DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 5 May 2026.
Claims 8-26 are pending. 8, 17, and 21 are independent claims. Claim 27 is cancelled.
Claim Objections
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
As noted by the examiner in the office action mailed 5 February 2026, the claim sets filed 31 December 2021 and 13 October 2025 failed to include claim 18. The applicant appears to have corrected this by shifting claims. Claim 19 is now renumbered claim 18; claim 20 is now renumbered claim 19; claim 21 is now renumbered claim 20; claim 22 is now renumbered claim 21; claim 23 is now renumbered claim 22; claim 24 is now renumbered claim 23; claim 25 is now renumbered claim 24; claim 26 is now renumbered claim 25; claim 27 is now renumbered claim 26.
However, this results in the current claim set not including claim 27 and the appropriate status identifier. For the purpose of examination, the examiner will treat claim 27 is though it is cancelled.
In any subsequent reply, the applicant is required to indicate that claim 27 is cancelled.
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 8-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 8-16 are directed toward a system (machine). Claims 17-19are directed toward a computer storage media (manufacture). Claims 20-26 are directed toward a method (process) Therefore, each of these claims falls within one of the four statutory categories.
Claim 8:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
Claim 8 recites the elements:
generating a first sub-model of the classifier model based on a first lexicon that includes a first plurality of strings that are included in a first plurality of training records that are labeled as belonging to the positive class (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a judgement or evaluation to sort/filter strings that belong to the positive class)
generating a second sub-model of the classifier model based on a first lexicon that includes a second plurality of strings that are included in a second plurality of training records that are labeled as belonging to the negative class (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a judgement or evaluation to sort/filter strings that belong to the negative class)
generating a third sub-model of the classifier model based on a first lexicon that includes a third plurality of strings that are included in both the first plurality of training records and the second plurality of training records (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a judgement or evaluation to sort/filter strings that belong to both the positive class and negative class)
integrating the first sub-model, the second sub-model, and the third sub-model to generate the classifier model, wherein a balance parameter associated with the classifier model controls a weighting applied, during inference and without retraining the classifier model, to relevance scores output by the first, second, and third sub-models, to modify a false positive threshold or a false negative threshold of the classifier model (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitations encompasses performing a judgement based upon the previously evaluations of different models to sort/filter items into positive/negative/both positive and negative classes)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more hardware processors
one or more computer-readable media having executable instructions embodied thereon, which, when executed by the one or more processors, cause the one or more hardware processors to execute actions
These additional elements 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 (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more hardware processors
one or more computer-readable media having executable instructions embodied thereon, which, when executed by the one or more processors, cause the one or more hardware processors to execute actions
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
With respect to dependent claim 9, the claim depends upon independent claim 8. The analysis of claim 8 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
generating a fourth lexicon based on the first plurality of training records
generating a fifth lexicon based on the second plurality of training records
generating the first, second, and third lexicons based on the fourth lexicon an the fifth lexicon
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
generating a fourth lexicon based on the first plurality of training records
generating a fifth lexicon based on the second plurality of training records
generating the first, second, and third lexicons based on the fourth lexicon an the fifth lexicon
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 10:
With respect to dependent claim 10, the claim depends upon dependent claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
determining an intersection of the fourth and fifth lexicons (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine the intersection between the fourth and fifth lexicons)
determining a set difference of the fourth and fifth lexicons (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine the set difference between the fourth and fifth lexicons)
determining a set difference of the fifth and fourth lexicons (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation to determine the set difference between the fifth and fourth lexicons)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
generating the first lexicon to include the determined set difference of the fourth and fifth lexicons
generating the second lexicon to include the determined set difference of the fifth and fourth lexicons
generating the third lexicon to include the determined intersection of the fourth and fifth lexicons
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
generating the first lexicon to include the determined set difference of the fourth and fifth lexicons
generating the second lexicon to include the determined set difference of the fifth and fourth lexicons
generating the third lexicon to include the determined intersection of the fourth and fifth lexicons
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 11:
With respect to dependent claim 11, the claim depends upon independent claim 8. The analysis of claim 8 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
accessing labeled archive data
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 claims further disclose the additional elements:
segmenting the labeled archived data into a set of testing data and a set of training data
segmenting the training data, based on the labels included in the set of training data, into the first plurality of training records and the second plurality of training records
employing the labeled testing data to validate the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
accessing labeled archive data
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 claims further disclose the additional elements:
segmenting the labeled archived data into a set of testing data and a set of training data
segmenting the training data, based on the labels included in the set of training data, into the first plurality of training records and the second plurality of training records
employing the labeled testing data to validate the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 12:
With respect to dependent claim 12, the claim depends upon dependent claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
employing the set of training data to train the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
employing the set of training data to train the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 13:
With respect to dependent claim 13, the claim depends upon dependent claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
segmenting the set of training data into a set of lexicon training data and a set of scoring threshold data
segmenting the set of lexicon training data into the first plurality of training records and the second plurality of training records
updating the classifier model such that the updated classifier model, when benchmarked against the set of scoring threshold data, exhibits a predetermined tradeoff between a false positive error rate (FPR) and a false negative error rate (FNR) of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
segmenting the set of training data into a set of lexicon training data and a set of scoring threshold data
segmenting the set of lexicon training data into the first plurality of training records and the second plurality of training records
updating the classifier model such that the updated classifier model, when benchmarked against the set of scoring threshold data, exhibits a predetermined tradeoff between a false positive error rate (FPR) and a false negative error rate (FNR) of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 14:
With respect to dependent claim 14, the claim depends upon independent claim 8. The analysis of claim 8 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
receiving a balance parameter that indicates a target tradeoff between a false positive error rate (FPR) of the classifier model and a false negative error rate (FNR) of the classifier model
employing the balance parameter to update the classifier model such that the updated classifier model, when benchmarked against a third plurality of training records, exhibits the target tradeoff between the FPR of the classifier model and the FNR of the classifier
employing the tuned classifier model to classify the text-based content as belonging to a positive class of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
receiving a balance parameter that indicates a target tradeoff between a false positive error rate (FPR) of the classifier model and a false negative error rate (FNR) of the classifier model
employing the balance parameter to update the classifier model such that the updated classifier model, when benchmarked against a third plurality of training records, exhibits the target tradeoff between the FPR of the classifier model and the FNR of the classifier
employing the tuned classifier model to classify the text-based content as belonging to a positive class of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 15:
With respect to dependent claim 15, the claim depends upon dependent claim 14. The analysis of claim 14 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
iteratively employing one or more threshold parameters of the classifier model to determine a classification of each record of the third plurality of training records
iteratively employing the label and the classification of each record of the third plurality of records to determine each of the FPR and the FNR of the classifier model
iteratively adjusting the one or more threshold parameters of the classifier model such that the classifier model, when benchmarked against the third plurality of training records, exhibits the indicative tradeoff between the FRP of the classifier model and the FNR of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
iteratively employing one or more threshold parameters of the classifier model to determine a classification of each record of the third plurality of training records
iteratively employing the label and the classification of each record of the third plurality of records to determine each of the FPR and the FNR of the classifier model
iteratively adjusting the one or more threshold parameters of the classifier model such that the classifier model, when benchmarked against the third plurality of training records, exhibits the indicative tradeoff between the FRP of the classifier model and the FNR of the classifier model
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 16:
With respect to dependent claim 16, the claim depends upon independent claim 8. The analysis of claim 8 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
deploying the classifier model in a compliance enforcement pipeline
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
deploying the classifier model in a compliance enforcement pipeline
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 17:
With respect to independent claim 17, the claim recites the elements substantially similar to those in claim 8. The analysis of claim 8 is incorporated herein by reference.
Claim 19:
With respect to dependent claim 19, the claim depends upon independent claim 17. The analysis of claim 17 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
receiving text-based content
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 claims further recite the elements:
employing the classifier model to classify the text-based content as belonging to the positive class
in response to classifying the text-based content as belonging to the positive class of the classifier model, performing one or more mitigation actions that alters subsequent transmissions of the text-based content
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
receiving text-based content
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 claims further recite the elements:
employing the classifier model to classify the text-based content as belonging to the positive class
in response to classifying the text-based content as belonging to the positive class of the classifier model, performing one or more mitigation actions that alters subsequent transmissions of the text-based content
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19:
With respect to dependent claim 19, the claim recites the limitations substantially similar to those in claim 14. The analysis of claim 14 is incorporated herein by reference.
Claim 20:
With respect to independent claim 20, the claim recites the elements substantially similar to those in claim 8. The analysis of claim 8 is incorporated herein by reference.
Claim 21:
With respect to dependent claim 21, the claim recites the limitations substantially similar to those in claim 18. The analysis of claim 18 is incorporated herein by reference.
Claim 22:
With respect to dependent claim 22, the claim recites the limitations substantially similar to those in claim 18. The analysis of claim 18 is incorporated herein by reference.
Claim 23:
With respect to dependent claim 23, the claim depends upon dependent claim 22. The analysis of claim 22 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
wherein the mitigation action includes at least one of: providing an alter indicating the text-based content, deleting the text-based content, replacing the text-based content, quarantining the text-based content, or any combination thereof
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
wherein the mitigation action includes at least one of: providing an alter indicating the text-based content, deleting the text-based content, replacing the text-based content, quarantining the text-based content, or any combination thereof
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Claim 24:
With respect to dependent claim 24, the claim recites the limitations substantially similar to those in claim 14. The analysis of claim 14 is incorporated herein by reference.
Claim 25:
With respect to dependent claim 25, the claim depends upon dependent claim 24. The analysis of claim 24 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the elements:
determine a first score for the second text-based content, wherein the first score indicates a likelihood that the second text-based content is associated with the positive class (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a evaluation to determine a first score indicating a likelihood that the text is associated with the positive class)
determine a second score for the second text-based content, wherein the second score indicates a likelihood that the second text-based content is associated with the negative class (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a evaluation to determine a second score indicating a likelihood that the text is associated with the negative class)
determine a third score for the second text-based content, wherein the third score indicates a likelihood that the text-based content is associated with both the positive class of the classifier model and the negative class (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a evaluation to determine a third score indicating a likelihood that the text is associated with both the positive class and the negative class)
generate an overall score for the second text-based content that is based on a combination of the first score, the second score, and the third score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing a evaluation to determine an overall score based on the first, second, and third scores)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
causing the first sub-model to determine likelihood that the second text-based content is associated with the positive class of the classifier model
causing the second sub-model to determine a likelihood that the second text-based content is associated with the negative class of the classifier model
causing the third sub-model to determine a likelihood that the text-based content is associated with both the positive class of the classifier model and the negative class of the classifier model
causing the updated classifier model based on a combination of the first score, the second score, and the third score
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
causing the first sub-model to determine likelihood that the second text-based content is associated with the positive class of the classifier model
causing the second sub-model to determine a likelihood that the second text-based content is associated with the negative class of the classifier model
causing the third sub-model to determine a likelihood that the text-based content is associated with both the positive class of the classifier model and the negative class of the classifier model
causing the updated classifier model based on a combination of the first score, the second score, and the third score
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Claim 26:
With respect to dependent claim 26, the claim depends upon dependent claim 24. The analysis of claim 24 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims further disclose the additional elements:
causing the tuned classifier model to be deployed in a compliance enforcement pipeline
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims further disclose the additional elements:
causing the tuned classifier model to be deployed in a compliance enforcement pipeline
These elements recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 8, 16-18, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (Sentiment Analysis with Automatically Constructed Lexicon and Three-Way Decision, 2014, hereafter Zhou) and further in view of Tiong et al. (US 2022/0156530, published 19 May 2022, hereafter Tiong) and further in view of Ram et al. (US 2022/0076144, filed 9 September 2020, hereafter Ram).
As per independent claim 8, Zhou discloses a system for generating an integrated classifier model that has a positive class and a negative class, the system comprising:
generating a first sub-model of the classifier model based on a first lexicon that includes a first plurality of strings that are included in a first plurality of training records that are labeled as belonging to the positive class (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (strings) based on sentiment analysis. This is performed via pattern extraction and sentiment polarity assignment. In this instance, each noun-adjective pattern is identified and a sentiment is applied to the extracted pattern. Based upon the sentiments, the noun-adjective pairs are stored in a data structure. Finally, each sentiment score is transformed to either “+1” (positive sentiment) or “-1” (negative sentiment).” In this instance the first sub-model is the “labeled data” set having strings labeled with a positive sentiment by both the general lexicon and the feature lexicon)
generating a second sub-model of the classifier model based on a second lexicon that includes a second plurality of strings that are included in a second plurality of training records that are labeled as belonging to the negative class (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (strings) based on sentiment analysis. This is performed via pattern extraction and sentiment polarity assignment. In this instance, each noun-adjective pattern is identified and a sentiment is applied to the extracted pattern. Based upon the sentiments, the noun-adjective pairs are stored in a data structure. Finally, each sentiment score is transformed to either “+1” (positive sentiment) or “-1” (negative sentiment).” In this instance the first sub-model is the “labeled data” set having strings labeled with a negative sentiment by both the general lexicon and the feature lexicon)
generating a third sub-model of the classifier model based on a third lexicon that includes a third plurality of strings that are included in both the first plurality of training records and the second plurality of training records (Figure 2; Section 3.3: Here, there exists a set of nouns such that the nouns are the same but the adjective portions are antonyms (classified as both the first plurality of training records (positive) and second plurality of training records (negative)). In this instance, the pattern with the larger sentiment score is given the sentiment polarity of “+1” (positive) and the other the sentiment polarity of “-1” (negative). In this instance the first sub-model is the “labeled data” set having strings labeled with a positive sentiment and a negative sentiment by the general lexicon and the feature lexicon)
integrating the first sub-model, the second sub-model, and the third sub-model to generate the classifier model (Figure 3; Section 3.4: Here, the classified data define lexicons. These lexicons are used to generate a plurality of “labeled data” sets. These labeled data sets are combined to train a supervised learning model that is used to classify the dataset of labeled data that has been labeled with different polarities by the general lexicon and the feature lexicon)
Zhou fails to specifically disclose:
one or more hardware processors
one or more computer-readable media having executable instructions embodied thereon, which, when executed by the one or more processors, cause the one or more hardware processors to execute actions
wherein a balance parameter associated with the classifier model controls a weighting applied, during inference and without retraining the classifier model, to relevance scores output by the first, second, and third sub-models, to modify a false positive threshold or a false negative threshold of the classifier model
However, Tiong, which is analogous to the claimed invention because it is directed toward use of classifier balancing, discloses:
one or more hardware processors and one or more computer readable media having executable instructions embodied thereon, which, when executed by the one or more processors, cause the one or more hardware processors to execute actions (Figure 2; paragraphs 0035-0036).
wherein a balance parameter associated with the classifier model controls a weighting applied (paragraph 0073: Here, in the classifier balancing stage, the sample applies the weight), during inference and without retraining the classifier model, to relevance scores output by the first, second, and third sub-models (Figure 4; paragraphs 0049-0055: Here, the model is trained and the classifier update parameters are updated based on the sum of the classification loss, the classification loss, and the ICCL loss. These represent the first, second, and third sub-models. This provides the initialization of the centroids. After this training is performed, and during inference, the classifiers may be rebalanced. This rebalancing occurs without additional retraining the classifier model)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Tiong with Zhou, with a reasonable expectation of success, as it would have allowed for storing programmatic instructions executable by a processor to perform rebalancing of classifiers to improve classification of long tailed representations learning (Tiong: paragraphs 0052-0053).
Finally, Ram discloses modify a false positive threshold or a false negative threshold of the classifier model (paragraph 0038: Here, a set of constraints are provided to the machine learning classification model. These constraints include false positive and false negative error rates. The model is then trained to adhere to these constraints)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ram with Zhou-Tiong, with a reasonable expectation of success, as it would have allowed for providing constraints, such as false positive and false negative error rates, that are acceptable in training the model (Ram: paragraph 0038).
As per dependent claim 16, Zhou, Tiong, and Ram disclose the limitations similar to those in claim 8, and the same rejection is incorporated herein. Zhou fails to specifically disclose deploying the classifier model in a compliance enforcement pipeline.
However, Ram, which is analogous to the claimed invention because it is directed toward a classification model, discloses a classifier model in a compliance enforcement pipeline (paragraph 0030: Here an ML model pipeline is used to model the correlation between data and classifications of the data. Various pipelines may classify the data based upon various parameters including false positive rates and false negative rates and use these hyperparameters to constrain the models (paragraph 0038)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ram with Zhou, with a reasonable expectation of success, as it would have allowed for generating classification models pipelines that conform to specified hyperparameters (Ram: paragraph 0038).
With respect to independent claim 17, the claim recites the limitations substantially similar to those in claim 8. Claim 17 is rejected under similar rationale.
With respect to dependent claim 18, Zhou discloses wherein the actions further comprise:
receiving text-based content class (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (text) based on sentiment analysis)
employing the classifier model to classify the text-based content as belonging to a positive class records (Figure 2; Section 3.3: Here, there exists a set of nouns such that the nouns are the same but the adjective portions are antonyms (classified as both the first plurality of training records (positive) and second plurality of training records (negative)). In this instance, the pattern with the larger sentiment score is given the sentiment polarity of “+1” (positive) and the other the sentiment polarity of “-1” (negative). Based upon the determination that the general lexicon and feature lexicon both classify the content as having a positive polarity, the content are put into the “labeled data” having a positive polarity for use in training the supervised learning model)
in response to classifying the text-based content as belonging to the positive class of the classifier model, performing one or more mitigation actions that alters subsequent transmissions of the text-based content (Figure 3; Section 3.4: Here, a three-way decision is performed. Specifically, a general lexicon and the feature lexicon are compared. If both lexicons agree on the sentiment polarity label, the sentiment polarity label is treated as correct and it is added to the labeled data set. If the polarity is different, the result is placed into a rejection set for additional processing. The removal of text-based content to the rejection set is a mitigation action that alters the subsequent transmission and processing of the text-based content)
As per independent claim 20, Zhou discloses a method comprising:
generating training records that include a first lexicon, a second lexicon, and a third lexicon, the first lexicon including a first plurality of strings that are included in the training records that are labeled as belonging to a positive class, a second plurality strings that are included in the training records that are labeled as belonging to the negative class, and the third lexicon includes a plurality of strings that are included in both the first plurality of strings and the second plurality of strings (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (strings) based on sentiment analysis. This is performed via pattern extraction and sentiment polarity assignment. In this instance, each noun-adjective pattern is identified and a sentiment is applied to the extracted pattern. Based upon the sentiments, the noun-adjective pairs are stored in a data structure. Finally, each sentiment score is transformed to either “+1” (positive sentiment) or “-1” (negative sentiment).” Further, there exists a set of nouns such that the nouns are the same but the adjective portions are antonyms (classified as both the first plurality of training records (positive) and second plurality of training records (negative)). In this instance, the pattern with the larger sentiment score is given the sentiment polarity of “+1” (positive) and the other the sentiment polarity of “-1” (negative))
generating a first sub-model based on a first lexicon, a second sub-model based on a second lexicon, and a third sub-model based on a third lexicon (Figure 3; Section 3.4: Here, a plurality of sub-models “labeled data” are generated from application of the general lexicon and the feature lexicon. These sub-models are labeled data sets that are used to train the supervised learning model. The first sub-model comprises the set of items that are identified by the general lexicon and the feature lexicon as having a positive polarity; the second sub-model comprises the set of items that are identified by the general lexicon and the feature lexicon as having a negative polarity; the third sub-model comprises the set of items that are identified as having different polarities as labeled by the general lexicon and the feature lexicon)
generating a classifier model by at least integrating the first sub-model, the second sub-model, and the third sub-model (Figure 3; Section 3.4: Here, the classified data define lexicons. These lexicons are used to generate a plurality of “labeled data” sets. These labeled data sets are combined to train a supervised learning model that is used to classify the dataset of labeled data that has been labeled with different polarities by the general lexicon and the feature lexicon)
Zhou fails to specifically disclose:
wherein a balance parameter associated with the classifier model controls a weighting applied, during inference and without retraining the classifier model, to relevance scores output by the first, second, and third sub-models, to modify a false positive threshold or a false negative threshold of the classifier model
However, Tiong, which is analogous to the claimed invention because it is directed toward use of classifier balancing, discloses:
wherein a balance parameter associated with the classifier model controls a weighting applied (paragraph 0073: Here, in the classifier balancing stage, the sample applies the weight), during inference and without retraining the classifier model, to relevance scores output by the first, second, and third sub-models (Figure 4; paragraphs 0049-0055: Here, the model is trained and the classifier update parameters are updated based on the sum of the classification loss, the classification loss, and the ICCL loss. These represent the first, second, and third sub-models. This provides the initialization of the centroids. After this training is performed, and during inference, the classifiers may be rebalanced. This rebalancing occurs without additional retraining the classifier model)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Tiong with Zhou, with a reasonable expectation of success, as it would have allowed for storing programmatic instructions executable by a processor to perform rebalancing of classifiers to improve classification of long tailed representations learning (Tiong: paragraphs 0052-0053).
Finally, Ram discloses modify a false positive threshold or a false negative threshold of the classifier model (paragraph 0038: Here, a set of constraints are provided to the machine learning classification model. These constraints include false positive and false negative error rates. The model is then trained to adhere to these constraints)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ram with Zhou-Tiong, with a reasonable expectation of success, as it would have allowed for providing constraints, such as false positive and false negative error rates, that are acceptable in training the model (Ram: paragraph 0038).
As per dependent claim 21, Zhou discloses wherein the method further comprises causing the classifier model to classifying a text-based content as belonging to a positive class (Figure 2; Section 3.3: Here, there exists a set of nouns such that the nouns are the same but the adjective portions are antonyms (classified as both the first plurality of training records (positive) and second plurality of training records (negative)). In this instance, the pattern with the larger sentiment score is given the sentiment polarity of “+1” (positive) and the other the sentiment polarity of “-1” (negative)).
As per dependent claim 22, Zhou discloses wherein the method further comprises, in response to classifying the text-based content as belonging to the positive class, performing a mitigation action that alters subsequent transmission of the text-based content (Figure 3; Section 3.4: Here, a three-way decision is performed. Specifically, a general lexicon and the feature lexicon are compared. If both lexicons agree on the sentiment polarity label, the sentiment polarity label is treated as correct and it is added to the labeled data set. If the polarity is different, the result is placed into a rejection set for additional processing. The removal of text-based content to the rejection set is a mitigation action that alters the subsequent transmission and processing of the text-based content)
As per dependent claim 23, Zhou discloses wherein the mitigation action includes at least one of: providing an alter indicating the text-based content, deleting the text-based content, replacing the text-based content, quarantining the text-based content (Figure 3; Section 3.4: Here, a three-way decision is performed. Specifically, a general lexicon and the feature lexicon are compared. If both lexicons agree on the sentiment polarity label, the sentiment polarity label is treated as correct and it is added to the labeled data set. If the polarity is different, the result is placed into a rejection set for additional processing. The removal of text-based content to the rejection set is a mitigation action that alters the subsequent transmission and processing of the text-based content), or any combination thereof.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, Tiong, and Ram, and further in view of Alspector et al. (US 8713014, patented 29 April 2014, hereafter Alspector).
As per dependent claim 9, Zhou, Tiong, and Ram disclose the limitations similar to those in claim 8, and the same rejection is incorporated herein. Zhou fails to specifically disclose:
generating a fourth lexicon based on the first plurality of training records
generating a fifth lexicon based on the second plurality of training records
generating the first, second, and third lexicons based on the fourth and fifth lexicons
However, Alspector discloses:
generating a fourth lexicon based on the first plurality of training records (column 2, lines 34-43: Here, a lexicon of attributes may include a primary lexicon and a secondary lexicon. In this instance, both the primary and secondary lexicons are generated from the same set of training records)
generating a fifth lexicon based on the second plurality of training records (column 2, lines 34-43: Here, a lexicon of attributes may include a primary lexicon and a secondary lexicon. In this instance, both the primary and secondary lexicons are generated from the same set of training records. Further, it is noted that Zhou discloses a first and second lexicons. Applying Alspector’s teaching of generating primary and secondary lexicons for each of the first and second lexicons would result in the generation of corresponding fourth and fifth lexicons)
generating the first, second, and third lexicons based on the fourth and fifth lexicons (column 2, lines 34-43: Here, a lexicon of attributes may include a primary lexicon and a secondary lexicon. In this instance, both the primary and secondary lexicons are generated from the same set of training records)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Alspector with Zhou, with a reasonable expectation of success, as it would have allowed for generation of primary and secondary lexicons for each generated lexicon (Alspector: column 2, lines 34-43). This would have provided the advantage of modifying the secondary lexicon to create an augmented intersection in order to use for calculating a similarity in addition to calculating the similarity based upon the original primary lexicon (Alspector: column 2, lines 34-43).
As per dependent claim 10, Zhou, Tiong, Ram, and Alspector disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Zhou discloses:
determining an intersection of two lexicons (Section 3.4: Here, the results of applying the general lexicon and the feature lexicon are compared. If both results have the same polarity, the lexicons intersect and the label is applied)
determining a set of differences between the lexicons (Section 3.4: Here, the results of applying the general lexicon and the feature lexicon are compared. If both results have different polarities, the difference is noted and the associated data is placed in the rejection set)
generating the third lexicon to include the determined intersection of the two lexicons (Section 3.4: Here, the results of applying the general lexicon and the feature lexicon are compared. If both results have the same polarity, the lexicons intersect and the label is applied)
Zhou fails to specifically disclose:
generating the first lexicon to include the determined set of differences of the fourth and fifth lexicons
generating the second lexicon to include the determined set of differences of the fifth and fourth lexicons
However, Alspector, which is analogous to the claimed invention because it is directed toward classifying data based upon lexical analysis, discloses:
generating the first lexicon to include the determined set of differences of the fourth and fifth lexicons (column 2, lines 34-43: Here, a lexicon of attributes may include a primary lexicon and a secondary lexicon. In this instance, both the primary and secondary lexicons are generated from the same set of training records)
generating the second lexicon to include the determined set of differences of the fifth and fourth lexicons (column 2, lines 34-43: Here, a lexicon of attributes may include a primary lexicon and a secondary lexicon. In this instance, both the primary and secondary lexicons are generated from the same set of training records)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Alspector with Zhou, with a reasonable expectation of success, as it would have allowed for generation of primary and secondary lexicons for each generated lexicon (Alspector: column 2, lines 34-43). This would have provided the advantage of modifying the secondary lexicon to create an augmented intersection in order to use for calculating a similarity in addition to calculating the similarity based upon the original primary lexicon.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, Tiong, and Ram, and further in view of Pushkin et al. (US 11734937, filed 2 January 2020, hereafter Pushkin).
As per dependent claim 11, Zhou, Tiong, and Ram disclose the limitations similar to those in claim 8, and the same rejection is incorporated herein. Zhou further discloses wherein the actions further comprise:
accessing labeled archive data (Figure 3; Section 3.4: Here, a set of data is labeled based upon the general lexicon and the feature lexicon)
segmenting the labeled archive data into a training set and a rejection set (Section 3.4: Here, the results of applying the general lexicon and the feature lexicon are compared. If both results have the same polarity, the lexicons intersect and the label is applied. Otherwise the data is added to a rejection set)
segmenting the training data, based on the labels included in the set of training data, into the first plurality of training records and the second plurality of training records (Figure 3; Section 3.4: Here, the labeled data includes two different result sets based upon the polarity of the sentiment analysis)
Zhou fails to specifically disclose:
a set of testing data and employing the labeled testing data to validate the classifier model
However, Pushkin, which is analogous to the claimed invention because it is directed toward testing text classification, discloses a set of testing data and employing the labeled testing data to validate the classifier model (Figure 2, item 208; column 1, line 55- column 2, line 22; column 10, lines 1-44: Here, a set of training data and testing data is obtained from the dataset. The testing dataset is used to test the classifier model and fine tune hyperparameters to improve classification (column 7, lines 46-67)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Pushkin with Zhou, with a reasonable expectation of success, as it would have allowed for testing and fine tuning hyperparameters to improve model classification (Pushkin: column 7, lines 46-67).
As per dependent claim 12, Zhou, Tiong, Ram, and Pushkin disclose the limitations similar to those in claim 11, and the same rejection is incorporated herein. Zhou discloses wherein the actions further comprise employing the set of training data to train the classifier model (Figure 3; Section 3.4).
As per dependent claim 13, Zhou, Tiong, Ram, and Pushkin disclose the limitations similar to those in claim 11, and the same rejection is incorporated herein. Zhou disclose wherein the actions further comprise:
segmenting the set of training data into a set of lexicon training data and a set of scoring threshold data (Figure 3; Section 3.4: Here, the training data is segmented based upon the polarity of the classification data by the general lexicon and feature lexicon)
segmenting the set of lexicon training data into the first plurality of training records and the second plurality of training records (Figure 3; Section 3.4: Here, based upon the textual data being classified as either positive or negative (polarity), and having the same polarity between the general and feature lexicons, the record is added to the appropriate lexicon)
updating the classifier model (Figure 3; Section 3.4: Here, the lexicon data is used to train the supervised learning classification model)
Zhou fails to specifically disclose:
updating the classifier model such that the updated classifier model, when benchmarked against the set of scoring threshold data, exhibits a predetermined tradeoff between a false positive error rate (FPR) and a false negative error rate (FNR) of the classifier model
However, Ram, which is analogous to the claimed invention because it is directed toward classification models having constraints, discloses:
updating the classifier model such that the updated classifier model, when benchmarked against the set of scoring threshold data, exhibits a predetermined tradeoff between a false positive error rate (FPR) and a false negative error rate (FNR) of the classifier model (paragraph 0038: Here, a set of constraints are provided to the machine learning classification model. These constraints include false positive and false negative error rates. The model is then trained to adhere to these constraints)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ram with Zhou-Pushkin, with a reasonable expectation of success, as it would have allowed for providing constraints, such as false positive and false negative error rates, that are acceptable in training the model (Ram: paragraph 0038).
Claims 14, 19, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, Tiong, and Ram, and further in view of Alspector, and further in view of Templeton (US 2019/0259095, published 22 August 2019).
As per dependent claim 14, Zhou, Tiong, and Ram discloses the limitations similar to those in claim 8, and the same rejection is incorporated herein. Zhou discloses employing the tuned classifier model to classify the text-based content as belonging to a positive class of the classifier model (Section 3.4), but Zhou fails to specifically disclose wherein the actions further comprise:
receiving a balance parameter that indicates a target tradeoff between a false positive error rate (FPR) of the classifier model and a false negative error rate (FNR) of the classifier model
employing the balance parameter to update the classifier model such that the updated classifier model, when benchmarked against a third plurality of training records, exhibits the target tradeoff between the FPR of the classifier model and the FNR of the classifier
Alspector discloses:
such that the updated classifier model, when benchmarked against a third plurality of training records, exhibits the target tradeoff (column 2, lines 34-43: Here, lexicon used for classification is modified by adding additional content to the intersection to create an augmented intersection. This causes a larger threshold that may result in false positive/negatives based upon the expanded dataset. However, this is a tradeoff made by extending to a larger set that does not meet the original threshold)
employing the tuned classifier model to classify the text-based content as belonging to a positive class of the classifier model (column 2, lines 34-43: Here, the augmented lexicon classifies items that originally do not meet the threshold as being positively associated with the lexicon)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Alspector with Zhou-Templeton, with a reasonable expectation of success, as it would have allowed for generation of primary and secondary lexicons for each generated lexicon (Alspector: column 2, lines 34-43). This would have provided the advantage of modifying the secondary lexicon to create an augmented intersection in order to use for calculating a similarity in addition to calculating the similarity based upon the original primary lexicon.
However, Templeton discloses:
receiving a balance parameter that indicates a target tradeoff between a false positive error rate (FPR) of the classifier model and a false negative error rate (FNR) of the classifier model and meeting a target tradeoff between the FPR of the classifier model and the FNR of the classifier (paragraph 0092: Here, a virtual balance model (Geometric Brownian Motion Monte Carlo) is used to balance error rates including false positive rate and false negative rates)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Templeton with Zhou, with a reasonable expectation of success, as it would have allowed for balancing a plurality of error rates to improve the model (Templeton: paragraph 0092).
With respect to claim 19, the claim recites the limitations substantially similar to those in claim 14. Claim 19 is rejected under similar rationale.
With respect to claim 24, the claim recites the limitations substantially similar to those in claim 14. Claim 24 is rejected under similar rationale.
As per dependent claim 25, Zhou, Tiong, Ram, Alspector, and Templeton disclose the limitations similar to those in claim 24, and the same rejection is incorporated herein. Zhou further discloses wherein causing the tuned classifier model to classify a second text-based content further comprises:
causing the first sub-model to determine a first score for the second text-based content, wherein the first score indicates a likelihood that the second text-based content is associated with the positive class of the classifier model (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (strings) based on sentiment analysis. This is performed via pattern extraction and sentiment polarity assignment. In this instance, each noun-adjective pattern is identified and a sentiment is applied to the extracted pattern. Based upon the sentiments, the noun-adjective pairs are stored in a data structure. Finally, each sentiment score is transformed to either “+1” (positive sentiment) or “-1” (negative sentiment)”)
causing the second sub-model to determine a second score for the second text-based content, wherein the second score indicates a likelihood that the second text-based content is associated with the negative class of the classifier model (Figure 2; Section 3.3: Here, a feature lexicon is used to analyze and categorize comments (strings) based on sentiment analysis. This is performed via pattern extraction and sentiment polarity assignment. In this instance, each noun-adjective pattern is identified and a sentiment is applied to the extracted pattern. Based upon the sentiments, the noun-adjective pairs are stored in a data structure. Finally, each sentiment score is transformed to either “+1” (positive sentiment) or “-1” (negative sentiment)”)
causing a third sub-model to determine a third score for the second text-based content, wherein the third score indicates a likelihood that the text-based content is associated with both the positive class of the classifier model and the negative class of the classifier model (Figure 2; Section 3.3: Here, there exists a set of nouns such that the nouns are the same but the adjective portions are antonyms (classified as both the first plurality of training records (positive) and second plurality of training records (negative)). In this instance, the pattern with the larger sentiment score is given the sentiment polarity of “+1” (positive) and the other the sentiment polarity of “-1” (negative))
causing the updated classifier model to generate an overall score for the second text-based content that is based on a combination of the first score, the second score, and the third score (Figure 3; Section 3.4: Here, the classified data define lexicons. These lexicons are used to train a supervised learning model to perform sentiment analysis)
As per dependent claim 26, Zhou, Tiong, Ram, Alspector, and Templeton discloses the limitations similar to those in claim 25, and the same rejection is incorporated herein. Zhou fails to specifically disclose deploying the classifier model in a compliance enforcement pipeline.
However, Ram, which is analogous to the claimed invention because it is directed toward a classification model, discloses a classifier model in a compliance enforcement pipeline (paragraph 0030: Here an ML model pipeline is used to model the correlation between data and classifications of the data. Various pipelines may classify the data based upon various parameters including false positive rates and false negative rates and use these hyperparameters to constrain the models (paragraph 0038)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ram with Zhou-Templeton-Alspector, with a reasonable expectation of success, as it would have allowed for generating classification models pipelines that conform to specified hyperparameters (Ram: paragraph 0038).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zhou, Tiong, Ram, Alspector, and Templeton and further in view of Hayman et al. (US 12229777, filed 1 March 2019, hereafter Hayman).
As per dependent claim 15, Zhou, Tiong, Ram, Alspector, and Templeton disclose the limitations similar to those in claim 14, and the same rejection is incorporated herein. Zhou discloses iteratively employing one or more threshold parameters of the classifier model to determine a classification of each record of the third plurality of training records (Figure 3; Section 3.4: Here, a three-way decision is performed. Specifically, a general lexicon and the feature lexicon are compared. If both lexicons agree on the sentiment polarity label, the sentiment polarity label is treated as correct and it is added to the labeled data set. If the polarity is different, the result is placed into a rejection set for additional processing. The removal of text-based content to the rejection set is a mitigation action that alters the subsequent transmission and processing of the text-based content. This constitutes classification of each record of the plurality of training records).
Zhou fails to specifically disclose:
iteratively employing the label and the classification of each record of the third plurality of records to determine each of the FPR and the FNR of the classifier model
iteratively adjusting the one or more threshold parameters of the classifier model such that the classifier model, when bookmarked against the third plurality of training record, exhibits the indicated tradeoff between the FPR of the classifier model and the FNR of the classifier model
However, Templeton discloses:
receiving a balance parameter that indicates a target tradeoff between a false positive error rate (FPR) of the classifier model and a false negative error rate (FNR) of the classifier model and meeting a target tradeoff between the FPR of the classifier model and the FNR of the classifier (paragraph 0092: Here, a virtual balance model (Geometric Brownian Motion Monte Carlo) is used to balance error rates including false positive rate and false negative rates)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Templeton with Zhou, with a reasonable expectation of success, as it would have allowed for balancing a plurality of error rates to improve the model (Templeton: paragraph 0092).
Further, Alspector discloses:
such that the updated classifier model, when benchmarked against a third plurality of training records, exhibits the target tradeoff (column 2, lines 34-43: Here, lexicon used for classification is modified by adding additional content to the intersection to create an augmented intersection. This causes a larger threshold that may result in false positive/negatives based upon the expanded dataset. However, this is a tradeoff made by extending to a larger set that does not meet the original threshold)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Alspector with Zhou-Templeton, with a reasonable expectation of success, as it would have allowed for generation of primary and secondary lexicons for each generated lexicon (Alspector: column 2, lines 34-43). This would have provided the advantage of modifying the secondary lexicon to create an augmented intersection in order to use for calculating a similarity in addition to calculating the similarity based upon the original primary lexicon.
Finally, Hayman, which is analogous to the claimed invention because it is directed toward training a classifier, discloses iteratively adjusting hyperparameters of the classifier to achieve satisfactory predicted performance (column 11, lines 6-33: Here, classification data and hyperparameter data are iteratively tuned to improve the accuracy of the machine learning model). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Hayman’s iterative training to Zhou’s classification of each record, with a reasonable expectation of success, as it would have provided the advantage of improving classification of each record (Hayman: column 11, lines 6-33).
Additionally, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Hayman’s iterative training to Templeton’s balancing of FPR and FNR, with a reasonable expectation of success, as it would have allowed for improving balancing through optimization achieved by multiple iterations (Hayman: column 11, lines 6-33).
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhou, Tiong, and Ram.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant’s initial argument is that the claimed invention is not an abstract idea (page 13). To support this position, the applicant argues that the claims are directed to “generating a first sub-model based on a first lexicon derived from training records labeled as positive, generating a second sub-model based on a second lexicon derived from training records labeled as negative, generating a third sub-model based on a third lexicon derived from strings appearing in both sets of records, and integrating the three sub-models into a single classifier model… a balance parameter controls, during inference without retraining, a weighting applied to relevance scored output by the first, second, and third sub-models to shift an operating point between false positive and false negative thresholds (page 14)” are a “concrete runtime control mechanism for a classifier’s operating characteristics -– not a human “judgement” or “opinion” (page 14).”
While the examiner appreciates the applicant’s arguments, the examiner respectfully disagrees. Receiving a lexicon that includes a first plurality of strings that belong to a positive class and determining a sub-model of a classifier to identify strings that belong to the class is a mental process. Specifically, based upon a set of examples, a mental process is performed to evaluate/judge additional textual strings to determine if the strings belong to the positive class.
Similarly, receiving a lexicon that includes a first plurality of strings that belong to a negative class and determining a sub-model of a classifier to identify strings that belong to the class is a mental process. Specifically, based upon a set of examples, a mental process is performed to evaluate/judge additional textual strings to determine if the strings belong to the negative class.
Further, receiving a third plurality of strings and determining that the strings belong to both a positive and negative class is a mental process.
Finally, mentally applying some sort of “weighting” when determining the classification of a string, based upon the first, second, and third sub-models to improve false positive/false negative rates when classifying the string is a mental process. Specifically, based upon the user evaluation/judgement/opinion regarding the received textual strings, the user’s evaluation/judgement/opinion may be balanced/modified during the inference by some weight factor.
For this reason, this argument is not persuasive.
The applicant further argues that the claims integrate the abstract idea into a practical application because the result in an improvement to computer functionality and/or computer-related technology (pages 15-17).
While the examiner appreciates the applicant’s arguments, the argument is not persuasive.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
Additionally an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
In this instance, the identified improvement is provided by the abstract idea itself. The claim appears to invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field
For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bloomberg et al. (US 11055730): Discloses parameter balancing based on gradient boosted classifier prediction (Claim 17)
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 KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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