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
Claims 15-17 are considered as independent claims because they simply refer to other claims as a matter of short-hand drafting technique.
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-6, 11, 15-17- is/are rejected under 35 U.S.C. 102(a(2) as being anticipated by Jiang (US 11,908,222).
As to claim 1, Jiang discloses a pedestrian detection method, comprising:
acquiring an image to be recognized (col. 3, line 24, col. 4, lines 35-36);
inputting the image to be recognized into a pre-trained multi-task recognition network, the multi-task recognition network comprising a backbone network, a pedestrian detection network and an attribute recognition network (col. 3, lines 13-58);
acquiring a backbone feature map according to the input image to be recognized based on the backbone network (col. 3, lines 49-58). ;
acquiring a predicted pedestrian location according to the backbone feature map based on the pedestrian detection network (col. 3, line 59 - col. 4, line 32);
acquiring a predicted pedestrian attribute according to the backbone feature map based on the attribute recognition network (col. 4, line 35 - col. 5, line 40); and
correlating the predicted pedestrian location and the predicted pedestrian attribute to output a detection result (col. 3, lines 29-46, col. 5, lines 38-40, col. 5, line 29 – col. 7, line 35).
As to claim 5, Jiang discloses a method for training a pedestrian detection model, comprising:
constructing a multi-task recognition network, the multi-task recognition network comprising a backbone network, a pedestrian detection network and an attribute recognition network (col. 3, lines 13-58, col. 7, lines 1-7);
acquiring a training set image (col. 6, lines 34-46); and
inputting the training set image into the multi-task recognition network for training to obtain the pedestrian detection model (col. 3, lines 42-58, col. 6, line 34 – col. 7, line 7); wherein:
the backbone network is configured for acquiring a backbone feature map according to the training set image (col. 3, lines 49-58);
the pedestrian detection network is configured for acquiring a predicted pedestrian location according to the backbone feature map (col. 3, line 59 - col. 4, line 32); and
the attribute recognition network is configured for acquiring a predicted pedestrian attribute according to the backbone feature map (col. 4, line 35 - col. 5, line 40).
As to claim 6, Jiang discloses the method for training a pedestrian detection model according to claim 5, wherein the inputting the training set image into the multi-task recognition network for training to obtain the pedestrian detection model, comprises:
acquiring a training location feature map and a training attribute feature map based on the training set image (col. 6, line 34 – col. 7, line 7);
calculating total loss according to the training location feature map and the training attribute feature map (col. 7, lines 5-35); and
updating preset parameters of the pedestrian detection model according to the total loss to obtain the pedestrian detection model (col. 7, lines 5-35).
As to claims 11, 15-17, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
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.
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.
Claim(s) 2-4, 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US 11,908,222) in view of Chen (US 2023/0085518).
As to claim 2, Jiang is silent regarding converging at least two backbone feature maps with different resolutions based on a first converged network of the pedestrian detection network to acquire a plurality of first converged feature maps, the plurality of first converged feature maps having different resolutions; and acquiring the predicted pedestrian location according to the plurality of first converged feature maps input based on a first task head network of the pedestrian detection network.
Chen teaches converging at least two backbone feature maps with different resolutions based on a first converged network of the pedestrian detection network to acquire a plurality of first converged feature maps, the plurality of first converged feature maps having different resolutions (Figs. 4, para. 0024, 0029, 0030); and acquiring the predicted pedestrian location according to the plurality of first converged feature maps input based on a first task head network of the pedestrian detection network (para. 0024, 0025, 0030).
It would have been obvious to one of ordinary skill in the art to incorporate Chen’s teachings into Jiang since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve performance.
As to claim 3, Jiang is silent regarding converging at least two backbone feature maps with different resolutions based on a second converged network of the attribute recognition network to acquire a plurality of second converged feature maps, the plurality of second converged feature maps having different resolutions; and acquiring the predicted pedestrian attribute according to the plurality of second converged feature maps input based on a second task head network of the attribute recognition network.
Chen teaches converging at least two backbone feature maps with different resolutions based on a second converged network of the attribute recognition network to acquire a plurality of second converged feature maps, the plurality of second converged feature maps having different resolutions (Figs. 4, para. 0024, 0029, 0030); and acquiring the predicted pedestrian attribute according to the plurality of second converged feature maps input based on a second task head network of the attribute recognition network (para. 0024, 0025, 0030).
It would have been obvious to one of ordinary skill in the art to incorporate Chen’s teachings into Jiang since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve performance.
As to claim 4, Jiang is silent regarding outputting the detection result having a detection box if a predicted pedestrian score of the predicted pedestrian location is greater than a preset pedestrian score; and outputting the detection result having a visible attribute if a predicted attribute score of the predicted pedestrian attribute is greater than a preset attribute score.
Chen teaches outputting the detection result having a detection box if a predicted pedestrian score of the predicted pedestrian location is greater than a preset pedestrian score (para. 0045, 0046); and outputting the detection result having a visible attribute if a predicted attribute score of the predicted pedestrian attribute is greater than a preset attribute score (para. 0045, 0046).
It would have been obvious to one of ordinary skill in the art to incorporate Chen’s teachings into Jiang since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve performance.
As to claims 12-14, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claims 7-10 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.
The following is a statement of reasons for the indication of allowable subject matter: The prior art discloses the claim limitations discussed above, but fails to disclose the combined features required by dependent claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mao et al. a method for simultaneous object detection and semantic segmentation including processing the image using a neural network backbone to obtain a feature map; processing the feature map using an object detection module to obtain object detection result of the image; and processing the feature map using a semantic segmentation module to obtain semantic segmentation result of the image.
Zhang et al. discloses an object detection model training method in which a classifier that has been trained in a first phase is duplicated to at least two copies, and in a training in a second phase, each classifier obtained through duplication is configured to detect to-be-detected objects with different sizes, and train an object detection model based on a detection result.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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