DETAILED ACTION
This action is in response to claims filed 27 September 2023 for application 18475995 filed 27 September 2023. Currently claims 1-33 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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claim 33 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the means, when interpreted in light of the specification (particularly [0151]), can be interpreted to be only software per se.
Claims 1-8, 11-21 and 24-33 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 1, 14, 27 and 33 are directed to the statutory category of a method, a system, an article of manufacture and a system respectively.
In step 2a prong 1, claims 1, 14, 27 and 33 recite, in part, accessing an observed tensor signal, generating a subset of elements of a matrix using a neural network, generating a first subset of elements of a reconstructed signal tensor using a reconstruction neural network, and outputting the first subset of elements of the reconstructed signal tensor. The limitations of accessing, generating and outputting 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 “computer implemented”, “neural network”, “memory”, “processors”, and “computer-readable medium” in the context of the claims, the limitations encompass generating information in response to an input and outputting reconstructed information in the mind or with aid. 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 “computer implemented”, “neural network”, “memory”, “processors”, and “computer-readable medium”. 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 “computer implemented”, “neural network”, “memory”, “processors”, and “computer-readable medium” 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 2-8, 11-13, 15-21, 24-26, and 28-32 recite further limitations, in part, generating a second subset, particular encoder layout, shared encoder subnet, methods of generating a latent tensor and subset, processing the latent tensor using a decoder subnet, generating distribution parameter and sampling the distribution, processing the distribution parameters with the decoder subnet, generating distribution parameter and sampling the distribution, generating an angle parameter, and wherein the tensors differ by at least one element. No further additional elements are presented in these claims, thus, the claims do not amount to a practical application in step 2a prong 2 nor significantly more than the abstract idea itself in step 2b.
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.
Claims 1, 2, 11, 13-15, 24, 26-28, 32 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Content-aware Scalable Deep Compressed Sensing) in view of Zhou et al. (20190205606).
Regarding claims 1, 14, 27 and 33, Chen discloses: A computer-implemented method, comprising:
accessing an observed signal … comprising a plurality of elements (Figs. 2 and 3);
generating a first subset of elements of a sensing matrix based on processing, from among the plurality of elements, a first subset of elements of the observed signal … using an acquisition neural network
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Fig. 4;
generating a first subset of elements of a reconstructed signal … based on processing, from among the plurality of elements, a second subset of elements of the observed signal … and the first subset of elements of the sensing matrix using a reconstruction neural network
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Fig. 6; and
outputting at least the first subset of elements of the reconstructed signal Fig. 6.
Chen disclose two dimensional matrices for all of the elements, however, does not explicitly disclose a tensor. Zhou teaches: tensors (“FIG. 12 illustrates a framework for deep learning partial inference based medical image segmentation according to an embodiment of the present invention. As shown in FIG. 12, the segmentation framework 1200 performs medical image segmentation in a two stage workflow. In the first stage (Stage 1), a first deep convolutional encoder decoder (CED) 1204 is used to learn a mapping from an input medical image 1202 (e.g., MR image) to a segmentation mask. The first deep CED 1204 is referred to herein as the initial CED (CED_Init). In the second stage (Stage 2), a multi-channel representation is used to embed the input medical image 1202 and the previous segmentation results into a unified tensor, which is fed into a second deep CED 1206 to generate an updated segmentation mask. The second CED 1206 is referred to herein as the partial inference CED (CED_PI). The second stage is applied in an iterative fashion in which the updated segmentation mask generated by CED_PI 1206 is iteratively combined with the input image 1202 to generate a new tensor input that is input to CED_PI 1206 to generated a new updated segmentation mask until the updated segmentation masks generated by CED_PI 1206 converge. Once the updated segmentation masks generated by CED_PI 1206 converge, the final segmentation mask 1208 is output, and can be used to extract contours from the original input image 1202.” [0103], use of compressed sensing [0042]).
Chen and Zhou disclose compressed sensing of image signals and are analogous. Chen discloses a compressed sensing system with acquisition and reconstruction neural networks used to process input signals. Zhou teaches a compressed sensing system that specifically uses tensors. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the matrices of Chen with the known tensors of Zhou to yield predictable results.
Regarding claims 2, 15 and 28, Chen discloses: The computer-implemented method of claim 1, further comprising generating a second subset of elements of the sensing matrix based on processing the second subset of elements of the observed signal tensor and the first subset of elements of the sensing matrix using the acquisition neural network Fig 4.
Regarding claims 11 and 24, Chen discloses: The computer-implemented method of claim 1, wherein generating the first subset of elements of the sensing matrix comprises: generating a set of distribution parameters by processing the first subset of elements of the observed signal tensor using the acquisition neural network; and sampling a distribution, defined by the set of distribution parameters, to generate the first subset of elements of the sensing matrix (“We propose to adopt a lightweight CNN to adaptively detect the image saliency distribution, and design a block ratio aggregation (BRA) strategy to achieve block-wise CS ratio allocation instead of using a handcrafted detecting function adopted by previous saliency-based methods [38], [39].” P2 ¶1, Fig 2).
Regarding claims 13 and 26, Chen does not explicitly disclose, however, Zhou teaches: The computer-implemented method of claim 1, wherein the first subset of elements of the observed signal tensor and the second subset of elements of the observed signal tensor differ by at least one element (“FIG. 12 illustrates a framework for deep learning partial inference based medical image segmentation according to an embodiment of the present invention. As shown in FIG. 12, the segmentation framework 1200 performs medical image segmentation in a two stage workflow. In the first stage (Stage 1), a first deep convolutional encoder decoder (CED) 1204 is used to learn a mapping from an input medical image 1202 (e.g., MR image) to a segmentation mask. The first deep CED 1204 is referred to herein as the initial CED (CED_Init). In the second stage (Stage 2), a multi-channel representation is used to embed the input medical image 1202 and the previous segmentation results into a unified tensor, which is fed into a second deep CED 1206 to generate an updated segmentation mask. The second CED 1206 is referred to herein as the partial inference CED (CED_PI). The second stage is applied in an iterative fashion in which the updated segmentation mask generated by CED_PI 1206 is iteratively combined with the input image 1202 to generate a new tensor input that is input to CED_PI 1206 to generated a new updated segmentation mask until the updated segmentation masks generated by CED_PI 1206 converge. Once the updated segmentation masks generated by CED_PI 1206 converge, the final segmentation mask 1208 is output, and can be used to extract contours from the original input image 1202.” [0103]).
Claim(s) 12 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Zhou and further in view of Zhu et al. (US 20220156957).
Regarding claims 12 and 25, Chen does not explicitly disclose: The computer-implemented method of claim 1, wherein generating the first subset of the sensing matrix comprises generating an angle parameter by processing the first subset of the observed signal tensor using the acquisition neural network.
Zhu teaches: wherein generating the first subset of the sensing matrix comprises generating an angle parameter by processing the first subset of the observed signal tensor using the acquisition neural network (“In an image depth estimation method proposed in an embodiment of the present disclosure, the input is two consecutive frames of images collected during the movement process of mobile equipment (for example, a webcam, a camera, etc.), and a network structure of the neural network includes a pose estimation network branch and a depth estimation network branch. The pose estimation network branch and the depth estimation network branch share a coding network for feature extraction. A reconstruction error between deep feature maps corresponding to the two frames of images is calculated based on an inter-frame geometrical relationship output from the pose estimation network branch, explicit geometrical constraints are added for the depth estimation network branch, and a depth map is finally obtained through decoding network regression of the depth estimation network branch. The key point of the image depth estimation method is that in case of not utilizing other motion sensing apparatuses, the pose estimation network outputs a scaled inter-frame geometrical relationship (the inter-frame geometrical relationship is configured to show a moving distance and an angle change of the mobile equipment, including a translation distance and a rotation matrix, wherein the scale refers to a proportion, and ‘scaled’ is compared with the case that an inter-frame geometrical relationship obtained by a method in a related art is scale-free.” [0061).
Chen, Zhou and Zhu disclose sensing of image signals and are analogous. Chen discloses a compressed sensing system with acquisition and reconstruction neural networks used to process input signals. Zhou teaches a compressed sensing system that specifically uses tensors. Zhu discloses acquisition of angle parameters from images using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the compressed sensing using acquisition and reconstruction neural network on images as disclosed by Chen and Zhou with the known angle parameters as taught by Zhu to yield predictable results.
Allowable Subject Matter
Claims 3-10, 16-23, and 29-31 are objected to as being dependent upon a rejected base claim, but would be allowable if written in independent form including all of the limitations of the base claim and any intervening claims as well as overcoming any other outstanding rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zamir et al. (US 20200234414) discloses encoder-decoder subnets but does not disclose the same configuration as claim 3.
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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