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 .
Status of Claims
This Office Action is in response to the application filed on February 28, 2025. Claims 1-20 are presently pending and are presented for examination.
Double Patenting
Claims 1-6, 8-14, and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-9, and 13-16 of U.S. Patent No. 12241756 as follows:
Current Application’s claims
Parent Application’s claims
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14
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Although the claims at issue are not identical, they are not patentably distinct from each other because they recite the same inventive concept of localizing a vehicle within an environment using sensor data associated with a multi-resolution voxel space, determining quality metrics for voxels, determining residuals between corresponding voxels of voxel spaces, and determining vehicle localization based on the residuals. The current claims merely implement and rearrange limitations already disclosed in the patented claims and provide only further details including determining whether a voxel quality metric satisfies a threshold quality prior to residual determination and determining the residual based on voxels that satisfy the threshold quality. These additional limitations merely represent a filtering or selection criterion applied before residual calculation and does not render the claims patentably distinct. Reference in under 35 U.S.C. 103 rejection below teaches the limitation and therefore it would have been obvious to modify the parent application to include the limitation(s) in this application.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/ patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/ patents/apply/applying-online/eterminal-disclaimer.
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 is incorrect, any correction of the statutory basis 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 discloses 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4, 7-9, 12, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210225074 (hereinafter,"Meilland"), in view of U.S. Pub. No. 20210381843 (hereinafter,"Montemerlo"), and in further view of U.S. Pub. No. 20190066344 (hereinafter,"Luo").
Regarding claim 1, Meilland discloses a system comprising:
one or more processors (“a device includes one or more processors” (para 0015)); and
one or more non-transitory computer readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations (“a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing” (para 0015)) comprising:
receiving sensor data from a vehicle traversing an environment (“obtaining depth data of a physical environment using a sensor” (para 0007));
associating a portion of the sensor data with a multi-resolution voxel space representing at least a portion of the environment (“The system… generates a mesh 1144 representing the surfaces in a 3D environment using multi-resolution hashing data structures 1134. The mesh 1144 is based on depth information detected in the physical environment that is integrated (e.g., fused) to recreate the physical environment.” (para 0078));
However, Meilland does not explicitly teach
determining, for a voxel of the voxel space, a quality metric based at least in part on one or more of:
a number of points associated with the voxel,
a semantic class associated with the voxel,
an eigenvalue associated with the voxel, or
a resolution associated with the voxel;
determining whether the quality metric associated with the voxel satisfies a threshold quality;
determining, based at least in part on the quality metric satisfying the threshold quality, a residual value indicating a difference between data associated with the voxel and an additional voxel from an additional multi-resolution voxel space; and
determining, based at least in part on the residual value, a localization of the vehicle within the environment.
Montemerlo, in the same field of endeavor, teaches
determining, for a voxel of the voxel space, a quality metric (“the system can rank each surfel in the surfel data according to stability measure and determine each surfel to be included in the group of particular surfels, assigning a rank to each particular surfel according to the stability measure” (para 0063) and “the surfel data comprises data characterizing a voxel grid, wherein each surfel in the surfel data corresponds to a different voxel in the voxel grid” (para 0113)) based at least in part on one or more of: a number of points associated with the voxel, a semantic class associated with the voxel, an eigenvalue associated with the voxel, or a resolution associated with the voxel (“wherein the associated data of each surfel further comprises a respective class prediction for each of a plurality of classes of semantic information for the surface represented by the surfel” (para 0105) and “wherein the plurality of classes of semantic information comprise one or more of: an object class, a reflectivity, an opacity, or a color” (para 0106));
determining whether the quality metric associated with the voxel satisfies a threshold quality (“wherein selecting one or more high-stability surfels from the plurality of candidate surfels comprises selecting one or more surfels that have a stability measure that is higher than a particular threshold.” (para 0093));
…, based at least in part on the quality metric satisfying the threshold quality (“the system combines only the high-stability surfels identified in step 308 with the sensor measurements when determining the location of the vehicle. That is, the system can discard the surfels in the surfel data that were not identified as high-stability” (para 0067)), …; and
determining, based at least in part on the residual value, a localization of the vehicle within the environment (“the system can perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data” (para 0064)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Montemerlo in order to perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data; see Montemerlo at least at [0064].
Luo, in the same field of endeavor, teaches
determining, …, a residual value indicating a difference between data associated with the voxel and an additional voxel from an additional multi-resolution voxel space (“computing matching scores between corresponding features of a same class in the 3D submap and the global map” (para 0018)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Luo in order to select, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; see Luo at least at [0018].
Regarding claim 2, Meilland discloses the system of claim 1. Additionally, Meilland discloses wherein: the voxel of the voxel space has a first resolution, and the additional voxel of the additional voxel space has the first resolution (“the mesh is generated by positioning a vertices of the mesh along a line connecting a first voxel of the first set of voxels with a second voxel of the second set of voxels…Additionally, or alternatively, vertices are generated between voxels within the same resolution” (para 0063)).
Regarding claim 4, Meilland discloses the system of claim 1. Additionally, Meilland discloses wherein the additional voxel is determined based on a distance between a first centroid of the additional voxel and a second centroid of the voxel (“the mesh may be generated by positioning a vertices of the mesh along a line connecting a first voxel (e.g., a position at the center of the first voxel) of the first set of voxels with a second voxel (e.g., a position at the center of the second voxel) of the second set of voxels” (para 0063)).
Regarding claim 7, Meilland discloses the system of claim 1. However, Meilland does not explicitly teach wherein the voxel and the additional voxel are associated with a same semantic classification.
Luo, in the same field of endeavor, teaches
wherein the voxel and the additional voxel are associated with a same semantic classification (“computing matching scores between corresponding features of a same class in the 3D submap and the global map” (para 0018)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Luo in order to select, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; see Luo at least at [0018].
Regarding claim 8, Meilland discloses a method comprising:
receiving sensor data from a vehicle traversing an environment(“obtaining depth data of a physical environment using a sensor” (para 0007));
associating a portion of the sensor data with a multi-resolution voxel space representing at least a portion of the environment (“The system… generates a mesh 1144 representing the surfaces in a 3D environment using multi-resolution hashing data structures 1134. The mesh 1144 is based on depth information detected in the physical environment that is integrated (e.g., fused) to recreate the physical environment.” (para 0078));
However, Meilland does not explicitly teach
determining, for a voxel of the multi-resolution voxel space, a quality metric based at least in part on one or more of:
a number of points associated with the voxel,
a semantic class associated with the voxel,
an eigenvalue associated with the voxel, or
a resolution associated with the voxel;
determining, based at least in part on at least in part on the quality metric satisfying a threshold quality, a residual between the voxel and an additional voxel of an additional multi-resolution voxel space; and
determining, based at least in part on the residual, a localization of the vehicle in the environment.
Montemerlo, in the same field of endeavor, teaches
determining, for a voxel of the multi-resolution voxel space, a quality metric (“the system can rank each surfel in the surfel data according to stability measure and determine each surfel to be included in the group of particular surfels, assigning a rank to each particular surfel according to the stability measure” (para 0063) and “the surfel data comprises data characterizing a voxel grid, wherein each surfel in the surfel data corresponds to a different voxel in the voxel grid” (para 0113)) based at least in part on one or more of: a number of points associated with the voxel, a semantic class associated with the voxel, an eigenvalue associated with the voxel, or a resolution associated with the voxel (“wherein the associated data of each surfel further comprises a respective class prediction for each of a plurality of classes of semantic information for the surface represented by the surfel” (para 0105) and “wherein the plurality of classes of semantic information comprise one or more of: an object class, a reflectivity, an opacity, or a color” (para 0106));
…, based at least in part on at least in part on the quality metric satisfying a threshold quality (“the system combines only the high-stability surfels identified in step 308 with the sensor measurements when determining the location of the vehicle. That is, the system can discard the surfels in the surfel data that were not identified as high-stability” (para 0067)),...; and
determining, based at least in part on the residual, a localization of the vehicle in the environment (“the system can perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data” (para 0064)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Montemerlo in order to perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data; see Montemerlo at least at [0064].
Luo, in the same field of endeavor, teaches
determining, …, a residual between the voxel and an additional voxel of an additional multi-resolution voxel space (“computing matching scores between corresponding features of a same class in the 3D submap and the global map” (para 0018)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Luo in order to select, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; see Luo at least at [0018].
Regarding claim 9, Meilland discloses the method of claim 8. Additionally, Meilland discloses wherein:
the multi-resolution voxel space comprises a first plurality of voxels representative of discrete volumetric portions of the environment and includes a first resolution and a second resolution (“generating a first hash table storing three dimensional (3D) positions of a first set of voxels having a first resolution and signed distance values representing distances to the surfaces of the physical environment based on the depth data” (claim 1)), the first resolution being coarser than the second resolution (“the second resolution could be a finer resolution and include smaller voxels, and the first hash table may include a coarser resolution and include larger voxels” (para 0059)),
the additional multi-resolution voxel space comprises a second plurality of voxels representative of discrete volumetric portions of the environment and includes the first resolution and the second resolution (“second set of voxels having a second resolution and signed distance values representing distances to the surfaces of the physical environment based on the depth data” (claim 1)), and
the voxel is associated with the first resolution (“determining whether to represent 3D positions as voxels having the first resolution or voxels having the second resolution.” (claim 2)).
Regarding claim 12, Meilland discloses the method of claim 8. Additionally, Meilland discloses wherein the additional voxel is determined based on a distance between a first centroid of the additional voxel and a second centroid of the voxel (“the mesh may be generated by positioning a vertices of the mesh along a line connecting a first voxel (e.g., a position at the center of the first voxel) of the first set of voxels with a second voxel (e.g., a position at the center of the second voxel) of the second set of voxels” (para 0063)).
Regarding claim 15, Meilland discloses the method of claim 8. However, Meilland does not explicitly teach wherein the voxel and the additional voxel are associated with a same semantic classification.
Luo, in the same field of endeavor, teaches
wherein the voxel and the additional voxel are associated with a same semantic classification (“computing matching scores between corresponding features of a same class in the 3D submap and the global map” (para 0018)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Luo in order to select, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; see Luo at least at [0018].
Regarding claim 16, Meilland discloses one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform operations (“a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing” (para 0015)) comprising:
receiving sensor data from a vehicle traversing an environment (“obtaining depth data of a physical environment using a sensor” (para 0007));
associating the sensor data with a multi-resolution voxel space representing at least a portion of the environment (“The system… generates a mesh 1144 representing the surfaces in a 3D environment using multi-resolution hashing data structures 1134. The mesh 1144 is based on depth information detected in the physical environment that is integrated (e.g., fused) to recreate the physical environment.” (para 0078));
However, Meilland does not explicitly teach
determining, for a voxel of the multi-resolution voxel space, a quality metric based at least in part on one or more of:
a number of points associated with the voxel,
a semantic class associated with the voxel,
an eigenvalue associated with the voxel, or
a resolution associated with the voxel;
determining that the quality metric associated with the voxel satisfies a threshold quality;
based on determining that the quality metric satisfies the threshold quality, determining, based at least in part on the voxel and an additional voxel of an additional multi-resolution voxel space, a residual; and
determining, based at least in part on the residual, a localization of the vehicle within the environment.
Montemerlo, in the same field of endeavor, teaches
determining, for a voxel of the multi-resolution voxel space, a quality metric (“the system can rank each surfel in the surfel data according to stability measure and determine each surfel to be included in the group of particular surfels, assigning a rank to each particular surfel according to the stability measure” (para 0063) and “the surfel data comprises data characterizing a voxel grid, wherein each surfel in the surfel data corresponds to a different voxel in the voxel grid” (para 0113)) based at least in part on one or more of: a number of points associated with the voxel, a semantic class associated with the voxel, an eigenvalue associated with the voxel, or a resolution associated with the voxel (“wherein the associated data of each surfel further comprises a respective class prediction for each of a plurality of classes of semantic information for the surface represented by the surfel” (para 0105) and “wherein the plurality of classes of semantic information comprise one or more of: an object class, a reflectivity, an opacity, or a color” (para 0106));
determining that the quality metric associated with the voxel satisfies a threshold quality (“wherein selecting one or more high-stability surfels from the plurality of candidate surfels comprises selecting one or more surfels that have a stability measure that is higher than a particular threshold.” (para 0093));
based on determining that the quality metric satisfies the threshold quality (“the system combines only the high-stability surfels identified in step 308 with the sensor measurements when determining the location of the vehicle. That is, the system can discard the surfels in the surfel data that were not identified as high-stability” (para 0067)),...; and
determining, based at least in part on the residual, a localization of the vehicle within the environment (“the system can perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data” (para 0064)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Montemerlo in order to perform localization using the relative locations of the high-stability surfels and the relative locations of the sensor measurements in the sensor data; see Montemerlo at least at [0064].
Luo, in the same field of endeavor, teaches
…determining, based at least in part on the voxel and an additional voxel of an additional multi-resolution voxel space, a residual (“computing matching scores between corresponding features of a same class in the 3D submap and the global map” (para 0018)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Luo in order to select, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; see Luo at least at [0018].
Regarding claim 17, Meilland discloses the one or more non-transitory computer-readable media of claim 16. Additionally, Meilland discloses wherein:
the multi-resolution voxel space is represented by a plurality of resolutions, the additional multi-resolution voxel space is represented by the plurality of resolutions (“method 400 that generates a mesh using multiple hash tables that represent voxels of multiple resolutions in accordance with some implementations” (para 0054)),
the voxel has a first resolution, and the additional voxel has the first resolution (“the mesh is generated by positioning a vertices of the mesh along a line connecting a first voxel of the first set of voxels with a second voxel of the second set of voxels…Additionally, or alternatively, vertices are generated between voxels within the same resolution” (para 0063)).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210225074 (hereinafter,"Meilland"), in view of U.S. Pub. No. 20210381843 (hereinafter,"Montemerlo"), in further view of U.S. Pub. No. 20190066344 (hereinafter,"Luo") as applied to claims 1 and 8 above, and in further view of U.S. Pub. No. 20200088882 (hereinafter,"Shin").
Regarding claim 6, Meilland discloses the system of claim 1. However, Meilland does not explicitly teach wherein determining the residual value comprises:
determining a set of data associated with the voxel;
determining an eigenvector of the set of data; and
determining a vector between a first mean of the set of data and a second mean of data associated with the additional voxel,
wherein the residual value is based on the eigenvector and the vector.
Shin, in the same field of endeavor, teaches
wherein determining the residual value comprises:
determining a set of data associated with the voxel (“representing three-dimensional map data of a surrounding environment acquired by using a three-dimensional lidar attached to a moving object as voxels” (claim 1));
determining an eigenvector of the set of data ; and determining a vector between a first mean of the set of data and a second mean of data associated with the additional voxel (“acquiring an eigenvalue and an eigenvector for each voxel based on all three-dimensional points in a three-dimensional map represented as the voxels” (claim 1)),
wherein the residual value is based on the eigenvector and the vector (“calculating a rotation transformation and a translation transformation for minimizing an error by minimizing an inner product value between the eigenvector weighted by the eigenvalue of the voxel to which the three-dimensional corresponding point belongs and a vector generated from a three-dimensional corresponding point” (claim 1)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Shin in order to minimize an error based on the eigenvector and the vector; see Shin at least at [claim 1].
Regarding claim 14, Meilland discloses the method of claim 8. However, Meilland does not explicitly teach wherein determining the residual comprises:
determining a set of data associated with the voxel;
determining an eigenvector of the set of data; and
determining a vector between a first mean of the set of data and a second mean of data associated with the additional voxel,
wherein the residual is based on the eigenvector and the vector.
Shin, in the same field of endeavor, teaches
wherein determining the residual value comprises:
determining a set of data associated with the voxel (“representing three-dimensional map data of a surrounding environment acquired by using a three-dimensional lidar attached to a moving object as voxels” (claim 1));
determining an eigenvector of the set of data ; and determining a vector between a first mean of the set of data and a second mean of data associated with the additional voxel (“acquiring an eigenvalue and an eigenvector for each voxel based on all three-dimensional points in a three-dimensional map represented as the voxels” (claim 1)),
wherein the residual value is based on the eigenvector and the vector (“calculating a rotation transformation and a translation transformation for minimizing an error by minimizing an inner product value between the eigenvector weighted by the eigenvalue of the voxel to which the three-dimensional corresponding point belongs and a vector generated from a three-dimensional corresponding point” (claim 1)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Meilland with the teachings of Shin in order to minimize an error based on the eigenvector and the vector; see Shin at least at [claim 1].
Allowable Subject Matter
Claims 3, 5, 10-11, 13, and 18-20 are objected to as depending on a rejected claim but may be
found allowable if re-written in independent form including all intervening claims. Reasons for
indicating allowable subject matter will be provided once one or more claims is in a state of
allowance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM ALHARBI whose telephone number is (313)446-6621. The examiner can normally be reached on M-F 11:00AM – 7:30PM EST.
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663
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