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 .
IDS
The information disclosure statements (IDS) submitted on March 14, 2024, October 8, 2024 (2x), October 14, 2024, October 15, 2024, January 7, 2025, January 16, 2025, May 20, 2025, August 20, 2025, November 5, 2025, January 7, 2026, April 28, 2026 and May 12, 2026 are being considered by the Examiner.
Drawing
The drawing filed on June 25, 2024 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §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.
In reference to claims 1-33: the claimed invention is directed to a judicial exception (i.e., abstract idea) without significantly more.
The requirement for subject matter eligibility test for products and processes requires first, the claimed invention must be to one of the four statutory categories. 35
U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. The latter three categories define "things" or "products" while the first category defines "actions" (i.e., inventions that consist of a series of steps or acts to be performed).
Second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature).
In the first step, it is to be determined whether the patent claim under examination is directed to an abstract idea. If so, in the second step of analysis, it is to be determined whether the patent adds to the idea "something more" or "significantly more" that embodies an "inventive concept."
In the instant case, claim 1 is representative and it is reproduced here with the limitations that are part of the abstract idea in bold:
A method of training a stimulation model for a wellbore, comprising:
collecting a set of sensed signals (data collection) from downhole sensors, wherein the downhole sensors include at least one of distributed sensors or discrete sensors; and
training, using one or more machine learning algorithms, a stimulation model by learning relationships between a training data set based on the set of sensed signals and targeted parameters of a stimulation process.
Step 2A:
Prong I: The claim recites the steps of "collecting a set of sensed signals (data values), …. training …algorithms, [creating] a stimulation model by learning relationship between a training data set based on the set of sensed signals and targeted parameters of a stimulation process". These limitations are mathematical calculation or models without having to do any specific real word action (such as implemented in a system to carry out a real physical action, such as application in medial devices, oil rig, or drilling a borehole etc.…). Therefore, the recited method falls in the abstract idea grouping of mental processes and/or mathematical concepts at Prong 1 of the §101 analysis.
Prong II
This abstract idea is not integrated into a practical application at Prong 2 of the §101 analyses because the claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. The claim recites the method comprising the additional element steps of "collecting a set of sensed signals from downhole sensors, including at least one of distributed sensors or discrete sensors; and training using one or more machine learning algorithm". However, the first the sensors are utilized for data gathering step recited at a high level of generality, and the second simply training a “stimulation model” using one or more generic machine learning algorithm is merely a generic process which is invoked as a tool to perform the abstract idea, i.e., programming a stimulation model.
The courts have found that adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea (such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011 )) is not enough to integrate the abstract idea into a particular practical application or make the claim qualify as "significantly more" (see MPEP § 2106.05(g)).
The claim does not recite applying the abstract idea with, or by use of, any particular machine, nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing. The claim amounts to manipulating data: a method of training a stimulation model. The claim does not recite any particular real-world actions that are taken as a result of training a stimulation model. The claim creates “a stimulation model”' as the general field-of-use, but does not recite a particular practical application being carried out within that field-of-use. Therefore, the claimed invention does not appear to be limited to the use of the mental process or math in a particular practical application, but instead the claim appears to monopolize the mental process or math itself, in any practical application where it might conceivably be used.
Step 2B
Finally, at Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea for the same reasons as discussed above with regard to Prong 2. Claim 1 is rejected as ineligible under 35 USC §101.
Claims 18 and 24 are analogous to claim 1, except that claim 18 additionally recites that “altering one or more operating parameter of the stimulation process based on the evaluating”; however, the modification of the parameter values does not provide a significantly more than the abstract idea itself because modifying the stimulation parameter is not exactly doing anything; and claim 24 is directed to a computer system with one or more processor to perform the operation noted in claim 1 of the instant application, in other words, these additional elements are separate from the abstract idea that need to be considered at Prong 2 of the §101 analysis. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claims 18 and 24 are therefore rejected as ineligible under 35 USC §101 as well.
Dependent claims 2-4: the instant claims are directed to the sensors used for gathering various parameters of interest, and they are being used for data collection at a high level of generality.
Dependent claims 5-10: the instant claims are directed to some aspects of training the stimulation model, and is considered insignificant extra solution activity.
Dependent claims 11-12: the instant claims are directed to the variation of the machine learning algorithm and considered another mathematical algorithm.
Dependent claims 13-14: the instant claims are directed to additional variables considered in the formation of a stimulation model; which is directed a data gathering to the highest level.
Dependent claim 15: the instant claim is describing the type of the stimulation process which is not significantly more than the abstract idea itself.
Dependent claim 16: the instant claim is defining the type of data and it is part of the data gathering step at a higher level of generality.
Dependent claim 17: the instant claim is directed to the apparatus for collecting the sensed data and it is used for the purposes of data gathering, which is part of a generic data gathering means.
Dependent claim 19: the instant claim is directed to the intended purpose of the stimulation model or process, and is not significantly more than the abstract idea.
Dependent claims 20-23: the instant claims are related to the operating parameter of interest and is not significantly more than the abstract idea.
Dependent claims 25-27: the instant claims are directed to generating sensed data set and is considered forming data set at a high level of generality.
Dependent claims 28-29: the instant claims are directed to filtering of the sensed signal for the purposes of removing anomalies which a generic data processing to obtain data at a high level of generalities, and is not significantly more than the abstract idea.
Dependent claim 30: the instant claim is directed to characterizing the type of the machine learning algorithm, and is considered an abstract idea, i.e., computational analysis.
Dependent claim 31: the instant claim is directed to the characterization of the stimulation model, specifically directed to the fracking practice, which is abstract.
Dependent claim 32: the instant claim is directed to the sensed signal having a transient component to it, which is a gathered data.
Dependent claim 33: the instant claim is directed to the way the sensed signals were collected and it is insignificant extra-solution activity.
Claim rejection – 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-33 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Al Tammar et al. (U.S. PAP 2022/0243568, hereon Tammar).
In reference to claim 1: Tammar discloses a method of training a stimulation model for a wellbore (see Tammar, paragraphs [0034] and [0036]), comprising:
collecting a set of sensed signals (data collection) from downhole sensors (see Tammar, paragraph [0016]), wherein the downhole sensors include at least one of distributed sensors or discrete sensors (see Tammar, paragraph [0007] and [0055]); and
training, using one or more machine learning algorithms (see Tammar, paragraph [0033]), a stimulation model by learning relationships between a training data set based on the set of sensed signals and targeted parameters of a stimulation process (see Tammar, paragraph [0054] and [0055]).
With regard to claim 2: Tammar further discloses that the downhole sensors include sensors for sensing one or more of temperatures, vibrations, pressures, or sound (see Tammar, paragraph [0045] and [0055]).
With regard to claim 3: Tammar further discloses that the downhole sensors are stimulation task sensors (see Tammar, paragraph [0058], such as task being modeled).
With regard to claim 4: Tammar further discloses that the set of sensed signals includes transient data (see Tammar, paragraphs [0033] and [0066]).
With regard to claim 5: Tammar further discloses that the method comprising generating the training data set by processing the collected set of sensed signals before the training, wherein the processing includes extracting stimulation features from the sensed signals (see Tammar, paragraph [0052]).
With regard to claim 6: Tammar further discloses that the features include pressure measurements per foot or meter of a completion section of the wellbore and pressure measurements per change in frac pack per proppant size (see Tammar, paragraph [0035]).
With regard to claim 7: Tammar further discloses that generating the training data set further includes combining different types of stimulation features, discretizing the combined features per a particular distance or amount of time, and using the discretized features for the training data set (see Tammar, paragraph [0014], [0066] and [0090]).
With regard to claim 8: Tammar further discloses that the processing further includes filtering the set of sensed signals by removing anomalies (or smoothing the data values) (see Tammar, paragraph [0014] and [0066]).
With regard to claim 9: Tammar further discloses that filtering includes using one or more machine learning algorithms (see Tammar, paragraph [0112]).
With regard to claim 10: Tammar further discloses that the filtering is performed by one or more of an intelligent sensor, an edge computing device, or a data recorder (see Tammar, paragraph [0098]).
With regard to claim 11: Tammar further discloses that the one or more machine learning algorithms include a deep learning algorithm (deep neural networks) (see Tammar, paragraph [0057]).
With regard to claim 12: Tammar further discloses that the deep learning algorithm is a transformer (see Tammar, paragraph [0057], since a transformer is a deep neural network architecture).
With regard to claim 13: Tammar further discloses that generating the training data set further includes using additional data that includes one or more of: laboratory data, offset well data, engineering calculations, engineering formulas, or expert observations and analysis (see Tammar, paragraphs [0045] and [0112]).
With regard to claim 14: Tammar further discloses that the expert observations and analysis is from artificial intelligence (see Tammar, paragraph [0057] since neural network is the core type of AI).
With regard to claim 15: Tammar further discloses that the stimulation process is gravel packing (see Tammar, paragraph [0050]).
With regard to claim 16: Tammar further discloses that the set of sensed signals includes transient data (see Tammar, paragraph [0033], the transient noise in the data).
With regard to claim 17: Tammar further discloses that the collecting includes receiving the set of sensed signals via a permanent half wet mate connector (see Tammar, paragraph [0044], optical fiber cables distributed underground and the optical connectors would have similar or identical function).
In reference to claim 18: Tammar discloses a method of performing a stimulation process for a wellbore (see Tammar, paragraphs [0034] and [0036]), comprising:
collecting sensed signals (data collection) from sensors located in the wellbore during the stimulation process (see Tammar, paragraph [0007] and [0055]);
evaluating the stimulation process based on the sensed signals and using a stimulation model trained to detect relationships between a training data set based on a set of sensed signals and targeted parameters of stimulation processes (see Tammar, paragraph [0059]), wherein the set of sensed signals were collected from downhole sensors that included at least one of distributed sensors or discrete sensors (see Tammar, paragraph [0063]); and
altering one or more operating parameter of the stimulation process based on the evaluating (see Tammar, paragraph [0056]).
With regard to claim 19: Tammar further discloses that the stimulation process includes one or more of cementing, packing, acidizing, pumping, or fracking (see Tammar, paragraph [0049]).
With regard to claim 20: Tammar further discloses that the one or more operating parameter relates to proppant used for the fracking (see Tammar, paragraph [0035]).
With regard to claim 21: Tammar further discloses that the one or more operating parameter relates to surfactants of the stimulation process (see Tammar, paragraph [0036]).
With regard to claim 22: Tammar further discloses that the set of sensed signals include transient data (see Tammar, paragraph [0033], the transient noise in the data).
With regard to claim 23: Tammar further discloses that the set of sensed signals were collected via a permanent half wet mate connector (see Tammar, paragraph [0044], optical fiber cables distributed underground and the optical connectors would have similar or identical function).
In reference to claim 24: Tammar discloses a computing system (see Tammar, Fig. 1), comprising:
an interface for receiving a set of sensed signals from downhole sensors (see Tammar, paragraph [0007] and [0055]) wherein the downhole sensors include at least one of distributed sensors or discrete sensors; and
one or more processors to perform operations including: training, using one or more machine learning algorithms (see Tammar, paragraph [0033]), a stimulation model by learning relationships between a training data set based on the set of sensed signals and targeted parameters of a stimulation process (see Tammar, paragraph [0054] and [0055]).
With regard to claim 25: Tammar further discloses that the one or more operations further include generating the training data set by processing the collected set of sensed signals before the training and extracting stimulation features based on the sensed signals (see Tammar, paragraph [0052]).
With regard to claim 26: Tammar further discloses that the extracted features include at least one of pressure measurements per foot or meter of a completion section of the wellbore and pressure measurements per change in frac pack per proppant size (see Tammar, paragraph [0035]).
With regard to claim 27: Tammar further discloses that generating the training data set includes combining different types of features, discretizing the combined features per a particular distance or amount of time, and using the discretized features for the training data set (see Tammar, paragraph [0014], [0066] and [0090]).
With regard to claim 28: Tammar further discloses that the processing further includes filtering the set of sensed signals by removing anomalies (or smoothing the data values) (see Tammar, paragraph [0014] and [0066]).
With regard to claim 29: Tammar further discloses that filtering includes using one or more machine learning algorithms and the filtering is performed by one or more of an intelligent sensor, an edge computing device, or a data recorder (see Tammar, paragraph [0098]).
With regard to claim 30: Tammar further discloses that the one or more machine learning algorithms include at least one of a supervised learning algorithm, an unsupervised learning algorithm, and a deep learning algorithm (deep neural networks) (see Tammar, paragraph [0057]).
With regard to claim 31: Tammar further discloses that the stimulation process is fracking (see Tammar, paragraph [0049]).
With regard to claim 32: Tammar further discloses that the set of sensed signals include transient data (see Tammar, paragraph [0033], the transient noise in the data).
With regard to claim 33: Tammar further discloses that the set of sensed signals were collected via a permanent half wet mate connector (see Tammar, paragraph [0044], optical fiber cables distributed underground and the optical connectors would have similar or identical function).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ross et al. (U.S PAP 2022/0365239) discloses a system, method, and apparatus for preventing fracture communication between wells, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fracking fluid in the well into an electrical signal; a machine learning system configured to analyze current frequency components of the electrical signal in a window of time and identify impending fracture communication between the well and an offset well.
Lolla et al. (U.S. Patent No. 10,921,471) discloses methods and systems for monitoring operation integrity during hydrocarbon production or fluid injection operations. Further, the received microseismic data is processed to obtain a plurality of data panels corresponding to microseismic data measured over a predetermined time interval. For each data panel, trigger values are calculated for data traces corresponding to sensor receivers of the microseismic monitoring system.
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/ELIAS DESTA/
Primary Examiner, Art Unit 2857
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