Office Action Predictor
Last updated: April 15, 2026
Application No. 18/545,110

SYSTEMS AND METHODS FOR AUTOMATIC THREE-DIMENSIONAL OBJECT DETECTION AND ANNOTATION

Non-Final OA §101§102§103
Filed
Dec 19, 2023
Examiner
CRUZ, IRIANA
Art Unit
2681
Tech Center
2600 — Communications
Assignee
Torc Robotics, INC.
OA Round
1 (Non-Final)
81%
Grant Probability
Favorable
1-2
OA Rounds
2y 9m
To Grant
90%
With Interview

Examiner Intelligence

Grants 81% — above average
81%
Career Allow Rate
590 granted / 726 resolved
+19.3% vs TC avg
Moderate +9% lift
Without
With
+8.6%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
48 currently pending
Career history
774
Total Applications
across all art units

Statute-Specific Performance

§101
10.5%
-29.5% vs TC avg
§103
53.9%
+13.9% vs TC avg
§102
24.2%
-15.8% vs TC avg
§112
8.8%
-31.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 726 resolved cases

Office Action

§101 §102 §103
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 . Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1, 9 and 17 recite “determining a two-dimensional boundary of the three-dimensional object contained in an image”, “determining a subset of points of the points contained within the two-dimensional boundary” and assigning a unique identifier to the subset of points contained within the two-dimensional boundary”, this limitations recite mental process of evaluation since an user can look at two-dimensional and three-dimensional images and observe and judge thew subsets, points and boundaries and assign a unique identifier to them. This judicial exception is not integrated into a practical application because the capturing of an image using a camera and capturing a point cloud representation using LIDAR is mere instruction to apply an exception (see MPEP 2106.05(f)) wherein the camera and the LIDAR system are disclosed at a high level of generality and they mere gather data to present to the user. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements as disclosed do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity described a high level of generality. Claims 2, 10 and 18 are dependent on claims 1, 9 and 17 and includes all the limitations of claims 1, 9 and 17. Therefore, claims 2, 10 and 18 recites the same abstract idea of claims 1, 9 and 17. The claim recites the additional limitation of “determining a three-dimensional boundary of the three-dimensional object using extrema of the subset of points”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical concepts, therefore, does not amount to significantly more than the abstract idea. Claims 3 and 16 are dependent on claims 1 and 9 and includes all the limitations of claims 1 and 9. Therefore, claims 3 and 16 recites the same abstract idea of claims 1 and 9. The claim recites the additional limitation of “training a machine learning model using the three-dimensional object boundary and the unique identifier”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea. Claims 4, 11 and 19 are dependent on claims 1, 9 and 17 and includes all the limitations of claims 1, 9 and 17. Therefore, claims 4, 11 and 19 recites the same abstract idea of claims 1, 9 and 17. The claim recites the additional limitation of “wherein the three-dimensional boundary of the three-dimensional object includes a cuboid having six facets”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea. Claims 5, 12 and 20 are dependent on claims 1, 9 and 17 and includes all the limitations of claims 1, 9 and 17. Therefore, claims 5, 12 and 20 recites the same abstract idea of claims 1, 9 and 17. The claim recites the additional limitation of “wherein the LiDAR system includes at least one of a laser source and a detector”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea. Claims 6 and 13 are dependent on claims 1 and 9 and includes all the limitations of claims 1 and 9. Therefore, claims 6 and 13 recite the same abstract idea of claims 1 and 9. The claim recites the additional limitation of “wherein determining the two-dimensional boundary of the three-dimensional object includes determining an instance segmentation two-dimensional boundary”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical concepts, therefore, does not amount to significantly more than the abstract idea. Claims 7 and 14 are dependent on claims 1 and 9 and includes all the limitations of claims 1 and 9. Therefore, claims 7 and 14 recite the same abstract idea of claims 1 and 9. The claim recites the additional limitation of “wherein the unique identifier corresponds to a category of objects”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea. Claims 8 and 15 are dependent on claims 1 and 9 and includes all the limitations of claims 1 and 9. Therefore, claims 8 and 15 recite the same abstract idea of claims 1 and 9. The claim recites the additional limitation of “points contained in the point cloud each include three-dimensional coordinates and an intensity value”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical concepts, therefore, does not amount to significantly more than the abstract idea. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 1, 5-9, 12-15, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mahieu et al. (US 2025/0298135 A1). With respect to Claim 1, Mahieu’135 shows a method for automatically digitally annotating a three-dimensional object in a scene (paragraph [0015] annotate objects within two-dimensional images), the method comprising: capturing an image of the scene using a camera (paragraph [0023] two-dimensional images captured by image capturing devices (e.g., cameras)); capturing a point cloud representing the scene using a LiDAR system (paragraphs [0022] and [0025] lidar data captured, receive the lidar data as a single lidar point cloud); determining a two-dimensional boundary of the three-dimensional object contained in the image (paragraphs [0042]-[0044] objects within the two-dimensional images may be annotated by human and/or machine labelers. As illustrated in box 108, the object 112 may include an annotation 114, representative of a static object, figure 1); determining a subset of points of the point cloud contained within the two-dimensional boundary (figure 2 step 204); and assigning a unique identifier to the subset of points contained within the two-dimensional boundary (paragraphs [0015], [0019], [0028]-[0029] and [0058] identifying lidar points that are associated with static objects and using such lidar points to annotate objects within two-dimensional images). With respect to Claim 5, Mahieu’135 shows the method of Claim 1, wherein the LiDAR system includes at least one of a laser source and a detector (paragraph [0017] The sensor data, which may include image data, radar data, lidar data, time-of-flight data, etc., may be analyzed by the autonomous vehicle to detect and classify various objects within the operating environment ). With respect to Claim 6, Mahieu’135 shows the method of Claim 1, wherein determining the two-dimensional boundary of the three-dimensional object includes determining an instance segmentation two-dimensional boundary (paragraphs [0017]-[0018] perform semantic and/or instance segmentation of the objects). With respect to Claim 7, Mahieu’135 shows the method of Claim 1, wherein the unique identifier corresponds to a category of objects (paragraph [0091] object type). With respect to Claim 8, Mahieu’135 shows the method of Claim 1, wherein points contained in the point cloud each include three-dimensional coordinates and an intensity value (paragraphs [0098] intensity information (e.g., lidar information, radar information, and the like) and map may include a three-dimensional mesh of the environment, and paragraph [0134] lidar points to a global reference frame (e.g., global coordinate frame)). With respect to Claims 9 and 17, rejection analogous to those presented for claim 1, are applicable. With respect to Claims 12 and 20, rejection analogous to those presented for claim 5, are applicable. With respect to Claim 13, rejection analogous to those presented for claim 6, are applicable. With respect to Claim 14, rejection analogous to those presented for claim 7, are applicable. With respect to Claim 15, rejection analogous to those presented for claim 8, are applicable. Claim Rejections - 35 USC § 103 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 2-4, 10-11, 16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mahieu et al. (US 2025/0298135 A1) in view of Roy Chowdhury et al. (US 2020/0134372 A1). With respect to Claim 2, Mahieu’135 does not specifically show shows the method of Claim 1 further comprising determining a three-dimensional boundary of the three-dimensional object using extrema of the subset of points. Roy’372 shows the method of Claim 1 further comprising determining a three-dimensional boundary of the three-dimensional object using extrema of the subset of points (paragraphs [0007] and [0029] Fitting the three-dimensional bounding box around each of the one or more object clusters representing the one or more objects includes estimation of the point cloud cluster center as the center of the 3-D bounding box, followed by estimation of bounding box extrema positions in the x, y, and z axes, which is then followed by estimation of eight 3-D corner points). At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Mahieu’135 to include determining a three-dimensional boundary of the three-dimensional object using extrema of the subset of points method taught by Roy’372. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to provide fast and accurate annotation cluster pre-proposal labels based on the feature-based detection of similar objects in already-annotated frames (paragraph [0025]). With respect to Claim 3, the combination of Mahieu’135 and Roy’372 shows the method of Claim 2 further comprising training a machine learning model using the three-dimensional object boundary and the unique identifier (in Mahieu’135: paragraphs [0018] and [0037] machine learning models configured to perform various object detection functionality, such as object identification, classification, instance segmentation, semantic segmentation, object tracking, and the like, may be implemented using artificial neural networks and trained with model training data). With respect to Claim 4, the combination of Mahieu’135 and Roy’372 shows the method of Claim 2, wherein the three-dimensional boundary of the three-dimensional object includes a cuboid having six facets (in Mahieu’135: paragraphs [0027] and [0054] the object manager may have a total of six annotated images. Further, the object manager may project a lidar point into each of the six annotated image planes, figure 1 230 ). With respect to Claims 10 and 18, rejection analogous to those presented for claim 2, are applicable. With respect to Claim 16, rejection analogous to those presented for claim 3, are applicable. With respect to Claims 11 and 19, rejection analogous to those presented for claim 4, are applicable. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Svetal (US 2015/0347801 A1): paragraphs [0090]-[0093] annotated image includes an outline surrounding an exception object that and each of the DoG images is inspected to identify the pixel extrema including minima and maxima corresponds to a three-dimensional model of the object generated by the object measurement subsystem 204. Lin et al. (US 2024/0005547 A1): shows in paragraphs [0071]-[0073] instance-specific 3D CAD model of the object at training, annotation, and/or inference time, at least one example represents an object as a set of vertex key points from its 3D cuboid projected onto the image plane. Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRIANA CRUZ whose telephone number is (571)270-3246. The examiner can normally be reached 10-6. 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, Akwasi M. Sarpong can be reached at (571) 270-3438. 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. /IRIANA CRUZ/Primary Examiner, Art Unit 2681
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Prosecution Timeline

Dec 19, 2023
Application Filed
Dec 20, 2025
Non-Final Rejection — §101, §102, §103
Mar 20, 2026
Applicant Interview (Telephonic)
Mar 20, 2026
Examiner Interview Summary
Mar 26, 2026
Response Filed

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
81%
Grant Probability
90%
With Interview (+8.6%)
2y 9m
Median Time to Grant
Low
PTA Risk
Based on 726 resolved cases by this examiner. Grant probability derived from career allow rate.

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