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 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention falls under signal per se. The claim fails to add "non-transitory" to the CRM and the specification states that it could encompasses artificial-generated propagated signal. Claims 18-20 depend from Claim 17 and are rejected similarly.
Step 1: Claim 17 is a signal per se. Therefore, claim 17 does not fall within one of the statutory categories and is not eligible subject matter. Likewise with Claims 18-20.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 and 17 are machine type claims. Claim 9 is a process type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“identify a multiwavelet filter configured to take as input including items of data corresponding to a particular application;” A user mentally or with pencil and paper can identify a multiwavelet filter that takes input data that corresponds to a particular application.
“transform, by the filter, the data into one or more subsets;” A user mentally or with pencil and paper can transform data into one or more subsets
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
2B: The claim does not include additional elements individually or in combination that
are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
As per claim 2-4, these claims generally link the model of claim 1 to one or more of a deep neural network, a convolutional neural network, a recurrent neural network, and a fully connected neural network and must have both different and same hyperparameters. Therefore, claims 2-4 are rejected for similar reasons to claim 1.
As per claims 5-8, these claims contain additional mental steps of selecting, determining, and generating models, data groups, filters, and output data. Therefore, claims 2-4 are rejected for similar reasons to claim 1.
As per claim 9,
2A Prong 1:
“identify a multiwavelet filter configured to take as input including items of data corresponding to a particular application;” A user mentally or with pencil and paper can identify a multiwavelet filter that takes input data that corresponds to a particular application.
“transform, by the filter, the data into one or more subsets;” A user mentally or with pencil and paper can transform data into one or more subsets
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
2B: The claim does not include additional elements individually or in combination that
are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
As per claim 10-12, these claims generally link the model of claim 9 to one or more of a deep neural network, a convolutional neural network, a recurrent neural network, and a fully connected neural network and must have both different and same hyperparameters. Therefore, claims 10-12 are rejected for similar reasons to claim 9.
As per claims 14-16, these claims contain additional mental steps of selecting, determining, and generating models, data groups, filters, and output data. Therefore, claims 14-16 are rejected for similar reasons to claim 9.
As per claim 17,
2A Prong 1:
“identify a multiwavelet filter configured to take as input including items of data corresponding to a particular application;” A user mentally or with pencil and paper can identify a multiwavelet filter that takes input data that corresponds to a particular application.
“transform, by the filter, the data into one or more subsets;” A user mentally or with pencil and paper can transform data into one or more subsets
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
2B: The claim does not include additional elements individually or in combination that
are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“and generate, by a model receiving one or more of the transformed subsets as input, a set of output data corresponding to the particular application” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claim contains no additional detail or limitations beyond a generic, off the shelf model).
As per claim 18-20, these claims generally link the model of claim 17 to one or more of a deep neural network, a convolutional neural network, a recurrent neural network, and a fully connected neural network and must have both different and same hyperparameters. Therefore, claims 18-20 are rejected for similar reasons to claim 17.
Claim Rejections - 35 USC § 103
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.
Claims 1, 8, 9, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-On et al, U.S. PG PUB (US 20110032994 A1), filed on January 3, 2008 in view of Singh et al, NPL (An Advanced Technique of De-Noising Medical Images using ANFIS), filed on August 15, 2013.
With regard to independent claim 1,
Bar-On teaches, “A system to … multiwavelet-based operator learning,” (Paragraph 0044, 0089; EN: This denotes a system using multiwavelet coefficients and transforms). “the system comprising: a processor and memory to:” (Paragraph 0044; EN: This denotes the use of a data processor and volatile memory). “identify a multiwavelet filter configured to take as input including items of data corresponding to a particular application;” (Paragraph 0089, 0092-0094; EN: This denotes an MWT (multiwavelet transform) taking an input signal, which can comprise data such as video streams, video files, an image, or a text file, turning the input signal into transformed coefficients, and dividing the coefficients into sub bands of low pass filters and high pass filters).
“transform, by the filter, the data into one or more subsets;” (Paragraph 0089, 0092-0094; EN: This denotes an MWT (multiwavelet transform) taking an input signal, which can comprise data such as video streams, video files, an image, or a text file, turning the input signal into transformed coefficients, and dividing the coefficients into sub bands of low pass filters and high pass filters). “and generate, …, a set of output data corresponding to the particular application” (Paragraph 0183; EN: This denotes that a device comprising a entropy decoder will decode the encoded data according to a self-adjusted zero tree (SAZT), reconstruct the SAZT according to the decode data, forward the data and SAZT to an inverse multiwavelet transformation module that transforms the data, and can output the reconstructed data via an output interface).
However, Bar-On fails to disclose “… execute a model performing …,” and “…, by a model receiving one or more of the transformed subsets as input, …”.
Singh teaches, “… execute a model performing …,” (Pg. 79, Section: Testing phase; EN: This denotes the execution of a neural network and fuzzy system that will take a test image that was subjected to MWT (multiwavelet transform) as input). “…, by a model receiving one or more of the transformed subsets as input, …” (Pg. 79, Section: Testing phase; EN: This denotes that the neural network and fuzzy system will take the test image that was subjected to MWT (multiwavelet transform) as input).
Bar-On and Singh are considered to be analogous to the claimed invention due to the fact that they are both generally related to utilizing multiwavelet transformations. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On with the noise reduction of medical images through the use of multiwavelet transformations of Singh. One would be motivated to do so to improve multiwavelet transformations with the addition of neural networks. By incorporating a neural network into multiwavelet transformations, a user can increasingly suppress or reduce the amount of noise gained from high frequency signals through the use of both adaptive and predictive abilities, while also reducing the number of computational resources needed to run experiments with large datasets.
With regard to dependent claim 8,
Bar-On teaches, “The system of claim 1, the processor to: generate the output data set based on a portion of an output data corresponding to a previous iteration used to create the final iteration” (Paragraph 0096-0099, 0181-0183; EN: This denotes that during each iteration the multiwavelet coefficients are mapped to a self-adjusted zero tree (SAZT) and both the multiwavelet coefficients and the SAZT are outputted. The multiwavelet coefficients are then decoded and the SAZT is reconstructed, which is then sent to an inverse multiwavelet transformation module).
With regard to independent claim 9,
The rest of this claim is similar in scope to claim 1 and is rejected under
a similar rationale.
With regard to dependent claim 16,
The rest of this claim is similar in scope to claim 8 and is rejected under
a similar rationale.
With regard to independent claim 17,
The rest of this claim is similar in scope to claim 1 and is rejected under
a similar rationale.
Claims 2-4, 10-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-On et al, U.S. PG PUB (US 20110032994 A1), filed on January 3, 2008 in view of Singh et al, NPL (An Advanced Technique of De-Noising Medical Images using ANFIS), filed on August 15, 2013 as applied to claims 1, 9, and 17 above, and further in view of Pang et al, NPL (An Investigation Into Fault Diagnosis of Planetary Gearboxes Using A Bispectrum Convolutional Neural Network), published October 6, 2020.
With regard to dependent claim 2,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. However, Bar-On in view of Singh fail to teach “The system of claim 1, the model comprising one or more of a deep neural network, a convolutional neural network, a recurrent neural network, and a fully connected neural network”.
Pang teaches, “The system of claim 1, the model comprising one or more of a deep neural network, a convolutional neural network, a recurrent neural network, and a fully connected neural network” (Pg. 2029, Section: B; EN: This denotes the use of a pretrained convolutional neural networks).
Bar-On, Singh, and Pang are considered to be analogous to the claimed invention due to the fact that they are all generally related to utilizing multiwavelet transforms. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On and the noise reduction of medical images through the use of multiwavelet transformations of Singh with the bispectrum convolutional neural network (BSP-CNN) of Pang. One would be motivated to do so to improve multiwavelet transformations with the addition of a convolutional neural networks. By incorporating a neural network into multiwavelet transformations, a user can increasingly suppress or reduce the amount of noise gained from high frequency signals through the use of both adaptive and predictive abilities, while also reducing the amount of computational resources needed to run experiments with large datasets.
With regard to dependent claim 3,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. However, Bar-On in view of Singh fail to teach “The system of claim 1, the model comprising a plurality of models each having same hyperparameters”.
Pang teaches, “The system of claim 1, the model comprising a plurality of models each having same hyperparameters” (Pg. 2030-2031, Section 3; EN: This denotes that the pretrained CNN and the transfer network have the same hyperparameters).
With regard to dependent claim 4,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. However, Bar-On in view of Singh fail to teach “The system of claim 1, the model comprising a plurality of models each having different hyperparameters”.
Pang teaches, “The system of claim 1, the model comprising a plurality of models each having different hyperparameters” (Pg. 2030-2031, Section 3; EN: This denotes the use of a pretrained network that is trained with new training data and has its hyperparameters changed).
With regard to dependent claim 10,
The rest of this claim is similar in scope to claim 2 and is rejected under
a similar rationale.
With regard to dependent claim 11,
The rest of this claim is similar in scope to claim 3 and is rejected under
a similar rationale.
With regard to dependent claim 12,
The rest of this claim is similar in scope to claim 4 and is rejected under
a similar rationale.
With regard to dependent claim 18,
The rest of this claim is similar in scope to claim 2 and is rejected under
a similar rationale.
With regard to dependent claim 19,
The rest of this claim is similar in scope to claim 3 and is rejected under
a similar rationale.
With regard to dependent claim 20,
The rest of this claim is similar in scope to claim 4 and is rejected under
a similar rationale.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-On et al, U.S. PG PUB (US 20110032994 A1), filed on January 3, 2008 in view of Singh et al, NPL (An Advanced Technique of De-Noising Medical Images using ANFIS), filed on August 15, 2013 as applied to claims 1 and 9 above, and further in view of Vrhel et al, NPL (Projection based prefiltering for multiwavelet transforms), published November 30, 1998.
With regard to dependent claim 5,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. Bar-On teaches, “… based on the particular application, …” (Paragraph 0089 and 0092; EN: This denotes compressing one dimension or multi-dimensional digital data, which can possibly only be provided and used by various applications).
However, Bar-On in view of Singh fail to teach “…select, …, the model”.
Vrhel teaches, “…select, …, the model” (Pg.3089, Section: 3, paragraph 1; EN: This denotes choosing a model).
Bar-On, Singh, and Vrhel are considered to be analogous to the claimed invention due to the fact that they are all generally related to utilizing multiwavelet transforms. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On and the noise reduction of medical images through the use of multiwavelet transformations of Singh with the multiwavelet decomposition algorithm of Vrhel. One would be motivated to do so to improve multiwavelet transformations by incorporating a neural network, not putting restrictions on input, and utilizing a faster algorithm. By incorporating a neural network into multiwavelet transformations, a user can increasingly suppress or reduce the amount of noise gained from high frequency signals through the use of both adaptive and predictive abilities, while also reducing the amount of computational resources needed to run experiments with large datasets.
With regard to dependent claim 13,
The rest of this claim is similar in scope to claim 5 and is rejected under
a similar rationale.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-On et al, U.S. PG PUB (US 20110032994 A1), filed on January 3, 2008 in view of Singh et al, NPL (An Advanced Technique of De-Noising Medical Images using ANFIS), filed on August 15, 2013 as applied to claims 1 and 9 above, and further in view of Ruedin et al, NPL (Construction of Nonseparable Multiwavelets for Nonlinear Image Compression), published January 14, 2002.
With regard to dependent claim 6,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. However, Bar-On in view of Singh fail to teach “The system of claim 1, the processor to: determine, based on an amount of the data, to transform the data into two bisected groups of data having equal size”.
Ruedin teaches, “The system of claim 1, the processor to: determine, based on an amount of the data, to transform the data into two bisected groups of data having equal size” (Pg.3-4, Section 4; EN: This denotes splitting an original image into two diamond-like matrices of approximation coefficients, which is then decomposed into two diamond images with equal pixels. These images hold the same amount of storage as the original image).
Bar-On, Singh, and Ruedin are considered to be analogous to the claimed invention due to the fact that they are all generally related to utilizing multiwavelet transforms. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On and the noise reduction of medical images through the use of multiwavelet transformations of Singh with the construction of balanced orthogonal nonseparable quincunx multiwavelets of Ruedin. One would be motivated to do so to improve multiwavelet transformations by incorporating a neural network. By incorporating a neural network into multiwavelet transformations, a user can increasingly suppress or reduce the amount of noise gained from high frequency signals through the use of both adaptive and predictive abilities, while also reducing the amount of computational resources needed to run experiments with large datasets.
With regard to dependent claim 14,
The rest of this claim is similar in scope to claim 6 and is rejected under
a similar rationale.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-On et al, U.S. PG PUB (US 20110032994 A1), filed on January 3, 2008 in view of Singh et al, NPL (An Advanced Technique of De-Noising Medical Images using ANFIS), filed on August 15, 2013 as applied to claims 1 and 9 above, and further in view of Wu et al, NPL (Car Information Bus Image Restoration Using Multi-wavelet Transform Algorithm), published October 1, 2013 and Alpert et al, NPL (Adaptive Solution of Partial Differential Equations in Multiwavelet Bases), published January 17, 2002.
With regard to dependent claim 7,
As discussed above, Bar-On in view of Singh teaches the method of claim 1. However, Bar-On in view of Singh fail to teach “The system of claim 1, the processor to: select, based on a type of differential equation provided as input to the MWT filter, the MWT filter”.
Wu teaches, “The system of claim 1, the processor to: select, …, the MWT filter” (Pg. 6159-6161, Section: 2 and 3.1; EN: This denotes selecting multiwavelet filters by allowing the selection of scaling functions and wavelet functions, which can define a particular multiwavelet and leads to low-pass and high-pass filters after balancing the multiwavelet).
Bar-On, Singh, and Wu are considered to be analogous to the claimed invention due to the fact that they are all generally related to utilizing multiwavelet transforms. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On and the noise reduction of medical images through the use of multiwavelet transformations of Singh with GHM multi-wavelet algorithm of Wu. One would be motivated to do so to improve multiwavelet transformations.
However, Wu fails to teach “…, based on a type of differential equation provided as input to the MWT filter, …”.
Alpert teaches, “…, based on a type of differential equation provided as input to the MWT filter, …” (Pg. 179, Section: 6.1 and Pg.182; EN: This denotes the use of both the heat equation and the burgers’ equation, which can be utilized with multiwavelet bases).
Bar-On, Singh, Wu, and Alpert are considered to be analogous to the claimed invention due to the fact that they are all generally related to utilizing multiwavelet transformations. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the adjustment of zero tree datasets according to wavelet transforms of Bar-On, the noise reduction of medical images through the use of multiwavelet transformations of Singh, and the GHM multi-wavelet algorithm of Wu with the adaptive solvers for both linear and nonlinear partial differential equations (PDEs) of Alpert. One would be motivated to do so to improve both multiwavelet transformations and PDEs since multiwavelet bases can be used as integral operators, and with the addition of neural networks a user can utilize both adaptive and predictive abilities to help reduce the amount of computational resources needed to run various experiments and complex equations that require large datasets. Neural networks can also suppress or reduce the amount of noise within signals, used as input for multiwavelet transformations.
With regard to dependent claim 15,
The rest of this claim is similar in scope to claim 7 and is rejected under
a similar rationale.
Conclusion
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/SETH CAPRIANO-UMARI SHELTON/ Examiner, Art Unit 2141
/MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141
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