35 USC § 103
19. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
20. Claim(s) 2, 5, 13, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bajpayee et al. (U.S. Patent Application Publication No. 2025/0022217 A1), hereinafter referred to as Bajpayee, as applied to claim 1 above, and further in view of Fang et al. (U.S. Patent Application Publication No. 2022/0414388 A1), hereinafter referred to as Fang.
21. Regarding claim 2, Bajpayee teaches the limitations of claim 1. However, Bajpayee is not relied upon for the below claim language: the apparatus wherein the processor is further configured to: obtain second images including the first images through the plurality of cameras at a plurality of time points including a time point at which the first images are obtained; and generate the sets of first space tensors in each of the second images.
Fang teaches the apparatus wherein the processor is further configured to: obtain second images including the first images through the plurality of cameras at a plurality of time points including a time point at which the first images are obtained; and generate the sets of first space tensors in each of the second images (Paragraph 45 teaches LIDAR sensors can retrieve images at two different moments in time which teaches obtaining second images through the plurality of cameras at a plurality of time points. Paragraph 36 teaches there can be a plurality of LIDAR sensors or camera sensors. Paragraph 46 teaches the consecutive lidar sweeps can be combined so the first image at t-1 and image at time t can be aligned to obtain second images. Paragraph 47 teaches tensors are created for the LIDAR sweeps which teaches second images will also have sets of first space tensors generated).
Bajpayee and Fang are considered analogous to the claimed invention as because both are in the same field of analyzing the space around a car through sensors. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and creating modeling information for the space taught by Bajpayee with the second images taught by Fang in order to accurately detect external objects and estimate their movements (Fang Paragraph 4 and 59).
22. Regarding claim 5, Bajpayee teaches the limitations of claim 1. However, Bajpayee is not relied upon for the below claim language: the apparatus wherein the processor is further configured to perform clustering on the sets of second space tensors included in a specified duration based on performing voxelization on the sets of second space tensors.
Fang teaches the apparatus wherein the processor is further configured to perform clustering on the sets of second space tensors included in a specified duration based on performing voxelization on the sets of second space tensors (Paragraph 46 teaches LIDAR sweeps for a certain time frame. This teaches tensors included in a specified duration. Paragraph 47 teaches performing a voxelization step to those tensors in the specified duration by clustering the tensors into pillars. Thus, this teaches clustering sets of second space tensors in a certain duration by performing voxelization).
Bajpayee and Fang are considered analogous to the claimed invention as because both are in the same field of analyzing the space around a car through sensors. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and creating modeling information for the space taught by Bajpayee with the clustering and voxelization taught by Fang in order to accurately detect external objects and estimate their movements (Fang Paragraph 4 and 59).
23. Regarding claim 13, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 2. Therefore, similar rationale as applied in the rejection of claim 2 applies herein.
24. Regarding claim 16, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 5. Therefore, similar rationale as applied in the rejection of claim 5 applies herein.
25. Claim(s) 4, 7-9, 15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bajpayee et al. (U.S. Patent Application Publication No. 2025/0022217 A1), hereinafter referred to as Bajpayee, as applied to claim 1 above, and further in view Kajo et al. (“Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects”), hereinafter Kajo.
26. Regarding claim 4, Bajpayee teaches the limitations of claim 1. However, Bajpayee is not relied upon for the below claim language: the apparatus wherein the processor is further configured to remove a background space tensor from each of the sets of second space tensors based on identifying at least one background space tensor regarding a second specified type, that is different from the first specified type and that indicates a background, in each of the sets of second space tensors.
Kajo teaches the apparatus wherein the processor is further configured to remove a background space tensor from each of the sets of second space tensors based on identifying at least one background space tensor regarding a second specified type, that is different from the first specified type and that indicates a background, in each of the sets of second space tensors (Abstract teaches “decomposes the video tensor spatiotemporally and divides it into background and foreground components”; Section IV, Subsection A teaches separating a tensor into the background and foreground patterns or tensors as seen in Equation 3. The background space tensor is removed based on identifying the background pattern or second specified type which is different from the first specified type. The first specified type can be the foreground pattern).
Bajpayee and Kajo are considered analogous to the claimed invention as because both are in the same field of identifying objects in a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and outputting modeling data taught by Bajpayee with the separation of the background space tensor taught by Kajo in order to detect all types of stationary foreground objects (Kajo Abstract).
27. Regarding claim 7, Bajpayee teaches the limitations of claim 1. However, Bajpayee is not relied upon for the below claim language: the apparatus wherein the processor is further configured to:
calculate a mean space tensor change amount based on (i) a first portion of the sets of second space tensors identified at a first time point and (ii) a second portion of the sets of second space tensors identified at a second time point; and represent the modeling information by projecting a stationary object onto the reference coordinate system based on the mean space tensor change amount, wherein the mean space tensor change amount includes a change amount of a value representing a color of the second portion corresponding to the first portion.
Kajo teaches the apparatus wherein the processor is further configured to: calculate a mean space tensor change amount based on (i) a first portion of the sets of second space tensors identified at a first time point and (ii) a second portion of the sets of second space tensors identified at a second time point (Section IV, Subsection D teaches taken difference between pixel intensities from the space tensors at a first and second time point. For example, δ1 teaches a difference between the pixel intensity tensor and background component intensity over a time period. δ2 teaches a difference between the pixel process tensor and background component over a time period. These differences are then used to create a stationary foreground map which is “constructed by computing the average of the absolute foreground processes”. This teaches a calculated mean space tensor change amount based on a first portion of tensors at a first time point and a second portion of tensors at a second time point);
and represent the modeling information by projecting a stationary object onto the reference coordinate system based on the mean space tensor change amount (Section IV, Subsection E and Figure 5(e) teach the stationary object is projected onto a frame and is done based on the stationary foreground map calculated which teaches the mean space tensor change amount. The projection onto a frame teaches projecting the stationary object onto a reference coordinate system since the frame has a window length and thus coordinates on where to highlight the foreground object as seen in Figure 5(e). The process explained in Section IV, Subsection D and Subsection E which creates the stationary foreground map teaches the stationary object detection algorithm),
wherein the mean space tensor change amount includes a change amount of a value representing a color of the second portion corresponding to the first portion (Section IV, Subsection D teaches δ1 which is a difference between the pixel intensity tensor and background component intensity over a time period. This teaches a change amount of pixel intensities over a time period. The change amount of pixel intensities teaches a change amount of color).
Bajpayee and Kajo are considered analogous to the claimed invention as because both are in the same field of identifying objects in a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and outputting modeling data taught by Bajpayee with the calculation of the mean space tensor change amount taught by Kajo in order to detect all types of stationary foreground objects (Kajo Abstract).
28. Regarding claim 8, Bajpayee in view of Kajo teaches the limitations of claim 7. However, Bajpayee is not relied upon for the below claim language: wherein the processor is further configured to project the stationary object onto the reference coordinate system based on the mean space tensor change amount and a stationary object detection algorithm.
Kajo teaches wherein the processor is further configured to project the stationary object onto the reference coordinate system based on the mean space tensor change amount and a stationary object detection algorithm (Section IV, Subsection E and Figure 5(e) teach the stationary object is projected onto a frame and is done based on the stationary foreground map calculated which teaches the mean space tensor change amount. The projection onto a frame teaches projecting the stationary object onto a reference coordinate system since the frame has a window length and thus coordinates on where to highlight the foreground object as seen in Figure 5(e). The process explained in Section IV, Subsection D and Subsection E which creates the stationary foreground map teaches the stationary object detection algorithm).
Bajpayee and Kajo are considered analogous to the claimed invention as because both are in the same field of identifying objects in a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and outputting modeling data taught by Bajpayee with the calculation of the mean space tensor change amount taught by Kajo in order to detect all types of stationary foreground objects (Kajo Abstract).
29. Regarding claim 9, Bajpayee in view of Kajo teaches the limitations of claim 8. However, Bajpayee is not relied upon for the below claim language: wherein the stationary object detection algorithm includes at least one of a region of interest (ROI)-based algorithm, a difference frame algorithm, an optical flow-based algorithm, a histogram-based algorithm, an algorithm using a motion sensor, or any combination thereof.
Kajo teaches wherein the stationary object detection algorithm includes at least one of a region of interest (ROI)-based algorithm, a difference frame algorithm, an optical flow-based algorithm, a histogram-based algorithm, an algorithm using a motion sensor, or any combination thereof (Section IV, Subsection D teaches a region-of-interest based algorithm through the calculation of differences in pixel intensities. Specifically, Subsection D(3) teaches isolating and removing the foreground object and checking if the region remains stationary which teaches a region-of-interest).
Bajpayee and Kajo are considered analogous to the claimed invention as because both are in the same field of identifying objects in a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating space tensors and outputting modeling data taught by Bajpayee with stationary object detection algorithm taught by Kajo in order to detect all types of stationary foreground objects (Kajo Abstract).
30. Regarding claim 15, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 4. Therefore, similar rationale as applied in the rejection of claim 4 applies herein.
31. Regarding claim 18, Bajpayee teaches the limitations of claim 12. The claim is similar in scope to claim 7. Therefore, similar rationale as applied in the rejection of claim 7 applies herein.
32. Regarding claim 19, Bajpayee in view of Kajo teaches the limitations of claim 18. The claim is similar in scope to claim 8. Therefore, similar rationale as applied in the rejection of claim 8 applies herein.
33. Regarding claim 20, Bajpayee in view of Kajo teaches the limitations of claim 19. The claim is similar in scope to claim 9. Therefore, similar rationale as applied in the rejection of claim 9 applies herein.
34. Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bajpayee et al. (U.S. Patent Application Publication No. 2025/0022217 A1), hereinafter referred to as Bajpayee, as applied to claim 1 above, and further in view of You et al. (“A Quality Prediction Method for Building Model Reconstruction Using LiDAR Data and Topographic Maps”), hereinafter referred as You.
Regarding claim 11, Bajpayee teaches the limitations of claim 1. However, Bajpayee is not relied upon for the below claim language: wherein the specified algorithm includes a mesh generation algorithm, and wherein the mesh generation algorithm includes at least one of a ball rolling algorithm, a Delaunay triangulation, a marching cubes algorithm, a Voronoi diagram, a geometry-based mesh generation algorithm, a polygon mesh generation algorithm, or any combination thereof.
You teaches wherein the specified algorithm includes a mesh generation algorithm, and wherein the mesh generation algorithm includes at least one of a ball rolling algorithm, a Delaunay triangulation, a marching cubes algorithm, a Voronoi diagram, a geometry-based mesh generation algorithm, a polygon mesh generation algorithm, or any combination thereof (Section II teaches using Delaunay triangulation on the tensors to create triangular patches and a TIN mesh. Section III, Subsection C, Figure 3 and Figure 4 teach using the outlines calculated in Section II to create the 3D building models).
Bajpayee and You are considered analogous to the claimed invention as because both are in the same field of using image tensors to generate a three-dimensional modeling output. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the apparatus of generating tensors and outputting modeling data taught by Bajpayee with the mesh generation algorithm taught by You in order to create accurate building models from LiDAR data (You Introduction Section).
Conclusion
35. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Karasev et al. (U.S. Patent Application Publication No. 2023/0260266 A1) teaches converting mapping feature tensors into cartesian coordinates.
Murashkin et al. (U.S. Patent Application Publication No. 2020/0090352 A1) teaches using sensors to create images and generating image tensors to represent objects around a vehicle.
36. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
37. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 9-5pm.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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