DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 4/3/2026.
Claims 17-18 have been amended.
Claims 1-3, 9-13, and 15-18 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application claims priority as a Divisional of Application 16/855668 filed on 4/22/2020. Applicant's claim for the benefit of this prior-filed application is acknowledged.
Response to Amendments
The previously pending 112f and corresponding 112b rejections have been withdrawn in response to Applicant’s claim amendments.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-3, 9-13, and 15-18, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims do not recite an abstract idea. The Examiner respectfully disagrees. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that a human using the results of the analysis of market conditions and recommendations does not make the claims eligible (See MPEP 2106). The Examiner notes that sending instructions in the form of recommendations to human users does not improve the functioning of the computer itself. Applicant’s arguments are not persuasive.
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 1-3, 9-13, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 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. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-3, 9-13, 15-16, and 18 are directed toward a process and system; which are statutory categories of invention. Claim 17 is directed towards “one or more computer readable storing instructions”, which is not a statutory category of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward one or more computer-readable storing instructions that, when executed by one or more computing devices cause performance of: generate a training dataset by: determining, for a given process complex, breakeven values for a candidate feedstock under market conditions by executing multiple planning scenarios in a computer executed process complex planning module, the market conditions comprising at least feedstock prices and product prices; responsively generating a set of vectors, wherein each vector comprises at least: (i) the determined breakeven value for the candidate feedstock under an individual market condition, (ii) the feedstock prices of the individual market condition, and (iii) the product prices of the individual market condition, different vectors being with respect to different individual market conditions; determining, based on properties of the given process complex, multipliers for the feedstock prices and product prices of the generated set of vectors; and adjusting the feedstock prices and the product prices of the vectors by the determined multipliers; train a classifier to divide the generated set of vectors of the training dataset into distinct subsets after said adjusting by the determined multipliers, wherein the dividing into subsets: training a support vector machine to divide the set of vectors into classes based at least on the determined breakeven values for the candidate feedstock; performing a Principle Component Analysis to reduce dimensionality of vectors in at least one of the classes; and using a clustering model on the reduced dimensionality vectors to subdivide the at least one of the classes into clusters; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm to fit a correlation between the market conditions and the breakeven value of the candidate feedstock based on a relationship between: (i) distance between the determined breakeven value of a pair of vectors in the subset, and (ii) errors of the determined breakeven value of the pair of vectors, each trained predictive model configured to calculate a predicted breakeven value of the candidate feedstock as a function of the market conditions for the given process complex, different trained predictive models optimized for different market conditions represented by areas of a space defined by the generated set of vectors; subsequent to said training the plurality of predictive models, receive data describing a current market condition; select a particular trained predictive model from the plurality of predictive models based on applying the classifier, including the support vector machine, Principle Component Analysis, and clustering model, to the current market condition, said selecting resulting in a selected trained predictive model optimized for the current market condition; execute the selected trained predictive model and therefrom determine whether the candidate feedstock is a target feedstock for the given process complex under the existing market condition based on the calculated predicted breakeven value of the candidate feedstock; and send, using results of said executing, a decision instruction communication regarding spot purchasing the candidate feedstock for the given process complex (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are determining breakeven values for candidate feedstock under market conditions using machine learning, generating vector inputs including breakeven values, feedstock prices and product prices to determine multipliers for the feedstock prices and product prices in order to adjust the prices for certain target market conditions to send decision recommendations to human users, where analyzing pricing and breakeven values under current market conditions and making recommendations for humans to interpret is commercial interactions.
Dependent claims 2-3, 9-13, and 15-16 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “one or more computer-readable storing instructions that, when executed by one or more computing devices cause; in a computer executed process complex planning module; train a classifier; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 17);” “computer; in a computer executed process complex planning module; train a classifier; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 1);” “a computer implemented system; one or more processors; memory storing instructions that, when executed by the one or more processors, cause the system to; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 18)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-3, 9-13, and 15-16 further narrow the abstract idea and present no additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method, system, and product Independent claims 1 and 17-18 recite “one or more computer-readable storing instructions that, when executed by one or more computing devices cause; in a computer executed process complex planning module; train a classifier; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 17);” “computer; in a computer executed process complex planning module; train a classifier; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 1);” “a computer implemented system; one or more processors; memory storing instructions that, when executed by the one or more processors, cause the system to; training a support vector machine; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm; execute the selected trained predictive model (claim 18)”; however, these additional elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0040-0042 and Figures 2 and 6. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-3, 9-13, and 15-16 further narrow the abstract idea identified in the independent claims and present no additional elements. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 17 are directed toward “one or more computer readable storing instructions” (a machine under 35 U.S.C. 101). Claim limitations however do not recite any structural elements of the device. The system elements in claim 17 include “instructions” (which may be entirely software). Thus each of the limitations can be interpreted as computer programs (Software per se). Since a computer program product is merely a set of instructions capable of being executed by a computer, the computer program itself is not a process or physical structural elements of the apparatus and the examiner therefore will treat a claim for a computer program, without the structures needed to realize the computer program’s functionality, as nonstatutory functional descriptive material. The Examiner recommends amending the claims to recite “one or more non-transitory computer readable storage mediums storing instructions” to overcome the rejection.
Allowable over 35 USC 103
Claims 1-3, 9-13, and 15-18 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1-3, 9-13, and 15-18 disclose generating vectors for breakeven values of candidate feedstock under market conditions with a focus on dividing and transforming the vectors into subsets and reduced dimensional space.
Regarding a possible 103 rejection: The closest prior art of record is:
Apap et al. (US 2017/0308831 A1) – which discloses a feedstock selection system for the chemical process industries using market and operational uncertainty.
Zhao et al. (US 2016/0320768 A1) – which discloses causality analysis using hybrid first principles and inferential models.
Horn et al. (US 2016/0260041 A1) – which discloses managing web based refinery performance optimization using cloud computing.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1-3, 9-13, and 15-18, such as generating vectors for breakeven values of candidate feedstock under market conditions with a focus on dividing and transforming the vectors into subsets and reduced dimensional space. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “one or more computer-readable program products storing instructions that, when executed by one or more computing devices cause performance of: generate a training dataset by: determining, for a given process complex, breakeven values for a candidate feedstock under market conditions by executing multiple planning scenarios in a computer executed process complex planning module, the market conditions comprising at least feedstock prices and product prices; responsively generating a set of vectors, wherein each vector comprises at least: (i) the determined breakeven value for the candidate feedstock under an individual market condition, (ii) the feedstock prices of the individual market condition, and (iii) the product prices of the individual market condition, different vectors being with respect to different individual market conditions; determining, based on properties of the given process complex, multipliers for the feedstock prices and product prices of the generated set of vectors; and adjusting the feedstock prices and the product prices of the vectors by the determined multipliers; train a classifier to divide the generated set of vectors of the training dataset into distinct subsets after said adjusting by the determined multipliers, wherein the dividing into subsets: training a support vector machine to divide the set of vectors into classes based at least on the determined breakeven values for the candidate feedstock; performing a Principle Component Analysis to reduce dimensionality of vectors in at least one of the classes; and using a clustering model on the reduced dimensionality vectors to subdivide the at least one of the classes into clusters; train a plurality of predictive models by, for each subset, training a different predictive model using a point interpolation machine learning algorithm to fit a correlation between the market conditions and the breakeven value of the candidate feedstock based on a relationship between: (i) distance between the determined breakeven value of a pair of vectors in the subset, and (ii) errors of the determined breakeven value of the pair of vectors, each trained predictive model configured to calculate a predicted breakeven value of the candidate feedstock as a function of the market conditions for the given process complex, different trained predictive models optimized for different market conditions represented by areas of a space defined by the generated set of vectors; subsequent to said training the plurality of predictive models, receive data describing a current market condition; select a particular trained predictive model from the plurality of predictive models based on applying the classifier, including the support vector machine, Principle Component Analysis, and clustering model, to the current market condition, said selecting resulting in a selected trained predictive model optimized for the current market condition; execute the selected trained predictive model and therefrom determine whether the candidate feedstock is a target feedstock for the given process complex under the existing market condition based on the calculated predicted breakeven value of the candidate feedstock; and send, using results of said executing, a decision instruction communication regarding spot purchasing the candidate feedstock for the given process complex (as required by claims 1-3, 9-13, and 15-18)”, thus rendering claims 1-3, 9-13, and 15-18 as allowable over the prior art.
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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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