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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - Claim 1 recites:
An apparatus for training a noise-resilient machine learning (ML) model, the apparatus comprising: at least one processor coupled to memory and arranged to: receive a training data set comprising a plurality of data items; initialise weights of at least one neural network layer of the ML model; and train, using an iterative process, the at least one neural network layer of the ML model by: inputting, into the at least one neural network layer, the plurality of data items, processing the plurality of data items using the at least one neural network layer and the weights, optimising a loss function of the weights by simultaneously minimising a loss value and a loss sharpness using weights that lie in a neighbourhood having a similar low loss value, wherein the neighbourhood is determined by a geometry of a parameter space defined by the weights of the ML model, and updating the weights of the at least one neural network layer using the optimised loss function. These claim elements are considered to be abstract ideas because they are directed to “mathematical concepts” which include “mathematical formulas or equations.” In this case, claim 1 discloses using a loss function of the weights and updating the weights. If a claim limitation, under its broadest reasonable interpretation, covers mathematical formulas or equations, then it falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a memory; and a processor circuitry.
The memory is merely used to store instructions. The processor is merely used to execute instructions. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “memory” and “processor” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Accordingly, alone and in combination, 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. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept. The specification shows that the memory is merely used to store instructions. The processor is merely used to execute instructions. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent Claim 12 is rejected similarly with respect to 101 as discussed above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 10-12, and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dwivedi (2022/0215201).
As for claims 1 and 12, Dwivedi discloses an apparatus for training a noise-resilient machine learning (ML) model, the apparatus comprising:
at least one processor coupled to memory and arranged to ([0022]):
receive a training data set comprising a plurality of data items ([0074]-[0077], [0023], [0024], [0051]);
initialise weights of at least one neural network layer of the ML model ([0074]-[0077], [0026], [0029], [0031]); and
train, using an iterative process, the at least one neural network layer ([0026], [0033], [0038], [0063], [0065]) of the ML model by:
inputting, into the at least one neural network layer, the plurality of data items, processing the plurality of data items using the at least one neural network layer and the weights, optimising a loss function of the weights by simultaneously minimising a loss value and a loss sharpness ([0030]) using weights that lie in a neighbourhood having a similar low loss value, wherein the neighbourhood is determined by a geometry ([0054]) of a parameter space defined by the weights of the ML model ([0032], [0033], [0037], [0038]), and
updating the weights of the at least one neural network layer using the optimised loss function ([0075], [0083]).
As for claims 2 and 14, Dwivedi discloses wherein the ML model is used to perform a computer vision task, and wherein the plurality of data items of the training data set are images and/or frames of videos ([0079]).
As for claims 3 and 15, Dwivedi discloses wherein the computer vision task is any one of:
object recognition, object detection, scene analysis, image or video segmentation, and image or video enhancement ([0079], [0086]).
As for claim 4, Dwivedi discloses wherein the ML model is robust to noise in the images and/or frames of videos ([0030]).
As for claim 5, Dwivedi fails to disclose wherein the noise in the images and/or frames of videos is any one or more of: occlusion of a target object, noise due to changes in lighting, and noise due to camera shake.
The Examiner takes Official Notice that it is well-known wherein the noise in the images and/or frames of videos is any one or more of: occlusion of a target object, noise due to changes in lighting, and noise due to camera shake.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to modify Dwivedi’s invention, to include the abovementioned limitation, as taught by the Examiner’s statement of Official Notice, for the purpose of efficient image processing.
As for claim 10, Dwivedi discloses wherein the ML model comprises a pre-trained backbone network, wherein initialising weights comprises using weights of the pre-trained backbone network, and wherein the training data set is the same as data used to train the pre-trained backbone network ([0074], [0098], [0099]).
As for claims 11, Dwivedi discloses wherein the ML model comprises a pre-trained network, wherein initialising weights comprises using weights of the pre-trained network, and wherein the training data set is different to data used to train the pre-trained network ([0074], [0098], [0099]).
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.
Claim(s) 6-9, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi as applied to claim 4 above, and further in view of Wexler (2021/0366479).
As for claim 6, Dwivedi fails to disclose:
wherein the ML model is used to perform an audio analysis task, and wherein the plurality of data items of the training data set are audio files.
In an analogous art, Wexler discloses:
wherein the ML model is used to perform an audio analysis task, and wherein the plurality of data items of the training data set are audio files ([0201], [0203], [0210], [0225], [0231], [0240]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dwivedi’s invention to include the abovementioned limitation, as taught by Wexler, for the advantage of improving audio for a user.
As for claim 7, Wexler discloses:
wherein the audio analysis task is any of:
audio recognition ([0210]), speech processing ([0231]), speech-to-text ([0225], [0240]), and speech recognition ([0201], [0203]).
As for claim 8, Wexler discloses:
wherein the ML model is robust to noise in the audio files ([0175], [0144], [0149], [0181], [0186], [0197]).
As for claims 9, Wexler discloses,
wherein the audio files contain speech of a target speaker, and the noise in the audio files is one or both of:
background noise ([0175], [0144]), and noise due to speaker state variation ([0174], [0204]-[0206]).
As for claims 13, Dwivedi discloses further comprising determining the geometry of a parameter space defined by the weights of the ML model ([0030], [0031], [0042], [0047], [0053]), but fails to by calculating a Fisher information metric of the parameter space.
In an analogous art, Wexler discloses calculating a Fisher information metric of the parameter space ([0231]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dwivedi’s invention to include the abovementioned limitation, as taught by Wexler, for the advantage of improving audio for a user.
Relevant Prior Art
Chen (2022/0318995) discloses optimizing a loss function using machine learning.
Conclusion
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SUMAIYA A. CHOWDHURY
Examiner
Art Unit 2421
/SUMAIYA A CHOWDHURY/Primary Examiner, Art Unit 2421
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