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 April 2, 2025 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Replacement Drawings
The drawings were received on April 2, 2025. These drawings are acceptable.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 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, 5-12 and 16-22 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Lin et al. (U.S. Patent Application Publication No. US 2021/0103726 A1) (hereafter referred to as “Lin”).
With regard to claim 1, Lin describes a processor in communication with a database (see Figure 3, element 326 and refer for example to paragraph [0043]), the processor receiving an aerial image and at least one heightmap (see Figure 3, elements 104 and 108, and refer for example to paragraphs [0022], [0025] and [0026]); merging the aerial image with the at least one heightmap to create a combined image (see Figure 3 and refer for example to paragraph [0027]); and processing the combined image using a computer vision model to detect one or more objects in the combined image (see Figure 3 and refer for example to paragraph [0029]).
As to claim 5, Lin describes wherein the processor merges the aerial image with the at least one heightmap by concatenating the aerial image with the heightmap to create the combined image (refer for example to paragraph [0037]).
In regard to claim 6, Lin describes wherein the computer vision model comprises a convolutional neural network (refer for example to paragraph [0027]).
With regard to claim 7, Lin describes wherein the detected one or more objects comprises one or more of a roof, a pool, a fence, or a boundary of a land property (refer for example to paragraphs [0019], [0032] and [0034]).
As to claim 8, Lin describes wherein the processor generates and places a bounding box or a polygon around each of the detected one or more objects in the image (refer for example to paragraphs [0019] and [0024]).
In regard to claim 9, Lin describes wherein the processor generates and assigns a structure classification to the bounding box or the polygon to indicate the structure of the object (refer for example to paragraphs [0019] and [0024]).
With regard to claim 10, Lin describes wherein the processor determines a geographic location of each of the one or more objects using two-dimensional spatial information of the aerial image and depth information of the heightmap (refer for example to paragraph [0024]).
As to claim 11, Lin describes wherein the processor stores data associated with the combined image including one or more of geographic coordinates, footprint polygons, bounding boxes, structure classifications, timestamps of the aerial image or the heightmap, or metadata in a geospatial database (refer for example to paragraphs [0019] and [0024]).
In regard to claim 12, Lin describes receiving by a processor an aerial image and at least one heightmap (see Figure 3, element 104, 108 and 326. and refer for example to paragraphs [0022], [0025], [0026] and [0043]); merging the aerial image with the at least one heightmap to create a combined image (see Figure 3 and refer for example to paragraph [0027]); and processing the combined image using a computer vision model executed by the processor to detect one or more objects in the combined image (see Figure 3 and refer for example to paragraph [0029]).
With regard to claim 16, Lin describes merging the aerial image with the at least one heightmap by concatenating the aerial image with the heightmap to create the combined image (refer for example to paragraph [0037]).
As to claim 17, Lin describes wherein the computer vision model comprises a convolutional neural network (refer for example to paragraph [0027]).
In regard to claim 18, Lin describes wherein the detected one or more objects comprises one or more of a roof, a pool, a fence, or a boundary of a land property (refer for example to paragraphs [0019], [0032] and [0034]).
With regard to claim 19, Lin describes generating a bounding box or a polygon around each of the detected one or more objects in the image (refer for example to paragraphs [0019] and [0024]).
As to claim 20, Lin describes generating and assigning a structure classification to the bounding box or the polygon to indicate the structure of the object (refer for example to paragraphs [0019] and [0024]).
In regard to claim 21, Lin describes determining a geographic location of each of the one or more objects using two-dimensional spatial information of the aerial image and depth information of the heightmap (refer for example to paragraph [0024]).
With regard to claim 22, Lin describes storing data associated with the combined image including one or more of geographic coordinates, footprint polygons, bounding boxes, structure classifications, timestamps of the aerial image or the heightmap, or metadata in a geospatial database (refer for example to paragraphs [0019] and [0024]).
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 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(a) 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.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-4 and 13-15 are rejected under 35 U.S.C. §103(a) as being unpatentable over Lin et al. (U.S. Patent Application Publication No. US 2021/0103726 A1) in view of Taylor et al. (U.S. Patent Application Publication No. US 2022/0027622 A1) (hereafter referred to as “Taylor (‘622)”).
The arguments advanced in section 7 above, as to the applicability of Lin, are incorporated herein.
Although Lin does not expressly describe the merging the aerial image with the at least one heightmap by aligning the heightmap with the aerial image, merging the aerial image with the at least one heightmap by mean shifting a plurality of values in the heightmap to zero and merging the aerial image with the at least one heightmap by resizing the heightmap to the size of the aerial image, such techniques are well known and widely utilized in the prior art.
Taylor discloses a system for detecting features in aerial images using disparity mapping and segmentation techniques (see Figures 1 and 6, and refer for example to the abstract) which describes a processor in communication with a database (see Figure 1, element 14 and refer for example to paragraphs [0035] and [0036]), the processor receiving an aerial image and at least one heightmap (see Figure 1, element 14, Figure 5, element 114, and refer for example to paragraphs [0035], [0036] and [0046], the disparity map in Taylor is a map providing elevation information of structures and corresponds to applicant’s heightmap); merging the aerial image with the at least one heightmap to create a combined image (refer for example to paragraph [0046]); and processing the combined image using a computer vision model to detect one or more objects in the combined image (refer to paragraphs [0053], [0055] and [0056]), and for merging the aerial image with the at least one heightmap by aligning the heightmap with the aerial image (refer for example to paragraphs [0046], [0047] and [0057]), merging the aerial image with the at least one heightmap by mean shifting a plurality of values in the heightmap to zero (refer for example to paragraphs [0046] and [0047]) and merging the aerial image with the at least one heightmap by resizing the heightmap to the size of the aerial image (refer for example to paragraphs [0046] and [0047]).
Given the teachings of the two references and the same environment of operation, namely that of systems for detecting structures using aerial images and heightmap data, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lin system in the manner described by Taylor (‘622) according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Taylor (‘622) (refer for example to paragraph [0006]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hsu, Taylor (‘872) and (‘980), Kottenstette and Bhattacharjee all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
July 10, 2026
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