DETAILED ACTION
This action is in response to the claims filed 03/13/2024 for Application number 18/604,157. Claims 1-18 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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/13/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
feature weight generation module configured to generate... in claims 1 and 12
data sampling module configured to generate…in claims 1 and 12
a mutual information amount module configured to generate…in claim 6
a weight integration module configured to generate…in claim 6
a data collector configured to collect… in claim 12
a classifier learning system configured to train… in claim 12
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-18 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:
to generate a feature weight based on an artificial neural network and an amount of mutual information between the plurality of features of the training data can be considered to be an evaluation in the human mind,
to generate sampling data by performing a feature space restoration operation based on the training data and a previous feature space of previous data on which the training is completed in the classifier can be considered to be an evaluation in the human mind
These limitations 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 only recites the additional elements – “a classifier configured to train training data having a feature space including a plurality of features based on a classification algorithm;”, “a feature weight generation module configured to generate…”, “a data sampling module configured to”, and “wherein the classifier is configured to train the sampling data and wherein the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned.”. These elements invoke 112(f) and can be interpreted to be programmable circuitry as disclosed on ¶0024 of the specification. Thus, the elements in the claim are recited at a high level of generality (i.e. as a generic processor performing a generic computer function of generating an index) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. 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.
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 “classifier”, “a feature weight generation module”, and “a data sampling module” 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 data sampling module is configured to perform the feature space restoration operation when the feature space of the training data includes the feature space of the previous data. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein, when the feature space restoration operation is performed, the data sampling module is configured to sample the training data to generate a plurality of data instances having the same feature space as the feature space of the previous 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 2 is further incorporated, and further, the claim recites: wherein the feature space restoration operation includes a data augmentation and a random data sampling. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the classification algorithm includes a Naive Bayes algorithm. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 2 is further incorporated, and further, the claim recites: generate a first weight for each feature based on interdependence between the plurality of features of the training data
generate a second weight for each feature based on the training data and the artificial neural network;
generate the feature weight based on the first weight for each feature and the second weight for each feature.
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 recite the additional elements of “a mutual information amount module configured to”, “an artificial neural network module configured to” and “a weight integration module configured to”, however they do not amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception, for the reasons set forth in connection with the rejection of claim 1 above. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 6 is further incorporated, and further, the claim recites: wherein the mutual information amount module is configured to calculate the first weight VW1 for each feature based on the mutual information amount to which an mRMR (Minimum Redundancy Maximum Relevance) technique is applied.
This claim recites additional mathematical 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 8, the rejection of claim 6 is further incorporated, and further, the claim recites: generate preprocessed data based on the feature space of the training data;
update the second weight for each feature such that an objective function is minimized based on the target weight for each feature and a classification result of the classifier
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 recite the additional elements of “a data preprocessor configured to”, “an artificial neural network configured to input the preprocessed data and to output the second weight for each feature”, “a target weight memory configured to store the second weight for each feature output from the artificial neural network as a target weight for each feature, after the training on the training data is completed,” and “wherein the artificial neural network is configured to...”, however they do not amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception, for the reasons set forth in connection with the rejection of claim 1 above. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein, in the preprocessed data, components corresponding to the plurality of features included in the feature space of the training data have a value of ‘1’, and components corresponding to the features not included in the feature space of the training data have a value ‘0’. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the number of input nodes of the artificial neural network is the same as a total number of trainable features in the classifier, and wherein the number of output nodes of the artificial neural network is the same as the number of the input nodes. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 10 is further incorporated, and further the claim recites:
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This claim recites additional mathematical 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.
Claim 12 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 12 additionally requires analysis for “a data collector configured to collect training data having a feature space including a plurality of features from each of a plurality of environments” however this is an additional element that amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding claim 13, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the plurality of environments include first to third environments, wherein the training data includes first training data collected from the first environment, second training data collected from the second environment, and third training data collected from the third environment, and wherein the first training data, the second training data, and the third training data have a different feature space. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 14, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein a first feature space of the first training data is included in a second feature space of the second training data, and wherein a third feature space of the third training data includes some of features of the second feature space of the second training data. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 15, the rejection of claim 14 is further incorporated, and further, the claim recites: wherein, when the classifier completes training on the first training data, the data sampling module is configured to perform a feature space restoration operation on the second training data based on the first feature space of the first 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 Claims 16-17, they recite features similar to claims 8 and 11 and is rejected for at least the same reasons therein.
Regarding claim 18, the rejection of claim 17 is further incorporated, and further, the claim recites: w wherein, when the classifier completes training on the second training data, the target weight memory is configured to store the second weight for each feature output from the artificial neural network as the target weight for each feature. 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 exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Allowable Subject Matter
Claims 1-18 are objected to as being allowable over prior art if all outstanding rejections were withdrawn. None of the prior art, either alone or in combination, fairly discloses limitations of claims 1 and 12 in particular:
…a data sampling module configured to generate sampling data by performing a feature space restoration operation based on the training data and a previous feature space of previous data on which the training is completed in the classifier, and wherein the classifier is configured to train the sampling data, and wherein the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned.
The closest prior art uncovered was Jiang et al. (“A Correlation-Based Feature Weighting Filter for Naive Bayes”, cited by Applicant in the IDS filed 03/13/2024) which discloses mutual relevance and mutual redundancy of features using Naïve Bayes classification, however the reference does not explicitly disclose generating sampling data by performing a feature space restoration operation based on training data and a previous feature space of previous data on which the training is completed and wherein the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned.
Ruan et al. (“Class-Specific Deep Feature Weighting for Naïve Bayes Text Classifiers”) discloses assigning each feature a specific weight for each class using Naïve Bayes classifiers, however the reference fails to explicitly teach generating sampling data by performing a feature space restoration operation based on training data and a previous feature space of previous data on which the training is completed and wherein the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned.
Kiziloz (“Classifier ensemble methods in feature selection”) discloses a plurality of classifier ensemble methods in feature selection however the reference does not explicitly disclose wherein the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned or disclose generating sampling data by performing a feature space restoration operation based on training data and a previous feature space of previous data on which the training is completed.
No prior art was uncovered which fairly discloses all of the specific steps recited in the independent claims.
Conclusion
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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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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