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 4/3/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al ("Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pages 17010-17020, retrieved from the Internet on 2/25/2026) in view of Mur-Artal et al ("Orb-slam2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras." IEEE transactions on robotics 33.5, 2017, pages 1255-1262, retrieved from the Internet on 2/26/2026).
Regarding claim 1, Shi teaches a computer comprising a processor and a memory, the memory storing instructions executable by the processor to:
determine a first set of SLAM poses of a camera with respect to an environment by performing a simultaneous localization and mapping (SLAM) algorithm (section 2, They first estimate relative camera poses between consecutive image frames…it can complement the SLAM/VO; section 7, Our ground-to-satellite pose optimization method can also help the conventional SLAM and visual odometry methods for camera tracking as a novel mechanism for “loop closure” in SLAM); and
determine a second set of G2O poses of the camera based on a plurality of ground-view images from the camera and an overhead image depicting the environment (abstract, matching a ground-level image with an overhead-view satellite map; section 1, minimizing the differences between the predicted and observed features setting, i.e., localization by matching a ground-level image
to an overhead-view satellite map to determine the ground camera’s pose).
Shi fails to teach determining a third set of final poses of the camera by minimizing a loss function derived from a pose graph of the final poses, the loss function based on the SLAM poses in the first set and the G2O poses in the second set.
However Mur-Artal teaches determining a set of poses of a camera by minimizing a loss function derived from a pose graph of the poses (section II.A, When closing a loop, our system aligns first both sides, similar to RSLAM so that the tracking is able to continue localizing using the old map, and then, performs a pose-graph optimization that minimizes the drift accumulated in the loop; section III.C, minimizing the reprojection error as shown in equation 2; section III.D, optimizing a pose graph), the loss function based on the SLAM poses (section I, the first open-source1 SLAM system for monocular, stereo, and RGB-D cameras, including loop closing, relocalization, and map reuse; section III.D, loop closing). It would be obvious to incorporate the G2O poses taught by Shi into the loss function as a component.
Therefore taking the combined teachings of Shi and Mur-Artal as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Mur-Artal into the apparatus of Shi. The motivation to combine Mur-Artal and Shi would be to achieve more accuracy (section I of Mur-Artal).
Regarding claim 16, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Claim(s) 2 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al ("Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pages 17010-17020, retrieved from the Internet on 2/25/2026) and Mur-Artal et al ("Orb-slam2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras." IEEE transactions on robotics 33.5, 2017, pages 1255-1262, retrieved from the Internet on 2/26/2026) in view of Ozog et al (US20250086984).
Regarding claim 2, the modified apparatus of Shi fails to teach a computer wherein the instructions further include instructions to actuate a component of a vehicle including the camera based on the final poses.
However Ozog teaches instructions to actuate a component of a vehicle including a camera based on poses (para. [0005], A SLAM technique can use proprioception information to estimate a pose; para. [0152], The instructions to correct the estimate of the pose of the camera can include instructions to adjust a position of the pose of the camera in a direction perpendicular to a direction of a major axis of the object that has the one or more of the linear feature or the planar feature).
Therefore taking the combined teachings of Shi and Mur-Artal with Ozog as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Ozog into the apparatus of Shi and Mur-Artal. The motivation to combine Ozog, Mur-Artal and Shi would be to correct an alignment of positions of points affiliated with an object (para. [0008] of Ozog).
Regarding claim 17, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Claim(s) 3, 10, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al ("Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pages 17010-17020, retrieved from the Internet on 2/25/2026) and Mur-Artal et al ("Orb-slam2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras." IEEE transactions on robotics 33.5, 2017, pages 1255-1262, retrieved from the Internet on 2/26/2026) in view of Eade et al (US20160154408).
Regarding claim 3, the modified apparatus of Shi fails to teach a computer wherein the instructions further include instructions to, before determining the third set of the final poses, remove a first G2O pose from the second set upon determining that the first G2O pose is outside a spatial bound.
However Eade teaches removing a first pose from a second set upon determining that the first pose is outside a spatial bound (para. [0122], In one embodiment, when the Pre-Filter module 622 identifies a potentially inaccurate visual measurement, the Pre-Filter module 622 does not pass the identified visual landmark data onto the SLAM module 604 such that the VSLAM system 600 effectively ignores the potentially inaccurate landmark measurement; para. [0135], the robot can be determined to be located relatively far away when the difference between the pose estimated by SLAM and the pose estimated by the visual measurement exceed a predetermined threshold).
Therefore taking the combined teachings of Shi and Mur-Artal with Eade as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Eade into the apparatus of Shi and Mur-Artal. The motivation to combine Eade, Mur-Artal and Shi would be to maintain the accuracy of the global robot pose estimate to exceed the accuracy of the visual measurements (para. [0107] of Eade).
Regarding claim 10, the modified apparatus of Shi fails to teach a computer wherein the pose graph includes the final poses as graph nodes and a plurality of error terms as graph edges, and the loss function includes the error terms.
However Eade teaches wherein a pose graph includes final poses as graph nodes (para. [0144]) and a plurality of error terms as graph edges (para. [0046], generating a residual value for each edge in the graph, the residual value being based at least in part on the difference between the relative pose of the nodes connected by the edge in the graph and the relative pose given by the transformation value associated with the same edge), and a loss function includes the error terms (equations 5-8, para. [0209]).
Therefore taking the combined teachings of Shi and Mur-Artal with Eade as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Eade into the apparatus of Shi and Mur-Artal. The motivation to combine Eade, Mur-Artal and Shi would be to maintain the accuracy of the global robot pose estimate to exceed the accuracy of the visual measurements (para. [0107] of Eade).
Regarding claim 18, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recites similar subject matter as claim 10 and is rejected for the same reasons as stated above.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al ("Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pages 17010-17020, retrieved from the Internet on 2/25/2026) and Mur-Artal et al ("Orb-slam2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras." IEEE transactions on robotics 33.5, 2017, pages 1255-1262, retrieved from the Internet on 2/26/2026) in view of Rukhovich et al (US20160121534).
Regarding claim 15, the modified apparatus of Shi fails to teach a computer wherein the SLAM poses, the G2O poses, and the final poses each include two spatial dimensions and one angular dimension.
However Rukhovich teaches, in a SLAM system (para. [0033]), wherein poses include two spatial dimensions and one angular dimension (para. [0082]).
Therefore taking the combined teachings of Shi and Mur-Artal with Rukhovich as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Rukhovich into the apparatus of Shi and Mur-Artal. The motivation to combine Rukhovich, Mur-Artal and Shi would be to achieve accurate object detection (para. [0008] of Rukhovich).
Allowable Subject Matter
Claims 4-9, 11-14, and 19 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.
Related Art
Ahmed et al (US20250014200) – see para. [0062], [0093], [0125]
Bosse et al (US20240094009) – see para. [0042]
Examiner Notes
Regarding independent claims 1 and 16, the examiner determines that the claims should not be given a 101 rejection because the determining steps amount to a technological improvement. Paragraph [0007] of applicant’s specification states that the combination of SLMA poses of a camera and G2O poses of the camera provide higher accuracy than each one individually. Although the improvement is not explicitly claimed, MPEP 2106.05(a) states that the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEON VIET Q NGUYEN/ Primary Examiner, Art Unit 2663