DETAILED ACTION
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 § 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.
Step 1: Claim 1 is a method claim. Claim 10 is a device claim. Claim 19 is a CRM claim. Therefore, claims 1, 10, and 19 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier, a plurality of expert labels for the sample (mental process – user can manually generate a plurality of expert labels for the sample)
determining, using the at least one processor, that an expert label among the plurality of expert labels is an expert consensus label representing consensus among the plurality of machine learned classifiers (mental process – user can manually determine that an expert label among the plurality of expert labels is an expert consensus label representing consensus among the plurality of machine learned classifiers)
comparing, using the at least one processor, the expert consensus label to the ground truth label in response to determining that the expert label among the plurality of expert labels is the expert consensus label (mental process – user can manually compare the expert consensus label to the ground truth label in response to determining that the expert label among the plurality of expert labels is the expert consensus label)
identifying, using the at least one processor, the ground truth label as a clean label in response to determining that the expert consensus label and the ground truth label match (mental process – user can manually identify the ground truth label as a clean label in response to determining that the expert consensus label and the ground truth label match)
placing, using the at least one processor, the ground truth label in a training dataset in response to identifying the ground truth label as a clean label (mental process – user can manually place the ground truth label in a training dataset in response to identifying the ground truth label as a clean label)
identifying, using the at least one processor, a problem with the ground truth label in response to determining that the expert consensus label and the ground truth label do not match (mental process – user can manually identify a problem with the ground truth label in response to determining that the expert consensus label and the ground truth label do not match, wherein reassessment of one or more of the ground truth label, the guidelines, and the sample is triggered by identification of the problem with the ground truth label)
triggering reassessment of the at least one guideline among the one or more guidelines in response to the identification of the problem with the ground truth label (mental process – user can manually trigger reassessment of the at least one guideline among the one or more guidelines in response to the identification of the problem with the ground truth label)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
obtaining, using at least one processor of an electronic device, a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines, wherein the ground truth label is one of correct or incorrect (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
obtaining, using at least one processor of an electronic device, a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines, wherein the ground truth label is one of correct or incorrect (mere instructions to apply the exception using a generic computer component)
generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier, a plurality of expert labels for the sample (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
obtaining, using at least one processor of an electronic device, a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines, wherein the ground truth label is one of correct or incorrect (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer)
obtaining, using at least one processor of an electronic device, a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines, wherein the ground truth label is one of correct or incorrect (mere instructions to apply the exception using a generic computer component)
generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier, a plurality of expert labels for the sample (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Conclusion: The claim is not patent eligible.
Claims 10 and 19 are rejected on the same grounds as claim 1. Additionally for claims 10 and 19: Claim 10 has the additional elements of a memory and a processing device. These elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B. Claim 19 has the additional element of a non-transitory machine-readable medium. This element is mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claims 2, 11, 20: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually identifying that the at least one guideline needs to be revised based on a degree of mismatch between the expert consensus label and the ground truth label.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 3 and 12: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually determining whether to reassess the sample using a revised guideline for the at least one guideline after the at least one guideline is revised.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 4 and 13: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the ground truth label determined by the human grader using the one or more guidelines classifies the sample based on content.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 5 and 14: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually determining a measure of the plurality of machine learned classifiers that agree on the expert label; and marking the sample for reassessment in response to determining that measure of the plurality of machine learned classifiers that agree on the expert label is less than a threshold.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 6 and 15: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of wherein the machine learned classifiers are trained using multi-fold cross validation.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the machine learned classifiers are trained using multi-fold cross validation recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does 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 element(s) of wherein the machine learned classifiers are trained using multi-fold cross validation recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claims 7 and 16: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of wherein the machine learned classifiers include different types of classifiers selected to reduce bias in label generation.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the machine learned classifiers include different types of classifiers selected to reduce bias in label generation recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does 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 element(s) of wherein the machine learned classifiers include different types of classifiers selected to reduce bias in label generation recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claims 8 and 17: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the consensus is based on a largest number of matches among the plurality of expert labels.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 9 and 18: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein: the sample is one of a plurality of samples; and each of the plurality of samples is associated with a verbal utterance.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, no additional elements are cited. Accordingly, the claim is not patent eligible.
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.
Claim(s) 1-4, 8-13, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rawi et al. (hereinafter Al-Rawi) On the Labeling Correctness in Computer Vision Datasets in view of Komedani et al. (hereinafter Komedani), U.S. Patent Application Publication 2017/0255628, further in view of Chavez et al. (hereinafter Chavez), U.S. Patent 9,985,984.
Regarding Claim 1, Al-Rawi discloses a method comprising:
generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier, a plurality of expert labels for the sample [“measure the per sample confidence-level is by using ensemble classification methods. In ensemble learning, multiple classifiers can be combined to solve a specific classification task” §1 ¶2];
determining, using the at least one processor, that an expert label among the plurality of expert labels is an expert consensus label representing consensus among the plurality of machine learned classifiers [“majority voting ensemble…ensemble chooses the category/class that receives the largest total vote” §2.1 ¶1];
comparing, using the at least one processor, the expert consensus label to the ground truth label in response to determining that the expert label among the plurality of expert labels is the expert consensus label [“verify the labeling is by having a system that returns a confidence-level for each sample in the dataset” §1 ¶1; Fig. 1; Table 2];
identifying, using the at least one processor, the ground truth label as a clean label in response to determining that the expert consensus label and the ground truth label match [“Correct labels” Fig. 1];
placing, using the at least one processor, the ground truth label in a training dataset in response to identifying the ground truth label as a clean label [“Data ready for usage” Fig. 1]; and
identifying, using the at least one processor, a problem with the ground truth label in response to determining that the expert consensus label and the ground truth label do not match [“Incorrect labels” Fig. 1]; and
triggering reassessment of at least one guideline among the one or more guidelines in response to the identification of the problem with the ground truth label [“Annotation/Labeling” after “Incorrect labels” Fig. 1; “verify the labeling is by having a system that returns a confidence-level for each sample in the dataset” §1 ¶1; Fig. 1; Table 2].
However, Al-Rawi fails to explicitly disclose obtaining, using at least one processor of an electronic device, a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines for classifying the sample, wherein the ground truth label is one of correct or incorrect;
triggering reassessment of at least one guideline among the one or more guidelines in response to the identification of the problem with the ground truth label.
Komedani discloses obtaining, using at least one processor of an electronic device [Figs. 1 and 2], a ground truth label associated with a sample among a plurality of samples, the ground truth label determined by a human grader according to one or more guidelines for classifying the sample [“annotators may add annotations to document elements constituting the document. The annotations may be categories of the document elements. The document elements may include words, phrases, and sentences. Thus, an annotation "Person" is assumed to be added to a word "Lincoln". Meanwhile, the annotators may be persons who are responsible for adding the annotations to the document elements. In this exemplary embodiment, the annotators are assumed to add the annotations to the document elements according to an annotation guideline as one example of a guideline. The annotation guideline may establish standards regarding what kinds of annotations are to be added to what kinds of document elements.” ¶13], wherein the ground truth label is one of correct or incorrect [“If an annotation has low quality, this exemplary embodiment may detect whether it is because the annotation guideline has low quality, or because any of the annotators has low proficiency.” §14; Note: a low quality label is incorrect];
triggering reassessment of at least one guideline among the one or more guidelines in response to the identification of the problem with the ground truth label [“If the evaluation result shows that the low quality of the annotation guideline has caused the low quality of the annotation, the evaluation module 240 may output information to that effect and the support information for supporting revision of the annotation guide” ¶20].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi and Komedani before him before the effective filing date of the claimed invention, to modify the method of Al-Rawi to incorporate the human grader and guidelines of Komedani.
Given the advantage of using machine learning to verify human labeled data to ensure proper labels for better training data, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Al-Rawi fails to explicitly disclose generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier, a plurality of expert labels for the sample.
Chavez discloses generating, using a plurality of machine learned classifiers of different types executing on the at least one processor and including at least one of a random forest classifier, a gradient boosted classifier, and a support vector machine classifier [“An ensemble of classifiers are trained on each of the feature subsets 327 and 328. Each ensemble contains one each of the Naïve Bayes, logistic regression, support vector machine (SVM), and random forest classifiers, and are termed level 1 classifiers 330 (for application with subset A 327) and 331 (for application with subset B 328). It is to be appreciated that as well as the previously mentioned classifiers, any suitable classifier can be utilized” col. 16, lines 3-12; “Combining heterogeneous classifiers into an ensemble in this way has been shown to increase prediction accuracy” col. 16, lines 61-63], a plurality of expert labels for the sample.
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, and Chavez before him before the effective filing date of the claimed invention, to modify the combination to incorporate heterogeneous classifiers of Chavez.
Given the advantage of increase prediction accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Al-Rawi, Komedani, and Chavez disclose the method of Claim 1.
However, Al-Rawi fails to explicitly disclose identifying that the at least one guideline needs to be revised based on a degree of mismatch between the expert consensus label and the ground truth label.
Komedani discloses identifying that the at least one guideline needs to be revised based on a degree of mismatch between the expert consensus label and the ground truth label [If the evaluation result shows that the low quality of the annotation guideline has caused the low quality of the annotation, the evaluation module 240 may output information to that effect and the support information for supporting revision of the annotation guide” ¶20].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, and Chavez before him before the effective filing date of the claimed invention, to modify the combination to incorporate the adjustment of guidelines of Komedani.
Given the advantage of improving guidelines to improve further annotations, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 3, Al-Rawi, Komedani, and Chavez disclose the method of Claim 2.
However, Al-Rawi fails to explicitly disclose determining whether to reassess the sample using a revised guideline for the at least one guideline after the at least one guideline is revised.
Komedani discloses determining whether to reassess the sample using a revised guideline for the at least one guideline after the at least one guideline is revised [“The operation above is repeated until all annotation types are processed” ¶58].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, and Chavez before him before the effective filing date of the claimed invention, to modify the combination to incorporate the use of the revised guidelines of Komedani.
Given the advantage of improving guidelines to improve further annotation accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 4, Al-Rawi, Komedani, and Chavez disclose the method of Claim 2.
However, Al-Rawi fails to explicitly disclose wherein the ground truth determined by the human grader using the one or more guidelines classifies the sample cased on content.
Komedani discloses wherein the ground truth determined by the human grader using the one or more guidelines classifies the sample cased on content [“The annotations may be categories of the document elements.” ¶13].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, and Chavez before him before the effective filing date of the claimed invention, to modify the combination to incorporate the guidelines for annotation of Komedani.
Given the advantage of using guidelines for increased consistency and accuracy of labeling, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 8, Al-Rawi, Komedani, and Chavez disclose the method of Claim 1. Al-Rawi further discloses wherein the consensus is based on a largest number of matches among the plurality of expert labels [“a majority voting ensemble” Abstract].
Regarding Claim 9, Al-Rawi, Komedani, and Chavez disclose the method of Claim 1.
However, Al-Rawi fails to explicitly disclose wherein: the sample is one of a plurality of samples; and each of the plurality of samples is associated with a verbal utterance.
Komedani discloses wherein: the sample is one of a plurality of samples; and each of the plurality of samples is associated with a verbal utterance [annotations may be manually added in order give a semantic structure to a text document.].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, and Chavez before him before the effective filing date of the claimed invention, to modify the combination to incorporate the plurality of samples that are associated with a verbal utterance.
Given the advantage of using the method efficiently on many samples that pertain of words used in numerous fields, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 10-13 are rejected on the same grounds as claims 1-4 respectively.
Claims 17-18 are rejected on the same grounds as claims 8-9 respectively.
Claims 19-20 are rejected on the same grounds as claims 1-2 respectively.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rawi, Komedani, and Chavez, in view of Misra et al. (hereinafter Misra), Patch-based CNN evaluation for bark classification.
Regarding Claim 5, Al-Rawi, Komedani, and Chavez disclose the method of Claim 1. Al-Rawi further disclose determining a measure of the plurality of machine learned classifiers that agree on the expert label [“majority voting ensemble based on the classifiers’ output labels, and the ensemble chooses the category/class that receives the largest total vote” §2.1 ¶1].
However, Al-Rawi fails to explicitly disclose marking the sample for reassessment in response to determining that measure of the plurality of machine learned classifiers that agree on the expert label is less than a threshold.
Misra discloses marking the sample for reassessment in response to determining that measure of the plurality of machine learned classifiers that agree on the expert label is less than a threshold [“there may be cases when more than one class gets the largest number of votes. There is no one major class and ties can be found, i.e. multiple classes can have the highest count of votes.” §3.6 ¶2; “more particular to neural network classifiers is the breaking of ties in ensembles by using Soft Max Accumulations [22]. Neural network classifiers output, by default, the class label prediction accompanied by a confidence of prediction (by using soft max function) and this information is leveraged to resolve ties in [22]. In case of a tie in the voting process, the confidences for the received votes of the tied classes, are summed up. Finally, the class that accumulates the Maximum Confidence sum is selected.” §3.6 ¶2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, Chavez, and Misra before him before the effective filing date of the claimed invention, to modify the combination to incorporate the ensemble tie breaking procedures of Misra.
Given the advantage of breaking a tie in an ensemble to intelligently select a result, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 14 is rejected on the same grounds as claim 5.
Claim(s) 6, 7, 15, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rawi, Komedani, and Chavez, in view of Doan et al. (hereinafter Doan), Recognition of medication information from discharge summaries using ensembles of classifiers.
Regarding Claim 6, Al-Rawi, Komedani, and Chavez discloses the method of Claim 1.
However, Al-Rawi fails to explicitly disclose wherein the machine learned classifiers are trained using multi-fold cross validation.
Doan discloses wherein the machine learned classifiers are trained using multi-fold cross validation [“10-fold cross-validation” pg. 5 §Experimental settings ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, Chavez, and Doan before him before the effective filing date of the claimed invention, to modify the combination to incorporate the cross-validation of Doan.
Given the advantage of training and testing the models in the ensemble to ensure accurate results, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 7, Al-Rawi, Komedani, and Chavez discloses the method of Claim 1.
However, Al-Rawi fails to explicitly disclose wherein the machine learned classifiers include different types of classifiers selected to reduce bias in label generation.
Doan discloses wherein the machine learned classifiers include different types of classifiers selected to reduce bias in label generation [“ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Al-Rawi, Komedani, Chavez, and Doan before him before the effective filing date of the claimed invention, to modify the combination to incorporate the heterogeneous classifiers of Doan.
Given the advantage of using different models to improve accuracy and robustness, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 15-16 are rejected on the same grounds as claims 6-7 respectively.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the 101 rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) the claims provide a practical application and are directed to an improvement of machine learning technology, and 2) claim 1 is analogous to Example 48’s claim 3 which was found to have a practical application. Examiner disagrees for at least the following reasons.
First, any alleged improvement to technology is actually an improvement to the abstract idea: the process of reviewing labels for accuracy. Reviewing labeled data through the claimed steps can be done manually by a person with the exceptions of the additional elements which do not integrate the judicial exception into a practical application nor provide significantly more. Merely automating an abstract idea does not render the claim patent eligible.
Second, Example 48’s claim 3 includes steps (e) and (f) which integrate the abstract idea into a practical application. Specifically, these limitations offer an improvement over existing speech-separation methods by (e) converting the masked clusters into N separate speech signals in time domain, and (f) extracting spectral features from only one target source sd of the N separate signals from the output of step (e) and generating a sequence of words from the spectral features to produce a transcript. These additional limitations integrate the abstract idea recited in steps (b), (c), and (d) into a practical application of speech-to-text conversion. Conversely, the instant claims are directed to improving the reliability of human labeled data by identifying an expert consensus label (if any) and including the human labeled data within the training dataset if the expert consensus label matches the human labeled data. Though consensus labels are implemented in machine learning, this additional element does not integrate the abstract idea into a practical application because it is equivalent to merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Therefore, the rejections are maintained.
Regarding the prior art rejections, Applicant's arguments with respect to the claims have been considered but are moot because the arguments do not apply to the references being used in the current rejection of the limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) 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 ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148