This Office action is in response to RCE filed on 12/15/2025.
DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/15/2025 has been entered.
Response to Amendments
3. Applicant’s amendments filed 10/23/2025 to claims are accepted and entered. In this amendment,
Claims 1 and 10-13 have been amended.
Claims 1-13 have been examined.
Response to Arguments
4. Applicant’s arguments filed 10/23/2025 have been fully considered.
Claims 1 and 10-13 were amended and thus, the 112(b) rejection has been withdrawn.
Regarding the 101 rejection, Applicant’s arguments are not persuasive.
Applicant submits that the rejection no longer is tenable in the wake of the
recent Appeals Review Panel decision by Director Squires in Ex parte Desjardins because the claims at issue here are directed to precisely the sort of improvement in AI technology that Director Squires warned should not be subjected to "overbroad reasoning" that risks categorically excluding AI innovations from patent protection in the United States ... " Ex parte Desjardins (Appeal No. 2024-000567) at 9. Since the claimed invention is directed to an improvement in AI technology, the reasoning of Desjardins applies to the present claims.
In response, the Examiner respectfully disagrees. In the Ex parte Desjardins, the Board agreed with Appellant that “the citing paragraph 21 of the Specification for support. Id. at 7-9; see also id. at 8 ("This training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems." The Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ¶ 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "use less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the
improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.
On the other hand, regarding the current claims, Applicant mentioned the innovation of the claimed invention as in paragraph 13 of the specification is to use an evaluation spectrum to establish a “pre-test” for a classifier before testing under real conditions, as explained in the Specification paras 11-12 and 15. However, para 13 and the whole specification do not disclose any specific classifier for a pre-test to be performed. As it is known in the art, a pre-testing in machine learning is a pre-trained classifier, such as using a validation set or cross-validation to measure the performance of a classifier, i.e., a decision tree or random forest, …) on unseen data before full development. Using a validation set or cross-validation are standard part of the overall machine learning development process. In addition, independent claims 10-11 recite “optimizing at least one hyperparameter in an objective that, when the classifier is trained and its performance is measured again, the performance is likely to improve”. However, the specification does not disclose how hyperparameter is created or built, i.e., by programmer or end-user to customize the hyperparameter to allow for, i.e., specific calculations, tailored workflow, and improved is designed software, i.e., to reducing redundancy, lowering development time, and/or relying on pre-test code.
The additional element of “classifier” when evaluating in the claims as a whole is used merely as a generic, routine tool to perform an abstract idea without solving a specific technical problem. The current claims do recite a specific way of an improvement of how machine learning model itself operates as in Ex parte Desjardins. Thus, the current claims are not eligible.
Please refer to the section of the 101 rejection below for further details regarding
the eligibility analysis.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-13 are rejected under 35 U.S.C. 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon,
or an abstract idea) without significantly more.
Regarding claims 1 and 10-13, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claims are to processes and machines, which are the statutory categories of invention.
Regarding independent claim 1, under Step 2A, Prong One of the test, the limitations below in “italic font”
“providing a set of test radar, ultrasound, or audio spectra that form
part of, and/or define, a common distribution or manifold” is process that, under its broadest reasonable interpretation, falls in the group of mental process, except for the recitation of the extra-solution activities (e.g., source/type of data).
“obtaining at least one evaluation spectrum that is a modification of at least one
test spectrum with a different semantic content as the at least one test spectrum, and/or does not form part of the common distribution or manifold; mapping, using the classifier, the at least one evaluation spectrum to the set of classification scores; determining the performance based on the set of classification scores, and/or on a further outcome produced by the classifier during processing of the evaluation spectrum; wherein the determining is based at least in part on a comparison between an outcome of the classifier for the evaluation spectrum, and an outcome that the classifier has outputted or should output for: the test spectrum from which the evaluation spectrum has been derived, and/or at least one other test spectrum from the set of test spectra,” are processes that, under their broadest reasonable interpretation, fall into the groups of mathematical concepts and mental processes (see underline text), except for the recitation of the extra-solution activities (i.e., obtaining data, test spectrum with a different semantic content as the at least one test spectrum, outcome produced by classifier, outputted or should output for) that are recited at a high level of generality (e.g., obtaining data, outputted or should output for).
Thus, claim 1 recites a judicial exception under Step 2A – prong One of the test.
Regarding independent claim 11, under Step 2A, Prong One of the test, the limitations below in “italic font”
“providing a classifier for radar, ultrasound, or audio spectra; training the
classifier by: setting at least one hyperparameter that affects an architecture of the
classifier, and/or the behavior of the training of the classifier; providing training spectra that are labelled with ground truth classification scores; training the classifier with an objective that, when given the training spectra, the classifier maps the training spectra to the ground truth classification scores; mapping, using the classifier, the at least one evaluation spectrum to the set of classification scores; and determining the performance based on the set of classification scores, and/or on a further outcome produced by the classifier during processing of the evaluation spectrum; wherein the determining is based at least in part on a comparison between an outcome of the classifier for the evaluation spectrum and an outcome that the classifier has outputted or should output for: the test spectrum from which the evaluation spectrum has been derived, and/or
at least one other test spectrum from the set of test spectra; optimizing the at least one hyperparameter with an objective that, when the classifier is trained and its performance is measured again, the performance is likely to improve; acquiring, using at least one radar, ultrasound or audio sensor carried by a vehicle, at least one radar, ultrasound, or audio spectrum; mapping, using the trained classifier, the at least one radar, ultrasound or audio spectrum to classification scores; determining an actuation signal based at least in part on the classification scores; and actuating a physical action of the vehicle with the actuation signal”, are processes that, under their broadest reasonable interpretation, fall into the groups of mathematical concepts and mental processes (see underline text above), except for the recitation of the extra-solution activities (i.e., source/type of data).
Thus, claim 11 recites a judicial exception under Step 2A – prong One of the test.
Further, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular,
the additional elements recited in the claim 1:
“A method for measuring performance of a classifier for radar, ultrasound, or
audio spectra, the spectrum includes a dependence of at least one measurement
quantity that has been derived from a radar, ultrasound, or audio signal on spatial coordinates, the classifier is configured to map a radar, ultrasound, or audio spectrum to a set of classification scores with respect to classes of a given classification, and the classifier is configured for use in actuating a vehicle based on its classification” generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), add extra-solution activities (e.g., measuring performance of a classifier for appending sensors, such as radar, ultrasound, or audio spectra) using elements recited at a high level of generality (i.e., a CRM in claim 12 or one or more computers in claim 13) (see MPEP 2106.05(g)) and implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f));
“an outcome that the classifier has outputted or should output for: the test
spectrum from which the evaluation spectrum has been derived, and/or at least one other test spectrum from the set of test spectra” adds extra-solution activities (i.e., output data) (see MPEP 2106.05(g)).
Accordingly, these additional elements, when considered individually and in
combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
The additional elements in claim 11:
“measuring the performance of the trained classifier by: providing a set of
test radar, ultrasound, or audio spectra that form part of, and/or define, a common distribution or manifold; obtaining at least one evaluation spectrum that: is a modification of at least one test spectrum with different substantially the same semantic content as the at least one test spectrum, and/ or does not form part of the common distribution or manifold; acquiring, using at least one radar, ultrasound or audio sensor carried by a vehicle, at least one radar, ultrasound, or audio spectrum,” generally link the use of the judicial exception to a particular technological environment of field of use (see MPEP 2106.05(h)) and add extra-solution activities (e.g., measuring the performance of the trained classifier, obtaining at least one evaluation spectrum, acquiring at least one radar, ultrasound, audio spectrum) using elements recited at a high level of generality (e.g., measuring, obtaining, and acquiring data) (see MPEP 2106.05(g)).
Accordingly, these additional elements, when considered individually and in
combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Furthermore, under Step 2B of the test, claims 1 and 11 as stated above under step 2A Prong Two, when analyzed individually and as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim amounts to significantly more than the judicial exception. The claims, when considered as a whole, do not provide significantly more under Step 2B of the test.
Based on the analysis, the claims are not patent eligible.
Similarly, independent claims 12 and 13 are directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
and independent claim 10 is directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 11.
With regards to the dependent claims 2-9, they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional
limitations, that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims (2-9), when considered
individually and in combination, refer to extra-solution activities (e.g., mere data gathering or measuring data), which as indicated in the Office's guidance does not integrate the judicial exception into a practical application (Step 2A -Prong Two) and/or does not provide significantly more (Step 2B).
Thus, the dependent claims are ineligible.
Examiner’s Notes
7. Claims 1-13 are distinguished over the prior art of record as indicated in the previous office action.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 AM - 6 PM ET.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857