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 § 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, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatfield et al. (PGPUB Document No. US 2022/0138860) in view of Li et al. (PGPUB Document No. US 2018/0260793).
Regarding claim 8, Chatfield teaches a non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations, comprising:
Capturing, as a captured set of images, a set of images of a vehicle, wherein the set of images are captured from different viewpoints around the vehicle (capturing images of the vehicle from varying angles as demonstrated in FIG.3A-C (Chatfield: 0019, 0021, 0023));
Mapping, as a mapped image set, the captured set of images using an image classification model (mapping regions of the images to vehicle parts using deep learning classifiers (Chatfield: 0024-0026), wherein “the classifiers utilized may be trained to evaluate multiple images, such as evaluating multi-frame portions of video files” (Chatfield: 0028));
Performing image-level damage detection on each image in the aligned image set to estimate a damage probability for each pixel in each image; and predicting part damage and severity for each image (dynamic classifiers used in the application make damage assessments (such as whether a part is damaged or not, whether a part should be repaired or replaced, labor hours, or damage severity level), and, for these assessments, generate confidence levels associated with these assessments (Chatfield: 0036). The Examiner submits that damage assessment based on the “multiple images” (Chatfield: 0028) is consistent with pixels that are part of the images also being assessed for damage, as presently claimed).
However, Chatfield does not expressly teach but Li teaches aligning, as an aligned image set, the mapped image set onto a three-dimensional vehicle model for the vehicle (Some embodiments then project the images onto the 3D model of the vehicle using the camera angles determined during the alignment process (Li: 0164, 0249-0250, 0287, FIG.27));
Organizing the captured set of images (applying the damage classifying (“organizing”) AI of Chatfield to the set of images captured by Li (Li: 0233, FIG.26, step 2606));
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of an ordinary skill in the art to create and display a 3D model from the images captured by Chatfield according to the teachings of Li, because this enables applying the teachings of Li for assessing damages only from a set of still images.
Claim(s) 1 is a corresponding method claim(s) of claim(s) 8. The limitations of claim(s) 1 are substantially similar to the limitations of claim(s) 8. Therefore, it has been analyzed and rejected substantially similar to claim(s) 1.
Claim(s) 15 is a corresponding computer system claim(s) of claim(s) 8. The limitations of claim(s) 15 are substantially similar to the limitations of claim(s) 8. Therefore, it has been analyzed and rejected substantially similar to claim(s) 15. Note, the combined teachings above teach discloses a computing system (Chatfield: 00016, FIG.2).
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatfield in view of Li as applied to the claim(s) above, and further in view of Murez (PGPUB Document No. US 2021/0279943).
Regarding claim 11, the combined teachings above do not expressly teach but Murez teaches the non-transitory, computer-readable medium of claim 8, wherein the aligning the mapped image set onto a three-dimensional vehicle model for the vehicle, comprises:
Discretizing a surface of the three-dimensional vehicle model, wherein each discretized point is referred to as a voxel (features projected into a 3D voxel volume (Murez: 0024));
Learning a high-dimensional embedding to represent each voxel and each image pixel, wherein a high similarity score in an embedding space yields a pixel-voxel correspondence (using convolutional neural network (high-dimensional embedding), features are projected (“embedding”) into a 3D voxel volume (Murez: 0024));
Determining an optimal camera pose using at least the pixel-voxel correspondence (the disclosed camera intrinsics and extrinsics (Murez: 0024) are known in the art as parameters comprising pose information);
And determining a mapping between each image pixel and each voxel using ray tracing with the optimal camera pose (“the extracted features from each frame are then back-projected using known camera intrinsics and extrinsics into a 3D voxel volume wherein each pixel of the voxel volume is mapped to a ray in the voxel volume” (Murez: 0024)).
Claims 4 and 18 are similar in scope to claim 11.
Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatfield in view of Li as applied to the claim(s) above, and further in view of Bouette et al. (PGPUB Document No. US 2023/0377047).
Regarding claim 12, the combined teachings above teach the non-transitory, computer-readable medium of claim 8, wherein aligning, as an aligned image set, the mapped image set onto a three-dimensional vehicle model for the vehicle yields a part correspondence for each pixel in each image of the aligned image set (“classifying AI displays a list 315 of parts of the vehicle having a greatest probability of being classified correctly from the image.” (Chatfield: 0024)) and artificial intelligence used to estimate the damage probability for each pixel in each image (providing a real-time damage estimate using an artificial intelligence (AI) (Chatfield: 0011)).
However, the combined teachings above do not expressly teach the artificial intelligence being a neural network model (“convolutional neural network is trained to recognize and assess damage” (Bouette: 0048)).
The combined teachings above contained a device which differed the claimed process by the substitution of the steps of using artificial intelligence for predicting damage.
Bouette teaches the substituted step of using a neural network for predicting damage.
Both methods disclosed by the combined teachings and Bouette were known in the art to effectively assess damage.
The artificial intelligence teaching of the combined teachings above could have been substituted with the neural network teaching of Bouette.
The results would have been predictable and resulted in equally predicting damage. Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 5 and 19 are similar in scope to claim 12.
Allowable Subject Matter
Claims 2, 3, 6, 7, 9, 10, 13, 14, 16, 17, 19 and 20 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David H Chu whose telephone number is (571)272-8079. The examiner can normally be reached M-F: 9:30 - 1:30pm, 3:30-8:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel F Hajnik can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID H CHU/Primary Examiner, Art Unit 2616