The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 29 November 2022. THIS ACTION IS NON-FINAL.
Status of Claims
Claims 1-15 are pending.
Claim 1-15 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
There is no art rejection for claims 1-15.
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.
Judicial Exception
Claims 1-15 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
Regarding claims 1-7, 10-13,
(Independent Claims) With regards to claim 1 / 10, the claim recites a process / machine, which falls into one of the statutory categories.
2A – Prong 1: the claim, in part, recites “… dividing the sample dataset into K sample sub-datasets, determining a group of data from the K sample sub-datasets as a test dataset, and using sample sub-datasets other than the test dataset in the K sample sub-datasets as a train dataset, wherein K is an integer greater than 1” (mental process) ; “obtaining a first indicator and a first hyper-parameter at least based on the first label and the second label, wherein the first indicator is a ratio of a quantity of samples each having a second label that is not equal to the first label in the test dataset to a total quantity of samples in the test dataset; obtaining a loss function of the classifier at least based on the first hyper-parameter, wherein the classifier is updated using the loss function” (mental process).
The limitation “dividing the sample dataset into K sample sub-datasets, determining a group of data from the K sample sub-datasets as a test dataset, and using sample sub-datasets other than the test dataset in the K sample sub-datasets as a train dataset, wherein K is an integer greater than 1”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computer elements, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “dividing”, “determining”, “using” in the limitation citied above encompasses observe / evaluate data for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The limitation “obtaining a first indicator and a first hyper-parameter at least based on the first label and the second label, wherein the first indicator is a ratio of a quantity of samples each having a second label that is not equal to the first label in the test dataset to a total quantity of samples in the test dataset; obtaining a loss function of the classifier at least based on the first hyper-parameter, wherein the classifier is updated using the loss function” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computer elements, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “obtaining … indicator / loss function”, “updated using the loss function”, in the limitation citied above encompasses observe / evaluate data for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: (a) generic computer elements (like “memory storing executable instructions”, “processor configured to execute the executable instructions …”, etc.), which is mere instructions to implement an abstract idea using generic computing device, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)); (b) “obtaining a sample dataset, wherein the sample dataset comprises a plurality of samples, and each of the plurality of samples comprises a first label” (insignificant extra solution activity, MPEP 2106.05(g)); (c) “training the classifier by using the train dataset, and classifying the test dataset by using a trained classifier, to obtain a second label of each sample in the test dataset; … and completing training of the classifier when the first indicator meets a condition” (mere instructions to apply an exception, MPEP 2106.05(f)). For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) which is mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). For (b), these steps are recited at a high level of generality and amounts to extra-solution activity of mere data gathering as described in MPEP.2106.05(g). For (c), it appeared that this limitation is using computer to implement classification models. These limitations are 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, as discussed in MPEP 2106.05(f). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of generic computer element merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The additional element of “obtaining a sample dataset, wherein the sample dataset comprises a plurality of samples, and each of the plurality of samples comprises a first label”, is insignificant extra solution activity (MPEP 2106.05(g)). the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). The additional element of “training the classifier by using the train dataset, and classifying the test dataset by using a trained classifier, to obtain a second label of each sample in the test dataset; … and completing training of the classifier when the first indicator meets a condition” is mere instructions to apply an exception (MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
(Dependent claims)
Claims 2-7 / 11-13 are dependent on claim 1 / 10, and include all the limitations of claim 1 / 10. Therefore, claims 2-7 / 11-13 recite the same abstract ideas.
With regards to claim 2 / 11, the claim recites further limitation of “wherein the first hyper-parameter is determined based on the first indicator and a second indicator, wherein the second indicator is an average value of loss values of all samples each having a second label that is not equal to the first label in the test dataset”, recites further element of data processing for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 3, the claim recites further limitation of “wherein the first hyper-parameter is determined by using the following formula:
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”, recites math concept. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 4 / 12, the claim recites further limitation of “wherein the obtaining of the loss function of the classifier at least based on the first hyper-parameter comprises: obtaining the loss function of the classifier at least based on the first hyper-parameter and a cross entropy” (mental process and/or math concept), as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts but for the recitation of generic computer components. That is, other than reciting server computer, processor, computer-readable medium coupled with the processors, the description of loss function, based on their broadest reasonable interpretation, describe mathematical relationships and algorithms. Mathematical relationship and algorithms have been found by the courts to be abstract ideas, e.g., see MPEP 2106.04(a)(2) A. Mathematical Relationships, iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The claim is directed to an abstract idea. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 5, the claim recites further limitation of “wherein the loss function is obtained by using the following formula:
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”, recites math concept. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 6 / 13, the claim recites further limitation of “wherein the dividing of the sample dataset into K sample sub-datasets comprises: equally dividing the sample dataset into the K sample sub-datasets”, recites further element of data processing for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 7, the claim recites additional element of “wherein the classifier comprises a convolutional neural network (CNN) and a residual network ResNet”, which is mere instruction to implement an abstract idea (MPEP2106.05(f)) and/or merely indicates a field of use or technological environment in which the judicial exception is performed (MPEP 2106.05(h)). The claim is not patent eligible.
Regarding claims 8-9, 14-15,
(Independent Claims) With regards to claim 8 / 14, the claim recites a process / machine,, which falls into one of the statutory categories.
2A – Prong 1: the claim, in part, recites “… dividing the dataset into K sub-datasets, wherein K is an integer greater than 1; performing at least one classification on the dataset, to obtain first clean data of the dataset, wherein any classification in the at least one classification comprises: determining a group of data from the K sub-datasets as a test dataset, and using sub-datasets other than the test dataset in the K sub-datasets as a train dataset; … and classifying the test dataset by using a trained classifier, to obtain a second label of each sample in the test dataset; and performing comparison based on the second label and the first label of each sample, to determine samples each having a second label that is equal to the first label in the test dataset, wherein the first clean data comprises the determined samples” (mental process), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computer elements, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “dividing”, “performing … classification”, “determining”, “performing comparison” in the limitation citied above encompasses observe / evaluate data for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: (a) generic computer elements (like “memory storing executable instructions”, “processor configured to execute the executable instructions …”, etc.), which is mere instructions to implement an abstract idea using generic computing device, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)); (b) “obtaining a sample dataset, wherein the sample dataset comprises a plurality of samples, and each of the plurality of samples comprises a first label” (insignificant extra solution activity, MPEP 2106.05(g)); (c) “training a classifier by using the train dataset” (mere instructions to apply an exception, MPEP 2106.05(f)). For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) which is mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). For (b), these steps are recited at a high level of generality and amounts to extra-solution activity of data input/output as described in MPEP.2106.05(g). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). For (c), it appeared that this limitation is using computer to implement classification models. These limitations are 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, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of generic computer element merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The additional element of “obtaining a sample dataset, wherein the sample dataset comprises a plurality of samples, and each of the plurality of samples comprises a first label”, is insignificant extra solution activity (MPEP 2106.05(g)). the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). The additional element of “training a classifier by using the train dataset” is mere instructions to apply an exception (MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of generic computer element merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). the additional element of “obtaining a sample dataset, wherein the sample dataset comprises a plurality of samples, and each of the plurality of samples comprises a first label”, is insignificant extra solution activity (MPEP 2106.05(g)). the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). The additional element of “training a classifier by using the train dataset” is mere instructions to apply an exception (MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
(Dependent claims)
Claims 9 / 15 are dependent on claim 8 / 14, and include all the limitations of claim 8 / 14. Therefore, claim 9 / 15recite the same abstract ideas.
With regards to claim 9 / 15, the claim recites further limitation of “dividing the dataset into M sub-datasets, wherein M is an integer greater than 1, and the M sub-datasets are different from the K sub-datasets; performing at least one classification on the dataset, to obtain second clean data of the dataset, wherein any classification in the at least one classification comprises: determining a group of data from the M sub-datasets as a test dataset, and using sub-datasets other than the test dataset in the M sub-datasets as a train dataset; … and classifying the test dataset by using the trained classifier, to obtain a second label of each sample in the test dataset; performing comparison based on the second label and the first label of each sample, to determine samples each having a second label that is equal to the first label in the test dataset, wherein the second clean data comprises the determined samples; and determining third clean data based on the first clean data and the second clean data, wherein the third clean data is an intersection set between the first clean data and the second clean data”, recites further element of data processing for classification model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The claim recites further limitation “training the classifier by using the train dataset”, which merely use computer to implement the exception. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
Allowable Subject Matter Analysis
Claims 1-15 include allowable subject matter since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-15.
In interpreting the claims, in light of the specification filed on 29 November 2022, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
Regarding the amended independent claims, the primary reason for the allowance is the inclusion of the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
None of the cited prior art references, singly or in combination, fully teaches all limitations of independent claims 1, 8, 10, and 14.
Regarding the dependent claims, which include all the limitations of the independent claims, are also allowed.
The followings are references close to the invention claimed:
Pusztai et al., US-PGPUB NO.20050266420A1 [hereafter Pusztai] teaches dividing data sample & training classifiers. However Pusztai does not teach the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
Principe et al., US-PGPUB NO.20160242690A1 [hereafter Principe] teaches training with data sample division into clusters. However Principe does not teach the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
Dasgupta et al., US-PATENT NO.11556746B1 [hereafter Dasgupta] teaches ML model annotation with multiple labels. However Dasgupta does not teach the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
Xiao et al., “Improving the performance of sentiment classification on imbalanced datasets with transfer learning”, IEEE Access, January 11, 2019 [hereafter Xian] teaches dataset division for balanced training. However Xiao does not teach the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
Noi et al., “Comparison of random forest, k-nearest neighbor and support vector machine classifiers for land cover classification using sentinel-2 imagery”, Sensors2018, 18, 18 [hereafter Noi] shows multiple classifiers models with performance indicators. However Noi does not teach the specific claimed process / structure of dataset division, training, generating indicator & hyperparameters with ratio value related to multiple labels, and using the indicator as training condition controller.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
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