DETAILED ACTION
This action is in response to the claims filed 04/21/2026 for Application number 18/367,223. Claims 1, 3, 5-8, 10, 12-15, 17, and 19-20 have been amended. Thus, claims 1-20 are currently pending.
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 .
Claim Rejections - 35 USC § 112
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 3, 5, 6, 10, 12, 13, 17, and 19 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.
Regarding claims 3, 5, 6, 10, 12, 13, 17, and 19, the claims recite: “wherein the respective annotation scores and the respective annotation scores are…”. It appears that one of the claimed annotation scores should be the confidence score as recited in claim 1. The claim is unclear as it repeats the respective annotation score, thus is rejected as s 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.
For purposes of examination, the examiner will interpret one of the recited respective annotation scores to be “confidence score”.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
[A processor comprising: one or more circuits to cause] one or more labeled neural network training data samples to be relabeled based, at least in part, determining respective confidence scores indicating a confidence of neural network inferences with respect to the one or more labeled neural network training data samples can be considered to be an evaluation in the human mind
determining respective annotation scores indicating difference between respective labels of the one or more labeled neural network training data samples and the neural network inferences can be considered to be an evaluation in the human mind
and comparing the respective confidence scores and the respective annotation scores with one or more thresholds to determine that the one or more labeled neural network training data samples are mislabeled can be considered to be an evaluation in the human mind.
This limitation as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “A processor comprising: one or more circuits to cause…”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processor comprising one or more circuits to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more circuits are to cause the relabeling to be performed by one or more annotators. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the respective annotation scores and the respective annotation scores are combined for the one or more labeled neural network data samples before comparison with the one or more thresholds. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more circuits are to further train the neural network using the relabeled training data. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the respective annotation scores and the respective annotation scores are separately compared with different ones of the one or more thresholds. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more labeled training data samples are ranked according to the respective annotation scores and the respective annotation scores. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more circuits are further to cause one or more neural network training data samples to be used to train a neural network without relabeling when further respective confidence scores and further respective annotation scores computed for the one or more neural network training data samples satisfy the one or more thresholds. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding Claims 8-14, they recite features similar to claims 1-7 and are rejected for at least the same reasons therein.
Regarding Claims 15-20, they recite features similar to claims 1-5 and 7 and are rejected for at least the same reasons therein.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 5-9, 12-16, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al. ("US 20240412037 A1" hereinafter "Chen").
Regarding claim 1, Chen teaches A processor comprising: one or more circuits (¶0085, “electronic circuitry”) to cause one or more labeled neural network training data samples to be relabeled (Abstract) based, at least in part, on:
determining respective confidence scores indicating a confidence of neural network inferences with respect to the one or more labeled neural network training data samples; (“For example, in the context of binary classification, machine learning models often output probabilities ranging from 0 to 1 to represent their confidence in assigning data points to the positive class. In some cases, a probability of 0 signifies high confidence in the negative class, while a probability of 1 represents high confidence in the positive class. When a model or a committee member outputs a probability of 0.5, such a probability value would indicate uncertainty or a lack of confidence in classifying the data point to either positive or negative class.” [¶0012])
determining respective annotation scores indicating difference between respective labels of the one or more labeled neural network training data samples and the neural network inferences (“At 240, the label uncertainty score (“annotation score”) is used to determine whether to re-label one or more labeled data in the set of labeled data. In some cases, for each labeled data, a label uncertainty score is calculated based on the classification results produced by the different models by using equations (1)-(3), as discussed previously.” [¶0042]); and
comparing the respective confidence scores and the respective annotation scores with one or more thresholds to determine that the one or more labeled neural network training data samples are mislabeled (“A threshold can be configured. If the label uncertainty score for one particular labeled data exceeds the threshold, the one particular labeled data may be determined as having higher data uncertainty.” [¶0042; See also: “For example, in the context of binary classification, machine learning models often output probabilities ranging from 0 to 1 to represent their confidence in assigning data points to the positive class. In some cases, a probability of 0 signifies high confidence in the negative class, while a probability of 1 represents high confidence in the positive class.” [¶0012]).
Regarding claim 2, Chen teaches The processor of claim 1, wherein the one or more circuits are to cause the relabeling to be performed by one or more annotators. (“The re-labeling operation can include an automatic labeling operation to regenerate the label, submitting to domain expert for further review, or both.” [¶0044])
Regarding claim 5, Chen teaches The processor of claim 1, wherein the respective annotation scores and the respective annotation scores are separately compared with different ones of the one or more thresholds. (“A threshold can be configured. If the label uncertainty score for one particular labeled data exceeds the threshold, the one particular labeled data may be determined as having higher data uncertainty.” [¶0042; See also: “For example, in the context of binary classification, machine learning models often output probabilities ranging from 0 to 1 to represent their confidence in assigning data points to the positive class. In some cases, a probability of 0 signifies high confidence in the negative class, while a probability of 1 represents high confidence in the positive class.” [¶0012])
Regarding claim 6, Chen teaches The processor of claim 1, wherein the one or more labeled training data samples are ranked according to the respective annotation scores and the respective annotation scores. (“Alternatively, or additionally, the labeled data can be ranked based on their label uncertainty score. A configured percentage of the labeled data that have the highest label uncertainty scores, e.g., 1%, are determined to have higher data uncertainty and will be re-labeled.” [¶0042; See also ¶0012: “In some cases, a probability of 0 signifies high confidence in the negative class, while a probability of 1 represents high confidence in the positive class. When a model or a committee member outputs a probability of 0.5, such a probability value would indicate uncertainty or a lack of confidence in classifying the data point to either positive or negative class.” (probabilities closer to 1 would “rank” labeled data higher than probabilities closer to 0 as having correct labels])
Regarding claim 7, Chen teaches The processor of claim 1, wherein the one or more circuits are further to cause one or more neural network training data samples to be used to train a neural network without relabeling when further respective confidence scores (See ¶0012; probability of 1 represents high confidence thus would imply using data samples without re-labeling) and further respective annotation scores computed for the one or more neural network training data samples satisfy the one or more thresholds. (“The label uncertainty calculation module 130 determines whether to re-label the labeled data 140 based on the label uncertainty score.” [¶0017; note: ¶0042 mentions the use of a threshold thus it is implicit that satisfying the threshold would determine whether or not to use data samples without re-labeling.])
Regarding claims 8-9, and 12-14 they are substantially similar to claims 1-2 and 5-7 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Regarding claims 15-16 and 19-20, they are substantially similar to claims 1-2 and 5 and 7 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 3, 4, 10, 11, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Torkzadehmahani et al. ("Label Noise-Robust Learning using a Confidence-Based Sieving Strategy", hereinafter "Torkz").
Regarding claim 3, Chen teaches The processor of claim 1, however fails to explicitly teach wherein the respective annotation scores (in light of the 112(b) interpreted as confidence scores) and the respective annotation scores are combined for the one or more labeled neural network data samples before comparison with the one or more thresholds.
Torkz teaches wherein the respective annotation scores (in light of the 112(b) interpreted as confidence scores) and the respective annotation scores are combined for the one or more labeled neural network data samples before comparison with the one or more thresholds. (“The confidence error EC(S) for sample S is defined as the difference between the probability assigned to the predicted label ˆ yi and the probability associated with the original label ˜yi:.. where EC(S) ∈ [0, 1]. In other words, the confidence error states the possibility that the predicted class is the same as the original class. The confidence error of zero implies that the original and predicted classes are the same.” [pg. 4, bottom left col – top right col; note the confidence error would be a combination of model confidence and predicted label/annotation probability scores])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Chen’s teachings by combining the confidence and confidence error scores together as taught by Torkz. One would have been motivated to make this modification in order to use confidence error as a metric to differentiate clean samples from noisy ones. [pg. 4, top right col, Torkz]
Regarding claim 4, Chen/Torkz teaches The processor of claim 3, Chen teaches wherein the one or more circuits are to further train the neural network using the relabeled training data. (“Mislabeled data may be corrected through automatic label correction or submitted to a domain expert for further review. FIGS. 1-3 and associated descriptions provide additional details of these implementations… Techniques described herein produce one or more technical effects. For example, this approach improves accuracy of labeled data that are used to train machine learning models and, therefore, improves the performance of machine learning operations.” [¶0013-¶0014])
Regarding claims 10 and 11, they are substantially similar to claims 3 and 4 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Regarding claims 17 and 18, they are substantially similar to claims 3 and 4 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Response to Arguments
Applicant's arguments filed 04/21/2026 have been fully considered but they are not persuasive.
Regarding the Prior art Rejection:
Applicant’s arguments regarding the prior art of Umaithanu failing to teach the newly recited limitations of claim 1 have been considered but are moot because the newly applied prior art of Chen is now relied upon to teach these new features. Please see the updated prior art rejection above.
Regarding the 35 U.S.C. §101 Rejection:
Applicant asserts the claimed system provides technical advantages including computing efficiency and speed in training neural networks by selecting labeled training data samples to relabel according to a determination that the labeled training data samples are mislabeled. Examiner respectfully disagrees. The claims as currently recited under broadest reasonable interpretation amount to a mental process. There are no additional elements/details in the claims that would reflect any improvement in computer technology. The claims appear to be directed towards an improvement in the abstract idea (improved data classification/labeling) rather than any improvement in the functioning of a computer/processor or training of a neural network. Merely stating the technical advantages without any details in the claim to reflect those assertions does not provide a persuasive argument. Thus, the examiner asserts that the claim is not patent eligible.
Applicant’s arguments with respect to the rejections of the dependent claims have been fully considered but they are not persuasive as they rely upon the allowability of the independent claims
Conclusion
Applicant's amendment necessitated the new grounds 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 MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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