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 6/12/2024 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 § 102
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 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 1-20 are rejected under 35 U.S.C. 102a1 as being anticipated by the NPL article to Fu et al, titled “Semantic Scene Completion through Multi-level Feature Fusion,” hereinafter referred to as Fu et al.
As per claim 1,Fu et al teaches a computer-implemented method comprising: receiving a set of digital images (Section IIIA), the set of digital images displaying at least an environment and a set of objects, see Fig. 2; generating, via a first machine learning system, feature maps using the set of digital images (Section IIIB, “generate a subset of input points (key points) with high-dimensional feature); generating, via a second machine learning system, object boundary data of the set of objects using the feature maps (Section IIIB, “These features are then aggregated and fed into another MLP to generate final proposals (objectness score, bounding box parameters, and classification score)”); generating three-dimensional (3D) feature volume data using the feature maps (Section IIIC, 2D-3D projection with Vtsdf and Vrgb); generating a coarse occupancy map using the 3D feature volume data (see final sentence of section IIIC - “coarse scene completion result Scoarse), the coarse occupancy map having a first resolution of a first range, the coarse occupancy map including the environment and the set of objects (see Fig 2); generating surface data of the set of objects using the object boundary data and the 3D feature volume data (Section IIID where further feature refinement of the segmented objects is performed in part by considering the bounding box data), the surface data having a second resolution of a second range, the second range being different than the first range (section IIID, where S”i is embedded back into Scoarse in place of Si to get a final result, see Fig 3); and generating a hybrid occupancy map by combining the coarse occupancy map and the surface data, the hybrid occupancy map displaying the environment with the first resolution and the set of objects with the second resolution (resulting in 3D hybrid occupancy map with objects being refined, resulting in higher resolution objects than the surrounding area of the map, see Section I, paragraph 3).
As per claim 2, Fu et al teaches the computer-implemented method of claim 1, wherein the second machine learning system includes a region proposal network (RPN) that generates the object boundary data using the feature maps (Section IIIB, the center of certain object is learned using a voting module based on a multi-layer perceptron).
As per claim 3, Fu et al teaches the computer-implemented method of claim 1, wherein the coarse occupancy map is generated via a third machine learning system that decodes the 3D feature volume data (Section IIIC, final sentence “decoder”).
As per claim 4, Fu et al teaches the computer-implemented method of claim 1, wherein the surface data is generated via another machine learning system using the object boundary data and the 3D feature volume data (Section IIID and the Geometric refinement model).
As per claim 5, Fu et al teaches the computer-implemented method of claim 4, wherein the another machine learning system includes a series of transformation matrices that generate the surface data of the set of objects (Section IIID, Si is up sampled with trilinear interpolation and three SAB blocks, see Fig 3).
As per claim 6, Fu et al teaches the computer-implemented method of claim 1, wherein the second range is greater than the first range such that the second resolution of the set of objects is greater than the first resolution of the environment (Section I, third paragraph).
As per claim 7, Fu et al teaches the computer-implemented method of claim 1, further comprising: controlling an actuator using the hybrid occupancy map, wherein the actuator is a component of a vehicle (Section I, first paragraph - method is implemented in robot navigation and intelligent robots which would inherently have actuators for autonomous control within the 3D environment).
Claims 8 and 15 are rejected for reasoning, mutatis mutandis, as that of claim 1 above. Furthermore, see Section IVB for the processor (GPU and inherent memory storing the MFFNet algorithm).
Claims 9 and 16 are rejected for reasoning, mutatis mutandis, as that of claim 2 above.
Claims 10 and 17 are rejected for reasoning, mutatis mutandis, as that of claim 3 above.
Claims 11 and 18 are rejected for reasoning, mutatis mutandis, as that of claim 4 above.
Claims 12 and 19 are rejected for reasoning, mutatis mutandis, as that of claim 5 above.
Claims 13 and 20 are rejected for reasoning, mutatis mutandis, as that of claim 6 above.
Claim 14 is rejected for reasoning, mutatis mutandis, as that of claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references cited all relate to the state-of-the-art surround semantic scene completion for creating a 3D map with semantic occupancy.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID OMETZ whose telephone number is (571)272-7593. The examiner can normally be reached M-F, 8am-4pm.
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, Sumati Lefkowitz can be reached at 571-272-3638. 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.
DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672