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 INTERPRETATION
Claim limitation “vehicle image acquisition unit, component identification unit, damage identification unit, fusion unit” has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “unit” coupled with functional language “…configured to…” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 20 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: physical units as described in pages 17-21 of the specification and excluding any software or non physical units.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-3, 15-18, 20 and 30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu (U.S. PG-PUB NO. 2020/0210786).
-Regarding claim 1, Xu discloses a method for assessing damage of vehicle (see abstract), comprising: acquiring vehicle images (step 21, FIG. 2); processing the vehicle images by a first model to obtain a component identification result, wherein the component identification result comprises a component name, and at least one of a component region and a component mask of a vehicle component (step 23, FIG. 2, paragraph 92, 98-99); processing the vehicle images by a second model to obtain a damage identification result, wherein the damage identification result comprises a damage morphology, and at least one of a damage region and a damage region mask of the vehicle component (step 22, FIG. 2, paragraph 91); and fusing the component identification result and the damage identification result to obtain a damage assessment result (step 24, FIG. 2, paragraph 104-106).
-Regarding claim 2, Xu further discloses the first model comprises at least one of: a component detection branch, configured to perform component detection processing on the vehicle images to acquire the component region; and a component segmentation branch, configured to perform component segmentation processing on the vehicle images to acquire the component mask; and the first model further comprises: a component identification branch, configured to perform component identification processing on the vehicle images to acquire the component name (paragraph 97-99).
-Regarding claim 3, Xu further discloses the second model comprises at least one of: a damage detection branch, configured to perform damage detection processing on the vehicle images to acquire the damage region; and a damage segmentation branch, configured to perform damage segmentation processing on the vehicle images to acquire the damage region mask; and the second model further comprises: a damage identification branch, configured to perform damage identification processing on the vehicle images to acquire the damage morphology (paragraph 90-95).
-Regarding claim 15, Xu further discloses fusing the component identification result and the damage identification result to obtain the damage assessment result comprises: calculating an Intersection Over Union (IOU) value between the component region and the damage region, or calculating an IOU value between the component region and the damage region mask, or calculating an IOU value between the component mask and the damage region, or calculating an IOU value between the component mask and the damage region mask; judging whether a matching is successful according to the IOU value, and if the matching is successful, determining that the vehicle component is a damaged component, and determining the damage morphology so as to obtain the damage assessment result; and if the matching is unsuccessful, determining that the vehicle component is not damaged (paragraph 129-133).
-Regarding claim 16, Xu further discloses judging whether the matching is successful according to the IOU value comprises: judging whether the IOU value exceeds a second threshold, and if it is determined that the IOU value exceeds the second threshold, indicating that the matching is successful, and if it is determined that the IOU value does not exceed the second threshold, indicating that the matching is unsuccessful; or judging whether the IOU value exceeds the second threshold and whether the IOU value is the maximum and if it is determined that the IOU value exceeds the second threshold and the IOU value is the maximum, indicating that the matching is successful; if it is determined that the IOU value does not exceed the second threshold or the IOU value is not the maximum, indicating that the matching is unsuccessful (paragraph 129-133).
-Regarding claim 17, Xu further discloses determining the damage morphology comprises: determining the damage morphology with a most serious damage degree as the damage morphology of the damaged component, so that the damaged component and the damage morphology are determined, and the damage assessment result is obtained (step 24, FIG. 2, paragraph 104-106).
-Regarding claim 18, Xu further discloses determining the damage morphology comprises: fusing the damage identification results of multiple of the vehicle images, and on a basis of calculation and comparison of weights of the damage morphologies, obtaining and determining the damage morphology corresponding to the damaged component (step 24, FIG. 2, paragraph 104-106).
-Regarding claim 20, Xu discloses an apparatus for assessing damage of vehicle (see abstract), comprising: a vehicle image acquisition unit, configured to acquire vehicle images (step 21, FIG. 2); a component identification unit, configured to process the vehicle images by a first model to obtain a component identification result, wherein the component identification result comprises a component name, and at least one of a component region and a component mask of a vehicle component (step 23, FIG. 2, paragraph 92, 98-99); a damage identification unit, configured to process the vehicle images by a second model to obtain a damage identification result, wherein the damage identification result comprises a damage morphology, and at least one of a damage region and a damage region mask of the vehicle component (step 22, FIG. 2, paragraph 91); and a fusion unit, configured to fuse the component identification result and the damage identification result to obtain a damage assessment result (step 24, FIG. 2, paragraph 104-106).
-Regarding claim 30, Xu further discloses A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises a program which is stored, wherein when running, the program controls a device where the non-transitory computer-readable storage medium is located to execute the method for assessing the damage of the vehicle according to claims 1 (paragraph 338-339).
Claim Rejections - 35 USC § 103
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) 4-6 and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (U.S. PG-PUB NO. 2020/0210786) in view of Zhang (U.S. PG-PUB NO. 2018/0293664).
-Regarding claim 4, Xu is silent to teaching that after acquiring the vehicle images, the method for assessing the damage of the vehicle further comprises: evaluating a quality of the vehicle images to obtain an evaluation result; according to the evaluation result, classifying the vehicle images according to preset categories, selecting vehicle images of required categories and inputting the vehicle images of required categories to the first model and the second model; and if the vehicle images of required categories do not exist, stopping assessing the damage of the vehicle or returning to continue to acquire the vehicle image. However, the claimed limitation is well known in the art as evidenced by Zhang.
In the same field of endeavor, Zhang teaches after acquiring the vehicle images, the method for assessing the damage of the vehicle further comprises: evaluating a quality of the vehicle images to obtain an evaluation result; according to the evaluation result, classifying the vehicle images according to preset categories, selecting vehicle images of required categories and inputting the vehicle images of required categories to the first model and the second model; and if the vehicle images of required categories do not exist, stopping assessing the damage of the vehicle or returning to continue to acquire the vehicle image (paragraph 27).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Xu with the teaching of Zhang in order to provide accurate and reliable damage determination result.
-Regarding claim 5, the combination further discloses the preset categories are determined according to a distance from a photographing point to the damage region or according to the number of components in the vehicle images (Xu, paragraph 124-129).
-Regarding claim 6, the combination further discloses the preset categories comprise: an unqualified category, a qualified category, an ultra-close-shot category, a close-shot category, a medium-shot category, and a long-shot category (Xu, FIG. 4a-4d and FIG. 7a-7d).
-Regarding claim 10, the combination further discloses the vehicle images of the medium-shot category are inputted to the first model; and the vehicle images of the close-shot category are inputted to the second model (Xu, paragraph 128-129).
-Regarding claim 11, the combination further discloses at least one of a following is voted by a multi-model fusion technique: the component identification result and the damage identification result (Xu, paragraph 128-129).
-Regarding claim 12, the combination further discloses training the first model; wherein correlation constraints of the vehicle components are added in a training process, wherein the correlation constraints comprise at least one of: spatial position relationships between different vehicle components, and direction relationships between different vehicle components (Xu, paragraph 128-129).
-Regarding claim 13, the combination further discloses the first model or the second model comprises: an RPN network, configured to extract candidate frames in the vehicle images; and align the candidate frames and extract candidate frame features (Xu, paragraph 128-129).
Claim(s) 7-9 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (U.S. PG-PUB NO. 2020/0210786) in view of Zhang (U.S. PG-PUB NO. 2018/0293552).
-Regarding claim 7, Xu is silent to teaching that after the vehicle images are obtained, the method for assessing the damage of the vehicle further comprises: removing vehicle images having a similarity greater than a first threshold. However, the claimed limitation is well known in the art as evidenced by Zhang.
In the same field of endeavor, Zhang teaches after the vehicle images are obtained, the method for assessing the damage of the vehicle further comprises: removing vehicle images having a similarity greater than a first threshold (paragraph 38).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Xu with the teaching of Zhang in order to provide accurate and reliable, vehicle loss assessment processing.
-Regarding claim 8, the combination further discloses after acquiring the vehicle images, the method for assessing the damage of the vehicle further comprises: performing enhancement processing on the vehicle images to obtain enhanced vehicle images, and inputting the enhanced vehicle images to the first model and the second model (Zhang, preprocessed images, paragraph 62). Although the combination does not specify the enhancement processing comprises: reflection removal, shadow removal, denoising and night scene enhancement, the examiner takes official notice that reflection removal, shadow removal, denoising and night scene enhancement are well known image preprocessing enhancements without any additional technical details. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to include the enhancement processing in order to provide accurate and reliable, vehicle loss assessment processing.
-Regarding claim 9, the combination further discloses after acquiring the vehicle images, the method for assessing the damage of the vehicle further comprises: pre-processing the vehicle images, and inputting pre-processed vehicle images to the first model and the second model (Zhang, preprocessed images, paragraph 62). Although the combination does not specify the preprocessing comprises: performing scaling on the vehicle images and performing normalization processing on the vehicle images, the examiner takes official notice that performing scaling on the vehicle images and performing normalization processing on the vehicle images are well known image preprocessing enhancements without any additional technical details. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to include the enhancement processing in order to provide accurate and reliable, vehicle loss assessment processing.
-Regarding claim 14, the combination further discloses the candidate frames which are redundant are removed by non-maximum suppression (Zhang, paragraph 38).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/Primary Examiner, Art Unit 2664