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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 July 2026 has been entered.
This action is in reply to the entered RCE.
Claims 1-5, 7, 10-14 and 20-22 have been amended.
Claims 6, 8-9, 16 and 18-19 were previously 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 insufficient to overcome the 101 rejection previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Response to Arguments
Applicant’s arguments filed on 10 July 2026 have been fully considered but are not persuasive.
Regarding the 101, applicant argues that the claims are not directed to an abstract idea and even if a judicial exception were identified, the claims integrated such exception into a practical application and recite significantly more. Examiner respectfully disagrees.
Applicant argues that the claim receive sensor derived asset data. While the claim limitations describe that the asset data is from sensors. This does not demonstrate integration into a practical application nor does it amount to significantly more. The claims do not demonstrate meaningful implementation of the receiving function. The fact that the data is from sensors is merely description. Neither the sensors themselves nor the method of gathering the data through a particular ongoing monitoring implementation are part of the claimed invention.
Applicant argues that the claims integrate physics based simulation outputs and training a machine learning model. Examiner respectfully disagrees. The claim describes that the model is generated by inputting features and outputs into a machine learning model. What the inputs are, what the outputs are and how the model result is generated is not detailed in the claim. The description that the machine learning model is trained by iteratively updating parameters to minimize loss is outside of the scope of the invention, there is no detailed learning process or improvement that is realized by the claim or described in the specification, unlike in Desjardin. Merely using a machine learning model to generate a model and describing that the machine learning model has 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.
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. This is unlike Desjardin in that the alleged improvement is part of the business process for reservoir development and thus 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, extracting a plurality of features from the asset data, generating a performance model, generating asset intelligence, determining an optimized value 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, computer implementation and using machine learning model language, 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. The description that the received data is from sensors and includes pressure, flow rate or temperature does not meaningfully limit the receiving function, how the sensors gather data or are part of the system is not meaningfully integrated into the claims. Generating a performance model, generating asset intelligence, and determining an optimized value could also be considered mathematical concepts in that they demonstrate mathematical representations of data, i.e. a mathematical model of performance, intelligence determined by applying a mathematical 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, describing sensors as a source of data, using a machine learning model of a system that is generated by inputting features and outputs into a model that is trained iteratively to minimize loss and applying the model. These elements are recited at a high level of generality and merely automate the receiving, extracting generating, determining and developing steps by a computer or using specific instructions in a computerized environment. Using a machine learning model that by inputting data into the model to get output and describing that it is trained through iterative updating to minimize loss 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 that is broadly descriptive in nature. The details of a training process, how parameters are updated or any specific learning or improvement to the machine learning model is not realized in the claim or described in the specification. 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
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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