DETAILED ACTION
This action is in response to the claims filed 09/12/2023 for Application number 18/367,223. 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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed on 08/18/2023. It is noted, however, that applicant has not filed a certified copy of the PCT/CN2023/113674 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/21/2023 and 06/11/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 mislabeled neural network training data to be selected to be relabeled based, at least in part, on an amount by which the one or more mislabeled neural network training data 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 amount by which the one or more mislabeled neural network training data are mislabeled is computed based, at least in part, on a difference between an inference by a neural network and a label of the mislabeled neural network training data. 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 selection of data to be relabeled is further based, at least in part, on a confidence score associated with an inference by a neural network. 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 selection of data to be relabeled is based, at least in part, on the confidence score and the amount by which the one or more mislabeled neural network training data are mislabeled exceeds a threshold. 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 to be used to train a neural network without relabeling when the amount by which the training data is mislabeled is less than a threshold amount. 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, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Umaithanu et al. ("US 20240346322 A1", hereinafter "Umaithanu").
Regarding claim 1, Umaithanu teaches A processor comprising:
one or more circuits (¶0078-0080, “A processor 614, which can be a micro-controller, digital signal processor (DSP), or other processing component, processes these various signals, such as for display on the computer system 600 or transmission to other devices via a communication link 624.”) to cause one or more mislabeled neural network training data to be selected to be relabeled based, at least in part, on an amount by which the one or more mislabeled neural network training data are mislabeled. (“The efficacy determination module 308 may then determine whether the classification model 306 has detected any mislabeled training data set in the group 324 based on the threshold value. Once a mislabeled training data set (e.g., the mislabeled training data set 362) is detected, the data preparation module 302 may relabel the mislabeled training data set 362 (e.g., from the non-fraudulent classification to the fraudulent classification).” [¶0071])
Regarding claim 3, Umaithanu teaches The processor of claim 1, wherein the amount by which the one or more mislabeled neural network training data are mislabeled is computed based, at least in part, on a difference between an inference by a neural network and a label of the mislabeled neural network training data. (“After training the machine learning model using the modified training data and the objective function, the classification system may evaluate the trained machine learning model's ability to identify mislabeled data sets. In some embodiments, the classification system may evaluate the machine learning model's ability to identify mislabeled data sets based on the outputs (“inference”) produced by the trained machine learning model.” [¶0022])
Regarding claim 4, Umaithanu teaches The processor of claim 3, wherein the one or more circuits are to further train the neural network using the relabeled training data. (“In some embodiments, the classification system may re-train the machine learning model using the newly modified training data (after relabeling the one or more data sets that have been identified by the machine learning model as mislabeled) in an iterative process.” [¶0025])
Regarding claim 5, Umaithanu teaches The processor of claim 1, wherein the selection of data to be relabeled is further based, at least in part, on a confidence score associated with an inference by a neural network. (“In some embodiments, during each iteration, the efficacy determination module 308 may also calculate a pseudo F-measure (which can also be used as an efficacy score for determining an efficacy of the machine learning model in detecting mislabeled data) for the classification model 306 based on the outputs produced by the classification model 306.” [¶0064])
Regarding claim 6, Umaithanu teaches The processor of claim 5, wherein the selection of data to be relabeled is based, at least in part, on the confidence score and the amount by which the one or more mislabeled neural network training data are mislabeled exceeds a threshold. (“In a scenario where the increase of the pseudo F-measure exceeds the threshold (indicating a substantial improvement in the ability of the classification model 306 to detect mislabeled data sets) but no mislabeled data sets have been identified based on the threshold value, the efficacy determination module 308 of some embodiments may adjust the threshold value and determine if any mislabeled data sets can be identified based on the adjusted threshold value.” [¶0065])
Regarding claim 7, Umaithanu teaches The processor of claim 1, wherein the one or more circuits are further to cause one or more neural network training data to be used to train a neural network without relabeling when the amount by which the training data is mislabeled is less than a threshold amount. (“In a scenario where the increase of the pseudo F-measure exceeds the threshold (indicating a substantial improvement in the machine learning model's ability to detect mislabeled data sets) but no mislabeled data sets have been identified (implies that no relabeling is done) based on the threshold value, the classification system of some embodiments may adjust the threshold value and determine if any mislabeled data sets can be identified based on the adjusted threshold value.” [¶0030])
Regarding claims 8 and 10-14, they are substantially similar to claims 1 and 3-7 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Regarding claims 15 and 17-20, they are substantially similar to claims 1 and 3-5 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 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Umaithanu in view of Johnson et al. ("US 20050027664 A1", cited by Applicant in the IDS filed 06/11/2024, hereinafter "Johnson").
Regarding claim 2, Umaithanu teaches The processor of claim 1, however fails to explicitly teach wherein the one or more circuits are to cause the relabeling to be performed by one or more annotators.
Johnson teaches wherein the one or more circuits are to cause the relabeling to be performed by one or more annotators. (“the user can relabel and explicitly accept the presented annotation instance… Alternatively, all of the annotation instances which were corrected, relabeled, rebracketed or added by the user or any combination thereof may be accepted.” [¶0120], [¶0123])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Umaithanu’s semi-supervised learning technique by using a human annotator to relabel training data as taught by Johnson. One would have been motivated to make this modification in order to allow effective learning of subsequent training iterations. [¶0038, Johnson]
Regarding claims 9 and 16, they are substantially similar to claim 2 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Basak et al. ("US 20220335335 A1") discloses a method for identifying mislabeled data samples (Abstract).
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/MICHAEL H HOANG/Examiner, Art Unit 2122