DETAILED ACTION
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 .
Status of Claims
The present application is examined under the claims filed on 10/04/2023.
Claims 1-20 are rejected.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
first and second transformation modules configured to… – claim 12
first and second segmentation modules configured to… claim 12
first and second base paths, configured to… claim 12
first and second masking modules, configured to… claim 12
a first convolution module configured to… claim 12
a second convolution module configured… claim 12
first and second output modules configured to… claim 12
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-16 are rejected under 35 U.S.C 101 because the claimed invention
is directed to an abstract idea without significantly more. The analysis of the claims
will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg.
50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
The claim states: “A method of training a machine learning algorithm, comprising:” therefore it is directed to a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “performing first and second transforms on the input data to generate first and second augmented data, [to provide first and second transformed base paths into first and second machine learning algorithm encoders]” Under its broadest reasonable interpretation in light of the
specification, this limitation encompasses the mental process of evaluating and
observing data, which is an evaluation or observation that is practically capable of being
performed in the human mind with the assistance of pen and paper.
“Segmenting the first and second augmented data” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“Calculating first and second main base path outputs by applying a weighting to the segmented first and second augmented data” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“Calculating first and second pruning masks from the input and first and second augmented data to apply to the first and second base paths of the first and second machine learning algorithm encoders, the pruning masks having a binary value for each segment in the segmented first and second augmented data, respectively” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“Calculating first and second sparse conditional path outputs by performing a computation on the segments of the segmented first and second augmented data which are designated with a binary one in the first and second pruning masks, respectively” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“Calculating a final output as a sum of the first and second main base path outputs and the first and second sparse conditional path outputs” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Providing a set of input data” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP §2106.05(g)).
“[performing first and second transforms on the input data to generate first and second augmented data], to provide first and second transformed base paths into first and second machine learning algorithm encoders” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements, “Providing a set of input data” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). “[performing first and second transforms on the input data to generate first and second augmented data], to provide first and second transformed base paths into first and second machine learning algorithm encoders” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the input data is a set of two-dimensional images” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the input data is a set of two-dimensional images” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the machine learning algorithm is an unlabeled, self- supervised machine learning algorithm” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the machine learning algorithm is an unlabeled, self- supervised machine learning algorithm” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the machine learning algorithm does not use a pre- trained dense model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the machine learning algorithm does not use a pre- trained dense model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the second transform comprises an inverse diagonal transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the second transform comprises an inverse diagonal transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “The method of claim 1, further comprising eliminating irregular sparse indexes” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states no additional elements.
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The claim is subject matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, the method not comprising introducing additional feature importance predictors.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, the method not comprising introducing additional feature importance predictors.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the computation is a sparse computation.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the computation is a sparse computation.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “The method of claim 1, further comprising applying a conditional weighting to the first and second conditional path outputs” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states no additional elements.
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The claim is subject matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 1, wherein the first transform is different from the second transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 1, wherein the first transform is different from the second transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The method of claim 10, wherein the first and second transforms comprise color jittering.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The method of claim 10, wherein the first and second transforms comprise color jittering.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
The claim states: “A computer-implemented system for learning sparse features of a dataset” therefore it is directed to a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “first and second transformation modules configured to perform first and second transforms on an input data element selected from the plurality of input data elements, having as an output first and second augmented data” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“First and second segmentation modules configured to segment each of the first and second augmented data into informative features and uninformative features” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“First and second base paths, configured to apply a base path weight to the uninformative features and provide as an output first and second weighted uninformative features” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“first and second masking modules, configured to generate first and second sparse pruning masks from the first and second weighted uninformative features, each sparse pruning mask having a binary value for each segment in the segmented first and second augmented data, respectively” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
“A first convolution module configured to convolve the first sparse pruning mask, the first informative features, and a conditional weighting, and configured to provide as an output a first sparse feature output” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical relationships, mathematical formulas or equations, or mathematical calculations.
“A second convolution module configured to convolve the second sparse pruning mask, the second informative features, and the conditional weighting, and configured to provide as an output a second sparse feature output” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical relationships, mathematical formulas or equations, or mathematical calculations.
“First and second output modules configured to add the first and second sparse feature outputs to the first and second uninformative features, configured to provide first and second final outputs” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“A plurality of input data elements” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP §2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements, “A plurality of input data elements” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The system of claim 12, wherein the plurality of input data elements are two-dimensional images.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the plurality of input data elements are two-dimensional images.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The system of claim 12, wherein the first transform is different from the second transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the first transform is different from the second transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The system of claim 12, wherein the first or second transform comprises an inverse diagonal transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the first or second transform comprises an inverse diagonal transform.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of
matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: the abstract ideas of the claim on which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements: “The system of claim 12, wherein the first or second transform comprises color jittering.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which
provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the first or second transform comprises color jittering.” This is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
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.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (Chen et al, “A Simple Framework for Contrastive Learning of Visual Representations”, 2020, hereinafter Chen) in view of Jiang et al (Jiang et al, “Pruning-aware Sparse Regularization for Network Pruning”, 2022, hereinafter Jiang) further in view of Modi (Modi, “ResNet — Understand and Implement from scratch”, 2021).
Regarding claim 1 Chen discloses:
“Providing a set of input data” (“A stochastic data augmentation module that transforms any given data example randomly resulting in two correlated views of the same example” (Chen, pp.2 section 2.1)). Any given data example is interpreted as a set of input data.
“Performing first and second transforms on the input data to generate first and second augmented data, to provide first and second transformed base paths into first and second machine learning algorithm encoders” (“A stochastic data augmentation module that transforms any given data example randomly resulting in two correlated views of the same example, denoted Xi and Xj, which we consider as a positive pair. In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur” (Chen, pp.2 section 2.1)
“A neural network base encoder f(·) that extracts representation vectors from augmented data examples.” (Chen, pp.2 section 2.1))
Chen teaches a stochastic data augmentation module that transforms any given data example randomly (first and second transforms) resulting in two correlated views of the same example (first and second augmented data). The 2 augmented data is then fed into 2 neural network base encoders. There is one encoder f(·) for each augmented data path (first and second machine learning algorithm encoders), as shown in the diagram of the SimCLR framework. This passing of first and second augmented data to the 2 base encoders is interpreted as the first and second transformed base paths.
“Segmenting the first and second augmented data” (“A neural network base encoder f(·) that extracts representation vectors from augmented data examples. Our framework allows various choices of the network architecture without any constraints” (Chen, pp.2 section 2.1)). A function performed by base encoders is segmentation of data. This base encoder is applied to both contrastive branches after the data augmentation in the SimCLR framework of Chen resulting in segmentation of augmented data in both branches.
“Calculating first and second main base path outputs by applying a weighting to the segmented first and second augmented data” (“A small neural network projection head g(·) that maps representations to the space where contrastive loss is applied” (Chen, pp.2 section 2.1)). A function of projection heads is applying weights to data. This projection head is applied to both branches of the SimCLR framework after the base encoders resulting in a set of 2 weighted data.
Image is a diagram of the SimCLR framework shown in Chen
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Chen does not disclose:
“Calculating first and second pruning masks from the input and first and second augmented data to apply to the first and second base paths of the first and second machine learning algorithm encoders, the pruning masks having a binary value for each segment in the segmented first and second augmented data, respectively”
“Calculating first and second sparse conditional path outputs by performing a computation on the segments of the segmented first and second augmented data which are designated with a binary one in the first and second pruning masks, respectively”
“Calculating a final output as a sum of the first and second main base path outputs and the first and second sparse conditional path outputs”
However, Jiang discloses:
“Calculating first and second pruning masks from the input and first and second augmented data to apply to the first and second base paths of the first and second machine learning algorithm encoders, the pruning masks having a binary value for each segment in the segmented first and second augmented data, respectively” (“The indexes are transformed into a binary pruning mask in previous methods. In our method, we use the mask to identify which channels to apply the sparse regularization in the second stage” (Jiang pp. 4 section 3.4)) Jiang teaches indexes (first and second augmented data) that are transformed into a binary pruning mask (binary value for each segment).
“Calculating first and second sparse conditional path outputs by performing a computation on the segments of the segmented first and second augmented data which are designated with a binary one in the first and second pruning masks, respectively” (“According to Equation 4, the pruning masks Mi is a binary vector consisting of 0 and 1… Specifically, we propose to apply the sparse regularization only on the unimportant channels. Based on Equation 3, we can describe our MaskSparsity method as the Equation 5… where M denotes the binary mask, indicating the unimportant channels of the whole network. For the important channels, the values in the channel mask are 0. Therefore, these channels are not affected by the sparse regularization and are trained as normal” (Jiang pp.4 section 3.4)) Jiang teaches pruning masks which are binary vectors consisting of 0 and 1. The important channels (segments) are designated with a 0, and although not explicitly stated, it is implied that the unimportant channels are designated by a 1. Jiang further teaches calculating first and second sparse conditional path outputs by performing a computation on the segments of the segmented first and second augmented data which are designated with a binary one in the first and second pruning masks (Specifically, we propose to apply the sparse regularization only on the unimportant channels (designated by a 1)).
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Equation 5
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It would have been obvious to one of ordinary skill in the art before the
effective filing date of the present application to combine Chen and Jiang. Chen teaches SimCLR, a contrastive learning framework for learning visual representations. Jiang teaches MaskSparsity, a method of pruning aware sparse regularization for convolutional neural networks (CNNs). One of ordinary skill would have motivation to combine the framework of Chen with the pruning aware sparse regularization of Jiang as they are both related to image processing in machine learning. “Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity. In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. Moreover, it restricts the network’s capacity, which leads to under-fitting. To solve this problem, we propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization.” (Jiang pp.1 Abstract). By applying MaskSparsity to SimCLR it would reduce redundant channels while maintaining model accuracy and preventing under fitting.
Furthermore, Modi discloses:
“Calculating a final output as a sum of the first and second main base path outputs and the first and second sparse conditional path outputs” (“A Skip/Residual connection takes the activations from an (n-1)ᵗʰ convolution layer and adds it to the convolution output of (n+1)ᵗʰ layer and then applies ReLU on this sum, thus Skipping the nᵗʰ layer.” (Modi “Skip Connection/Residual Connection”)). As seen in the included ResNet architectural diagram the f(x(n-1)) path (main base path) is added with the X(n+1) path (conditional path) and a final output (f(x(n-1) + X(n+1)) is calculated.
ResNet Architecture
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Chen, Jiang, and Modi. Chen and Jiang teach the SimCLR framework in combination with pruning aware sparse regularization. Modi teaches Skip Connection/Residual Connection using Resnet architecture, which is often used with CNNs and computer vision machine learning. One of ordinary skill would have motivation to combine the framework and pruning aware sparse regularization of Chen and Jiang with Modi as they are all related to image processing in machine learning. “As a result, it was proposed that adding more layers to a deep neural network should either increase its performance or let it stay the same, but it should never decrease the performance. In order to achieve this, they came up with the concept of Skip connections/Residual connections, by use of which we can avoid loss of information flow” (Modi paragraph 2). By combining the Resnet architecture of Modi with the contrastive learning branches of Chen utilizing the pruning aware sparse regularization of Jiang, loss of information flow can be prevented during pruning and sparse regularization in each of the contrastive branches.
Regarding Claim 2 Chen in view of Jiang further in view of Modi discloses:
“Wherein the input data is a set of two-dimensional images” (“Most of our study for unsupervised pretraining (learning encoder network f without labels) is done using the ImageNet ILSVRC-2012 dataset” (Chen pp.3 section 2.3)). The ImageNet ILSVRC-2012 dataset contains two dimensional images.
Regarding Claim 3 Chen in view of Jiang further in view of Modi discloses:
“Wherein the machine learning algorithm is an unlabeled, self- supervised machine learning algorithm” (“This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank” (Chen pp.1 Abstract)). Self-supervised learning models imply unlabeled data is used for training.
Regarding Claim 4 Chen in view of Jiang further in view of Modi discloses:
“Wherein the machine learning algorithm does not use a pre- trained dense model” (“This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank” (Chen pp.1 Abstract)). A pre-trained dense model is interpreted as a specialized architecture.
Regarding Claim 5 Chen in view of Jiang further in view of Modi discloses:
“Wherein the second transform comprises an inverse diagonal transform” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur” (Chen pp.2 Section 2.1)). Color distortion is interpreted as a diagonal transform, and resizing back to original size is interpreted as an inverse operation.
Regarding Claim 6 Chen in view of Jiang further in view of Modi discloses:
“The method of claim 1, further comprising eliminating irregular sparse indexes” (“Therefore, in this paper, we design a fine-grained sparse training strategy to alleviate the damage of the sparse regularization loss on important channels. Specifically, we propose to apply the sparse regularization only on the unimportant channels” (Jiang pp.5 Section 3.4)). Jiang teaches eliminating irregular sparse indexes (apply the sparse regularization(eliminating) only on the unimportant channels(irregular sparse indexes).
Regarding Claim 7 Chen in view of Jiang further in view of Modi discloses:
“The method of claim 1, the method not comprising introducing additional feature importance predictors” (“Figure 2. A simple framework for contrastive learning of visual representations. Two separate data augmentation operators are sampled from the same family of augmentations (t ∼ T and t ∼T) and applied to each data example to obtain two correlated views. A base encoder network f(·) and a projection head g(·) are trained to maximize agreement using a contrastive loss. After training is completed, we throw away the projection head g(·) and use encoder f(·) and representation h for downstream tasks.”). No additional feature importance predictors are included in the SimCLR framework. Refer to diagram of SimCLR framework.
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Regarding Claim 8 Chen in view of Jiang further in view of Modi discloses:
“Wherein the computation is a sparse computation” (“Specifically, we propose to apply the sparse regularization only on the unimportant channels” (Jiang pp.5 Section 3.4)). Sparse regularization is interpreted as a sparse computation.
Regarding Claim 9 Chen in view of Jiang further in view of Modi discloses:
“The method of claim 1, further comprising applying a conditional weighting to the first and second conditional path outputs” (“A small neural network projection head g(·) that maps representations to the space where contrastive loss is applied” (Chen pp.2 section 2.1)). A function of projection heads is applying weights to data. This projection head is applied to the first and second conditional path outputs.
Regarding Claim 10 Chen in view of Jiang further in view of Modi discloses:
“Wherein the first transform is different from the second transform” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur. As shown in Section 3, the combination of random crop and color distortion is crucial to achieve a good performance” (Chen pp.2 section 2.1)). The applied transforms are random, so the first transform is different from the second.
Regarding Claim 11 Chen in view of Jiang further in view of Modi discloses:
“The method of claim 10, wherein the first and second transforms comprise color jittering” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur. As shown in Section 3, the combination of random crop and color distortion is crucial to achieve a good performance” (Chen pp.2 section 2.1)). Color jittering is interpreted as color distortion.
Regarding claim 12 Chen discloses:
“A plurality of input data elements” (“A stochastic data augmentation module that transforms any given data example randomly resulting in two correlated views of the same example” (Chen, pp.2 section 2.1)). Any given data example is interpreted as a set of input data.
“First and second transformation modules configured to perform first and second transforms on an input data element selected from the plurality of input data elements, having as an output first and second augmented data” (“A stochastic data augmentation module that transforms any given data example randomly resulting in two correlated views of the same example, denoted Xi and Xj, which we consider as a positive pair. In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur” (Chen, pp.2 section 2.1) Chen teaches a stochastic data augmentation module that transforms any given data example randomly (first and second transforms) resulting in two correlated views of the same example (first and second augmented data).
“First and second segmentation modules configured to segment each of the first and second augmented data into informative features and uninformative features” (“A neural network base encoder f(·) that extracts representation vectors from augmented data examples. Our framework allows various choices of the network architecture without any constraints” (Chen, pp.2 section 2.1)). A function of base encoders is segmentation of data. This base encoder is applied to both contrastive branches after the data augmentation in the SimCLR framework of Chen resulting in segmentation of augmented data in both branches.
“First and second base paths, configured to apply a base path weight to the uninformative features and provide as an output first and second weighted uninformative features” (“A small neural network projection head g(·) that maps representations to the space where contrastive loss is applied” (Chen, pp.2 section 2.1)). A function of projection heads is applying weights to data. The uninformative features are passed into the projection head, weights are applied, and weighted uninformative features are outputted in both contrastive branches.
Chen does not disclose:
“First and second masking modules, configured to generate first and second sparse pruning masks from the first and second weighted uninformative features, each sparse pruning mask having a binary value for each segment in the segmented first and second augmented data, respectively”
“A first convolution module configured to convolve the first sparse pruning mask, the first informative features, and a conditional weighting, and configured to provide as an output a first sparse feature output”
“A second convolution module configured to convolve the second sparse pruning mask, the second informative features, and the conditional weighting, and configured to provide as an output a second sparse feature output”
“First and second output modules configured to add the first and second sparse feature outputs to the first and second uninformative features, configured to provide first and second final outputs”
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the present application to combine Chen and Jiang. Chen teaches SimCLR, a contrastive learning framework for learning visual representations. Jiang teaches MaskSparsity, a method of pruning aware sparse regularization for convolutional neural networks (CNNs). One of ordinary skill would have motivation to combine the framework of Chen with the pruning aware sparse regularization of Jiang as they are both related to image processing in machine learning. “Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity. In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. Moreover, it restricts the network’s capacity, which leads to under-fitting. To solve this problem, we propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization.” (Jiang pp.1 Abstract). By applying MaskSparsity to SimCLR it would reduce redundant channels while maintaining model accuracy and preventing under fitting.
However Jiang discloses:
“First and second masking modules, configured to generate first and second sparse pruning masks from the first and second weighted uninformative features, each sparse pruning mask having a binary value for each segment in the segmented first and second augmented data, respectively” (“The first stage is the sparsity training stage with global sparse regularization, which is aimed to get the indexes of the unimportant channels. The indexes are transformed into a binary pruning mask in previous methods. In our method, we use the mask to identify which channels to apply the sparse regularization in the second stage” (Jiang pp.4 section 3.4)). Jiang teaches first and second masking modules, configured to generate first and second sparse pruning masks from the first and second weighted uninformative features (indexes of unimportant channels), each sparse pruning mask having a binary value for each segment in the segmented first and second augmented data, respectively (indexes are transformed into a binary pruning mask). This mask can be applied to both contrastive branches as a first and second masking module.
“A first convolution module configured to convolve the first sparse pruning mask, the first informative features, and a conditional weighting, and configured to provide as an output a first sparse feature output” (“These methods apply sparse regularization on the filter weights of the convolution layers [1,34] or scaling factors [14,26] of the batch normalization layers. After the sparsity training, the corresponding filter weights or scaling factors of unimportant channels are considered to be near zero. Then these channels could be safely pruned without affecting the output values of the corresponding layers too much. We call these methods sparsity-training-based methods” (Jiang, pp.1 Introduction)). Jiang teaches a first convolution module (convolution layers) configured to convolve the first sparse pruning mask, the first informative features, and a conditional weighting, and configured to provide as an output a first sparse feature output (scaling factors of unimportant channels are considered to be near zero).
“A second convolution module configured to convolve the second sparse pruning mask, the second informative features, and the conditional weighting, and configured to provide as an output a second sparse feature output” (“These methods apply sparse regularization on the filter weights of the convolution layers [1,34] or scaling factors [14,26] of the batch normalization layers. After the sparsity training, the corresponding filter weights or scaling factors of unimportant channels are considered to be near zero. Then these channels could be safely pruned without affecting the output values of the corresponding layers too much. We call these methods sparsity-training-based methods” (Jiang, pp.1 Introduction)). Jiang teaches a first convolution module (convolution layers) configured to convolve the first sparse pruning mask, the first informative features, and a conditional weighting, and configured to provide as an output a first sparse feature output (scaling factors of unimportant channels are considered to be near zero).
Furthermore Modi discloses:
“First and second output modules configured to add the first and second sparse feature outputs to the first and second uninformative features, configured to provide first and second final outputs” (“A Skip/Residual connection takes the activations from an (n-1)ᵗʰ convolution layer and adds it to the convolution output of (n+1)ᵗʰ layer and then applies ReLU on this sum, thus Skipping the nᵗʰ layer.” (Modi “Skip Connection/Residual Connection”)). As seen in the included ResNet architectural diagram the f(x(n-1)) path (uninformative features) is added with the X(n+1) path (sparse features) and a final output (f(x(n-1) + X(n+1)) is calculated.
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Chen, Jiang, and Modi. Chen and Jiang teach the SimCLR framework in combination with pruning aware sparse regularization. Modi teaches Skip Connection/Residual Connection using Resnet architecture, which is often used with CNNs and computer vision machine learning. One of ordinary skill would have motivation to combine the framework and pruning aware sparse regularization of Chen and Jiang with Modi as they are all related to image processing in machine learning. “As a result, it was proposed that adding more layers to a deep neural network should either increase its performance or let it stay the same, but it should never decrease the performance. In order to achieve this, they came up with the concept of Skip connections/Residual connections, by use of which we can avoid loss of information flow” (Modi paragraph 2). By combining the Resnet architecture of Modi with the contrastive learning branches of Chen utilizing the pruning aware sparse regularization of Jiang, loss of information flow can be prevented during pruning and sparse regularization in each of the contrastive branches.
Regarding Claim 13 Chen in view of Jiang further in view of Modi discloses:
“Wherein the plurality of input data elements are two-dimensional images” (“Most of our study for unsupervised pretraining (learning encoder network f without labels) is done using the ImageNet ILSVRC-2012 dataset” (Chen pp.3 section 2.3)). The ImageNet ILSVRC-2012 dataset contains two dimensional images.
Regarding Claim 14 Chen in view of Jiang further in view of Modi discloses:
“Wherein the first transform is different from the second transform” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur. As shown in Section 3, the combination of random crop and color distortion is crucial to achieve a good performance” (Chen pp.2 section 2.1)). The applied transforms are random, so the first transform is different from the second.
Regarding Claim 15 Chen in view of Jiang further in view of Modi discloses:
“Wherein the first or second transform comprises an inverse diagonal transform” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur” (Chen pp.2 Section 2.1)). Color distortion is interpreted as a diagonal transform, and resizing back to original size is interpreted as an inverse operation.
Regarding Claim 16 Chen in view of Jiang further in view of Modi discloses:
“Wherein the first or second transform comprises color jittering” (“In this work, we sequentially apply three simple augmentations: random cropping followed by resize back to the original size, random color distortions, and random Gaussian blur. As shown in Section 3, the combination of random crop and color distortion is crucial to achieve a good performance” (Chen pp.2 section 2.1)). Color jittering is interpreted as color distortion.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOWIE CHAN whose telephone number is (571)270-1110. The examiner can normally be reached 8:00am-5:00pm.
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/HOWIE CHAN/Examiner, Art Unit 2147 /MARC S SOMERS/Primary Examiner, Art Unit 2159
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