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 .
Priority
Acknowledgement is made of the application’s status as a continuation of KR 10-2023-0196227
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 11/21/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claim(s) 1-2, 5, 6-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Deephanphongs (US 9317966 B1) in view of Lin (US 20210103726 A1) and in further view of Raskob (US 20210158609 A1).
Claims 3-4 and 8-9 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.
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) 1-2, 5, 6-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Deephanphongs (US 9317966 B1) in view of Lin (US 20210103726 A1) and in further view of Raskob (US 20210158609 A1).
Regarding claim 1, Deephanphongs discloses A method of generating a synthetic image using a satellite image, (Deephanphongs: Col 1 lines 11-15 “computer-aided design (CAD) tools enable users to define three-dimensional models, such as a three-dimensional model of a building. Photographic images of the building may be available from, for example, satellite…”) which is performed by a processor, the method comprising: (Deephanphongs: Col 3 lines 1-2 “Computer system 100, in general, will be an electronic device with one or more processors”)
applying a satellite image captured by a satellite to (Deephanphongs: Col 1 lines 14-15 “Photographic images of the building may be available from, for example, satellite,”) to infer a boundary of an upper surface (Deephanphongs: Fig. 2 and Col 9 lines 9-14 “in general image processing using techniques such as edge detection may be used in combination with the first camera parameters to establish a first feature in the first photographic image that corresponds to at least a portion of a roof of the building.”) and a boundary of a side surface of a building included in the satellite image; and (Deephanphongs: Fig. 2 and Col 9 lines 48-52 “In stage 570, a three-dimensional model of the building is constructed based on the height and the legal boundaries, wherein the legal boundaries specify, at least in part, a perimeter of the model and the height of the building specifies, at least in part, a height of the model.” )
generating a new building based on the inferred boundary of the upper surface and the inferred boundary of the side surface of the building (Deephanphongs: Col 1 line 52-27 “A three-dimensional model of the building is determined by combining the height and the legal boundaries of the lot. The legal boundaries specify, at least in part, a perimeter of the model and the height of the building specifies, at least in part, a height of the model.”)
Deephanphongs fails to specifically disclose based on artificial intelligence
a neural network
and synthesizing the new building into the satellite image.
In related art, Lin discloses based on artificial intelligence (Lin discloses learning a building's boundaries such as roof (¶24) and (¶22) while making use of deep learning techniques (¶27))
a neural network (Lin: ¶27 “the building segmentation module 110 may apply machine learning in the form of a deep learning neural network 402 (FIG. 4) having multiple layers.”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate the use of a neural network disclosed by Lin into the method of three-dimensional building modelling disclosed by Deephanphongs to aid in extracting features from the building present in the images such as the roof or height.
In related art Raskob discloses and synthesizing the new building into the satellite image. (Raskob: ¶81 “The DSM model of the building is provided in block 906 with the vegetation.” Raskob discloses including a three-dimensional model into a satellite image)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate including the generated building in a satellite image disclosed by Raskob into the method of three-dimensional building generation disclosed by Deephanphongs to assess the accuracy of the three-dimensional model by placing it within the satellite image.
Regarding claim 2, Deephanphongs, as modified by Lin and Raskob, disclose wherein further comprising generating an inferred position relationship between the upper surface and the side surface of the building based on the inferred boundary of the upper surface and the inferred boundary of the side surface of the building. (Deephanphongs: Fig. 4 and Col 8 lines 15-18 “Since feature 320A and 320B are both located on the roof of building 202A and the roof of building 202A is assumed to be flat, the physical height at which features 320A and 320B are located is also known.”)
Regarding claim 5, Deephanphongs, as modified by Lin and Raskob, disclose wherein further comprising rotating the new building synthesized into the satellite image (Raskob: ¶81 “The DSM model of the building is provided in block 906 with the vegetation.” Raskob discloses including a three-dimensional model into a satellite image) according to an inclination of the inferred boundary of the upper surface of the building. (Raskob: ¶58 “the stereo point clouds and resulting DSM are used to determine the building geometry, roof shape, and height.”)
Regarding claim 6, Deephanphongs discloses A device comprising: (Deephanphongs: Col 1 lines 33 “ a computer-implemented method,”)
a processor configured to execute instructions (Deephanphongs: Col 3 lines 21 “processor and memory for executing and storing instructions.”) for generating a synthetic image using a satellite image; and (Deephanphongs: Col 1 lines 11-15 “computer-aided design (CAD) tools enable users to define three-dimensional models, such as a three-dimensional model of a building. Photographic images of the building may be available from, for example, satellite…”)
a memory configured to store the instructions, (Deephanphongs: Col 3 lines 21 “processor and memory for executing and storing instructions.”)
wherein the instructions are implemented to apply a satellite image captured by a satellite (Deephanphongs: Col 1 lines 14-15 “Photographic images of the building may be available from, for example, satellite,”) to infer a boundary of an upper surface (Deephanphongs: Fig. 2 and Col 9 lines 9-14 “in general image processing using techniques such as edge detection may be used in combination with the first camera parameters to establish a first feature in the first photographic image that corresponds to at least a portion of a roof of the building.”) and a boundary of a side surface of a building included in the satellite image, (Deephanphongs: Fig. 2 and Col 9 lines 48-52 “In stage 570, a three-dimensional model of the building is constructed based on the height and the legal boundaries, wherein the legal boundaries specify, at least in part, a perimeter of the model and the height of the building specifies, at least in part, a height of the model.” ) and generate a new building based on the inferred boundary of the upper surface and the inferred boundary of the side surface of the building (Deephanphongs: Col 1 line 52-27 “A three-dimensional model of the building is determined by combining the height and the legal boundaries of the lot. The legal boundaries specify, at least in part, a perimeter of the model and the height of the building specifies, at least in part, a height of the model.”)
Deephanphongs fails to specifically disclose based on artificial intelligence
to a neural network
to synthetize the new building into the satellite image.
In related art, Lin discloses based on artificial intelligence (Lin discloses learning a building's boundaries such as roof (¶24) and (¶22) while making use of deep learning techniques (¶27))
to a neural network (Lin: ¶27 “the building segmentation module 110 may apply machine learning in the form of a deep learning neural network 402 (FIG. 4) having multiple layers.”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate the use of a neural network disclosed by Lin into the method of three-dimensional building modelling disclosed by Deephanphongs to aid in extracting features from the building present in the images such as the roof or height.
In related art Raskob discloses and to synthesize the new building into the satellite image. (Raskob: ¶81 “The DSM model of the building is provided in block 906 with the vegetation.” Raskob discloses including a three-dimensional model into a satellite image)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate including the generated building in a satellite image disclosed by Raskob into the method of three-dimensional building generation disclosed by Deephanphongs to assess the accuracy of the three-dimensional model by placing it within the satellite image.
Regarding claim 7, Deephanphongs, as modified by Lin and Raskob, disclose wherein the instructions are further implemented (Deephanphongs: Col 3 lines 21 “processor and memory for executing and storing instructions.”) to generate an inferred position relationship between the upper surface and the side surface of the building based on the inferred boundary of the upper surface and the inferred boundary of the side surface of the building. (Deephanphongs: Fig. 4 and Col 8 lines 15-18 “Since feature 320A and 320B are both located on the roof of building 202A and the roof of building 202A is assumed to be flat, the physical height at which features 320A and 320B are located is also known.”)
Regarding claim 10, Deephanphongs, as modified by Lin and Raskob, disclose wherein the instructions are further implemented to rotate the new building synthesized into the satellite image (Raskob: ¶81 “The DSM model of the building is provided in block 906 with the vegetation.” Raskob discloses including a three-dimensional model into a satellite image) according to an inclination of the inferred boundary of the upper surface of the building. (Raskob: ¶58 “the stereo point clouds and resulting DSM are used to determine the building geometry, roof shape, and height.”)
Allowable Subject Matter
Claims 3-4 and 8-9 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Simonelli (US 20250131710 A1) discloses Methods, non-transitory computer readable media, and building analysis systems are disclosed that analyze an overhead image to generate a building outline for a building associated with a property represented by the overhead image. The overhead image is included in imagery data obtained from an overhead imagery server based on a received request comprising a geographic location for the property. The building outline is then shifted or rotated. Segment(s) of the building outline are slid to match identified wall(s) of the building. The building outline is then modified based on property feature(s) detected based on an application of one or more trained machine learning classifiers to the overhead image. At least a portion of the overhead image is output with a graphical overlay comprising the building outline via a user interface in response to the received request.
Kozikowski (US 20220383436 A1) discloses A property inspection service hosted on a web-based server system. The server system receives a list of physical addresses each corresponding to different parcels. For each address, the server system obtains multiple images including overhead images and perspective view images. A first trained model analyzes selected overhead images individually to identify one or more building structures. A second trained model analyzes selected perspective view images individually to identify a primary building structure. A third trained model analyzes the selected overhead images and the perspective view images together in an integrated approach to identify attributes associated with identified building structure. A digital report is generated as a graphical user interface configured to display the selected images and the attributes associated the identified building structure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL KIM MAIDEN whose telephone number is (703)756-1264. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm.
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, Stephen Koziol can be reached at 4089187630. 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.
/MICHAEL KIM MAIDEN/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
Prosecution Insights