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 .
Claims 1-15 are presented for examination.
Response to Amendment
Applicant’s amendments have obviated most, but not all, of the specification objections, as well as the claim objections and rejections under 35 USC § 112(b) (but see the new ground of rejection under § 112(b) infra). To the extent that an objection or rejection appears in the previous Office Action(s) but not this Office Action, that objection or rejection is withdrawn. To the extent that it appears both in a previous Office Action(s) and this Office Action, the objection or rejection is maintained.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 23, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The use of the term BLUETOOTH (paragraph 73), which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-15 are 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.
Claims 1 and 11 newly recite “preventative/mitigative actions”. The use of the slash makes it unclear whether both preventative and mitigative actions are required by the claim or only one of the two. For purposes of examination, Examiner will read this language as “preventative or mitigative actions”.
All claims dependent on a claim rejected hereunder are also rejected for being dependent on a rejected base claim.
Claim Rejections - 35 USC § 101
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim is directed to a system comprising physical processors; therefore, the claim is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
[D]etermin[ing] reconstructed operation characteristics of the well for the duration of time using … historical well operation information characterizing the operation characteristics of the well for a period of time preceding the duration of time: This limitation could encompass mentally determining reconstructed operation characteristics for the well using historical data.
[P]redict[ing] a future occurrence of an asphaltene anomaly at the well based on a difference between the operation characteristics of the well for the duration of time and the reconstructed operation characteristics of the well for the duration of time: This limitation could encompass mentally predicting a future occurrence of an asphaltene anomaly based on a difference between the operation characteristics and the reconstructed operation characteristics.
[O]ne or more preventative/mitigative actions are performed in response to the future occurrence of the asphaltene anomaly predicted by the system, the one or more preventative/mitigative actions including: controlling a production rate of the well, scheduling an intervention to be performed on the well, or controlling operations to prevent uncommanded shut-in of the well: Scheduling an intervention to be performed on the well may be performed with a pen and paper by marking the date and time of the intervention on a calendar.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determination of the characteristics is performed using “an unsupervised machine-learning model, wherein the unsupervised machine-learning model is trained using historical well operation information” and that the method is executed on a “system comprising: one or more physical processors configured by machine-readable instructions”. However, these are mere instructions to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f). The claim further recites “obtain[ing] well operation information, the well operation information characterizing operation characteristics of a well for a duration of time”. However, this limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2, with the exception that the obtaining limitation, in addition to reciting insignificant extra-solution activity, also recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As an ordered whole, the claim is directed to a mentally performable method of predicting asphaltene anomalies in wells using time-series data. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the unsupervised machine-learning model includes a linear unsupervised machine-learning model and/or a non-linear unsupervised machine-learning model.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the unsupervised machine-learning model includes a linear unsupervised machine-learning model and/or a non-linear unsupervised machine-learning model.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the linear unsupervised machine-learning model includes a principal component analysis algorithm.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the linear unsupervised machine-learning model includes a principal component analysis algorithm.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the non-linear unsupervised machine-learning model includes a long short-term memory autoencoder.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the non-linear unsupervised machine-learning model includes a long short-term memory autoencoder.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites “determination of an anomaly score based on the difference between the operation characteristics of the well for the duration of time and the reconstructed operation characteristics of the well for the duration of time; and prediction of the future occurrence of the asphaltene anomaly at the well based on a comparison between the anomaly score and an anomaly score threshold.” Both the determination and the prediction could be performed mentally.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the anomaly score threshold is determined based on the operation characteristics of the well for the period of time and reconstructed operation characteristics of the well for the period of time.” This limitation could encompass mentally determining the threshold based on the recited data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the anomaly score threshold is determined based on the operation characteristics of the well for the period of time and reconstructed operation characteristics of the well for the period of time such that a threshold percentage of historical anomaly scores satisfies the anomaly score threshold.” This limitation could encompass mentally determining the threshold based on the recited data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 6 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 6 analysis.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “present[ing] a visualization of the comparison between the anomaly score and the anomaly score threshold.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim further recites that “he one or more physical processors are further configured by the machine-readable instructions to” perform the presentation. However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The presenting limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing or retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Otherwise, the analysis mirrors that of step 2A, prong 2.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the unsupervised machine-learning model is retrained using the well operation information.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the unsupervised machine-learning model is retrained using the well operation information.” However, this is a mere instruction to apply the judicial exception using a generic computer programmed with generic classes of computer algorithm. MPEP § 2106.05(f).
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the operation characteristics of the well include pressure and temperature at the well.” Predicting the anomaly using these characteristics remains mentally performable under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claims 11-15
Step 1: The claims recite a method; therefore, they are directed to the statutory category of processes.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 1-5, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this stage mirrors that of claims 1-5, respectively.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this stage mirrors that of claims 1-5, respectively.
Claim Rejections - 35 USC § 103
Claims 1-2, 4-6, 8, 10-12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Heidari et al. (US 20210087925) (“Heidari”) in view of Chen et al. (US 20150211357) (“Chen”) and further in view of Ben Kimon et al. (US 20200210849) (“Ben Kimon”).
Regarding claim 1, Heidari discloses “[a] system for predicting …, the system comprising:
one or more physical processors configured by machine-readable instructions (Heidari paragraph 18 discloses that the method is performed by a processor coupled with a computer-readable storage medium containing instructions executed by the processor) to:
obtain well operation information, the well operation information characterizing operation characteristics of a well for a duration of time (time-series data [for a duration of time] may be different types of data associated with a hydraulic fracturing well; each type of data may be encoded separately using an LSTM encoder; time-series data may include flow rate, proppant concentration, fluid concentration, chemical additive concentration, and pressure [operation characteristics] – Heidari, paragraph 68);
determine reconstructed operation characteristics of the well for the duration of time using an unsupervised machine-learning model (time-series data may be different types of data associated with a hydraulic fracturing well; each type of data may be encoded separately using an LSTM encoder [unsupervised machine-learning model]; time-series data may include flow rate, proppant concentration, fluid concentration, chemical additive concentration, and pressure [operation characteristics]; the encoded data may be decoded [reconstructed] to retrieve the original time-series data – Heidari, paragraph 68), wherein the unsupervised machine-learning model is trained using historical well operation information, the historical well operation information characterizing the operation characteristics of the well for a period of time preceding the duration of time (hydraulic fracturing job optimization system can generate [train] a machine learning model based on historical [i.e., preceding the duration of time] production data [historical well operation information; operation characteristics include production as well as the above metrics] – Heidari, paragraph 78); and
predict a future occurrence … at the well based on … the operation characteristics of the well for the duration of time and the reconstructed operation characteristics of the well for the duration of time (hydraulic fracturing job optimization system can determine an optimized job design for a hydraulic fracturing well using a prediction based on the machine learning model – Heidari, paragraph 79 [future occurrence = implementation of design that will lead to optimized performance]; optimization system can generate the model based on the time-series data; each type of data may be encoded separately using an LSTM encoder; time-series data may include flow rate, proppant concentration, fluid concentration, chemical additive concentration, and pressure [operation characteristics]; the encoded data may be decoded [to create reconstructed operation characteristics] to retrieve the original time-series data – id. at paragraph 68 [since the prediction is based on the ML model, and the ML model encodes and decodes the operation characteristics, the prediction is based on these characteristics]) ...;
wherein one or more preventative/mitigative actions are performed in response to the future occurrence … predicted by the system, the one or more preventative/mitigative actions including: controlling a production rate of the well, scheduling an intervention to be performed on the well, or controlling operations to prevent uncommanded shut-in of the well (hydraulic fracturing job optimization system can determine an optimized job design for a hydraulic fracturing well having an objective function using a prediction based on the machine learning model; the objective function may include maximizing cumulative barrel of oil equivalent for a first six months of production [i.e., controlling the production rate, thereby preventing suboptimal production] – Heidari, paragraph 79).”
Heidari appears not to disclose explicitly the further limitations of the claim. However, Chen discloses “predict[ing] a future occurrence of an asphaltene anomaly…, wherein the asphaltene anomaly is a production of asphaltene in the well and/or an effect of asphaltene production in the well (based on known fluid parameters and asphaltene gradients determined at one or more depths in a well, an asphaltene onset pressure [asphaltene anomaly that causes the production of asphaltene] prediction can be made at an additional depth – Chen, paragraph 51; see also paragraph 47 (characterizing the predictions as future-regarding))….”
Chen and the instant application both relate to asphaltene prediction and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Heidari to predict the future occurrence of asphaltene-related events, as disclosed by Chen, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the user to take preventative measures before an anomaly occurs, thereby optimizing the well’s performance. See Chen, paragraph 51.
Neither Heidari nor Chen appears to disclose explicitly the further limitations of the claim. However, Ben Kimon discloses “predict[ing an] … anomaly … based on a difference between the … characteristics … for the duration of time and the reconstructed … characteristics … for the duration of time (reconstruction difference threshold for anomalies is determined by receiving a plurality of fraudulent transaction data, and generating a reconstruction difference [score based on a difference between characteristics and reconstructed characteristics] – Ben Kimon, paragraph 34) ….”
Ben Kimon and the instant application both relate to anomaly detection using autoencoders and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari and Chen to determine that an anomaly has occurred based on a reconstruction error threshold, as disclosed by Ben Kimon, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow for anomaly detection in the presence of noisy datasets, thereby preventing problems that require remediation. See Ben Kimon, paragraph 2.
Claim 11 is a method claim corresponding to system claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 2, Heidari, as modified by Chen/Ben Kimon, discloses that “the unsupervised machine-learning model includes a linear unsupervised machine-learning model and/or a non-linear unsupervised machine-learning model (optimization system can generate the model based on the time-series data; each type of data may be encoded separately using an LSTM encoder; time-series data may include flow rate, proppant concentration, fluid concentration, chemical additive concentration, and pressure; the encoded data may be decoded to retrieve the original time-series data – Heidari, paragraph 68 [LSTM encoder/decoder = LSTM autoencoder, i.e., non-linear unsupervised machine learning model]).”
Claim 12 is a method claim corresponding to system claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 4, Heidari, as modified by Chen/Ben Kimon, discloses that “the non-linear unsupervised machine-learning model includes a long short-term memory autoencoder (optimization system can generate the model based on the time-series data; each type of data may be encoded separately using an LSTM encoder; time-series data may include flow rate, proppant concentration, fluid concentration, chemical additive concentration, and pressure; the encoded data may be decoded to retrieve the original time-series data – Heidari, paragraph 68 [LSTM encoder/decoder = LSTM autoencoder, i.e., non-linear unsupervised machine learning model]).”
Claim 14 is a method claim corresponding to system claim 4 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 5, the rejection of claim 1 is incorporated. Chen further discloses that “prediction of the future occurrence of the asphaltene anomaly at the well based on the operation characteristics of the well for the duration of time … includes:
determination of an anomaly score based on … the operation characteristics of the well for the duration of time (based on known fluid parameters and asphaltene gradients [operation characteristics of the well for a duration of time] determined at one or more depths in a well, an asphaltene onset pressure [anomaly score] prediction can be made at an additional depth – Chen, paragraph 51; see also paragraph 47 (characterizing the predictions as future-regarding)) … ; and
prediction of the future occurrence of the asphaltene anomaly at the well (based on known fluid parameters and asphaltene gradients determined at one or more depths in a well, an asphaltene onset pressure [asphaltene anomaly] prediction can be made at an additional depth – Chen, paragraph 51; see also paragraph 47 (characterizing the predictions as future-regarding)) ....” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Heidari to predict asphaltene anomalies based on an anomaly score determined from well operation characteristics, as disclosed by Chen, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the user to take preventative measures before an anomaly occurs, thereby optimizing the well’s performance. See Chen, paragraph 51.
Neither Heidari nor Chen appears to disclose explicitly the further limitations of the claim. However, Ben Kimon discloses that “prediction of the … occurrence … based on … the reconstructed … characteristics … for the duration of time includes:
determination of [a] … score based on a difference between the … characteristics … for the duration of time and the reconstructed … characteristics … for the duration of time (reconstruction difference threshold for anomalies is determined by receiving a plurality of fraudulent transaction data, and generating a reconstruction difference [score based on a difference between characteristics and reconstructed characteristics] – Ben Kimon, paragraph 34); and
prediction of the … occurrence … based on a comparison between the anomaly score and an anomaly score threshold (first reversion transaction is determined to be anomalous by comparing the reconstruction difference [score] with the reconstruction error threshold for anomaly [anomaly score threshold] – Ben Kimon, claim 5).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari and Chen to determine that an anomaly has occurred based on a reconstruction error threshold, as disclosed by Ben Kimon, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow for anomaly detection in the presence of noisy datasets, thereby preventing problems that require remediation. See Ben Kimon, paragraph 2.
Claim 15 is a method claim corresponding to system claim 5 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 6, the rejection of claim 5 is incorporated. Heidari further discloses “operation characteristics of the well for the period of time”, as shown above in the rejection of claim 1.
Neither Heidari nor Chen appears to disclose explicitly the further limitations of the claim. However, Ben Kimon discloses that “the anomaly score threshold is determined based on the … characteristics … and reconstructed … characteristics (reconstruction difference threshold for anomalies is determined by receiving a plurality of fraudulent transaction data, and generating a reconstruction difference [difference between characteristics and reconstructed characteristics] – Ben Kimon, paragraph 34; reconstruction error threshold for fraud generator receives the reconstruction differences and determines a reconstruction error threshold for fraud (e.g., average of the reconstruction differences, weighted average of the reconstruction differences, etc.) – id. at paragraph 35) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari and Chen to determine the anomaly threshold based on the characteristics and reconstructed characteristics, as disclosed by Ben Kimon, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow for anomaly detection in the presence of noisy datasets, thereby preventing problems that require remediation. See Ben Kimon, paragraph 2.
Regarding claim 8, Heidari, as modified by Chen and Ben Kimon, discloses that “the one or more physical processors are further configured by the machine-readable instructions to present a visualization of the comparison between the anomaly score and the anomaly score threshold (first reversion transaction is determined to be anomalous by comparing the reconstruction difference [score] with the reconstruction error threshold for anomaly [anomaly score threshold] – Ben Kimon, claim 5; toolbar application may display a user interface in connection with a browser application [including the comparison] – id. at paragraph 47).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari and Chen to display the difference between the score and the threshold, as disclosed by Ben Kimon, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow for anomaly detection in the presence of noisy datasets, thereby preventing problems that require remediation. See Ben Kimon, paragraph 2.
Regarding claim 10, Heidari, as modified by Chen/Ben Kimon, discloses that “the operation characteristics of the well include[] pressure and temperature at the well (Heidari paragraph 26 discloses that the data collected include pressure data and digital temperature sensing data).”
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Heidari in view of Chen and Ben Kimon and further in view of Yoon et al. (US 20200320402) (“Yoon”).
Regarding claim 3, neither Heidari, Ben Kimon, nor Chen appears to disclose explicitly the further limitations of the claim. However, Yoon discloses that “the linear unsupervised machine-learning model includes a principal component analysis algorithm (some unsupervised learning models focus on characterization of normality and detecting samples out of the normality, e.g., PCA for linearity – Yoon, paragraph 22).”
Yoon and the instant application both relate to PCA and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari, Ben Kimon, and Chen to use PCA as part of the machine learning algorithm, as disclosed by Yoon, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would save processor resources by reducing the dimensionality of the data. See Yoon, paragraph 22.
Claim 13 is a method claim corresponding to system claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Heidari in view of Chen and further in view of Ben Kimon and Beggel et al. (US 20190130279) (“Beggel”).
Regarding claim 7, the rejection of claim 6 is incorporated. Heidari further discloses “operation characteristics of the well for the period of time”, as shown above in the rejection of claim 1.
Neither Heidari, Chen, nor Ben Kimon appears to disclose explicitly the further limitations of the claim. However, Beggel discloses that “the anomaly score threshold is determined based on the … characteristics … and reconstructed …characteristics … such that a threshold percentage of historical anomaly scores satisfies the anomaly score threshold (anomaly decision threshold can be chosen based on reconstruction error [i.e., difference between characteristics and reconstructed characteristics] and can depend on the distribution of reconstruction errors during training; several alternatives are available to determine this threshold, including a percentile of reconstruction errors, such that, e.g., only 5% of all training images exceed this reconstruction error [i.e., satisfy the threshold] – Beggel, paragraph 43).”
Beggel and the instant application both relate to anomaly detection using autoencoders and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari, Chen, and Ben Kimon to determine the anomaly score threshold based on a percentage, as disclosed by Beggel, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would provide an objective and simple-to-execute criterion for determining whether an anomaly has occurred, allowing the user to take corrective action based on the detected anomaly. See Beggel, paragraph 43.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Heidari in view of Chen and Ben Kimon and further in view of Marcharet (US 20130254153) (“Marcharet”).
Regarding claim 9, the rejection of claim 1 is incorporated. Heidari further discloses that “the unsupervised machine-learning model is [trained] using the well operation information (hydraulic fracturing job optimization system can generate [train] a machine learning model based on historical production data [well operation information] – Heidari, paragraph 78).”
Neither Heidari, Ben Kimon, nor Chen appears to disclose explicitly the further limitations of the claim. However, Marcharet discloses that “the unsupervised machine-learning model is retrained (illustrated method performs unsupervised retraining of a classification model – Marcharet, paragraph 45) ….”
Marcharet and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Heidari, Ben Kimon, and Chen to retrain the model in unsupervised fashion, as disclosed by Marcharet, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would improve performance of the model with a particular use case with a distribution of unlabeled test data. See Marcharet, paragraph 24.
Response to Arguments
Applicant's arguments filed March 23, 2026 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the entry of a new ground of rejection, not persuasive.
Applicant first argues that the claims as amended are eligible under 35 USC § 101, principally because the newly added limitation reciting that “one or more preventative/mitigative actions are performed in response to the future occurrence of the asphaltene anomaly predicted by the system, the one or more preventative/mitigative actions including: controlling a production rate of the well, scheduling an intervention to be performed on the well, or controlling operations to prevent uncommanded shut-in of the well” allegedly integrates any judicial exception recited into a practical application. Remarks at 10-14. However, this limitation cannot integrate any judicial exception recited into a practical application because it constitutes part of the judicial exception itself. MPEP § 2106.05(I). The three options are written in the alternative, and the second option of “scheduling an intervention” could be practically performed in the mind, as detailed further in the rejection.
Regarding the art rejection, Applicant argues that Chen fails to disclose a production and/or effect of production of asphaltene in a well because the asphaltene onset pressure is a parameter used for the evaluation of subterranean formations. Remarks at 15. However, even assuming that this characterization of Chen is correct, which Examiner does not concede, the onset pressure would not fail to be an “asphaltene anomaly [that] is a production of asphaltene in the well” because the pressure is specifically characterized as an “asphaltene onset pressure”, i.e., a pressure at which asphaltene begins to be produced. Applicant’s further assertion that Heidari/Chen do not disclose taking preventative or mitigative actions that include controlling a production rate of the well, Remarks at 15-16, is unaccompanied by substantive argument. It suffices to refer Applicant back to the rejection.
Conclusion
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 RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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, Kamran Afshar, can be reached at 571-272-7796. 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.
/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
Prosecution Insights