DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final action is in reply to the amendments and remarks filed on 27 January 2026.
Claims 1, 14 and 20 have been amended.
Claims 6, 8-9, 16 and 18-19 have been canceled.
Claims 1-5, 7, 10-15, 17 and 20-24 are currently pending and have been examined.
Response to Amendment
Applicant’s amendments are sufficient to overcome the 112 rejections previously raised. Those rejections are respectfully withdrawn.
Applicant’s amendments are insufficient to overcome the 101 rejection previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Applicant’s amendments are sufficient to overcome the 103 rejections previously raised. Those rejections are respectfully withdrawn. None of the prior art of record, taken individually or in combination, teach an obvious combination where machine learning algorithms are applied to received asset data with an associated life cycle and target variable to generate a model of a performance of the particular asset, then applying that generated model to the asset data to generate asset intelligence which is further used to optimize the target variable at the associated life cycle stage and developing a reservoir based on the optimized variable by drilling, performing completion of the asset, producing resources or abandoning the particular asset. The 103 rejections are respectfully withdrawn.
Response to Arguments
Applicant’s arguments filed on 27 January 2026 have been fully considered but are not persuasive.
Regarding the 101, applicant argues that the claims require analysis of received data to generate a model using one or more machine learning algorithms as well as utilizing the model to generate asset intelligence to optimize a target variable and cannot be performed mentally. Examiner respectfully disagrees. There are additional elements that meaningfully limits the implementation of any of the steps beyond applying machine learning algorithms. Applying a machine learning algorithm to data to generate a model having been trained using data and simulation models as well as applying the model to the asset data to generate asset intelligence are considered “apply it” types of limitations since the generation of the model and the generation of the asset intelligence is merely a provided solution without any specific steps of machine learning or details regarding how the algorithm or models which are applied to the data results in the generated model or generated intelligence, they are merely inputting data to a program or algorithm to output a result and are therefore considered an abstract idea, as indicated above, that is applied by a computer, see MPEP 2106.05.
Additionally, generating a model of performance by applying a machine learning algorithm of a development prediction system, generating asset intelligence by applying the model to asset data, and optimizing a target variable could also be considered mathematical concepts in that they demonstrate mathematical representations of data, i.e. a model of performance, intelligence determined by applying a performance model to asset data, and a mathematical optimization as is described in the specification in at least [0030-0035] and therefore demonstrate mathematical relationships between variables or numbers. The overall concept where a series of analytics are utilized for predictive reservoir development could also be considered a certain method of organizing human activity in that the claims are directed to a method of developing a particular reservoir based on the optimization of variables, which is a commercial interaction or business relation.
Applicant argues that the claim limitations as a whole integrate the abstract idea into a practical application and amount to significantly by improving the techniques for forecasting reservoir performance through computational modeling. Improved techniques for forecasting demonstrates an alleged improvement that is wholly within the identified abstraction. The analytic techniques do not demonstrate an improved way of training a particular model that solves a specifically recited/supported technical problem described in the specification nor does the claimed technique realize an improvement to the functioning of any computer component or other technology. The claims do not recite a specific technical solution to a technical problem describes in the specification but instead merely establish an analysis technique using computers that improves forecasting performance, which is a business/mathematical problem that exists outside of a specific technical implementation. The claims merely establish an idea of a solution or outcome using a tool to improve the judicial exception, e.g. they automate a mathematical analysis for a business process of forecasting and developing reservoirs which is not a technical solution to a technical problem. The 101 rejection is respectfully maintained and updated below as necessitated by the amendments to the claims.
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 1-5, 7, 10-15, 17 and 20-24 are rejected under U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 14 and 20 recite limitations for receiving asset data having an associated life cycle stage and a target variable, generating a model of performance, generating asset intelligence, optimizing the target variable at the associated life cycle state and developing the particular reservoir using intelligence including at least one of drilling, performing completion, producing or abandoning. These limitations, as drafted, illustrate a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. But for the executable instructions and computing system language including applying one or more machine learning algorithms, the claims encompass a user simply making observations and evaluations in their mind. Nothing in the claim precludes the functions from being performed the same way mentally or manually with pencil and paper. Generating a model of performance by applying a machine learning algorithm of a development prediction system, generating asset intelligence by applying the model to asset data, and optimizing a target variable could also be considered mathematical concepts in that they demonstrate mathematical representations of data, i.e. a model of performance, intelligence determined by applying a performance model to asset data, and a mathematical optimization as is described in the specification in at least [0030-0035] and therefore demonstrate mathematical relationships between variables or numbers. The overall concept where a series of analytics are utilized for predictive reservoir development could also be considered a certain method of organizing human activity in that the claims are directed to a method of developing a particular reservoir based on the optimization of variables, which is a commercial interaction or business relation. The mere nominal recitation of a generic system, computer process and executable instructions/applying algorithms does not take the claim limitations out of the mental processes grouping. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite additional elements including a computer readable media storing executable instructions for performing a computer process on a computing system, applying machine learning algorithms and models. These elements are recited at a high level of generality and merely automate the receiving, generating, optimizing and developing steps by a computer or using specific instructions in a computerized environment. Applying a machine learning algorithm to data to generate a model having been trained using data and simulation models as well as applying the model to the asset data to generate asset intelligence are considered “apply it” types of limitations since the generation of the model and the generation of the asset intelligence is merely a provided solution without any specific steps of machine learning or details regarding how the algorithm or models which are applied to the data results in the generated model or generated intelligence, they are merely inputting data to a program or algorithm to output a result and are therefore considered an abstract idea, as indicated above, that is applied by a computer, see MPEP 2106.05. The receiving is also recited at a high level of generality and are considered insignificant extra solution activity amounting to mere data gathering. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. The combination of these additional elements is no more than mere instructions to apply the exception or generically linking the claims to a computing environment, e.g. a computing system. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A Prong 2, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
For the receiving considered extra solution activity in step 2A above, this has been re-evaluated in step 2B and determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the system elements are anything other than generic computer components and the Symantec, TLI and OIP Techs court decisions in MPEP 2106.05 indicate that the mere collection, receipt or transmission of data over a network is a well-understood, routine and conventional function when claimed in a merely generic manner, as it is here.
Dependent claims 2-7, 10-13, 15-17 and 21-24 include all of the limitations of the independent claims and therefore recite the same abstract idea. The limitations merely narrow the abstract idea by describing different variables, stages, the models, the basis of the intelligence, data, intelligence strategies, intelligence analytics and the intended use of the intelligence but do not provide any limitations that would transform the claim into a patent eligible invention.
Accordingly, Claims 1-5, 7, 10-15, 17 and 20-24 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim 20, as recited, is directed towards a system for predictive reservoir development. The recited components of the system appear to lack the necessary physical components (hardware) to constitute a machine or manufacture under § 101. Therefore, these claim limitations can be reasonably interpreted as computer program modules or software per se due to the lack of sufficient structure. Software is not one of the defined statutory classes and is hence rendered non-patentable subject matter. The mere recitation of a system or apparatus in the preamble or as an element of another system does not satisfactorily depict the subject matter which the applicant regards as the invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Early et al. (US 10,345,764) Illustrates integrated modeling and monitoring of formation and well performance that includes simulating an energy industry operation parameter by an analysis model and predicting values of the operational parameter via mathematical models.
Phillips et al. (US 2017/0103433) illustrates generating and using estimated asset monetary values and estimated macroeconomic measure data values for estimating the value of a macroeconomic measure by generating a mapping from a set of data values for variables to monetary values for a second set of assets.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623
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