DETAILED ACTION
Allowable Subject Matter
Claims 3, 4, 7, 13, 14 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.
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, 2, 5, 6, 8-12, 15, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwon (US 20260141710 A1)
Regarding claim 1, Kwon discloses a method of visual task processing, comprising: (Kwon teaches analyzing pathology patterns of whole-slide images based on graph deep learning, see Abstract)
obtaining a to-be-processed image; (See Fig. 2 and ¶ 0054, acquiring whole-slide image (WSI).)
processing the to-be-processed image by using a target model, to obtain a feature of the to- be-processed image, wherein the target model comprises a first module and a second module connected to the first module, the first module comprises a graph neural network, and the second module is configured to implement at feature transformation; and (See Figs. 2, 3 and 5. In Fig. 5 the graph attention network (GAT) layers are the backbone of the first module/graph neural network. Feature transformation/second modules are both before and after the GAT layers with the 3-layer MLP in the node feature embedding prior to the GAT and the 2-layer MLP in postprocessing.)
completing a visual task for the to-be-processed image based on the feature of the to-be- processed image. (See Fig 4 and Fig. 5 calculating risk value associated with tumor.)
Kwon does not expressly disclose that all of its above-cited teachings are expressly disclosed as occurring in the same embodiment. That is, despite the reference being clear that these functions are disclosed, there is no express disclosure that the details are all found in the same embodiment. Instead, the reference presents some of the individual detailed disclosures as ‘according to some embodiments.’ It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the various teachings to provide a single system capable of the variety of tasks which are disclosed. In view of these teachings, this cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 2, the above combination discloses the method according to claim 1, wherein a quantity of second modules is N+M, N>1, and M>1, and processing the to-be-processed image comprises: performing at least N times of feature transformation on the to-be-processed image using N second modules connected in series, to obtain a first feature; performing feature aggregation on the first feature using the first module, to obtain a second feature; performing at least M times of feature transformation on the second feature using M second modules connected in series, to obtain a third feature; and obtaining the feature of the to-be-processed image based on the third feature. (As above, in Fig. 5 the graph attention network (GAT) layers are the backbone of the first module/graph neural network. Feature transformation/second modules are both before and after the GAT layers with the 3-layer MLP in the node feature embedding prior to the GAT and the 2-layer MLP in postprocessing. The GAT layers perform feature aggregation, ¶ 0063.)
Regarding claim 5, the above combination discloses the method according to claim 1, wherein the first module further comprises a normalization layer, and an activation function layer, and the graph neural network of the first module, the normalization layer of the first module, and the activation function layer of the first module are connected in series. (See ¶ 0065 a normalization layer and an activation function layer are used in series after each layer.)
Regarding claim 6, the above combination discloses the method according to claim 1, wherein the second module comprises a feature transformation layer and a normalization layer, the feature transformation layer of the second module and the normalization layer of the second module are connected in series, and the feature transformation layer of the second module is a fully-connected layer, a convolutional layer, or a Fourier transformation layer. (As above, see ¶ 0065 a normalization layer is used after each layer. Also see Fig. 5, the second module/feature transformation is composed of MLP layers which are fully-connected layers.)
Regarding claim 8, the above combination discloses the method according to claim 1, wherein before processing the to-be-processed image using a-the target model, the method further comprises: dividing the to-be-processed image, to obtain P patches, wherein P>1; mapping the P patches, to obtain vector representations of the P patches; and constructing an initial feature of the to-be-processed image based on the vector representations of the P patches; and processing the to-be-processed image using the target model, comprises: processing the initial feature of the to-be-processed image using the target model, to obtain the feature of the to-be-processed image. (¶ 0056 and Fig. 2 teaches dividing the image into P patches followed by the steps which process their vector representations, first with initial features, then via the target model.)
Regarding claim 9, the above combination discloses the method according to claim 8, wherein before constructing the initial feature of the to-be-processed image, the method further comprises: adding positional encoding of an ith patch to a vector representation of the ith patch, to obtain the vector representation of the ith patch, wherein the positional encoding of the ith patch indicates a position of the ith patch in the to-be- processed image, wherein i=1, ..., P; and constructing the initial feature of the to-be-processed image comprises: constructing the initial feature of the to-be-processed image based on vector representations of the P patches. (The extracted patches preserve positional information as seen at Fig. 3, 6 and 8A. Also see ¶ 0061 which teaches that the edge graph features are positional.)
Regarding claim 10, the above combination discloses the method according to claim 1, wherein the visual task comprises at least one of: image classification, target detection, semantic segmentation, image denoising, or image super-resolution. (Kwon teaches pathology image classification, see Abstract)
Claims 11-12, 15-16 and 18 are the method of model training corresponding to the method of visual task processing of claims 1-2, 5-6 and 8-10. Kwon teaches model training including a target loss based on the feature of the to-be-processed image and a real feature of the to-be-processed image, wherein the target loss indicates a difference between the feature of the to-be-processed image and the real feature of the to-be-processed image; and updating parameters of the to-be-trained model based on the target loss, until a model training condition is met, to obtain a target model. (See Kwon ¶ 0051, 0077, 0091-0092, 0114-0115, 0126 and 0131). Remaining limitations are rejected similarly. See detailed analysis above.
Claim 19 is the visual task processing apparatus corresponding to the method of visual task processing of claim 1. Kwon teaches a processor and memory at ¶ 0119-0120. Remaining limitations are rejected similarly. See detailed analysis above.
Claim 20 is the model training apparatus corresponding to the method of visual task processing of claim 11. Kwon teaches a processor and memory at ¶ 0119-0120. Remaining limitations are rejected similarly. See detailed analysis above.
Conclusion
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
