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. This action is a non-final First Office Action. This action is in response to communications filed on 1 2/20/2022 . Claims 1- 6 are pending and have been considered. Specification is objected – Abstract exceeds 150 words. Claims 1- 6 are rejected under 3 5 U.S.C. 112(b) . The scope of the claims cannot be determined with reasonable certainty , however an interpretation is stated and an analysis is performed to advance prosecution. The claims are rejected under 35 USC 101 as being directed to non-statutory subject matter, a judicial exception, an abstract idea ( m athematical concept), without significantly more . Claims 1-4 , 6 rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) Claims 5 rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) in further view of Maddineli et Pavoni , US 20130103627 A1 (“MAD”) Priority The application claims priority to the Brazilian Application BR10 2021 0258780 , filed on 12/20/2021 . The priority is acknowledged. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). No translation of the application has been provided. Information Disclosure Statement (IDS) The information disclosure statement (IDS) submitted on 02/22/2023 is in compliance with the provisions of 37 CFR 1.97. Specification Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art. If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives. Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps. Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. Abstract is 180 words. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts. Claim Rejections - 35 USC § 112 Claim 1 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim is drafter as a method and recites a plurality of steps, however the recited steps do not define any affirmative acts or actions to be performed. Instead the claim merely lists noun phrase such as “Database fo r obtaining industrial and oil information and similar expressions, without reciting verbs of functional language specifying what is actually done in each step. A method claim must recite active steps that define a series of acts to be performed. See MPEP 2173. Because the claim fails to specify what actions are performed with respect to the recited elements , it is unclear whether the claim is directed to consulting a database, updating a database, or other unspecified activity involving the database. As a result the metes and bounds of the claim method can not be reasonably determined. Accordingly the claim is indefinite because it does not clearly set forth the steps of the method and fails to inform with reasonable certainly the practitioners about the scope of the invention. Claims 2-6 are rejected as dependent on the claim 1 and thus inheriting its deficiencies. Regarding the claim recit ation of trademarks, “Power BI” “BDEMQ” “BDAP”, “OAC”, “LOGISTICA” . The Examiner interprets the use of these as limitations indicating databases which contain a specific type of data, (e.g. compositions and volumes processed in the refineries) thus a data source and not a source of indefiniteness. 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. Claim Interpretation While the claims are indefinite an attempt is made to perform the analysis in broadest reasonable interpretation with best assumption on the intended meaning of the claims. Claim 1 is interpreted to recite a method with the steps: Obtain information about oils and their use in industry from databases Preprocess data to obtain information for machine learning Perform machine learning and calculate logarithmic probability of being suitable for pavement product Claim 2 is interpreted as adding the following limitation to claim 1: At step ( i ) Information is obtained from databases containing refinery composition processing data, oil properties, crude streams or logistic information. Claim 3 is interpreted as adding the following limitation to claim 1: At step (ii) information obtained for machine learning consists the types of oils used and their properties, processing ( production and refining) product properties performance metrics and operational data. Claim 4 is interpreted as adding the following limitation to claim 1: At step (iii) one of the following – or equivalent class methods – is selected for machine learning: Logistic Regression , hierarchical or multi-level , Gaussian Processes frameworks , Neural Networks, Support Vector Machines (SVM) , and Random Forests . Claim 5 is interpreted as adding the following limitation to claim 1. At step (iii) v ariables selected in step (iii) are a numerical parameter for oil density or classification (often API gravity) , amount or fraction of saturated hydrocarbons, fraction or amount of aromatics, asphaltenes insoluble in n-heptane, carbon residue – measure of coke-forming tendency, and the oil viscosity parameters Claim 6 is interpreted as adding the following limitation to claim 1. At step (iii) the model is implemented in software, including a web-based statistical computing platform, and spreadsheet interface, and using the model to computer probabilities that indicate the suitability of considered oils and production routes for asphalt production. Claims 1-6 are rejected under 3 5 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, a judicial exception (abstract idea, mathematical concepts ) without significantly more. (S1) Prima facie, claims 1- 6 are each directed to a statutory category of invention: process (Claims 1-6 directed to a method). Independent claim (S2A1) Claim 1 recites calculat ion of logarithmic probability which are a M athematical C oncept s (see MPEP 2106.04(a)(2) subsection I ) , thus the claim recites an abstract idea . (S2A2) The additional elements are Insignificant Extra (Pre-Solution and/or Post-Solution) Activity (MPEP 2106.05(g)) limitations of data gatherin g and data manipulation . These additional elements alone or in combination are not sufficient to integrated the abstract idea into a practical application. The claim is directed to an abstract idea. (S2B) The additional limitations of Insignificant Extra (Pre-Solution and/or Post-Solution) Activity (MPEP 2106.05(g)) , i.e. the limitations of data gatherin g and data manipulation recited at high level of generality were found by the courts to be Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)( ll )). Thus these additional al elements, alone, in combination – or the claim as a whole do not provide significantly more. The claim is thus ineligible under 35 US 101. Dependent claims Claim 2 , in the BRI presented above , only limits claim elements in the parent claim, specifically limits from which kinds of databases the information is obtained from. Claim 3 , in the BRI presented above, only limits claim elements in the parent claim, specifically limits Types of information for machine learning, including types of oils used and their properties, processing and product properties and performance metrics Claim 4 , in the BRI presented above, only limits claim elements in the parent claim, specifically limits the types of machine learning methods used. Claim 5, in the BRI presented above, only limits claim elements in the parent claim, specifically limits Variables and parameters used in machine learning Claim 6 , in the BRI presented above, only limits claim elements in the parent claim, specifically limits the computational platform and tools to web-based statistical computing platform, and spreadsheet interface, and using the model to compute probabilities of suitability of oils Th us the se further elements in the dependent claims only limit other claim elements, as indicated above, by describing their nature, structure and/or content, thus further limiting the form of the transactions that are acted upon in the parent claim. The nature, form or structure of these elements themselves do not provide more than a general link to a technological environment and do not practically or significantly alter how the identified abstract idea would be performed. Moreover, under the broadest reasonable interpretation, the further elements in these dependents claims, respectively, do not perform any claimed method steps and are not part of the claimed system. They are outside the scope of the claimed invention and, as such, cannot change the nature of the identified abstract idea (“ Calculating probabilities of OAC suitability ”), from a judicial exception into an eligible application, because they do not represent significantly more. In summary, in none of the claims there is an inventive concept beyond the judicial exception, and thus, when each of these claims is considered as a whole, it does not amount to significantly more than the judicial exception itself. Therefore, claims 2- 6 are deemed ineligible. 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows: i . Determining the scope and contents of the prior art. ii. Ascertaining the differences between the prior art and the claims at issue. iii. Resolving the level of ordinary skill in the pertinent art. iv. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claims that share substantially similar limitations (even though not verbatim) are grouped and analyzed together; the analysis is done on the claim with most comprehensive limitations. The parenthesis following a claim number indicates the parent claim. In BRI the independent claim is interpreted as having two components : 1) using oil information to predict a property of a product of asphalt/ b itumum and 2) using machine to make the prediction of fitting or not fitting a class. Claims 1- 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 ( “ JUS ” ) Re claim 1 KR I teaches : ( i ) Database for obtaining industrial and oil information; (ii) Treatment and modeling of data through the use of Business Intelligence (Power BI) to integrate data and obtain information for machine learning; {[Title] PROPERTY PREDICTION FOR ASPHALTS FROM BLENDED SOURCES ; Absract : Methods are provided for predicting the properties of an asphalt fraction that contains two or more asphalt components based on measurements of the viscosity for the asphalt fraction. ] [0027] One initial step in predicting the properties of an asphalt fractions is to characterize the properties of individual asphalt components in the asphalt fraction. This can represent testing performed specifically to develop the model or accumulated data from prior testing on asphalt fractions derived from single crude sources. The testing for asphalt fractions from individual crude sources will preferably include measurements for kinematic viscosity as well as any other property that is desired for prediction. [0035] [0035] FIG. 1 shows a schematic example of a refinery configuration for using a viscosity based predictive model to provide real time feedback for asphalt formation. } Accumulated data interpreted as database KRI teaches predictive model and asphalt formation . KRI does not teach machine learning for binary decision. J US however teaches (iii) Machine learning and implementation of algorithms through the use of the R platform with different machine learning techniques, which selects the variables for calculating the logarithmic probability of fitting or not fitting OAC of a given list. : { Title Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models ; [0035] FIG. 1 shows a schematic example of a refinery configuration for using a viscosity based predictive model to provide real time feedback for asphalt formation. ; 4.1. Data Pre-Analysis - 4.3. Data Preparation } In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention , to combine the teachings of KRI with MAD to have the advantage of using the modern prediction tools that developments and available tools in machine learning have provided in recent years with high accuracy capability. Both KRI and JUS are dealing with the field of asphalt/pavement solutions, and implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. S ince the elements disclosed by KRI and JUS would function in the same manner in combination as they do in their separate embodiments, the results of the combination would be predictable. Accordingly, the claimed subject matter would have been obvious over KRI and JUS. Regarding claim 2 KRI/JUS teaches the limitations of the parent claim. KRI further teaches industrial and oil information of step ( i ) are taken from the following databases: BDEMQ - to obtain the compositions and the volumes processed in the refineries, the production and storage of asphalts, and the results of laboratory analyses, BDAP - to determine the properties of the oils used in the OAC campaigns, and LOGISTICA - to evaluate the pre-salt loads in the oil streams {[0039] [0039] Still another potential modification is to expand a model to incorporate data from multiple refineries that have formed asphalt fractions from a given crude source. In some embodiments, the data in the model can be based on forming asphalt fractions at a single refinery from various crude sources. However, data from multiple refineries can be incorporated into a single model if desired. The data from each refinery can be given the same weight, or the data can be weighted based on the refinery the model is being used at, so that historical data from the refinery currently making a prediction is given greater weight than data from other refineries. Incorporating data from multiple refineries can allow information a given crude source and/or interactions of pairs of crudes to be built up in a more rapid manner. I n BRI Information is obtained from enumerated databases is interpreted as incorporating data from multiple refineries Regarding c laim 3 KRI/JUS teaches the limitations of the parent claim. KRI further teaches t he information obtained for the machine learning of step (ii) are oils used in OAC campaigns, properties of the oil lists, routes of production and refining, product properties, indices for evaluating product fit, and operational difficulties. { [0039] Incorporating data from multiple refineries can allow information a given crude source and/or interactions of pairs of crudes to be built up in a more rapid manner. [0040] FIG. 2 shows an example of constructing a predictive model that includes at least some of the model re [0040] FIG. 2 shows an example of constructing a predictive model that includes at least some of the model refinements described above. In FIG. 2, a crude slate 1 specifies a type and amount for a plurality of components that are included in a feed for forming an asphalt fraction . Based on a virtual cut point 6, a yield 2 for each of the feed components within a virtual asphalt blend is determined. The yield values combined with the crude slate composition can be used to determine a virtual composition 3 for the virtual asphalt blend corresponding to the virtual cut point. A calculated viscosity 4 for the virtual asphalt blend can then be determined. ; [0023] Another example of a feedstock suitable for forming asphalt is a feedstock derived from an atmospheric resid fraction or a similar petroleum fraction. For example, when a whole crude oil, partial crude oil, or other feedstock is processed in a refinery, one common type of processing is to distill or fractionate the crude oil based on boiling point. One type of fractionation is atmospheric distillation, which can result in one or more fractions that boil at less than 650.degree. F. (343.degree. C.) or less than 700.degree. F. (371.degree. C.), and a bottoms fraction. This bottoms fraction corresponds to an atmospheric resid. } Regarding claim 4 KRI/JUS teaches the limitations of the parent claim. JUS further teaches m achine learning techniques of step (iii) are chosen among Hierarchical Logistic Regression, Gaussian Processes, Neural Networks, Vectors Supported by Machines, and Random Forests. { TITLE: Machine Learning Techniques for Developing Pavement Performance Prediction Models } Re Claim 6 KRI/JUS teaches the limitations of the parent claim including qualities properties of asphalt. JUS further teaches the model is implanted in a web application, based on the R platform, and also in an electronic spreadsheet, to select the list and production route and calculate the probabilities of fitting and not fitting OAC in the OAC production campaign . { p12. t he routines for its computation are available in a vast number of software packages . }the prediction is for binary outcome which is the interpretation for fitting or not fitting. Web application and R platform or spreadsheet are interpreted broadly as implementation in various software packages. A POSITA would have naturally chosen R first as being the main software for statistical processing. R is also available as anyone in the art knows, as a web-based application. Thus it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of KRI/JUS with further teaching of JUS . One would have been motivated to do so, in order to obtain the advantage of using state of the art commercial tools of availability and accuracy and implement available software functions, such as logit to give the predicted binary answer Accordingly, the claimed subject matter would have been obvious over KRI/JUS. Claim 5 rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) in further view of Maddineli et Pavoni , US 20130103627 A1 (“MAD”) Regarding claim 5 KRI/JUS teach the limitations of the parent claim. KRI/JUS does not disclose, however MAD discloses variables selected in step (iii) are a numerical parameter for oil density or classification (often API gravity), amount or fraction of saturated hydrocarbons, fraction or amount of aromatics, asphaltenes insoluble in n-heptane, carbon residue – measure of coke-forming tendency, and the oil viscosity parameters { [Abstract] the most representative physico -chemical factors of crude oils; Claim 1 entering said selected values as input data for a multilayer neural network of the back propagation type, trained and optimized by means of genetic algorithms; predicting, by means of said trained and optimized neural network, at least one of the following physico -chemical factors of the unknown crude oil: TBP yield, API degree, viscosity, sulfur content, acidity, paraffin content, naphthene content, aromatic content, naphthene+2 aromatic content, smoke point, freezing point, cloud point, pour point, cetane index, Nickel content, Vanadium content, asphaltene content, or carbonaceous residue content . } In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of KRI/JUS with MAD. One would have been motivated to do so, in order to obtain the advantage of using the most important physico -chemical factors of the crude oil . Both KRI/JUS and MAD are in the same art dealing with properties of crude oil, and implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Since the elements disclosed by KRI/JUS and MAD would function in the same manner in combination as they do in their separate embodiments, the results of the combination would be predictable. Accordingly, the claimed subject matter would have been obvious over KRI/JUS in further view of MAD. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ADRIAN STOICA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571) 272-3428 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday to Friday, 9 a.m. -5 p.m. PT . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached on ( FILLIN "SPE Phone?" \* MERGEFORMAT 571) 272- 4071 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.S./ Examiner, Art Unit 2188 /RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188