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
The following is Final Office Action in response to request for amendments received on 02/05/2026.
Claims 1, 2, 19, and 20 are amended. Claims 4-8, 15, and 18 are cancelled. Claims 27-28 are newly added. Claims 1-4, 9-14, 16-17, and 19-26 are considered in this Office Action. 1-3, 9-14, 16-17, and 19-28 are currently pending.
Response to Arguments
Applicant’s amendments necessitated new grounds of rejections set forth in this Office Action.
Applicant’s amendments and arguments have been considered with respect to 35 U.S.C. 101, however the arguments are primarily raised in light of applicant’s amendments. An updated 35 U.S.C. 101 rejection will address applicant’s amendments.
Applicant asserts that the claims independent claims 1, 19, and 20 do not recite mental processes. Applicant submits that the recitations of amended independent claim 1 cannot be reasonably construed as merely directed to a mental process. Instead, the claim recites technical steps including, for example, "retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model, “predicting, using the retrained machine learning model, a third treatment pressure of the hydraulic fracturing operation, “obtaining a cost function defined by at least a first variable associated with a design of the hydraulic fracturing operation and a second variable associated with an execution of the hydraulic fracturing operation, “determining, using the retrained machine learning model, that the control action increases efficiency based on the cost function," and "causing an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation." As with the examples set forth in M.P.E.P. § 2106.04(a)(2)(III)(A), Applicant respectfully submits that at least the recited steps cannot be practically performed in the human mind. Applicant believes amended independent claims 19 and 20 are not directed to mental processes for substantially the same reasons.
The examiner respectfully disagrees. The examiner notes the claim recites concepts of “mental process” such as observing a second number of treatment stages, comparing the predicted third treatment pressure to an actual third treatment pressure corresponding to the third number of treatment stages, receiving microseismic data indicative of a microseismic event within a wellbore undergoing the hydraulic fracturing operation, determining a control action for adjusting a slurry rate provided by one or more pumps associated with the hydraulic fracturing operation based on the microseismic data, obtaining a cost function defined by at least a first variable associated with a design of the hydraulic fracturing operation and a second variable associated with an execution of the hydraulic fracturing operation, determining that the control action increases efficiency based on the cost function, determining one or more mechanical properties associated with degradation of a plug within the wellbore undergoing the hydraulic fracturing operation, and assessing a viability of the control action based on the one or more mechanical properties associated with the degradation of the plug within the wellbore. The examiner notes the cited limitation are steps can be performed in the human mind or by the aid of pen/paper, where they recited steps that constitute as an observation, evaluation, judgment, and opinion. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed “conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally,” i.e., “as a person would do it by head and hand.”); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of “translating a functional description of a logic circuit into a hardware component description of the logic circuit” are directed to an abstract idea, because the claims “read on an individual performing the claimed steps mentally or with pencil and paper”). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. The examiner next notes the claims further recite concepts that fall within “the mathematical concept”. The examiner notes the use of machine learning model and generating a retrained machine learning model by observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model are recited at high level which amounts to performing other basic mathematical calculations that can be performed mentally. The examiner notes the steps “retraining”, “predicting”, and “determining using the model” is recited as generic data analysis and mathematical concept with no specific machine learning architecture, particular training technique or a technological improvement to machine learning itself. The examiner further notes that the “machine learning” and “retrained machine learning model” merely represents computer/ processor environment automatically executing predefined models, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)).
Next the step of “causing an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action,” the examiner notes the step is recited at high level of generality and is considered an additional element. However, the step amounts to extra-solution activity. The step is analogous to i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. See MPEP 2106.05(f).
In response to applicant’s analogizing the instant claims with SiRF Tech., Inc. v. Int'l Trade Comm'n, the examiner respectfully disagrees. The examiner notes that SiRF Tech, the claims are directed to calculating an absolute position of a GPS receiver and an absolute time of reception of satellite signals cannot be practically performed in the human mind. Unlike SiRF Tech, the instant claims are reciting concepts of “mental process” and “mathematical concept” of a method for evaluating the efficiency and viability of operation of a well and generate a plan within the enumerated groupings of abstract ideas. The examiner notes the claim recites concepts of “mental process” such as observing a second number of treatment stages, comparing the predicted third treatment pressure to an actual third treatment pressure corresponding to the third number of treatment stages, receiving microseismic data indicative of a microseismic event within a wellbore undergoing the hydraulic fracturing operation, determining a control action for adjusting a slurry rate provided by one or more pumps associated with the hydraulic fracturing operation based on the microseismic data, obtaining a cost function defined by at least a first variable associated with a design of the hydraulic fracturing operation and a second variable associated with an execution of the hydraulic fracturing operation, determining that the control action increases efficiency based on the cost function, determining one or more mechanical properties associated with degradation of a plug within the wellbore undergoing the hydraulic fracturing operation, and assessing a viability of the control action based on the one or more mechanical properties associated with the degradation of the plug within the wellbore. The examiner notes the cited limitation are steps can be performed in the human mind or by the aid of pen/paper, where they recited steps that constitute as an observation, evaluation, judgment, and opinion. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. The examiner next notes the claims further recite concepts that fall within “the mathematical concept”. The examiner notes the use of machine learning model and generating a retrained machine learning model by observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model are recited at high level which amounts to performing other basic mathematical calculations that can be performed mentally. The examiner notes the steps “retraining”, “predicting”, and “determining using the model” is recited as generic data analysis and mathematical concept with no specific machine learning architecture, particular training technique or a technological improvement to machine learning itself.
In response to applicant’s analogizing the instant claims with Example 39, the examiner respectfully disagrees. Example 39 is directed to facial detection is a computer technology for identifying human faces in digital images by using a combination of features to more robustly detect human faces. The first feature is the use of an expanded training set of facial images to train the neural network. This expanded training set is developed by applying mathematical transformation functions on an acquired set of facial images. These transformations can include affine transformations, for example, rotating, shifting, or mirroring or filtering transformations, for example, smoothing or contrast reduction. The neural networks are then trained with this expanded training set using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network. the second feature of applicant’s invention is the minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives. Unlike Example 39, the instant claims are reciting concepts of “mental process” and “mathematical concept” of a method for evaluating the efficiency and viability of operation of a well and generate a plan within the enumerated groupings of abstract ideas. The examiner notes the claim recites concepts of “mental process” such as observing a second number of treatment stages, comparing the predicted third treatment pressure to an actual third treatment pressure corresponding to the third number of treatment stages, receiving microseismic data indicative of a microseismic event within a wellbore undergoing the hydraulic fracturing operation, determining a control action for adjusting a slurry rate provided by one or more pumps associated with the hydraulic fracturing operation based on the microseismic data, obtaining a cost function defined by at least a first variable associated with a design of the hydraulic fracturing operation and a second variable associated with an execution of the hydraulic fracturing operation, determining that the control action increases efficiency based on the cost function, determining one or more mechanical properties associated with degradation of a plug within the wellbore undergoing the hydraulic fracturing operation, and assessing a viability of the control action based on the one or more mechanical properties associated with the degradation of the plug within the wellbore. The examiner notes the cited limitation are steps can be performed in the human mind or by the aid of pen/paper, where they recited steps that constitute as an observation, evaluation, judgment, and opinion. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. The examiner next notes the claims further recite concepts that fall within “the mathematical concept”. The examiner notes the use of machine learning model and generating a retrained machine learning model by observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model are recited at high level which amounts to performing other basic mathematical calculations that can be performed mentally. The examiner notes the steps “retraining”, “predicting”, and “determining using the model” is recited as generic data analysis and mathematical concept with no specific machine learning architecture, particular training technique or a technological improvement to machine learning itself. The examiner further notes that the “machine learning” and “retrained machine learning model” merely represents computer/ processor environment automatically executing predefined models, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)).
Applicant asserts that amended independent claims 1, 19, and 20 recite a judicial exception, the amended independent claims are integrated into a practical application of "assessing accuracy of one or more predictive models. .. . For example, .. during execution of the first three stages of a stimulation treatment, the model can be fit using a current data set with current weights of the parameters.... [I]f the model accuracy is below the QA parameters, then the method can call for retraining the model based on the first observed x stages and test on consequent x stages." Application, 164. This is particularly useful where "the system [] utilizes a trained ML model that was trained using analogue data [because] the assessment provides for some assurances as to whether or not the data are sufficiently analogous." Id. at 121. Furthermore, the amended independent claims are integrated into an additional practical application of "provid[ing] for real-time fracturing treatment cost optimization via issuance of one or more control actions." Id. at 165.
The examiner respectfully disagrees. The additional elements are directed to a system, a processor, memory accessible to the processor, processor-executable instructions stored in the memory, executable to instruct the system (recited at high level of generality), machine learning model (recited at high level of generality), a retrained machine learning model(recited at high level of generality), cause an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action (recited at high level of generality amounts to post-solution activity), one or more computer-readable storage media comprising tangible, non- transitory computer- executable instructions executable to instruct a computing system, and using a machine learning model to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification fig. 16 describes high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. The examiner notes the use of machine learning model and generating a retrained machine learning model by observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model are recited at high level which amounts to performing other basic mathematical calculations that can be performed mentally. The examiner notes the steps “retraining”, “predicting”, and “determining using the model” is recited as generic data analysis and mathematical concept with no specific machine learning architecture, particular training technique or a technological improvement to machine learning itself. The examiner further notes that the “machine learning” and “retrained machine learning model” merely represents computer/ processor environment automatically executing predefined models, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). With respect to “cause an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action”, the step amounts to extra-solution activity. The step is analogous to i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. See MPEP 2106.05(f).
Lastly, Applicant cites the Berkheimer decision and suggests that “Independent claims 1, 19, and 20 include limitations, or combinations of limitations, that are not well-understood, routine, conventional activity in the field.”
As best understood by the Examiner, Applicant’s argument appears to be based on a misunderstanding of the recent Berkheimer decision, which the Examiner emphasizes is germane only to Step 2B eligibility inquiry and only for “additional elements” (i.e., not the elements that actually recite the abstract idea). In particular, the Berkheimer memo provides guidelines for evaluating whether certain claim limitations (the “additional elements”) are well-understood, routine, and conventional, and describes the evidentiary requirements to support factual findings related thereto. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
Accordingly, the Examiner emphasizes that a §101 rejection, including one based on a judicial exception, does not hinge on whether or not the entire claimed subject matter is directed to “well-understood, routine, and conventional activities,” as suggested by Applicant. Notably, a §101 rejection may be proper even if there are no claim elements deemed well-understood, routine, and conventional. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
Accordingly, with respect to 35 U.S.C. 101 the arguments are found not persuasive, and an updated 35 U.S.C. 101 rejection will address applicant’s amendments.
Applicant’s amendments and arguments have been considered with respect to 35 U.S.C. 103, and are found persuasive. The 35 U.S.C. 103 is withdrawn.
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, 9-14, 16-17, 19-21, and 23-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-3, 9-14, 16-17, 19-21, and 23-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-3, 9-14, 16-17, 21, and 23-27), the system (claims 19 and 28), and the one or more computer-readable storage media comprising tangible, non- transitory computer-executable instruction (claim 20) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of “mental process” of a method for evaluating the efficiency and viability of operation of a well and generate a plan within the enumerated groupings of abstract ideas. The claims further fall within mathematical concept. The use of computer/computer components to perform the abstract idea does not negate the abstractness of the claims. See MPEP 2106.04(a)(2)(III). The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 19, are: A system comprising: a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable to instruct the system to: predict, using a machine learning model, a first treatment pressure of a hydraulic fracturing operation corresponding to a first number of treatment stages; determine that an accuracy of the machine learning model is below a threshold by comparing the predicted first treatment pressure to an actual first treatment pressure corresponding to the first number of treatment stages; receiving, by the retrained machine learning model, microseismic data indicative of a microseismic event within a wellbore undergoing the hydraulic fracturing operation; in response to determining that the accuracy of the machine learning model is below the threshold, generate a retrained machine learning model by: observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of stages to generate the retrained machine learning model; and predicting, using the retrained machine learning model, a third treatment pressure of the hydraulic fracturing operation corresponding to a third number of treatment stages; determine that the accuracy of the retrained machine learning model is above the threshold by comparing the predicted third treatment pressure to an actual third treatment pressure corresponding to the third number of treatment stages; in response to determining that the accuracy of the retrained machine learning model is above the threshold, determine a control action for adjusting a slurry rate provided by one or more pumps associated with the hydraulic fracturing operation based on the microseismic data; obtain a cost function defined by at least a first variable associated with a design of the hydraulic fracturing operation and a second variable associated with an execution of the hydraulic fracturing operation; determining, using the retrain-machine learning model, that the control action increases efficiency based on the cost function; determine one or more mechanical properties associated with degradation of a plug the wellbore undergoing the hydraulic fracturing operation; assess a viability of the control action based on the one or more mechanical properties associated with the degradation of the plug within the wellbore; and cause an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action. Claims 1 and 20 recite substantially the same limitation as claim 11 and therefore subject to the same rationale.
With respect to Step 2A Prong Two of MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a system, a processor, memory accessible to the processor, processor-executable instructions stored in the memory, executable to instruct the system (recited at high level of generality), machine learning model (recited at high level of generality), a retrained machine learning model(recited at high level of generality), cause an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action (recited at high level of generality amounts to post-solution activity), One or more computer-readable storage media comprising tangible, non- transitory computer- executable instructions executable to instruct a computing system, and using a machine learning model to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification fig. 16 describes high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. The examiner notes the use of machine learning model and generating a retrained machine learning model by observing a second number of treatment stages and retraining the machine learning model based on an actual second treatment pressure corresponding to the second number of treatment stages to generate the retrained machine learning model are recited at high level which amounts to performing other basic mathematical calculations that can be performed mentally. Further, the steps “retraining”, “predicting”, and “determining using the model” is recited as generic data analysis and mathematical concept with no specific machine learning architecture, particular training technique or a technological improvement to machine learning itself. The examiner further notes that the “machine learning” and “retrained machine learning model” merely represents computer/ processor environment automatically executing predefined models, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). With respect to “cause an adjustment to the slurry rate provided by the one or more pumps associated with the hydraulic fracturing operation based on assessing the viability of the control action”, the step amounts to extra-solution activity. The step is analogous to i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. See MPEP 2106.05(f).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed a system, a processor, memory accessible to the processor, processor-executable instructions stored in the memory, executable to instruct the system (recited at high level of generality), machine learning model (recited at high level of generality), a retrained machine learning model(recited at high level of generality), automatically issue the control action for implementation during the hydraulic fracturing operation based on assessing the viability of the control action, wherein the control action causes an adjustment to an amount of fluid provided by one or more pumps associated with the hydraulic fracturing operation(recited at high level of generality amounts to post-solution activity), One or more computer-readable storage media comprising tangible, non- transitory computer- executable instructions executable to instruct a computing system, and using a machine learning model. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (fig. 16) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). Furthermore, the “machine learning model” and “retrained machine learning model” are described in the specification in in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). With respect to “automatically issue the control action for implementation during the hydraulic fracturing operation based on assessing the viability of the control action, wherein the control action causes an adjustment to an amount of fluid provided by one or more pumps associated with the hydraulic fracturing operation”, the step amounts to extra-solution activity. The step is analogous to i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. See MPEP 2106.05(f).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims recite the following additional elements the issuing comprises rendering a graphical user interface to a display wherein the graphical user interface comprises a visualization of the control action (claim 2; recited at high level of generality amounts to displaying data), the graphical user interface comprises a visualization derived from real-time data acquired during performance of the hydraulic fracturing operation (claim 3; recited at high level of generality and amounts to extra-solution activity), and implementing the control action to adjust the hydraulic fracturing operation (claim 4; recited at high level of generality), claims 9, 11, 12, and 13 recite machine learning model and retrain machine learning model at high level of generality. Claims 27-28 recites “the control action comprises a friction reducer (FR) concentration control action, a proppant concentration control action, a viscosifier concentration control action, a proppant ramp schedule control action, or any combination thereof(recited at high level of generality). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification [0016] describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification ([fig. 16]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The examiner further notes that the “machine learning model” and “retrain machine learning model” merely represents computer/ processor environment automatically executing predefined models, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). Furthermore, the “machine learning model” and “retrained machine learning model” are described in the specification in in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a).
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of mental processes and certain method of organizing human activity, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Examiner Notes
Claims 1-3, 9-14, 16-17, and 19-28 are objected, but would be allowable, if they were amended in such a way to overcome the 35 USC 101 rejection set forth in the action.
Independent claims 1, 1, and 20 are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art combination of the record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. The closest prior of records is Kris Bliesner Peyman Heidari (WO 2020139346 A1, hereinafter “Heidari”), Manisha Bhardwaj (US 20210087925 A1, hereinafter “Bhardwaj”), Dykstra (US 20160230513 A1, hereinafter “Dykstra”), Nan Mu (WO 2020097060 A2, hereinafter “Mu”), Fulton (WO 2020139344 A1, hereinafter “Fulton”), and Xiaohua Yi (US 2019/0203574 A1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20210230992 A1
Fracturing Control
RUHLE; William Owen Alexander et al.
US 20210199110 A1
Systems And Methods for Fluid End Early Failure Prediction
Albert; Arden et al.
US 20190120024 A1
Smart Fracturing System and Method
Oehring; Jared et al.
US 20200065677 A1
Machine Learning Assisted Events Recognition on Time Series Completion Data
Iriarte Lopez; Jessica G. et al.
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 REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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/REHAM K ABOUZAHRA/Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625