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 .
Response to Amendment
This Office Action is in response to applicant’s communication filed 16 December 2025, in response to the Office Action mailed 17 September 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 2-4, 7-17, 21-23, and 25-28 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 2-4 and 7-17 recite a method and claims 21-23 and 25-28 recite a device. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 2:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
using a self-supervised loss function – this is describing a mathematical function/formula (the self-supervised loss function is a mathematical formula – see, e.g., Equation (1) in the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method of cross-domain adaptive machine learning, comprising: tuning a target domain feature extraction model that comprises a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the source domain feature extraction model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and deploying the target domain feature extraction model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method of cross-domain adaptive machine learning, comprising: tuning a target domain feature extraction model that comprises a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the source domain feature extraction model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and deploying the target domain feature extraction model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
The claim recites the following additional mathematical concept elements:
wherein the self-supervised loss function comprises a contrastive loss function – this is describing a mathematical function/formula (the contrastive, self-supervised loss function is a mathematical formula – see, e.g., Equation (1) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
The claim recites the following additional mathematical concept elements:
augmenting the source data set by performing one or more transformations on one or more samples of the source data set – this is describing a mathematical function/formula (the transformations are a mathematical function – see, e.g., para. [0036] of the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
wherein generating the set of binary masks based on the set of masks comprises: adding logistic noise to the set of masks – this is describing a mathematical function/calculation (producing random noise from a logistic distribution).
applying a nonlinear activation function to the set of masks – this is describing a mathematical function/formula (the nonlinear activation function is a mathematical formula – see, e.g., Equation (2) in the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein training the mask generation model comprises: generating a set of positive features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of negative features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating a set of masks using the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of binary masks based on the set of masks – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein training the mask generation model comprises: generating a set of positive features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of negative features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating a set of masks using the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of binary masks based on the set of masks – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mathematical concept elements:
wherein the nonlinear activation function comprises a sigmoid function – this is describing a mathematical function/formula (see, e.g., Equation (2) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
using a loss function comprising a cross-entropy loss component – this is describing a mathematical function/formula (see, e.g., Equation (1) in the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein training the mask generation model comprises: generating a set of positive features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of negative features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the mask generation model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
based on the set of positive features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein training the mask generation model comprises: generating a set of positive features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and generating a set of negative features based on the target data set and the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the mask generation model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
based on the set of positive features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
The claim recites the following additional mathematical concept elements:
wherein the loss function further comprises a maximum entropy loss component – this is describing a mathematical function/formula (see, e.g., Equation (4) in the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
based on the set of negative features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
based on the set of negative features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
The claim recites the following additional mathematical concept elements:
wherein the loss function further comprises a divergence loss component – this is describing a mathematical function/formula (see, e.g., Equation (5) in the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
based on the set of positive features and the set of negative features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
based on the set of positive features and the set of negative features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
The claim recites the following additional mathematical concept elements:
wherein the loss function further comprises: a first weighting parameter for the cross-entropy loss component; a second weighting parameter for the maximum entropy loss component; and a third weighting parameter for the divergence loss component – this is describing parameters of a mathematical function/formula (see, e.g., Equation (6) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 13:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
using a loss function comprising a regularization loss component – this is describing a mathematical function/formula (see, e.g., Equation (7) in the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the target domain feature extraction model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method, comprising: tuning a target domain feature extraction model using a source domain feature extraction model trained on a source data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein: the tuning is performed using a mask generation model trained on a target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the tuning is performed using the target data set – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the target domain feature extraction model is trained – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 14:
The claim recites the following additional mathematical concept elements:
wherein the regularization loss component comprises a Euclidean distance function – this is describing a mathematical function/formula (see, e.g., Equation (7) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 15:
The claim recites the following additional mathematical concept elements:
wherein the loss function further comprises a cross-entropy loss component – this is describing a mathematical function/formula (see, e.g., Equation (1) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 16:
The claim recites the following additional mathematical concept elements:
wherein for a given sample, the cross-entropy loss component is configured to generate a cross-entropy loss value – this is describing a mathematical function/formula (see, e.g., Equation (1) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
based on a positive feature generated by the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
based on the given sample and a classification output generated by a linear classification model based on the given sample – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
based on a positive feature generated by the mask generation model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
based on the given sample and a classification output generated by a linear classification model based on the given sample – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 17:
The claim recites the following additional mathematical concept elements:
wherein the loss function further comprises a weighting parameter for the regularization loss component – this is describing parameters of a mathematical function/formula (see, e.g., Equation (6) in the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 21:
See the rejection of claim 2 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A processing system, comprising – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
a memory comprising computer-executable instructions – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 22, see the rejection of claim 3, above.
As per claim 23, see the rejection of claim 4, above.
As per claim 25, see the rejection of claim 7, above.
As per claim 26, see the rejections of claims 9-11, above.
As per claim 27, see the rejection of claim 12, above.
As per claim 28, see the rejections of claims 13-14, above.
Claim Rejections - 35 USC § 102
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 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, 9-12, 18-20, 26, 27, and 29 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jin et al. (Feature Alignment and Restoration for Domain Generalization and Adaptation, June 2020, pgs. 1-15 – cited in an IDS).
As per claim 1, Jin teaches a method of cross-domain adaptive machine learning, comprising: tuning a target domain feature extraction model that comprises a source domain feature extraction model trained on a source data set [a Feature Alignment and Restoration (FAR) framework is used to tune an unsupervised domain adaptation (UDA) model (pg. 1, abstract; etc.) including a feature extraction model for a target domain(s) (tuning a target domain feature extraction model) trained from a backbone network including feature extraction (pg. 3, fig. 2; etc.); where the backbone network is the source domain feature extraction model trained on the source data set (see, e.g., pgs. 3-4, sections 3-3.1; where Fs1 is a feature map extracted from data of a first source domain; etc.)], wherein: the tuning is performed using a mask generation model trained on a target data set [In the FA phase, we first leverage spatial and channel attention to adaptively select features from the extracted feature map F of the backbone network and enforce across domain alignment constraints on them. Then, the FR step distills task-relevant (discriminative) features from the residual to compensate for the aligned features. Moreover, in the FR phase, we design a dual ranking entropy loss constraint to promote the distillation of the task-relevant features from the residual, trained on unlabeled images from a target domain (pgs. 3-4, sections 3-3.1; pg. 5, section 3.2; etc.); where the spatial/channel attention selecting features is the mask generation model], and the tuning is performed using the target data set [the target models are trained using a target domain set of unlabeled images (target data set) (pg. 3, section 3; pg. 5, section 3.2; etc.)]; and deploying the target domain feature extraction model [the model is deployed and tested on a target domain (pg. 3, section 3; pg. 6, table 1; etc.)].
As per claim 5, Jin teaches wherein training the mask generation model comprises: generating a set of positive features based on the target data set and the mask generation model; and generating a set of negative features based on the target data set and the mask generation model [the FA and FR phases are used to disentangle the residual, R, into task-relevant (positive) features R+ and task-irrelevant (negative) features R- while training on the target domain image data set (pg. 4, section 3.1; see “Feature Alignment (FA) Phase” and “Feature Restoration (FR) Phase”, etc.)].
As per claim 9, Jin teaches wherein the mask generation model is trained using a loss function comprising a cross-entropy loss component based on the set of positive features [the FA/FR model loss function includes a weighted summation of alignment loss Lalign, dual ranking entropy loss LDRE, and basic classification loss (i.e., cross-entropy loss) Lcls (pg. 5, section 3.2; pg. 11, supplementary 2; etc.)].
As per claim 10, Jin teaches wherein the loss function further comprises a maximum entropy loss component based on the set of negative features [dual entropy loss functions are calculated from the enhanced feature vector f+, the contaminated feature vector f-, and the reference feature vector f; where the loss for the contaminated feature vector is L-DRE (maximum entropy loss component) (pg. 4, section 3.1 and equation (2), etc.)].
As per claim 11, Jin teaches wherein the loss function further comprises a divergence loss component based on the set of positive features and the set of negative features [dual entropy loss functions are calculated from the enhanced feature vector f+, the contaminated feature vector f-, and the reference feature vector f; which includes a divergence between the feature vectors for enhanced and contaminated features (pg. 4, section 3.1 and equation (2); pg. 7, section 4.4; etc.)].
As per claim 12, Jin teaches wherein the loss function further comprises: a first weighting parameter for the cross-entropy loss component; a second weighting parameter for the maximum entropy loss component; and a third weighting parameter for the divergence loss component [the FA/FR model loss function includes a weighted summation of alignment loss Lalign, dual ranking entropy loss LDRE, and basic classification loss (i.e., cross-entropy loss) Lcls (pg. 5, section 3.2; pg. 11, supplementary 2; etc.)].
As per claim 18, Jin teaches wherein the target domain feature extraction model comprises a neural network model [feature extraction can utilize a DNN (pg. 3, fig. 2; etc.)].
As per claim 19, Jin teaches generating an inference using the target domain feature extraction model [the trained model produces inferences and is compared to multiple baseline models (pgs. 5-6, sections 4-4.2 and Table 1; pg. 8, Tables 4-5; etc.)].
As per claim 20, see the rejection of claim 1, above, wherein Jin also teaches a processing system, comprising: a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation comprising [the method] [the trained model produces inferences and is compared to multiple baseline models (pgs. 5-6, sections 4-4.2 and Table 1; pg. 8, Tables 4-5; etc.); which requires implementing the model using instructions stored in a memory and executed by a processor].
As per claim 26, see the rejections of claims 9-11, above.
As per claim 27, see the rejection of claim 12, above.
As per claim 29, see the rejection of claim 19, above.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-4 and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (Feature Alignment and Restoration for Domain Generalization and Adaptation, June 2020, pgs. 1-15 – cited in an IDS) in view of Chen et al. (A Simple Framework for Contrastive Learning of Visual Representations, July 2020, pgs. 1-20 – cited in an IDS).
As per claim 2, Jin teaches the method of claim 1, as described above.
While Jin also teaches wherein the source domain feature extraction model is trained with a loss function (see above), it has not been relied upon for teaching wherein the source domain feature extraction model is trained using a self-supervised loss function.
Chen wherein the source domain feature extraction model is trained using a self-supervised loss function [a linear classifier is trained for image classification on self-supervised representations using a contrastive loss function (pg. 1, abstract; pg. 2, section 2.1; etc.), which includes the model learning generalizable features (i.e., feature extraction) (pg. 5, section 3.1; etc.); where the loss function is thus a self-supervised loss function; for the source domain feature extraction model of Jin, above].
Jin and Chen are analogous art, as they are within the same field of endeavor, namely building/training image classifier models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the self-supervised contrastive loss function for training the feature extraction and classifier model(s), as taught by Chen, for the loss function used to train the source domain feature extraction model in the system taught by Jin.
Chen provides motivation as [the classifier trained using self-supervised contrastive loss outperforms prior models, including supervised models, without requiring labeled data (pg. 1, abstract and section 1; etc.)].
As per claim 3, Jin/Chen teaches wherein the self-supervised loss function comprises a contrastive loss function [a backbone network, including a source domain feature extraction model, is trained on the source data set using a multi-part loss function (see, e.g., pgs. 3-4, sections 3-3.1; where Fs1 is a feature map extracted from data of a first source domain; pg. 5, section 3.2; etc.); where the classifier is trained for image classification on self-supervised representations using a contrastive loss function (Chen: pg. 1, abstract; pg. 2, section 2.1; etc.)].
As per claim 4, Jin/Chen teaches augmenting the source data set by performing one or more transformations on one or more samples of the source data set [the training data set is augmented using various transformations, such as random cropping, resizing, random color distortion, random Gaussian blur, etc. (Chen: pg. 2, section 2.1; etc.)].
As per claim 21, see the rejection of claim 2, above.
As per claim 22, see the rejection of claim 3, above.
As per claim 23, see the rejection of claim 4, above.
Claim(s) 6 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (Feature Alignment and Restoration for Domain Generalization and Adaptation, June 2020, pgs. 1-15 – cited in an IDS) in view of Li et al. (US 2021/0046861).
As per claim 6, Jin teaches generating a set of masks using the mask generation model [To enable the content-adaptive disentanglement, we use a spatial and channel attention module (similar to the attention in the FA, see Supplementary) as the gate (generated masks) to obtain task-relevant feature R+ and the remaining task-irrelevant feature R- (Jin: pg. 4, section 3.1; see “Feature Alignment (FA) Phase” and “Feature Restoration (FR) Phase”, etc.)];
While Jin teaches a mask generation model generating a set of masks (see above), it has not been relied upon for teaching generating a set of binary masks based on the set of masks.
Li teaches generating a set of binary masks based on the set of masks [the DNN may be trained to output a binary confidence mask indicating confidence that a pixel belongs to a class, using the activation function output, which may include ReLU or sigmoid functions (paras. 0025-26); based on the activation function output from the attention gates (masks) of Jin, above].
Jin and Li are analogous art, as they are within the same field of endeavor, namely building/training image classifiers.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate a set of binary masks, for the classifier, from the activation function outputs in the trained DNN, as taught by Li, from the activation function outputs generated from the gated attention (mask) generation in the DNN classifier taught by Jin.
Li provides motivation as [the binary masks produced by the DNN provide more accurate identification and localization for items/segments in the image(s) (para. 0005, etc.)].
As per claim 24, see the rejections of claims 5-6, above.
Claim(s) 7, 8, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin and Li as applied to claims 6 and 24, above, and further in view of Van den Oord et al. (US 2018/0365554).
As per claim 7, Jin/Li teaches wherein generating the set of binary masks based on the set of masks comprises: applying a nonlinear activation function to the set of masks [the channel attention gates are followed by activation function layers, including a ReLU or sigmoid (nonlinear) activation functions (Jin: pg. 10, supplementary 1; etc.); where the DNN may be trained to output a binary confidence mask indicating confidence that a pixel belongs to a class, using the activation function output, which may include ReLU or sigmoid functions (Li: paras. 0025-26)].
While Jin/Li teaches that the generated masks may include noise (see, e.g., Li: para. 0026), it has not been relied upon for teaching adding logistic noise to the set of masks.
Van den Oord teaches adding logistic noise to the set of masks [the feedforward network can add noise to generated outputs using a random noise vector that includes a noise value for each sample in the output, where the noise values in the noise vector come from a logistic distribution (i.e., logistic noise) (paras. 0065-66, etc.); for the set of masks of Jin/Li, above].
Jin/Li and Van den Oord are analogous art, as they are within the same field of endeavor, namely training convolutional neural networks.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include added logistic noise to the output vector generation of the DNN, as taught by Van den Oord, to generating the set of masks in the DNN taught by Jin/Li.
Van den Oord provides motivation as [using added (logistic) noise facilitates parallel processing and faster output generation (para. 0006, etc.) and allows the neural network to be effectively conditioned on the context input to generate a high-quality output example while having fewer parameters and being less computationally complex (para. 0024, etc.)].
As per claim 8, Jin/Li/Reisser/Van den Oord teaches wherein the nonlinear activation function comprises a sigmoid function [the channel attention gates are followed by activation function layers, including a ReLU or sigmoid (nonlinear) activation functions (Jin: pg. 10, supplementary 1; etc.); where the DNN may be trained to output a binary confidence mask indicating confidence that a pixel belongs to a class, using the activation function output, which may include ReLU or sigmoid functions (Li: paras. 0025-26)].
As per claim 25, see the rejection of claim 7, above.
Claim(s) 13-17 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (Feature Alignment and Restoration for Domain Generalization and Adaptation, June 2020, pgs. 1-15 – cited in an IDS) in view of Li et al. (US 2019/0258925 – hereinafter Shen to distinguish it from the reference cited above)
As per claim 13, Jin teaches wherein the target domain feature extraction model is trained using a loss function [we design a dual ranking entropy loss constraint to promote the distillation of the task-relevant features from the residual, trained on unlabeled images from a target domain (pgs. 3-4, sections 3-3.1; pg. 5, section 3.2; etc.)].
While Jin teaches training the target domain feature extraction model using a loss function (see above), it has not been relied upon for teaching the loss function comprising a regularization loss component.
Shen teaches a loss function comprising a regularization loss component [the loss function includes a number of weighted components, including a regularization loss function component (paras. 0103-104; equation (9); etc.)].
Jin and Shen are analogous art, as they are within the same field of endeavor, namely building/training an attention controlled neural network for image classification.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the regularization loss function component in the loss function, as taught by Shen, in the loss function used for training the target domain feature extraction model in the system taught by Jin.
Shen provides motivation as [adding the regularization loss improves the accuracy of the loss function in training the neural network (para. 0103, etc.)].
As per claim 14, Jin/Shen teaches wherein the regularization loss component comprises a Euclidean distance function [the regularization loss term includes a distance function (Shen: paras. 0103-104, etc.) which can be based on Euclidean distance (Shen: para. 0061, etc.)].
As per claim 15, Jin/Shen teaches wherein the loss function further comprises a cross-entropy loss component [the FA/FR model loss function includes a weighted summation of alignment loss Lalign, dual ranking entropy loss LDRE, and basic classification loss (i.e., cross-entropy loss) Lcls (Jin: pg. 5, section 3.2; pg. 11, supplementary 2; etc.); where the overall loss function can include a cross-entropy loss function (Shen: para. 0068, etc.)].
As per claim 16, Jin/Shen teaches wherein for a given sample, the cross-entropy loss component is configured to generate a cross-entropy loss value based on a positive feature generated by the mask generation model based on the given sample and a classification output generated by a linear classification model based on the given sample [the FA/FR model loss function includes a weighted summation of alignment loss Lalign, dual ranking entropy loss LDRE, and basic classification loss (i.e., cross-entropy loss) Lcls (Jin: pg. 5, section 3.2; pg. 11, supplementary 2; etc.); where the classification loss is determined from outputs of the FA/FR (mask generation) and classification models (Jin: pg. 3, fig. 2; etc.); where the overall loss function can include a cross-entropy loss function (Shen: para. 0068, etc.)].
As per claim 17, Jin/Shen teaches wherein the loss function further comprises a weighting parameter for the regularization loss component [the loss function includes a number of components weighted with a hyper-parameter, including a regularization loss function component (Shen: paras. 0103-104; equation (9); etc.)].
As per claim 28, see the rejections of claims 13-14, above.
Response to Arguments
Applicant's arguments filed 16 December 2025 have been fully considered but they are not persuasive.
Applicant argues that the independent claims are not rejected under 35 U.S.C. 101, and therefore the dependent claims, which narrow the scope of the independent claims, should also be eligible under 35 U.S.C. 101.
However, the reason the independent claims are not rejected under 35 U.S.C. 101 is because the independent claims do not recite the identified abstract ideas included in the dependent claims (see above). Similarly, “one claim may be ineligible because it is directed to a judicial exception without amounting to significantly more, but another claim dependent on the first may be eligible because it recites additional elements that do amount to significantly more, or that integrate the exception into a practical application.” See MPEP § 2106.04(d).
Applicant also argues that the claimed invention is directed to a technical solution to problems arising in the field of machine learning, and allows for “adapting machine learning models to different domains using few training samples.”
However, applicant has described an improvement to the mathematical concept(s) identified above. Therefore, (assuming that the invention provides these advantages) this amounts to an improvement to an abstract idea rather than to a computer or technology. See MPEP 2106.05(a). It appears that any benefits to the computer itself are based solely on the use of an improvement to the abstract idea(s), using generic computer components to apply the abstract idea(s). Additionally, to find a valid improvement to a computer or technology the specification must disclose the improvement and the claim must include the necessary components to realize the improvement. MPEP 2106.05(d)(1).
Applicant further argues that this technical solution is accomplished by training a feature extraction model and training a mask generator to tune the feature extraction model. Applicant argues that the claims embody the described solution in (1) tuning a target domain feature extraction model that comprises a source domain feature extraction model trained on a source data set, wherein: (2) the tuning is performed using a mask generation model trained on a target data set, and (3) the tuning is performed using the target data set.
However, the claims do not describe any specific architecture or features of these models. Therefore, reciting generic models defined only by the type of data they utilize amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Applicant also argues that the claims integrate the abstract idea into a practical application – namely, training and deploying machine learning models, where the training involves adapting machine learning models to different domains using few training samples.
However, applicant has described an improvement to the mathematical concept(s) identified above. Therefore, (assuming that the invention provides these advantages) this amounts to an improvement to an abstract idea rather than to a computer or technology. See MPEP 2106.05(a). It appears that any benefits to the computer itself are based solely on the use of an improvement to the abstract idea(s), using generic computer components to apply the abstract idea(s). Additionally, to find a valid improvement to a computer or technology the specification must disclose the improvement and the claim must include the necessary components to realize the improvement. MPEP 2106.05(d)(1).
Applicant also argues that an improvement to a technology or technical field is achieved by the mask generators selecting salient features from output of the source domain feature extractor based on a target domain.
However, this is not recited in the claims. To find a valid improvement to a computer or technology the specification must disclose the improvement and the claim must include the necessary components to realize the improvement. MPEP 2106.05(d)(1).
Applicant also points to a Board decision and Appeal Review Panel decision
However, the claims in the instant application are not the same as in the cited cases, nor do the same arguments apply 1:1. Additionally, the rejections do not “equate any machine learning algorithm with an unpatentable ‘algorithm’ and the remaining additional elements as generic computing components.” See the rejections, above.
Applicant also argues, regarding the rejections under 35 U.S.C. 102, that the cited art does not teach “the tuning is performed using a mask generation model trained on a target data set.”
However, Jin teaches In the FA phase, we first leverage spatial and channel attention to adaptively select features from the extracted feature map F of the backbone network and enforce across domain alignment constraints on them. Then, the FR step distills task-relevant (discriminative) features from the residual to compensate for the aligned features. Moreover, in the FR phase, we design a dual ranking entropy loss constraint to promote the distillation of the task-relevant features from the residual, trained on unlabeled images from a target domain (pgs. 3-4, sections 3-3.1; pg. 5, section 3.2; etc.); where the spatial/channel attention selecting features is the mask generation model. This is a mask generation model trained on a target data set.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-29 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yosinski et al. (How transferable are features in deep neural networks, Nov 2014, pgs. 1-5 – cited in an IDS) – discloses determining the transferability of features and using transferred features to initialize a network.
Jin et al. (Style Normalization and Restitution for Domain Generalization and Adaptation, 3 Jan 2021, pgs. 1-16 – cited in an IDS) – discloses DG/UDA by adding SNR blocks/layers to a network, including attention on positive/negative features and dual causality loss function(s).
Bousmalis et al. (Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks, Aug 2017, pgs. 1-15) – discloses a system/method training models for pixel-level UDA, including generating and applying binary masks.
Saito et al. (Maximum Classifier Discrepancy for Unsupervised Domain Adaptation, April 2018, pgs. 1-12) – discloses UDA utilizing discrepancy loss, including maximizing and minimizing discrepancy between classifiers.
Reisser (US 2019/0354865) – discloses quantizing weights of a neural network using a distribution modeled using logistic noise.
Puscas (US 11,544,532) – discloses training a neural network including a sigmoid activation function used to generate a mask that produces a binary mask.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GEORGE GIROUX/Primary Examiner, Art Unit 2128
Prosecution Insights