DETAILED ACTION
This action is in response to the communication filed 04/15/2026. Claims 11, 13-16 and 25-31 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/15/2026 has been entered.
Response to Arguments
Applicant's arguments filed 04/15/2026 have been fully considered but they are not persuasive.
Examiner notes Applicant does not appear to advance any particular arguments beyond those filed after final on 02/17/2026. The response to these arguments is provided below.
Regarding the rejections under 35 U.S.C 101
Step 2A prong 1
Applicant appears to argue the claims fail to recite an abstract idea because at most they involve mathematical operations but do not describe in any detail the specific mathematical operations of the second order neurons.
Examiner disagrees. The claims clearly recite limitations such as “to determine a first dot product of an intermediate vector and the input vector” which is a recitation of an abstract idea. Determination of a dot product of two vectors is activity which can be performed in the mind. Even if these limitations did not describe specific mathematical operations, these are mental processes as identified in the prior rejection. The claims do not merely involve abstract ideas, but rather expressly recite preforming abstract ideas.
Applicant compares the instant application with Enfish noting that the claims involve mathematical manipulations but are directed to a second order neuron having a novel and non-obvious configuration. Applicant further argues the claims are directed to a real world problem.
Examiner disagrees. The claims in Enfish were found eligible because they recited particular additional elements (namely arrangement and configuration of data in memory) which reflect and improvement not because the claims merely are directed to novel and non-obvious configurations. The claims in Enfish describe the inner workings of memory storage technology, whose functions could not be considered abstract ideas. The claims merely being involved with a real world problem or involving unique properties is not a consideration for eligibility. Rather, The flow chart establishes in Step 2A prong 1 whether the claim recites abstract ideas (which applicant does not appear to dispute, and instead skips to whether the claim recites an improvement to computer capabilities), then under step 2A prong two the claims are evaluated to determine whether the additional elements integrate the judicial exception (for example because they provide an improvement to technology).
Indeed the claims recite the configuration of the 2nd order neuron, however the claims do not describe how this configuration affects an improvement (as in Enfish). Instead, the claims recite that the claimed device is configure to denoise an input image. The claims do not appear to recite using the autoencoder for denoising, much less how the configuration reflects and improvement technology for denoising images. At most the claim describes a configuration and how the certain elements (the second order neuron) includes circuitry for implementing the abstract ideas the neuron performs.
Step 2A prong 2
Applicant argues the claims as a whole reflect and improvement by providing an quadratic autoencoder, an improved neural network, for training to reduce noise in images. Applicant points out specifically the problem solved by denoising CT images.
Examiner disagrees. The claims do not reflect the supposed improvement. At most the claims recite the idea of a solution. The claim does not explain how the neural network or autoencoder is improved or even used, only that it includes certain named layers and neurons, that those neurons perform abstract ideas via circuitry, and separately that the device (not the neural network expressly) is configured to denoise an input image. Further, as pointed out in updated rejection devices for denoising CT images is well-understood routine and conventional. While the second order neuron is described in detail, it is described as including a series of labeled circuits for performing abstract ideas, the claims do not provide additional elements alone or in combination which reflect the improvement because any recited additional elements do not express how the improvement is accomplished. Further, the claim does not describe how the device is configured for image denoising. The claims merely recites that the device is configured to denoise. This corresponds squarely to reciting the idea of a solution generally linked to a particular environment.
Applicant suggests the rejection erred in proposing that “the device configured to denoise the input image” amounts to insignificant extra-solution activity. Applicant notes when considered as a whole the claim describes various features related to a real-world problem and does far more than merely use a computer as a tool to perform an abstract idea. Rather it describes a solution to a technical problem.
Examiner disagrees. The claim as a whole is considered via analysis of each of the recited additional elements and abstract ideas. While the claim recites various technical features, none of these features reflect the supposed improvement. As evidenced by each of the additional elements amounting to merely performing an abstract idea with named computer components, generally linking the abstract idea to the field of use, or well understood routine and conventional features, the claim provides no nexus to suggest how particular improvements to the technology are reflected.
Mere suggestion that past failure of image denoising exists does not suggest that a claim which simply states “the device configured to denoise the input image” is not insignificant extra solution activity. The thrust of the claim is related to features for a quadratic autoencoder. Simply limiting the claimed device to be used for a particular problem does not suggest the claim as a whole is directed to a improved technology. Further, The cited reference notes that denoising, or noise reduction is a common step in CT image processing.
Examiner highlights that simply claiming the use of a particular neuron for a particular purpose does not describe “how” the improvement is accomplished.
Step 2B
Applicant argues the claim limitations are not well-understood routine and conventional (WURC) and thus amount to significantly more.
Examiner highlights that a conclusion that the claim describes various features which in combination are unique and not well understood does not itself make the claim eligible. The 101 rejection is made based on a plurality of enumerated considerations (2106.05(a-h)).
Indeed the claim recites certain elements such as the described pretrained quadratic autoencoder, but the rejection is made based on a variety of considerations which include the recited abstract ideas and that the features related to the autoencoder claimed correspond to additional elements which are not indicative of a practical application (see MPEP 2106.05(f) and MPEP 2106.05(h)), and further include certain particular additional elements that are insignificant and WURC, therefore the claim as a whole is not indicative of significantly more.
In summary, the claims recite various named components for performing abstract idea, each of these components are only described as including certain nominal elements (such as convolutional layer). The claims do not describe the technical functioning of these elements or their service to affecting the solution of denoising an image, except that they perform certain abstract determinations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11, 13-16 and 25-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “low-dose patient image” in claim 11, 15 and 26 is a relative term which renders the claim indefinite. The term “low-dose” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 11, 13-16 and 25-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 11
Step 1 Analysis: Claim 11 is directed a system for denoising computed tomography images, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a machine each of the following limitations:
to determine a first dot product of an intermediate vector and an input vector… the intermediate vector corresponding to a product of the input vector and the first weight vector or the input vector and the weight matrix,… to determine a second dot product of the input vector and the second weight vector… to determine the output of the second order neuron based, at least in part, on the first dot product and the second dot product
As drafted, is a machine that, under its broadest reasonable interpretation recites mere instructions to implement an abstract idea on a computer. The above limitations in the context of this claim encompasses determining (mental processes). Determining an output of a dot product according to the mathematical equation, is a mathematical concept that can be evaluated in a human mind. The additional limitations identified above only serve to describe the values which are used to perform the abstract idea. As such the claim recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exception is not integrated into a practical application. In particular, the claim only recited additional elements that are mere instructions to implement an abstract idea, or merely uses a computer as a tool to perform an abstract idea. The additional elements (“a device comprising a processor circuitry, a memory circuitry and an artificial neural network (ANN) management circuitry; and an ANN comprising quadratic autoencoder…the second order neuron comprising a first dot product circuitry comprising a first multiplies block… a second dot product circuitry comprising a second multiplier block … a nonlinear circuitry configured…”) amounts to mere instructions to implement an abstract ideas on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP2106.05(f). In addition, the limitation “the quadratic autoencoder comprising a convolutional layer and a deconvolutional layer, wherein each of the convolutional layer and the deconvolutional layer include a plurality of second order neurons,…
each second order neuron of the plurality of second order neurons having an associated first weight vector and/or weight matrix, and a second weight vector, elements of the first weight vector, the weight matrix… and the second weight vector being determined by training on a training set of image data and validated on a validation set of image data wherein the input image comprises a low-dose patient image … the nonlinear circuitry comprising a sigmoid block or a rectified linear unit block, the input vector, the intermediate vector, the first weight vector and the second weight vector each containing a number, n, elements, and the weight matrix having dimension n x n;” only generally links the use of the judicial exception to a particular technological environment, i.e. neural network processing. None of these limitations describe how any functions are performed only that labeled computer components comprise other components and are configured for or determined by training and/or denoising and/or validation without any detail with respect to how these functions are performed. (see MPEP 2106.05(h)). Further, “the device configured to denoise an input image,” amounts to adding insignificant extra-solution activity to the judicial exception, See MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “the device configured to denoise an input image,” are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. As described in “Denoising Filter to Improve the Quality of CT images” Lanzolla et al., “Modeling noise is a common problem in most image processing application as evident in the extensive literature about the ways to reduce it. In many image processing applications a suitable denoising step is often required before any relevant information can be extracted from analyzed images. … A lot of studies have focused on the image noise reduction and some researchers have shown that the noise in CT image is in a first approximation Gaussian distributed”. The literature suggests that noise, and consequently denoising, is a common problem to be solved and note that extensively studies aim reduce it (i.e denoise). As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic computer to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer. Therefore, the claim is not patent eligible.
Regarding Claim 13-16
Step 1 Analysis: The rejection of Claim 11 is incorporated, therefore Claim 13-16 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: Further, the claim recites a computer machine each of the following limitations:
determine a third dot product…multiply the second dot product and the third dot product…a summer circuitry configured to add the intermediate product and the first dot product…summer circuitry configured to add the first dot product and the second dot product to yield an intermediate output
As the rejection of claim 11 is incorporated, the claim recites an abstract idea.
Step 2A Prong Two Analysis:
The judicial exception is not integrated into a practical application. In addition to those additional elements already identified in the parent claim, the claim only recites additional elements that are mere instructions to implement an abstract idea, or merely uses a computer as a tool to perform an abstract idea. The additional element of a matrix, vectors, circuitry, multiplier blocks, summer blocks, and second order neurons amounts to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP2106.05(f). Further as noted previously in the rejection of claim 11 the additional element “the elements of the third weight vector being determined by training on the training set of image data and validation on the validation set of image data” only generally links the use of the judicial exception to a particular technological environment, i.e. neural network processing. The claims sets no limits on how the functions are performed. (see MPEP 2106.05(h)).
Step 2B: The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 25
Step 1 Analysis: The rejection of Claim 11 is incorporated, therefore Claim 25 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis:
Further, the claim recites a computer machine each of the following limitations:
wherein an output of each second order neuron of the plurality of second order neurons corresponds to…
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As the rejection of claim 11 is incorporated, the claim recites an abstract idea. Further, these limitations recite mathematical operations performed to arrive at an output for a neuron. As such, these limitations recite an additional abstract idea.
Step 2B:
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 26
Step 1 Analysis: Claim 26 is directed A quadratic autoencoder trained on a set of images data to denoise an input image, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a machine each of the following limitations:
to determine a first dot product of an intermediate vector and an input vector… the intermediate vector corresponding to a product of the input vector and the first weight vector or the input vector and a weight matrix,… to determine a second dot product of the input vector and the second weight vector… to determine the output of the second order neuron based, at least in part, on the first dot product and the second dot product
As drafted, is a machine that, under its broadest reasonable interpretation recites mere instructions to implement an abstract idea on a computer. The above limitations in the context of this claim encompasses determining (mental processes). Determining an output of a dot product according to the mathematical equation, is a mathematical concept that can be evaluated in a human mind. The additional limitations identified above only serve to describe the values which are used to perform the abstract idea. As such the claim recites an abstract idea.
Step 2A Prong Two Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim only recited additional elements that are mere instructions to implement an abstract idea, or merely uses a computer as a tool to perform an abstract idea. The additional elements (“a device comprising a processor circuitry, a memory circuitry and an artificial neural network (ANN) management circuitry; and an ANN comprising quadratic autoencoder…the second order neuron comprising a first dot product circuitry comprising a first multiplies block… a second dot product circuitry comprising a second multiplier block … a nonlinear circuitry configured…”) amounts to mere instructions to implement an abstract ideas on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP2106.05(f). In addition, the limitation “a convolutional layer and a deconvolutional layer, wherein each of the convolutional layer and the deconvolutional layer include a plurality of second order neurons,… each second order neuron of the plurality of second order neurons having an associated first weight vector and/or weight matrix, and a second weight vector, elements of the first weight vector, the weight matrix, and the second weight vector being determined by training on a training set of image data, and validation on a validation set of image data… the nonlinear circuitry comprising a sigmoid block or a rectified linear unit block, the input vector, the intermediate vector, the first weight vector and the second weight vector each containing a number, n, elements, and the weight matrix having dimension n x n;” only generally links the use of the judicial exception to a particular technological environment, i.e. neural network processing. None of these limitations describe how any functions are performed only that labeled computer components comprise other components and are configured for training and/or denoising without any detail with respect to how these functions are performed. (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 27-30
The dependent claims 27-30 are rejected for the same reasons as the dependent claims 13-16 in connection with claim 26.
Regarding Claim 31
The dependent claims 31 is rejected for the same reasons as the dependent claims 25 in connection with claim 26.
Allowable Subject Matter
The closest prior art of record is the newly cited Yang et al. “High Order Neural Networks with Reduced Numbers of Interconnection Weights” which describes the generalized equation for a higher order neuron
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and specifically the equation for a parabolic neuron
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.
The cited art either alone or in combination do not teach all of the limitations of claims 11 and 26.
Conclusion
Prior art
Lu et al. “An efficient multilayer quadratic perceptron for pattern classification and function approximation” teaches a sigma pi style neural unit for implementing a neural unit for non-linear function approximation.
Ganesh et al “Pattern Classification using Quadratic Neuron: An Experimental Study” teaches a quadratic neuron model for classification of concentric separable classes.
Su et al. “A Neural-Network-Based Approach to Detecting Hyperellipsoidal Shells” disclosed neural networks that implement quadratic junctions in order to learn hyper spherical patterns.
Yang et al. “High Order Neural Networks with Reduced Numbers of Interconnection Weights” discloses a generalized mathematical form for higher order neurons in neural networks. It also specifically provides an example equation for a parabolic neuron:
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached on Monday-Friday 7:30 am – 4:00 pm (EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki, can be reached at telephone number 5712723719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./ Examiner, Art Unit 2122 /KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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