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 statement (IDS) submitted on 12/27/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 6, 10, 11 and 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lee et al (US20230206456).
Regarding claim 1, Lee teaches a processor-implemented method, the method comprising:
extracting initial feature maps from respective images extracted from a video (402 and 404 in fig. 4A, feature maps from the input data. Although Lee does not explicitly recite a video, it is known in the art that a video is a compilation of images. Lee teaches that object detection is employed as an autonomous vehicle travels within an environment in para. [0117]), wherein the extracting of the initial features maps is performed using a transformer (404 in fig. 4A; para. [0057], At operation 404, a feature extractor (or backbone network) of a point supervised transformer model extracts a hierarchical combination of features in the form of a set of feature maps having different levels);
generating a target feature map by fusing the initial feature maps using a feature fusion network comprising one or more layers (406 in fig. 4A; para. [0058], At operation 406, a feature pyramid network (FPN) fuses the feature maps of different levels. The feature pyramid network increases the feature resolution and fuses the information from the high-level semantic features and low-level finer features); and
identifying an object in the video based on the target feature map (408 and 410 in fig. 4A; para. [0060], The attention map is used to combine the values, which are the projected image features using a learned value mapping, and to update the corresponding object query. Each query can attend to the image features to obtain information about an object instance’s category, location, and boundary).
Regarding claim 6, Lee discloses a method wherein the one or more layers comprise feature fusion modules (para. [0058], In each layer of the FPN, the previous layer’s lower resolution feature map is upsampled and fused together with the corresponding higher resolution feature map from the feature extractor), each feature fusion module comprising:
a self-attention module configured to output a self-attention feature map from each fusion feature map in an input sub-set (para. [0070], self-attention); and
a cross-attention module configured to output a cross-attention feature map by crossing a fusion feature map in the input sub-set (para. [0073], cross attention maps).
Regarding claim 10, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 11, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated 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.
Claim(s) 2, 3, 8, 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al (US20230206456) in view of Xiao et al (US20230074706).
Regarding claim 2, Lee fails to teach a method wherein the identifying of the object in the video comprises:
extracting classification feature information from a target feature map output from a last layer of the feature fusion network;
obtaining a global feature vector of the video;
obtaining a final feature vector of the video based on the classification feature information and the global feature vector; and
identifying the object in the video based on the final feature vector.
However Xiao teaches extracting classification feature information from a target feature map output from a last layer of a network (para. [0032], image-level contrastive loss is essentially performing a (K+1)-way classification; para. [0033], An image-level contrastive loss is computed by the contrastive loss unit 135 from the outputs (predictions) of the neural network 130);
obtaining a global feature vector of the video (para. [0045], a global feature vector generated for the entire image);
obtaining a final feature vector of the video based on the classification feature information and the global feature vector (output of neural network 130 in fig. 1B after contrastive loss updates); and
identifying the object in the video based on the final feature vector (para. [0045], an attention map that identifies a foreground object in the first image 201).
It would be obvious to replace the network of Xiao with the feature fusion network of Lee.
Therefore taking the combined teachings of Lee and Xiao as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Xiao into the method of Lee. The motivation to combine Xiao and Lee would be to improve semantic correspondence performance (para. [0002] of Xiao).
Regarding claim 3, the modified method of Lee teaches a method wherein the obtaining of the global feature vector of the video comprises:
obtaining global feature vectors of the respective images (para. [0045] of Xiao);
obtaining weights for of the respective global feature vectors (para. [0063] of Xiao); and
obtaining the global feature vector of the video based on the weights and the global feature vectors (fig. 1B and para. [0045] of Xiao).
Regarding claim 8, the claim recites similar subject matter as claims 1 and 2 and is rejected for the same reasons as stated above.
Regarding claim 12, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 13, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al (US20230206456) in view of Liu et al (US20220036561).
Regarding claim 4, Lee teaches a method wherein the one or more layers comprise one feature fusion module and fusion feature maps corresponding to an output of a current layer (406 in fig. 4A; para. [0058]).
Lee fails to teach wherein an input of a next layer comprise the fusion feature maps, and
wherein the fusion feature maps are cascaded with the current layer.
However Liu teaches wherein an input of a next layer comprise the fusion feature maps, and wherein the fusion feature maps are cascaded with the current layer (fig. 6; para. [0135]).
Therefore taking the combined teachings of Lee and Liu as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Liu into the method of Lee. The motivation to combine Liu and Lee would be to reduce the incompleteness caused by artificial design features (para. [0028] of Liu).
Regarding claim 14, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al (US20230206456) and Xiao et al (US20230074706) in view of Kim et al (US20150206026).
Regarding claim 9, the modified method of Lee fails to teach a method wherein the global feature vector is obtained from a weighted average of global feature vectors extracted from the respective images.
However Kim teaches wherein a global feature vector is obtained from a weighted average of global feature vectors extracted from respective images (para. [0076], The histogram generator 110 generates global feature vectors by performing the frequency analysis for each of the plurality of selected frames, calculates an average of the generated global feature vectors, and generates the global feature vectors corresponding to training images).
Therefore taking the combined teachings of Lee and Xiao with Kim as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Kim into the method of Lee and Xiao. The motivation to combine Kim, Xiao and Lee would be to efficiently represent a context in an image (para. [0079] of Kim).
Allowable Subject Matter
Claims 5, 7, 15, and 17 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.
Related Art
Lu et al (US20220319155) – see para. [0048], [0055], [0059]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663