Prosecution Insights
Last updated: April 19, 2026
Application No. 18/466,374

METHOD AND APPARATUS FOR LEARNING OF NOISE LABEL BASED ON TEST-TIME AUGMENTED CROSS-ENTROPY AND NOISE MIXING

Non-Final OA §101§112
Filed
Sep 13, 2023
Examiner
LEVEL, BARBARA HENRY
Art Unit
2142
Tech Center
2100 — Computer Architecture & Software
Assignee
Korea Advanced Institute Of Science And Technology
OA Round
1 (Non-Final)
72%
Grant Probability
Favorable
1-2
OA Rounds
2y 8m
To Grant
98%
With Interview

Examiner Intelligence

Grants 72% — above average
72%
Career Allow Rate
236 granted / 330 resolved
+16.5% vs TC avg
Strong +27% interview lift
Without
With
+26.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 8m
Avg Prosecution
16 currently pending
Career history
346
Total Applications
across all art units

Statute-Specific Performance

§101
17.2%
-22.8% vs TC avg
§103
42.5%
+2.5% vs TC avg
§102
10.4%
-29.6% vs TC avg
§112
20.7%
-19.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 330 resolved cases

Office Action

§101 §112
CTNF 18/466,374 CTNF 91856 DETAILED ACTION This correspondence is responsive to the Application filed on September 13, 2023. Claims 1-16 are pending in the case, with claims 1 and 9 in independent form. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority Acknowledgment is made of applicant's claim for foreign priority based on an application filed in KR 10-2022-0115336 on September 14, 2022. 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Summary of Detailed Action Claims 1-2, 6-7, 9-10 and 14-15 are objected to regarding informalities. Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite. Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite. Claims 2-8 and 10-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite. Claims 3-7 and 11-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite Claims 1-2, 8, 9-10 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim Objections 07-29-01 AIA Claim s 1-2, 6-7, 9-10 and 14-15 are objected to because of the following informalities: Claims 1-2, 6-7, 9-10 and 14-15: change “cross entropy” to “cross-entropy” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claims 1 and 9 recite wherein the prediction score for separating the clean label data and the label noise data is “test-time augmentation cross entropy.” It is not clear what test-time augmentation cross entropy is or is not. It is further unclear how to calculate the prediction score that is the test-time augmentation cross entropy. It is yet further unclear as to what being tested? For example, is the classifier being tested? Or, is the weak classifier being tested? Or is some entirely different model being tested? It is still yet further unclear what the augmentation is referencing. For example, is there training data or testing data that has been generated, transformed or somehow augmented? Or is there the augmentation referring to something else entirely? Claims 2-8 depend directly or indirectly from claim 1 and are rejected for the same reasons discussed above with respect to claim 1. Claims 10-16 depend directly or indirectly from claim 9 and are rejected for the same reasons discussed above with respect to claim 9. Applicant may cancel claims 1-16 or amend claims 1-16 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “weak” classifier in claims 1-2, 4-6, 9-14, is a relative term which renders the claim indefinite. The term “weak” classifier is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claims 3, 7-8 depend directly or indirectly from claims 1-2, 4-6 and are rejected for the same reasons discussed above with respect to claims 1-2, 4-6. Claims 15, 16 depend directly or indirectly from claims 9-14 and are rejected for the same reasons discussed above with respect to claims 9-14. Claims 2-8 and 10-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Dependent claims 2-3 and 10-11 recite performing “warm-up” for training the weak classifier. It is not clear what performing warm-up for training the weak classifier is or is not. For example, does warm-up for training the weak classifier mean pre-training the weak classifier? Or does warm-up training for the weak classifier mean a certain limited size or amount of initial training of the weak classifier? Or does warm-up training for the weak classifier mean transfer learning for the weak classifier? Or does warm-up training for the weak classifier mean not full model training? Or does warm-up for training the weak classifier mean something else entirely? Claims 4-7 depend directly or indirectly from claim 3 and are rejected for the same reasons discussed above with respect to claim 3. Claim 8 depends from claim 2 and is rejected for the same reasons discussed above with respect to claim 2. Claims 12-15 depend directly or indirectly from claim 11 and are rejected for the same reasons discussed above with respect to claim 11. Claim 16 depends from claim 10 and is rejected for the same reasons discussed above with respect to claim 10. Applicant may cancel claims 2-8 and 10-16 or amend claims 2-8 and 10-16 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 3-7 and 11-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Dependent claims 3 and 11 recite “training the noisy training data” for a predetermined number of epochs. It is not clear how to train the noisy training data or what training noisy training data means. It is further unclear how to train the noisy training data for a predetermined number of epochs. It is not clear if the claim was trying to state that the classifier is trained on the noisy training data for a predetermined number of epochs. Or if the claim was trying to state that the weak classifier is trained on the noisy training data for a predetermined number of epochs. Or if the claim was trying to state that some other entirely different model is trained on the noisy training data for a predetermined number of epochs. Claims 4-7 depend from claim 3 and are rejected for the same reasons discussed above with respect to claim 3. Claims 12-15 depend from claim 11 and are rejected for the same reasons discussed above with respect to claim 11. Applicant may cancel claims 3-7 and 11-15 or amend claims 3-7 and 11-15 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-2, 8, 9-10 and 16 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) subject matter at a general high-level of a noise label learning method comprising selecting label noise for searching for mislabeled data by separating the clean label data and the label noise data from the noisy training data; and learning by mixing the noisy training data and the clean label data at a predetermined ratio, wherein the selecting of the label noise includes calculating a prediction score by predicting augmented training data and selecting label noise data from the noisy training data depending on the prediction score, and wherein the prediction score for separating the clean label data and the label noise data is test-time augmentation cross entropy, which are which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(III) MPEP 210604(a)(2)(I). This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-16 recite one of the four statutory categories of patent able subject matter and belong to the statutory class(es) of a process (method claims 1-8), a machine (system/apparatus claims 9-16), and an article of manufacture (non-transitory computer readable media claims). Claim 1 recites a method, thus a process and one of the four statutory categories of patentable subject matter. However, claim 1 further recites A noise label learning method, the method comprising selecting label noise for searching for mislabeled data by separating the clean label data and the label noise data from the noisy training data; and learning by mixing the noisy training data and the clean label data at a predetermined ratio, wherein the selecting of the label noise includes: calculating a prediction score by predicting augmented training data; and selecting label noise data from the noisy training data depending on the prediction score, and wherein the prediction score for separating the clean label data and the label noise data is test-time augmentation cross entropy , which are which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). Additionally, the recitations of learning by mixing the noisy training data and the clean label data at a predetermined ratio, wherein the selecting of the label noise includes: calculating a prediction score by predicting augmented training data; and selecting label noise data from the noisy training data depending on the prediction score, and wherein the prediction score for separating the clean label data and the label noise data is test-time augmentation cross entropy are also mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of: performed by an apparatus (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).). obtaining noisy training data including clean label data and label noise data (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.). a classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). training a weak classifier for the noisy training data (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). by using the weak classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). Thus, the claim is directed to the abstract idea. Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more, and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d), and generally linking the use of the judicial exception to a particular technological field of use does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible. Claim 2 , dependent on claim 1, recites additional abstract subject matter for wherein the selecting of the label noise data further includes: performing warm-up to obtain a prediction score of the noisy training data; calculating the prediction score of the noisy training data for separating the label noise data and the clean label data and obtaining the test-time augmentation cross entropy for distinguishing between the label noise data and the clean label data based on the calculated prediction score of the noisy training data , which are which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). Additionally, these recitations of obtain a prediction score of the noisy training data; calculating the prediction score of the noisy training data for separating the label noise data and the clean label data and obtaining the test-time augmentation cross entropy for distinguishing between the label noise data and the clean label data based on the calculated prediction score of the noisy training data are also mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Claim 2 does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of: for training the weak classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). by using the trained weak classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). Claim 8 , dependent on claim 2, recites additional abstract subject matter for wherein the learning by mixing the noisy training data and the clean label data at the predetermined ratio includes: forming mixed training data by mixing the clean label data and the noisy training data separated by selecting the label noise , which are which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). Claim 8 does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of: of the classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). Claim 9 recites an apparatus, thus a machine and one of the four statutory categories of patentable subject matter. However, claim 9 further recites noise label learning to select label noise for searching for mislabeled data by separating the clean label data and the label noise data from the noisy training data; learn by mixing the noisy training data and the clean label data at a predetermined ratio; when selecting the label noise, calculate a prediction score by predicting augmented training data; and select label noise data from the noisy training data depending on the prediction score, wherein the prediction score for separating the clean label data and the label noise data is test-time augmentation cross entropy , which are which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). Additionally, these recitations of learn by mixing the noisy training data and the clean label data at a predetermined ratio; when selecting the label noise, calculate a prediction score by predicting augmented training data; and select label noise data from the noisy training data depending on the prediction score, wherein the prediction score for separating the clean label data and the label noise data is test-time augmentation cross entropy are also mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of: apparatus, the apparatus comprising: a processor; and a memory configured to store a program executed by the processor, wherein the processor is configured to (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) obtain noisy training data including clean label data and label noise data (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.). a classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). training a weak classifier for the noisy training data (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). by using the weak classifier (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)). Thus, the claim is directed to the abstract idea. Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more, and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d), and generally linking the use of the judicial exception to a particular technological field of use does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible. Dependent claims 10 and 16 are comparably rejected as set forth above with respect to dependent claims 2 and 8. Allowable Subject Matter Claims 1-16 would be allowable if the rejections of claims 1-16 under 35 U.S.C. 112(b) as being indefinite are overcome and if the rejections of claims 1-2, 8, 9-10 and 16 under 35 U.S.C. 101 as being directed to an abstract idea are overcome and if the claim objections are overcome. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US-20220067506-A1, US-20210089883-A1, US-20210357776-A1, US-20230196184-A1, US-20210374553-A1, US-20230267713-A1, US-11663486-B2. Wang, Yisen et al., Iterative Learning with Open-set Noisy Labels, Cornell University, Computer Vision and Pattern Recognition , arXiv:1804.00092 [cs.CV] 2018. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARBARA LEVEL whose telephone number is (303)297-4748. The examiner can normally be reached Monday through Friday 8:00 AM - 5:00 PM MT. 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, Mariela Reyes can be reached at (571) 270-1006. 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. /BARBARA M LEVEL/Examiner, Art Unit 2142 Application/Control Number: 18/466,374 Page 2 Art Unit: 2142 Application/Control Number: 18/466,374 Page 3 Art Unit: 2142 Application/Control Number: 18/466,374 Page 4 Art Unit: 2142 Application/Control Number: 18/466,374 Page 5 Art Unit: 2142 Application/Control Number: 18/466,374 Page 6 Art Unit: 2142 Application/Control Number: 18/466,374 Page 7 Art Unit: 2142 Application/Control Number: 18/466,374 Page 8 Art Unit: 2142 Application/Control Number: 18/466,374 Page 9 Art Unit: 2142 Application/Control Number: 18/466,374 Page 10 Art Unit: 2142 Application/Control Number: 18/466,374 Page 11 Art Unit: 2142 Application/Control Number: 18/466,374 Page 12 Art Unit: 2142 Application/Control Number: 18/466,374 Page 13 Art Unit: 2142 Application/Control Number: 18/466,374 Page 14 Art Unit: 2142 Application/Control Number: 18/466,374 Page 15 Art Unit: 2142 Application/Control Number: 18/466,374 Page 16 Art Unit: 2142 Application/Control Number: 18/466,374 Page 17 Art Unit: 2142
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Prosecution Timeline

Sep 13, 2023
Application Filed
Mar 22, 2026
Non-Final Rejection — §101, §112 (current)

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Prosecution Projections

1-2
Expected OA Rounds
72%
Grant Probability
98%
With Interview (+26.9%)
2y 8m
Median Time to Grant
Low
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