DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 12/9/2020, the Remarks and Amendments filed on 5/9/2025.
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 1-3, 5, 7-10, 12, 14-17, and 19-20 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”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
obtaining a set of loss terms from known machine learning models that implement a domain adversarial neural network (DANN) architecture … extract a domain-invariant feature vector (F) and a deep label predictor (G), wherein features associated with the source domain are not distinguished from features associated with the target domain within the feature vector (F), and the backpropagation comprises a domain classifier (D) connected to the feature extractor via a gradient reversal layer, wherein the gradient reversal layer is configured to multiply a gradient by a negative constant during backpropagation-based training: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of obtaining loss terms from models that implement a DANN architecture, wherein the models use math to extract features and multiply gradients by a negative constant. These loss terms are obtained through mathematical calculations as evidenced by equation 3 and paragraph [0022] of the originally filed specification.
the DANN architecture being configured to minimize classification error E(F, G) and to minimize a statistical distance across a source domain distribution P and a target domain dataset O according to [equation omitted for clarity]: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of minimizing error and a statistical distance, wherein the models use math to do the minimizing as evidenced by the equation in the limitation.
generating … a target domain machine learning model based on the source domain dataset, the target domain dataset, the feature vector, and the set of loss terms without determining corresponding labels for the target domain dataset, determining a set of parameters including co (weights of the customized losses in the set of loss terms S), a (weights of potential operations between two nodes in the feature extractor F), and 6 (weights of a feature network), by iterating an outer loop until the set of loss terms converges, the outer loop including and inner loop having a loss function [(0) of the reinforcement learning model parameterized by 0, and iterating the inner loop within the outer loop until a value of 0 converges, and using the loss function at the converged value of 0 to determine a co for the current iteration of the outer loop, determining an a for the current iteration of the outer loop using the co and determining a 6 of the current iteration of the outer loop using the a and the w, wherein generating the set of loss terms S includes obtaining discriminability loss terms SD and transferability loss terms STto obtain S = {SDiT}, and wherein obtaining the transferability loss terms STincludes obtaining, from the known machine learning models, loss terms of the maximum portion: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of generating a target domain machine learning (ML) model based on the datasets, feature vector, and set of loss terms, determining parameters, and iterating a loop until convergence. The target domain ML model is generated through mathematical calculations as evidenced by the algorithm disclosed in Figure 4 of the originally filed specification.
Step 2A Prong 2: The claim does not recite any additional elements which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “obtaining, using a processor system, a source domain dataset, wherein the source domain dataset includes corresponding labels, and the source domain dataset and the corresponding labels are associated with training a source domain machine learning model; obtaining, using the processor system, a target domain dataset without corresponding labels; obtaining, using the processor system, a feature vector that identifies features in the source domain dataset and the target domain dataset without an indication of domain”, “wherein the DANN architecture includes feed-forward propagation and backpropagation and a formulation of the DANN architecture includes a minimum portion and a maximum portion”, “wherein the feed-forward propagation comprises a feature extractor” and “by the processor system”.
The additional elements of “using a processor system”, “a feature extractor that”, “by the processor system”, “wherein the DANN architecture includes feed-forward propagation and backpropagation and a formulation of the DANN architecture includes a minimum portion and a maximum portion” and “wherein the feed-forward propagation comprises a feature extractor” amount to invoking computers, other machinery, or a generic or well-known DANN architecture merely as a tool to perform existing processes or judicial exceptions. Thus, this additional element is recited in a manner that represents no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “obtaining, … a source domain dataset, wherein the source domain dataset includes corresponding labels, and the source domain dataset and the corresponding labels are associated with training a source domain machine learning model; obtaining, using the processor system, a target domain dataset without corresponding labels; obtaining, using the processor system, a feature vector that identifies features in the source domain dataset and the target domain dataset without an indication of domain” are insignificant extra solution activities (see MPEP § 2106.05(g)).
Thus, even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea
Step 2B: Finally, the claim taken as a whole does not contain any additional elements which provide significantly more than the abstract ideas.
The additional elements of “using a processor system”, “a feature extractor that”, “by the processor system”, “wherein the DANN architecture includes feed-forward propagation and backpropagation and a formulation of the DANN architecture includes a minimum portion and a maximum portion” and “wherein the feed-forward propagation comprises a feature extractor” amount to invoking computers, other machinery, or a generic or well-known DANN architecture merely as a tool to perform existing processes or judicial exceptions. Thus, this additional element is recited in a manner that represents no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “obtaining, … a source domain dataset, wherein the source domain dataset includes corresponding labels, and the source domain dataset and the corresponding labels are associated with training a source domain machine learning model; obtaining, using the processor system, a target domain dataset without corresponding labels; obtaining, using the processor system, a feature vector that identifies features in the source domain dataset and the target domain dataset without an indication of domain;” are insignificant extra solution activities (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d); “Receiving or transmitting data over a network” and “Storing and retrieving information in memory”).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“initializing a model with the DANN architecture”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of initializing a model, which achieved through mathematical calculation.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas. As such, the claim is ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining parameters for the model with the DANN architecture. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining parameters for a model, 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, Step 2B: The claim does not recite any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas. As such, the claim is ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
obtaining, from the known machine learning models, loss terms of the minimum portion: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of obtaining loss terms from models that implement a DANN architecture. These loss terms are obtained through mathematical calculations as evidenced by equation 3 and paragraph [0022] of the originally filed specification.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract ideas. As such, the claim is ineligible.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The claim recites the additional element of “wherein obtaining the target domain machine learning model includes using reinforcement learning” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the ML model is obtained using reinforcement learning. Thus, this additional element is recited in a manner that represents no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)). Nothing in the claim integrates the abstract idea into a practical application, nor does it provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Claim 8
Step 1: The claim recites a system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 1.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 2.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 3.
Claim 12
Step 1: A machine, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 5.
Claim 14
Step 1: A machine, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 7.
Claim 15
Step 1: The claim recites a computer program product; therefore, it is directed to the statutory category of a manufacture. Note that this claim has sufficient structure and is statutory because the specification at [0040] states that the computer-readable storage medium does not encompass signals.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 1.
Claim 16
Step 1: A manufacture, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 2.
Claim 17
Step 1: A manufacture, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 3.
Claim 19
Step 1: A manufacture, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 5.
Claim 20
Step 1: A manufacture, as above.
Step 2A Prong 1, Step 2A, Prong 2, and Step 2B: The claim is rejected for the using the same rationale as claim 7.
Response to Arguments
Applicant’s arguments and amendments, filed on 5/9/2025, with respect to the 35 USC § 101 rejection of the pending claims have been fully considered and are not persuasive.
Beginning on page 10 of the remarks, filed on 5/9/2025, Applicant argues “In light of these FIGS, and the particular description at paragraphs 23 and 24, Applicant has further amended the claims to define: … This feature is narrowly tailored to the new and inventive iterative operations that allow for the generation of the target domain machine learning model without generating labels for the target domain. Furthermore, these features provide for the new machine learning model for a target domain dataset using discriminability loss terms and transferability loss terms and are an improvement over the existing DANN technology. As the amendments are drawn to the structure of the DANN technology that achieves this, rather than to specific mathematical algorithms and operations, Applicant understands the claims to eligible under 35 USC 101”. Examiner respectfully disagrees.
The amendments to the independent claims recite further abstract ideas in the form of mathematical concepts. Specifically, the step of generating the target domain ML model is performed by implementing an algorithm that is depicted as a series of mathematical calculations or steps as shown in Figure 4 of the originally filed specification. Applicant has failed to provide any evidence that the newly presented amendments of the independent claims are anything but further details for the implementation of a mathematical algorithm. The alleged improvements appear to be an improvement to the algorithm or abstract idea in the form of a mathematical concept. Abstract ideas alone cannot reflect a technical improvement. See MPEP §2106.05(a).
Applicant next argues “the amendments, as well as the previously claimed features, define a specific practical structure for a DANN and do not purport to limit all applications of the alleged abstract idea. As such, the alleged abstract idea is incorporated into a practical application and is eligible for this reason as well”. Examiner respectfully disagrees.
The additional elements of the claims do not integrate the judicial exceptions or abstract ideas into a practical application.
The additional elements of “using a processor system”, “a feature extractor that”, “by the processor system”, “wherein the DANN architecture includes feed-forward propagation and backpropagation and a formulation of the DANN architecture includes a minimum portion and a maximum portion” and “wherein the feed-forward propagation comprises a feature extractor” amount to invoking computers, other machinery, or a generic or well-known DANN architecture merely as a tool to perform existing processes or judicial exceptions. Thus, this additional element is recited in a manner that represents no more than mere instructions to apply the judicial exceptions on a computer (see MPEP § 2106.05(f)).
The additional elements of “obtaining, … a source domain dataset, wherein the source domain dataset includes corresponding labels, and the source domain dataset and the corresponding labels are associated with training a source domain machine learning model; obtaining, using the processor system, a target domain dataset without corresponding labels; obtaining, using the processor system, a feature vector that identifies features in the source domain dataset and the target domain dataset without an indication of domain” are insignificant extra solution activities (see MPEP § 2106.05(g)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea. Applicant has failed to identify any specific additional element claim language or evidence from the specification to suggest that the claims are directed towards anything more than the abstract ideas of the claim or integrate the abstract ideas into a practical application.
As such, Applicant’s arguments are not persuasive, and the 35 USC § 101 rejection of the pending claims STANDS.
Conclusion
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 Brent Hoover whose telephone number is (303)297-4403. The examiner can normally be reached Monday - Friday 9-5 MST.
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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