DETAILED ACTION
This application is in response to claims filed 03 July 2024 for application 18769906 filed 11 July 2024. Currently claims 20-39 are pending.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 20-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 20, 27 and 34 are directed to the statutory category of a method, an article of manufacture and a system.
In step 2a prong 1, claims 20, 27 and 34 recite, in part, determining weights for an image, generating a feature map, generating an output from the weights and feature map and estimating an edge of an object. The limitations of determining, generating and estimating are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “neural network”, “computer-readable medium”, and “processor”, in the context of the claims, the limitations encompass identifying edges of objects in images with the aid of a neural network. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “neural network”, “computer-readable medium”, and “processor”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “neural network”, “computer-readable medium”, and “processor” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 21-26, 28-33 and 35-39 recite further limitations of a convolutional layer, a 1x1 kernel, a CNN, a three-dimensional tensor, a neural network is trained for semantic segmentation, and weights correspond to portions of the image. No further additional elements are recited. The claims amount to the same abstract idea identified above in step 2a prong 1. As no further additional elements are present, the claims amount to the same lack of practical application in step 2a prong 2 and lack of significantly more in step 2b. The claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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) 20-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalinovskii et al. (Compact Convolutional Neural Network Cascade for Face Detection) in view of Choi et al. (US 20170124409 A1).
Regarding claims 20, 27 and 34, Kalinovskii discloses: A method, comprising:
determining, by using a first neural network (Fig 1 CNN 1)
based on an input image (Fig 3 greyscale image);
generating, by using a second neural network, a feature map from the input image (Fig 1 CNN 2, “Compact design of the CNN architectures. The total number of feature maps in all CNNs is 355 (in [16] it amounts up to 1,949), but a samples with a smaller variation of face images was applied to train the model.” P2 §3 ¶3);
providing the one or more weights and the feature map to a layer that is outside the first neural network and the second neural network, the layer generating an output from the one or more weights and the feature map (Fig 3 decision rule); and
estimating an edge of an object in the input image based on the output of the layer (“The last stage of the pipeline detector is Non-Maximum Suppression (NMS) algorithm, which aggregates the found regions to form the resulting areas of faces localization.” P4 ¶2).
Kalinovskii does not explicitly disclose: one or more weights.
Choi teaches: one or more weights (“On top of the weak learner approximation, another FC layer is employed to construct the classifier F, where the weight is initialized as a diagonal matrix by w.sub.t and the bias is negative rejection threshold. Given a proposal and the convolutional feature maps as the inputs of the feature pooling layer, the output of the entire approximation is a number indicating whether the proposal should be rejected or not. By using a feature pooling layer, a hyperbolic layer and two FC layers, the rejection classifiers may be approximated by a network module that can be easily incorporated into the network and runs on a GPU.” [0047]).
Kalinovskii and Choi are in the same field of endeavor of image analysis using neural networks and are analogous. Kalinvskii teaches a CNN based pipeline of multiple neural networks for image analysis. Choi discloses CNN image analysis with semantic segmentation, multidimensional tensors and weights. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known CNN pipeline model of Kalinsovskii with the known CNN structure as taught by Choi to yield predictable results as these parameters are able to be easily implemented and are able to be implemented on GPU [0047].
Regarding claims 21, 28 and 35, Kalinovskii discloses: The method of claim 20, wherein the layer is a convolutional layer (Fig 1 conv 1x1).
Regarding claims 22, 29 and 36, Kalinovskii discloses: The method of claim 21, wherein the convolutional layer has a kernel size of 1×1 (Fig 1 conv 1x1).
Regarding claims 23, 30 and 37, Kalinovskii discloses: The method of claim 20, wherein the second neural network is a convolutional neural network (Fig 1 CNN2).
Regarding claims 24 and 31, Kalinovskii does not explicitly disclose, however, Choi teaches:
The method of claim 20, wherein the feature map is a three-dimensional tensor (“To approximate the weak learner, a feature pooling layer is implemented which is adapted from an ROI pooling layer by only pooling features at specific locations on the feature maps to form a T-dimensional vector rather than an m×m×c cuboid. ” [0047]).
Regarding claims 25, 32 and 38, Kalinovskii does not explicitly disclose, however, Choi teaches:
The method of claim 20, wherein the second neural network is trained for semantic segmentation (“Visual semantic concepts of an object can emerge in different convolutional layers 103 depending on a size of target object(s) within the image 101. These visual semantic concepts can include, for example, convolutional features 105 representative of a portion of a target object. Target objects may include objects to be detected within an image, such as cars or pedestrians. Visual semantic concepts include abstract visible elements, such as small parts of an objects (e.g., eye, wheel, etc.) or low level salient features (e.g., edges, corners, texture, etc.). For example, if a target object (e.g., a pedestrian) within the image 101 is small, a strong activation of convolutional neurons (e.g., convolutional features 105) may be present in an earlier convolutional layer 103c (e.g., conv3) which encodes specific parts of an object. On the other hand, if a target object is large (e.g., a car), the same part concept may emerge in a subsequent convolutional layer 103d (e.g., conv4).” [0036]).
Regarding claims 26, 33 and 39, Kalinovskii does not explicitly disclose, however, Choi teaches:
The method of claim 20, wherein the one or more weights comprise weights corresponding to different portions of the input image (“On top of the weak learner approximation, another FC layer is employed to construct the classifier F, where the weight is initialized as a diagonal matrix by w.sub.t and the bias is negative rejection threshold. Given a proposal and the convolutional feature maps as the inputs of the feature pooling layer, the output of the entire approximation is a number indicating whether the proposal should be rejected or not. By using a feature pooling layer, a hyperbolic layer and two FC layers, the rejection classifiers may be approximated by a network module that can be easily incorporated into the network and runs on a GPU.” [0047]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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