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 statements submitted on July 22, 2024 (3) have been considered by the examiner and made of record in the application file.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities:
On line 9 of claim 1, please replace “mode” with “model” as it appears applicant is referring to “vehicle model” which is recited previously.
On line 10 of claim 8, please replace “mode” with “model” as it appears applicant is referring to “vehicle model” which is recited previously.
On line 10 of claim 15, please replace “mode” with “model” as it appears applicant is referring to “vehicle model” which is recited previously.
Appropriate correction is required.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson et al. (US Patent 10,949,814 B1, hereinafter Nelson) in view of Wang et al. (US PGPUB 2018/0182039 A1, hereinafter Wang).
Consider claim 1 (and similarly applied to claims 8 and 15). Nelson discloses a method comprising:
processing a plurality of images of a vehicle captured from a plurality of viewpoints (fig. 3, column 17 lines 39-50, read as obtaining and processing a plurality of images of a damaged vehicle, where this includes perspective views, and/or angles of the vehicle or portions of the vehicle);
based on the plurality of images and the mappings, automatically detecting, using artificial intelligence, defects associated with the vehicle (fig. 3, column 9 lines 12-22, column 18 lines 48-67, read as the parts prediction model 30 may utilize one or more machine learning algorithms, statistical algorithms, rule-based logic techniques, and/or historical claim analyses in furtherance of analyzing the image attribute data. Based on the inputs (e.g., the independent variables of the model 30), the parts prediction model 30 determines one or more outputs (e.g., dependent variables of the model 30), including the predicted, identified, determined, generated, and/or created initial set of parts 28 corresponding to the damage indicated by the image attributes); and
using the vehicle mode, generating, based on detecting the defects, an automated vehicle inspection report including describing the detected defects and locations on the vehicle containing the defects based on the vehicle model (fig. 3, column 18 lines 61-67, column 19 lines 39-42, read as transforming the initial set of parts that are predicted to be needed to repair the vehicle into a jurisdictionally-based set of repairs (e.g. draft vehicle repair estimate) that are predicted to be needed to repair the damaged vehicle, where this transformation is based on the set of image attributes including the historical data that includes damaged vehicles depicted in historical images, such as make, model, year, year range, body style, vehicle owner name, street, city and state address, impact points, vehicle drivable condition, and/or other attributes).
Nelson further teaches a remote server system comprising one or more processors from claim 8 (column 7 lines 11-39, read as image processing system includes one or more processors may execute each set of instructions) and one or more non-transitory machine-readable media having instructions stored thereon for performing a method from claim 15 (column 7 lines 11-39, read as a respective set of computer-executable instructions stored on one or more tangible, non-transitory computer-readable storage media).
Nelson substantially discloses the claimed invention but fails to explicitly teach determining, using the plurality of images, a respective mapping between each of a plurality of viewpoints of the vehicle and a vehicle model representing the vehicle, each mapping identifying a location on the vehicle model corresponding with a portion of the vehicle captured in the respective viewpoint.
However, Wang teaches determining, using the plurality of images, a respective mapping between each of a plurality of viewpoints of the vehicle and a vehicle model representing the vehicle, each mapping identifying a location on the vehicle model corresponding with a portion of the vehicle captured in the respective viewpoint (paragraph 75, read as based on the preset number of damage assessing images of the front portion of the vehicle, the model training module 100 generates the analyzing model for determining that the damage portion in the damage assessing images is the front portion of the vehicle; based on the preset number of damage assessing images of the side surface of the vehicle, the model training module 100 generates the analyzing model for determining that the damage portion contained in the damage assessing images is the side surface of the vehicle; based on the preset number of the damage assessing mages of the rear portion of the vehicle, the model training module 100 generates the analyzing model for determining that the damage portion in the damage assessing images is the rear portion of the vehicle; based on the preset number of the damage assessing mages of the whole vehicle, the model training module 100 generates the analyzing model for determining that the damage portion contained in the damage assessing images is the whole vehicle).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Wang into the invention of Nelson in order to provide a less time consuming and more efficient and accurate means for detecting tampered images.
Consider claim 2 and as applied to claim 1. The combination of Nelson and Wang discloses wherein the report comprises an estimate of damage to the vehicle classified by type, location and severity (Nelson; column 21 lines 24-37).
Consider claim 3 and as applied to claim 1. The combination of Nelson and Wang discloses before processing the plurality of images, validating that the images meet technical, business and/or anti-fraud standards (Wang; paragraphs 86, 107).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Wang into the invention of Nelson in order to provide a less time consuming and more efficient and accurate means for detecting tampered images.
Consider claim 4 and as applied to claim 1. The combination of Nelson and Wang discloses wherein the plurality of images are not subject to capture conditions (Nelson; column 17 lines 39-51).
Consider claim 5 and as applied to claim 1. The combination of Nelson and Wang discloses wherein the images are captured via a smartphone or a fixed camera (Nelson; column 35 lines 45-50).
Consider claim 6 and as applied to claim 1. The combination of Nelson and Wang discloses wherein the mapping and detection of defects occurs at a pixel-by-pixel level (Nelson; column 18 lines 24-38).
Consider claim 7 and as applied to claim 1. The combination of Nelson and Wang discloses causing the automated report submitted to an insurance claim processing system (Nelson; column 21 lines 32-37).
Consider claim 9 and as applied to claim 8. The combination of Nelson and Wang discloses wherein the report comprises an estimate of damage to the vehicle classified by type, location and severity (Nelson; column 21 lines 24-37).
Consider claim 10 and as applied to claim 8. The combination of Nelson and Wang discloses the one or more processors further configurable to cause: before processing the plurality of images, validating that the images meet technical, business and/or anti-fraud standards (Wang; paragraphs 86, 107).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Wang into the invention of Nelson in order to provide a less time consuming and more efficient and accurate means for detecting tampered images.
Consider claim 11 and as applied to claim 8. The combination of Nelson and Wang discloses wherein the plurality of images are not subject to capture conditions (Nelson; column 17 lines 39-51).
Consider claim 12 and as applied to claim 8. The combination of Nelson and Wang discloses wherein the images are captured via a smartphone or a fixed camera (Nelson; column 35 lines 45-50).
Consider claim 13 and as applied to claim 8. The combination of Nelson and Wang discloses the one or more processors further configurable to cause: causing the automated report submitted to an insurance claim processing system (Nelson; column 21 lines 32-37).
Consider claim 14 and as applied to claim 8. The combination of Nelson and Wang discloses wherein the mapping and detection of defects occurs at a pixel-by-pixel level (Nelson; column 18 lines 24-38).
Consider claim 16 and as applied to claim 15. The combination of Nelson and Wang discloses wherein the report comprises an estimate of damage to the vehicle classified by type, location and severity (Nelson; column 21 lines 24-37).
Consider claim 17 and as applied to claim 15. The combination of Nelson and Wang discloses the method further comprising: before processing the plurality of images, validating that the images meet technical, business and/or anti-fraud standards (Wang; paragraphs 86, 107).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Wang into the invention of Nelson in order to provide a less time consuming and more efficient and accurate means for detecting tampered images.
Consider claim 18 and as applied to claim 15. The combination of Nelson and Wang discloses wherein the plurality of images are not subject to capture conditions (Nelson; column 17 lines 39-51).
Consider claim 19 and as applied to claim 15. The combination of Nelson and Wang discloses wherein the images are captured via a smartphone or a fixed camera (Nelson; column 35 lines 45-50).
Consider claim 20 and as applied to claim 15. The combination of Nelson and Wang discloses wherein the mapping and detection of defects occurs at a pixel-by-pixel level (Nelson; column 18 lines 24-38).
Relevant Prior Art Directed to State of Art
Knuffman et al. (US Patent 10,497,108 B1) is relevant prior art not applied in the rejections above. Knuffman discloses machine learning involving identifying and recognizing patterns in existing images of damages of vehicles and/or defects of vehicles in order to facilitate making predictions for subsequent images of subsequent vehicles.
Li et al. (US PGPUB 2018/0260793 A1) is relevant prior art not applied in the rejections above. Li discloses one or more images of a damaged vehicle from a client computing device; performing computerized image processing based on the one or more images to generate one or more damage detection images, wherein each damage detection image is a two-dimensional (2D) image that includes indications of areas of damage to the vehicle in the damage detection image; mapping the one or more damage detection images to a three-dimensional (3D) model of the vehicle to generate a damaged 3D model that indicates area of the vehicle that are damaged; and, calculating an estimated repair cost for the vehicle based on the damaged 3D model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER M BRANDT whose telephone number is (571)270-1098. The examiner can normally be reached Mon - Fri 8:00-5:00.
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/CHRISTOPHER M BRANDT/Primary Examiner, Art Unit 2645 July 23, 2026
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