Detailed Action
This action is in response to the amendment filed 10/17/2025 for application 17/740,745, in which:
Claims 1, 13, and 20 are independent claims.
Claims 1, 13, and 20 have been amended.
Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Regarding the 35 USC § 112 Rejections:
Applicant's amendments to the independent Claims overcome the previous 35 U.S.C. 112(b) due to previously being indefinite. The rejection has been withdrawn. Please note the objection for similar Claim 11 which also recites the ‘remaining ones’.
Response to Arguments
Applicant's arguments filed 10/17/2025 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. § 101 Rejections:
Applicant's arguments regarding the 35 U.S.C. § 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant disagrees and traverses the rejections of the pending claims (Pages 8-9), as the claims do not fall within the any groupings of abstract ideas under Step 2A Prong One. The limitations within the independent claims do not fall within the abstract idea groupings including “mental processes”, “mathematical concepts”, and “certain methods of organizing human activity” as the claims recite operations that the human mind is not equipped to perform. Rather, the operations can only be reasonably completed by a computer and are specifically tailored to a computing environment.
Examiner respectfully disagrees. For the reasons given below and in the 35 U.S.C. § 101 rejections, the claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The claims recites … identifying a generative model … (a human being can mentally apply evaluation to identify a generative model), determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features (a human being can mentally apply evaluation to determine respective reduced feature-set outputs with subset constraints), determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values that are lower than the first set of accuracy values (a human being can mentally apply evaluation to determine one of the reduced feature-set outputs corresponding to a threshold constraint for accuracy values), designating the iteratively removed data features that are associated with the one or more of the reduced feature-set outputs corresponding to the second set of accuracy values that are lower than the first set of accuracy values as accuracy-modifying data features included as part of an accuracy-modifying data feature-set (a human being can mentally apply evaluation to designate iteratively removed data features that have specific associations), generating a first linear model based on the accuracy-modifying data features (a human being can mentally apply evaluation to generate a linear model based on features), generating a second linear model that is based on one of the accuracy-modifying data features having a weight that is highest relative to a remainder of the accuracy-modifying data features (a human being can mentally apply evaluation to generate a linear model based on specific features), identifying the second linear model as the generative model responsive to determining that the linear model accuracy value of the second linear model exceeds each of the first set of accuracy values (a human being can mentally apply evaluation to identify a second linear model based on an accuracy threshold); where the abstract ideas are evaluations or judgements that can be performed in the human mind, or by a human using pen and paper.
Applicant asserts (Pages 9-11), under 2019 Revised Patent Subject Matter Eligibility Guidance even if there was an abstract idea recited… the alleged abstract idea is integrated into a practical implementation (Step 2A Prong Two). The applicant further notes that all the abstract ideas alleged by the examiner above should be considered additional elements. The additional elements identified at Step 2A Prong One, in combination with an alleged abstract idea, provide a solution to a technological problem encountered by current modeling systems. The applicant further support their assertion by noting the all limitations except the identifying … limitation; where all noted limitations allegedly improves upon currently modeling systems. The specification notes and highlights the improvement to provide solutions to a technical problem. Thus, the Applicant has shown a teaching in the Specification that describes a practical implementation and how the technology has established improvements and the recited features of the amended independent claims are not well-understood, routine, conventional activity in the field. The office action does not provide a statement, court decision, or publication indicating the limitations are widely prevalent or in common use. Therefore, the limitations of the claims individually and in combination amount to significantly more than the abstract idea. Thus, the independent claim, similar independent claims, and all dependent claims should be reconsidered as being patent eligible subject matter due to similarity/dependence.
Examiner respectfully disagrees. The office action establishes a proper and well-supported prima facie case as the claims are explained to be not patentable via the Patent Subject Matter Eligibility steps within MPEP 2106. The independent claim fails to recite the steps that achieve the improvement. The independent claim is no more detailed than generating specific data with specific restrictions, making determinations, removing data based on specific restrictions, and generating models to compare accuracy values. The comparison is done for identification with no application of the comparison to integrate it into a practical application. The limitations are unable to provide improvement as they are currently being evaluated as either abstract idea(s) or additional elements that fall within MPEP 2106.05. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea. Additionally, the Claims do not reflect any improvement in the functioning of a computer or hardware processor rather the additional elements merely use a generic computer component to perform the abstract idea or restricting the abstract idea to a particular technological environment. Therefore, the claims do not integrate the judicial exception into a practical application nor amount to significantly more. The claim is not patent eligible. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
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.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification. The independent Claim fails to recite any details as to how fitting … and iteratively fitting … improves the method for performing the abstract ideas. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exception. As noted in the rejection, the Claim does not include additional elements that are sufficient to amount to an integration of the identified abstract idea into a practical application, thus the claim is directed to an abstract idea. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained. More specific details are discussed below within the 35 USC § 101 Rejections.
Regarding the 35 U.S.C. § 103 Rejections:
Applicant's arguments regarding the 35 U.S.C. § 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the 103 rejections (Pages 11-12), as the independent are not rendered obvious by the combination of Liu and Wang. The applicant supports their traversal by noting that Liu and Wang do not teach the limitations generating a first linear model … , generating a second linear model … , identifying the second linear model … .
Examiner respectfully disagrees. Liu teaches both generating … limitations but does not disclose the identifying the second linear model … explicitly; however, Wang is able to cure these deficiencies. More detailed information will be addressed below within the specific assertions presented. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1 and all dependent Claims) are maintained. More specific details are discussed below within the Office Action (within the responses for the specific assertions and within the 35 USC § 103 Rejections).
Applicant asserts (Page 12-14), that Liu does not teach the generating a first linear model … limitations by noting that Liu teaches the process of LOFO methodology, performance of a model based on a full feature set, using error rate metrics for increasing prediction performance. The applicant further asserts, that the interpretation by the Examiner is inconsistent with the Applicant’s specification. Liu discloses the application of a linear model to a much broader set of features to generate the initial data feature set. The reference does not teach generating a linear model based on accuracy-modifying features, such as the most-relevant features (e.g., "Features #3 to #7, #9, #11 to #14, #17, #18, and #20" in Table IV) that are previously identified from the initial data feature set using the LOFO method.
Examiner respectfully disagrees. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims. The limitation recites generating a first linear model based on the accuracy-modifying data features; where the examiner interprets the accuracy-modifying data features as the most influential/relevant/important features within a dataset. Lin teaches generating a first linear model based on the most influential/relevant/important features (the accuracy-modifying data features) by utilizing an SVM kernel. The SVM Kernel is used to generate a first linear model based on all features within the dataset (which uses the full dataset X; which results in an error rate of 0.0797). The X dataset contains all features with no features removed; thus, containing the accuracy-modifying data features and all features. The limitation does not recite the first linear model to restricted by only the accuracy-modifying data features or specific thresholds the accuracy-modifying data features fall within. Thus, the rejection is maintained.
Applicant asserts (Pages 14-15), that Liu does not teach the generating a second linear model … limitation by noting that Liu only discloses ranking features in a much broader set of features; thus, Liu does not disclose ranking the accuracy-modifying data features according to weight values calculated from a SVM classification with linear kernel. The reference does not teach or suggest identifying an accuracy-modifying data feature that has the highest weight value from a set of accuracy-modifying data features (e.g., the most-relevant features). Nor does the reference disclose generating a second linear model based on the accuracy-modifying data feature with the highest weight value.
Examiner respectfully disagrees. The SVM kernel noted above is used for classification with a linear kernel for each dataset from X1 -> Xj (where the X(j) dataset has the j-th feature removed); thus, generating a second linear model that is based on one of the accuracy-modifying data features. This procedure noted by Liu, first selects the 20 most high ranked features for generating the second linear models; thus, interpreted by the examiner as generating second linear models that is based on one of the accuracy-modifying data features having a weight that is highest relative to a remainder of the accuracy-modifying features. Thus, the rejection is maintained.
Applicant asserts (Page 15), that Liu does not teach the … determining … limitation as the cited portion of the reference does not describe steps or features to compute the linear model accuracy value of the second linear model. Nor does the reference teach the linear model accuracy value of the second linear model exceeds each of the first set of accuracy values. Liu does not actually disclose a second linear model that is generated based on an accuracy-modifying data feature having the highest weight relative to other accuracy-modifying data features.
Examiner respectfully disagrees. The limitation recites … determining that the linear model accuracy value of the second linear model exceeds each of the first set of accuracy values. Earlier in the office action, the examiner notes the interpretation of the first set of accuracy values to be shown in Table 1; as Table 1 is generated with all features and different subsets of features; where all features (#1 -> #24 is interpreted to be the first set of accuracy values). The determination portion of the limitation does not recite any computations or specific steps or features to compute a specific linear model accuracy value for the second linear model. However, the determination taught by Liu does note the condition for comparison
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(noted within the previous office action) and also notes calculating these error rates within the Algorithm (interpreted by the examiner as computing)
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(Liu, Page 658, Column 1, The Algorithm: Leave-One-Feature-Out Feature Selection). The limitation does not recite/describe steps or features to compute "the linear model accuracy value of the second linear model" in a specific manner. As noted in the response above, Liu does indeed disclose the second linear model based on accuracy-modifying data feature having the highest weight relative to other accuracy-modifying data features. Thus, the rejection is maintained.
Applicant asserts (Pages 15-16), the examiner improperly relies on the same prior art element teaching two or more distinct, and separate, functions in claims 1, 13, and 20. By treating the two or more distinct, and separate, functions similarly, Applicant submits the Office Action implies that the two or more distinct functions are the same function. Applicant submits that the Office Action overlooks the distinction between the functions recited in the claims and the LOFO method described in Liu (as explained at length in the above remarks) as well as overlooks the interplay between the distinct functions recited in the claims. Liu is deficient and cannot be used to establish a prima facie case of obviousness. Additionally, Wang does not cure the deficiencies; thus, the independent claims and dependent claims should be considered allowable for over the prior art references.
Examiner respectfully disagrees. The same prior art element is indeed utilized for both generating a first linear model and a second linear model; however, they are different in terms of what data is being used between the first and second linear models (as noted above and in the office action below). The first linear model is the linear model with all features and the second linear model is the linear model with the j-th feature removed. Two different and distinct models. Thus, even though the same prior art reference is used to explain the generations they are two different models as one is using all features, and one is leaving a feature out for each iteration. Liu is not deficient for the first and second linear models (as noted above) when pertaining to generation and determining. The determination of obviousness is dependent on the facts of each case (which is noted within the motivation of the rejection). The Examiner maintains the rejections and notes establishing a prima facie case of obviousness to combine to the two references. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to the independent claims, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 103 Rejections.
Duplicate Claims, Warning
Applicant is advised that should Claim 1 be found allowable, Claim 20 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Objections
Claim 11 is objected to because of the following informalities: ‘remaining ones’ is recited instead of ‘remainder’ as the independent Claims. Appropriate correction is required. For the purposes of examination, the examiner interprets the limitation as “an additional remainder of the”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 1 further recites the method comprising of:
… identifying a generative model … (a human being can mentally apply evaluation to identify a generative model)
determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features (a human being can mentally apply evaluation to determine respective reduced feature-set outputs with subset constraints)
determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values that are lower than the first set of accuracy values (a human being can mentally apply evaluation to determine one of the reduced feature-set outputs corresponding to a threshold constraint for accuracy values)
designating the iteratively removed data features that are associated with the one or more of the reduced feature-set outputs corresponding to the second set of accuracy values that are lower than the first set of accuracy values as accuracy-modifying data features included as part of an accuracy-modifying data feature-set (a human being can mentally apply evaluation to designate iteratively removed data features that have specific associations)
generating a first linear model based on the accuracy-modifying data features (a human being can mentally apply evaluation to generate a linear model based on features)
generating a second linear model that is based on one of the accuracy-modifying data features having a weight that is highest relative to a remainder of the accuracy-modifying data features (a human being can mentally apply evaluation to generate a linear model based on specific features)
identifying the second linear model as the generative model responsive to determining that the linear model accuracy value of the second linear model exceeds each of the first set of accuracy values (a human being can mentally apply evaluation to identify a second linear model based on an accuracy threshold)
Claim 1 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
A method for generating an interpretive behavioral model that is implemented by a computing device, comprising: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
fitting a machine learning trained model to a plurality of data features included as part of a data feature-set for …, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
iteratively fitting, after an iterative removal of each data feature from the data feature-set, the machine learning trained model to subsets of the plurality of data features to … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional elements b-c are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 2 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 2 further recites the method comprising of comparing each of the reduced feature-set outputs with each of the complete data feature-set outputs (a human being can mentally apply evaluation to compare reduced and complete data feature-set outputs). Claim 2 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 3 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 3 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists wherein the first linear model including main effect components and interaction components for the accuracy-modifying data features (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 4 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 4 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists wherein each main effect component is representative of a direct influence that at least one of the accuracy-modifying data features has on the reduced feature-set outputs that correspond to the second set of accuracy values that are lower than the first set of accuracy values (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 5 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 5 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists wherein each interaction component is representative of an indirect influence that at one or more of the reduced feature-set outputs as corresponding to the second set of accuracy values that are lower than the first set of accuracy values (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 6 further recites the method comprising of ranking the accuracy-modifying data features from one of the accuracy-modifying data features having a highest weight to at least an additional one of the accuracy-modifying data features having a lower weight relative to the one of the accuracy-modifying data features having the highest weight (a human being can mentally apply evaluation to rank the features based on weights). Claim 6 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 7 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 7 further recites the method comprising of comparing the linear model accuracy value of the second linear model with the first set of accuracy values associated with the machine learning trained model (a human being can mentally apply evaluation to compare an accuracy value of the second linear model with the first set of accuracy values). Claim 7 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 8 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 8 further recites the method comprising of iteratively removing each data feature from the plurality of data features of the data feature-set (a human being can make a mental judgement and remove each data feature from a data feature-set). Claim 8 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 9 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 9 further recites the method comprising of determining a respective weight for each of the accuracy-modifying data features (a human being can mentally apply evaluation to determine a respective weight for each of the data features). Claim 9 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 10 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 10 further recites the method comprising of generating a third linear model responsive to … (a human being can mentally apply evaluation to generate a linear model responsive to a mental process). Claim 10 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 11 recites the method of Claim 10. Claim 10 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 11 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 10. Claim 11 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element consists wherein the generating of the third linear model is based the one of the accuracy-modifying data features having the weight that is the highest, and an additional one of the accuracy-modifying data features having an additional weight that is higher than an additional remaining ones of the accuracy-modifying data features, but lower than the one of the accuracy-modifying data features having the weight that is the highest (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 12 recites the method of Claim 10. Claim 10 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 12 further recites the method comprising of identifying the third linear model as the generative model responsive to determining that a third linear model accuracy value of the third linear model exceeds each of the first set of accuracy values (a human being can mentally apply evaluation to identify a third linear model based on an accuracy threshold). Claim 12 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claims 13-19:
Claims 13-19 incorporate substantively all the limitations of Claims 1-3, 6-8, and 10 in a system (thus, a machine) and further recites comprises: one or more processors included as part of a computing device; non-transitory computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 13-19 are rejected for reasons set forth in the rejections of Claims 1-4 and 7-8, respectively.
Regarding Claim 20:
Claim 20 incorporate substantively all the limitations of Claim 1 in a method (thus, a process (see Duplicate Claims, Warning)) and further recites no new limitations; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claim 20 is rejected for reasons set forth in the rejection of Claims 1, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al., “A Leave-One-Feature-Out Wrapper Method for Feature Selection in Data Classification” in view of Wang et al., “Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model”.
Regarding Claim 1:
Liu teaches:
A method for generating … model that is implemented by a computing device, comprising:
(Liu, Abstract, “… we propose a simple leave-one-feature-out wrapper method for feature selection. The main goal is to improve prediction accuracy … The strategy can be applied to any classifiers and the idea is intuitive. Given the wide availability of off-the-shelf machine learning software packages and computing power, the proposed simple method may be particularly attractive to practitioners …”).
fitting a machine learning trained model to a plurality of data features included as part of a data feature-set for identifying a generative model, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values;
(Liu, Page 659, Table I. Table 1 shows the error rate on the training set for all features; thus a trained model to a plurality of data features included as part of a data feature-set for identifying a generative model. Table I was used for the experiment LED24 which utilizes training the SVM on the full set of data features and subsets; thus, fitting a machine learning to generate complete data feature-set outputs that are associated with a first set of accuracy values (shown in Table 1)).
iteratively fitting, after an iterative removal of each data feature from the data feature-set, the machine learning trained model to subsets of the plurality of data features to determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features;
(Liu, Page 658, Column 1, Paragraph 1, “Let
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In other words, X(j) is the data set with the j-th feature removed … For each j = 1, 2, . . . , n, calculate ErrCV (X\X(j), Pk), where X\X(j) means the data set with X(j) removed, i.e., only the j-th feature is used … The proposed method will determine if a feature is “bad” for every feature by comparing the performance of a model on the full feature set with the performance of the model when a feature is left out”. The LOFO (leave-one-feature-out method) iteratively removes each data feature from the full data feature set to determine if a feature is “bad”/weak/low-influence/low-importance. Thus, X(j) removed for each j = 1, 2, . . . , n, where only the j-th feature is removed are the respective subsets of reduced feature-set outputs from the full data set of features).
determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values that are lower than the first set of accuracy values;
(Liu, Page 658, Column 2, Paragraph 3, “… The values of ErrCV (X, Pk) and ErrCV (X(j), Pk) (j = 1, 2, . . . , n) are
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respectively. As expected, our method selects all the relevant features (feature #1 to #7) …”. The ErrCV values note the error for the j-th feature that was removed (removal of one feature at a time) from the full set of data features. Thus, when ErrCV is higher than the full complete data feature set (X) the lower the accuracy due to more error; which notes the feature being relevant/helpful as the feature being removed led to more error and lower accuracy values than the first set of accuracy values (X = 0.5527). The method determines the “relevant features” by selecting the highest/relevant/important feature (features subsets: #1 to #7)).
designating the iteratively removed data features that are associated with the one or more of the reduced feature-set outputs corresponding to the second set of accuracy values that are lower than the first set of accuracy values as accuracy-modifying data features included as part of an accuracy-modifying data feature-set;
(Liu, Page 658, Column 2, Paragraph 3, “As expected, our method selects all the relevant features (feature #1 to #7) … We put the detailed numbers in Table I. The prediction accuracy is on the 3000 testing samples by the SVM trained on the 200 training samples, with the corresponding feature subsets”; Page 659, Table I. Reduced feature subsets: #1 to #7 are the features that are lower than the first set of accuracy values (due to higher ErrCV values) and are identified in Table I; thus, the method is designating the accuracy-modifying features set (known as the most influential/relevant/important features)).
generating a first linear model based on the accuracy-modifying data features;
(Liu, Page 659, Column 2, Paragraph 1, “For this type of application, SVM with linear kernel often works well. In addition, the features can be ranked by the values |wi| calculated from the SVM classification with linear kernel …
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, respectively”. The LOFO method with an SVM that utilizes a linear kernel (which generates a linear model) within the ProstateTumor experiment. Thus, the dataset X is used for generating a first linear model based on the accuracy-modifying features as this first linear model is generated (via SVM with linear kernel which is considered a linear model) based on the full complete data feature set (including the accuracy-modifying features). The linear models within Liu are generated for the full complete dataset X and then new linear models (second, third, … j-th) are generated for the X(j) datasets, where a j-th feature is removed one at a time).
generating a second linear model that is based on one of the accuracy-modifying data features having a weight that is highest relative to a remainder of the accuracy-modifying data features; and
(Liu, Page 659, Column 2, Paragraph 1, “For this type of application, SVM with linear kernel often works well. In addition, the features can be ranked by the values |wi| calculated from the SVM classification with linear kernel … Following this procedure, We first selected 20 genes (features) with the highest ranking …
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, respectively”. The LOFO method with an SVM that utilizes a linear kernel (which generates a linear model) is used within the ProstateTumor experiment. The features are ranked by |wi| and the top 20 were selected (interpreted as the accuracy-modifying data features) and the linear models for the j-th features are shown via X(j). Thus, the X(j) dataset with the j-th feature removed is used to generate a second linear model based on the accuracy-modifying features as the highest weighted features are processed in a descending order (which is interpreted as highest relative to a remainder of the accuracy-modifying data features)).
… determining that the linear model accuracy value of the second linear model exceeds each of the first set of accuracy values.
(Liu, Page 658, Column 1, Paragraph 4, “Definition. Suppose a learning machine is given with all parameters chosen and fixed. The j-th feature is a bad feature if
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”. The ErrCV values note the error for the j-th feature that was removed (removal of one feature at a time) for the learning machine. Thus, the method determines the generated linear model accuracy value of each of the j-th feature generated model to determine if they exceed the first set of accuracy values (X) as shown in the definition).
Liu does not explicitly teach:
… an interpretive behavioral model …
… identifying the second linear model as the generative model responsive to …
However, Wang does teach:
… an interpretive behavioral model …
(Wang, Abstract, “In this work, we propose a novel framework for building a Hybrid Predictive Model that integrates an interpretable model with any pre-trained black-box model to combine their strengths”).
… identifying the second linear model as the generative model responsive to …
(Wang, Page 11, Algorithm 1: Line 29. Line 29 of Algorithm 1 is identifying the best linear model as the new machine learning model where the machine learning model started as pure black-box model in line 3; thus, when the second linear model is considered the best solution the generative model is updated from the first linear model to the second linear model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the LOFO methodology of Liu for ranking the features and creating subsets to determine the best features for a linear model, with the use of the Wang’s explicit updating/identifying of the interpretable generative model with the linear model when a threshold is met. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to utilizing Liu’s LOFO method which can now explicitly update the ML model due to Wang’s explicit update threshold, interpretability, optimization, transparency, increase flexibility, allows for the combination multiple models, improve the interpretable model when performance improves (Wang, Page 25, Paragraph 3, “We proposed a novel Hybrid Predictive Model that integrates an interpretable model with any black-box model for classification. The interpretable model substitutes the black-box and Lin on a subset of data to gain transparency at an efficient cost of predictive performance. The model is trained to jointly optimize predictive performance, interpretability, and transparency, with carefully designed training algorithms to achieve the best balance among the three. Hybrid models provide more choices for users. Instead of choosing between black-box models (transparency equal to zero) and interpretable models (transparency equal to one), hybrid models span the entire spectrum of transparency, and users can choose the best operating point based on the desired transparency and tolerable loss in accuracy”).
Regarding Claim 2:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
comparing each of the reduced feature-set outputs with each of the complete data feature-set outputs.
(Liu, Page 658, Column 2, Paragraph 3, “… The values of ErrCV (X, Pk) and ErrCV (X(j), Pk) (j = 1, 2, . . . , n) are
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respectively. As expected, our method selects all the relevant features (feature #1 to #7) …”. The LED24 experiment shows how each and every j-th subset (where one feature is removed at a time) is compared to the complete data feature-set (X)).
Regarding Claim 3:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
wherein the first linear model including main effect components … for the accuracy-modifying data features.
(Liu, Page 659, Column 2, Paragraph 1, “… the features can be ranked by the values |wi| … We first selected 20 genes (features) with the highest ranking …
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, respectively”. The learned respective weights |wi| are used within the first linear model which utilizes the X data set; thus, the learned respective weights are interpreted as including main effect components as the weights restrictions/rules are for the most relevant features and the main effects of the independent variables (j-th features removed)).
Liu does not explicitly disclose:
… and interaction components …
However, Wang teaches:
… and interaction components …
(Wang, Page 12, Equation 11,
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. Equation 11 defines the Hybrid Linear Model in terms of weights/thresholds. Which is interpreted as the interaction components of the hybrid linear model (as w is a vector weight; thus, a combined weighted input score based for both black-box and interpretable models where the prediction is based on the interaction of predicting positive versus negative boundaries)).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 4:
Liu and Wang teach the method of Claim 3 and Liu further teaches:
wherein each main effect component is representative of a direct influence that at least one of the accuracy-modifying data features has on the reduced feature-set outputs that correspond to the second set of accuracy values that are lower than the first set of accuracy values.
(Liu, Page 659, Column 2, Paragraph 1, “… the features can be ranked by the values |wi| … We first selected 20 genes (features) with the highest ranking …
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, respectively”. The learned respective weights |wi| are representative of a direct influence as they notate how relevant the independent j-th feature is (by calculating ErrCV). Thus, each weight (main effect component) is representative of a direct influence that each j-th feature has on the complete full data set (which is where the first set of accuracy values are from) including the reduced feature-set outputs (subsets))).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 5:
Liu and Wang teach the method of Claim 3 and Wang further teaches:
wherein each interaction component is representative of an indirect influence that at one or more of the reduced feature-set outputs as corresponding to the second set of accuracy values that are lower than the first set of accuracy values.
(Wang, Page 12, Equation 11,
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; Page 13, Figure 2. Equation 11 shows how the decision boundaries that are shifted by either 1 or -1 within Figure 2; thus the interaction component is representative of an indirect influence as the value has bias due to being binary and is addressed via loss functions).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 6:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
ranking the accuracy-modifying data features from one of the accuracy-modifying data features having a highest weight to at least an additional one of the accuracy-modifying data features having a lower weight relative to the one of the accuracy-modifying data features having the highest weight.
(Liu, Page 659, Column 2, Paragraph 1, “… the features can be ranked by the values |wi| … We first selected 20 genes (features) with the highest ranking …
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, respectively”. The features are ranked by respective weight |wi| and the top 20 were selected; thus, the each of the top 20 features (accuracy-modifying data features) have a determined respective weight. Thus, X(j) is generating a j-th linear model based on the accuracy-modifying features as the highest weighted features are processed in a descending order; thus, the features having a highest weight to at least an additional one of the accuracy-modifying data features having a lower weight relative to the one of the accuracy-modifying data features having the highest weight)).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 7:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
comparing the linear model accuracy value of the second linear model with the first set of accuracy values associated with the machine learning trained model.
(Liu, Page 658, Column 2, Paragraph 3, “… The values of ErrCV (X, Pk) and ErrCV (X(j), Pk) (j = 1, 2, . . . , n) are
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respectively. As expected, our method selects all the relevant features (feature #1 to #7) …”. The LED24 experiment shows how each and every j-th subset (where one feature is removed at a time) is compared to the complete data feature-set (X)).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 8:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
iteratively removing each data feature from the plurality of data features of the data feature-set.
(Liu, Page 658, Column 1, Paragraph 1, “Let
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In other words, X(j) is the data set with the j-th feature removed … For each j = 1, 2, . . . , n, calculate ErrCV (X\X(j), Pk), where X\X(j) means the data set with X(j) removed, i.e., only the j-th feature is used … The proposed method will determine if a feature is “bad” for every feature by comparing the performance of a model on the full feature set with the performance of the model when a feature is left out”. The LOFO (leave-one-feature-out method) iteratively removes each data feature from the full data feature set. Thus, X(j) removed for each j = 1, 2, . . . , n, where only the j-th feature is removed are the respective subsets of reduced feature-set outputs from the full data set of features).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 9:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
determining a respective weight for each of the accuracy-modifying data features.
(Liu, Page 659, Column 2, Paragraph 1, “… the features can be ranked by the values |wi| … We first selected 20 genes (features) with the highest ranking …
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, respectively”. The features are ranked by respective weight |wi| and the top 20 were selected; thus, the each of the top 20 features (accuracy-modifying data features) have a determined respective weight).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 10:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
generating a third linear model responsive to determining that the linear model accuracy value of the second linear model does not exceed each of the first set of accuracy values.
(Liu, Page 658, Column 1, Paragraph 4, “Definition. Suppose a learning machine is given with all parameters chosen and fixed. The j-th feature is a bad feature if
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In other words, if the prediction performance improves when a feature is removed, that feature is bad and should be eliminated from the feature set … This process could be repeated recursively if the prediction performance can be further improved, … The Algorithm: Leave-One-Feature-Out Feature Selection Let a learning machine be given
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”; Page 658, The ErrCV values note the error for the j-th feature that was removed (removal of one feature at a time) for the learning machine which is done for all of j (shown in step 3 of LOFO Feature selection algorithm). Thus, the method determines the generated j-th linear model accuracy value (of each of the j-th feature) including the third linear model generated model to determine if they exceed or does not exceed the first set of accuracy values (X) as shown in the definition. This is responsive to determining if removing a feature increases or decreases prediction performance as each features is removed one at a time).
Regarding Claim 11:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
wherein the generating of the third linear model is based the one of the accuracy-modifying data features having the weight that is the highest, and an additional one of the accuracy-modifying data features having an additional weight that is higher than an additional remaining ones of the accuracy-modifying data features, but lower than the one of the accuracy-modifying data features having the weight that is the highest.
(Liu, Page 659, Column 2, Paragraph 1, “For this type of application, SVM with linear kernel often works well. In addition, the features can be ranked by the values |wi| calculated from the SVM classification with linear kernel … Following this procedure, We first selected 20 genes (features) with the highest ranking …
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, respectively”. The LOFO method with an SVM that utilizes a linear kernel (which generates a linear model) is used within the ProstateTumor experiment. The features are ranked by |wi| and the top 20 were selected (interpreted as the accuracy-modifying data features) and the linear models for the j-th features are shown via X(j). Thus, X(j) is generating a second, third, j-th … linear model based on the accuracy-modifying features as the highest weighted features are processed in a descending order (which is interpreted as highest relative to a remaining ones of the accuracy-modifying data features)).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claim 12:
Liu and Wang teach the method of Claim 1 and Liu further teaches:
… determining that a third linear model accuracy value of the third linear model exceeds each of the first set of accuracy values.
(Liu, Page 658, Column 1, Paragraph 4, “Definition. Suppose a learning machine is given with all parameters chosen and fixed. The j-th feature is a bad feature if
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”. The ErrCV values note the error for the j-th feature that was removed (removal of one feature at a time) for the learning machine. Thus, the method determines the generated linear model accuracy value of each of the j-th feature generated model to determine if they exceed the first set of accuracy values (X) as shown in the definition).
Liu does not explicitly teach:
identifying the third linear model as the generative model responsive to …
However, Wang does teach:
identifying the third linear model as the generative model responsive to …
(Wang, Page 11, Algorithm 1: Line 29. Line 29 of Algorithm 1 is identifying the best linear model as the new machine learning model where the machine learning model started as pure black-box model in line 3; thus, when the second, third, or n-th linear model is considered the best solution the generative model is updated from the first linear model to the third linear model (additional model)).
The motivation of Claim 1’s combination of Liu and Wang is still maintained.
Regarding Claims 13-19:
Claims 13-19 incorporate substantively all the limitations of Claims 1-3, 6-8, and 10 in a system and further recites comprises: one or more processors included as part of a computing device; non-transitory computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to (Wang, Page 658, Column 2, Paragraph 2, “The software package LIBSVM (Chang and Lin, 2011) was used for all the experiments. LIBSVM is a versatile package for support vector machines (SVM)”. LIBSVM is used for all experiments, which is a software package and implies that the experiments were done on a computing device (system), in which a processor and CRM are inherent); thus, Claims 13-19 are rejected for reasons set forth in the rejections of Claims 1-4 and 7-8, respectively.
Regarding Claim 20:
Claim 20 incorporate substantively all the limitations of Claim 1 in a method (see Duplicate Claims, Warning) and further recites no new limitations; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claim 20 is rejected for reasons set forth in the rejection of Claim 1 respectively.
Conclusion
THIS ACTION IS MADE FINAL. 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 IBRAHIM RAHMAN whose telephone number is (703)756-1646. The examiner can normally be reached M-F 8am-5pm.
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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122