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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims at a high level recite classifying and marching documents.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-20 recite a method and a system and therefore, are directed to the statutory class of machine and a product.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 1 recites -
▪ access a conduit pressure dataset comprising a multitude of measured pressure data samples of a conduit of a hydrocarbon well operation (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can mentally and logically analyze data samples);
▪ filter the pressure data samples of the conduit pressure dataset by applying a low-pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples, to generate a training dataset having a multitude of filtered pressure data samples (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user / analyst can apply corresponding filters);
▪ identify a plurality of key attributes in each of the filtered pressure data samples of the training dataset, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can mentally or on a piece of paper identify a plurality of key attributes);
▪ using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to transient object predictions calculated from at least some of the filtered pressure data samples in the training dataset (Abstract idea of “a mathematical concept” — see MPEP § 2106.04(a)(2)(l). Note: under the broadest reasonable interpretation of the claim, the claimed invention encompasses mathematical concept (e.g., Mathematical Formula or Equations - a user can mentally perform and apply mathematical concepts and evaluations).
These limitations, based on their broadest reasonable interpretation, recite a mental process, i.e. a judicial exception. For these reasons, the independent claim 1, as well as independents claims 8 and 20, which include limitations commensurate in scope with claim 1, recite a judicial exception.
A method, like the claimed method, “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) where collecting information, analyzing it, and displaying results from certain results of the collection and analysis was held to be an abstract idea. See In re Meyer, 688 F.2d 789, 795—96 (CCPA 1982), which held that “a mental process that a neurologist should follow” when testing a patient for nervous system malfunctions was not patentable.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
Next determine whether the claims recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)-(c), (e)-(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)).
Additional elements:
▪ a processor; and a memory including instructions, non-transitory computer-readable medium (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note - high level application of using routine computer hardware to merely invoking a computer component to apply the exception);
▪ train a machine learning model … to generate a predictive model (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical / logical reasoning).
The term “additional elements” for claim features, limitations, or steps that the claim recites beyond the identified judicial exception. Claim 1 additionally recite “A system, comprising: a processor; and a memory” and claim 15 recites “non-transitory computer-readable medium.” However, claims do not recite any improvements to these additional elements, nor does the claims recite any particularly programmed or configured computer system, device, or machine learning. Rather, the additional elements in claims 1, 15 and 16 serve merely to automate the abstract idea. See Int’l Bus. Machs. Corp. v. Zillow Group, Inc., 50 F. 4" 1371, 1382 (Fed. Cir. 2022) (“[A] patent that ‘automate[s] “pen and paper methodologies” to conserve human resources and minimize errors’ is a ‘quintessential “do it on a computer” patent’ directed to an abstract idea.”) (quoting Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019)). Therefore, none of these recited additional elements, whether considered individually or in combination, integrates the judicial exception into a practical application.
The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception.
For these reasons, independent claim 1, as well as independent claims 8 and 15, which include similar additional elements as claim 1, are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, determine whether the claims recite an “inventive concept” that “must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer.” BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016); see MPEP § 2106.05(d). There must be more than “computer functions [that] are “well-understood, routine, conventional activit[ies]’ previously known to the industry.” Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014) (second alteration in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 73 (2012)); see MPEP § 2106.05(d).
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception.
Additional elements (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
Note, that in similar case, such as Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), the Courts have identified that the additional elements of displaying and analyzing data, as shown in the independent claims 1, 8, 20 do not amount to significantly more than the judicial exception. Consequently, that is not enough to transform an abstract idea into a patent-eligible invention.
No “inventive concept” sufficient to transform the abstract method of organizing human activity into a patent-eligible application. See MPEP § 2106.05. Rather, the additional elements identified above are merely well-understood, conventional computer components, as confirmed by the Specification. See MPEP § 2106.05(d)(1). For example, the Specification refers to the additional elements in generic terms.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. For example, the claimed processor and machine learning merely communicates and processes known data. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claims 1, 8 and 20 contain the identified abstract ideas, with the additional elements of a processor, hardware and the media, which is a generic computer component, and thus not significantly more for the same reasons and rationale above.
Dependent claims further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible. As such, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 8, 10-12, 15-16, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duckworth et al. (US 6178141) in view of AlSinan et al. (US 20250035802).
Regarding claim 1, Duckworth teaches a system, comprising: a processor; and a memory including instructions that are executable by the processor for causing the processor to:
access a
filter the pressure data samples of the NOTE);
identify a plurality of key attributes in each of the filtered pressure data samples of the training dataset (C14L6-10), at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy
train a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation (C14L45-54, C15L50-59) and mean average between distance to transient object predictions (C8L63-64, C14L36-38, C15L4-16, C15L30-55, C19L8-10) calculated from at least some of the filtered pressure data samples in the training dataset, to generate a predictive model (C10L20-30, C12L10-14).
Duckworth does not explicitly teach, however AlSinan discloses - access a conduit pressure dataset comprising a multitude of measured pressure data samples of a conduit of a hydrocarbon well operation ([0040], [0046], [0055]).
NOTE Duckworth explicitly teaches training data collected from various sensor for input into a prediction model, which construed to be analogous to “generate a training dataset.” However, given that Duckworth does not explicitly teach machine learning per se, to further obviate the limitation - generate a training dataset having a multitude of filtered pressure data samples, AlSinan discloses the same in [0041], [0068], [0087]-[0088], [0090].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Duckworth to include data samples of a conduit of a hydrocarbon well operation as disclosed by AlSinan. Doing so would improve the generalization of machine-learned models for seismic processing tasks (AlSinan [0067]).
Regarding claim 8, Duckworth teaches a computer-implemented method comprising: accessing, by a processor, a conduit pressure dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation; filtering, by the processor, the pressure data samples of the conduit pressure dataset by applying a low-pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples, to generate a training dataset having a multitude of filtered pressure data samples; identifying a plurality of key attributes in each of the filtered pressure data samples of the training dataset, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; training, by the processor, a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to transient object predictions calculated from at least some of the filtered pressure data samples in the training dataset, to generate a predictive model.
Claim 8 recites substantially the same limitations as claim 1 and is rejected for substantially the same reasons.
Regarding claim 15, Duckworth teaches a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to: access a conduit pressure dataset comprising a multitude of measured pressure data samples of a conduit of a hydrocarbon well operation; filter the pressure data samples of the conduit pressure dataset by applying a low-pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples, to generate a training dataset having a multitude of filtered pressure data samples; identify a plurality of key attributes in each of the filtered pressure data samples of the training dataset, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; train a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to transient object predictions calculated from at least some of the filtered pressure data samples in the training dataset, to generate a predictive model.
Claim 15 recites substantially the same limitations as claim 1 and is rejected for substantially the same reasons.
Regarding claims 2 and 16, Duckworth as modified teaches the system and the medium, wherein: the pressure data samples are produced by a conduit monitoring system configured to introduce a pressure wave into the conduit and to measure a magnitude of reflected pressure waves using at least one sensor (AlSinan [0001], [0056]-[0059], [0108], [0117], [0120], [0142], Duckworth C4L33-34, C6L30-33, C25L39-40);
the conduit monitoring system is communicatively coupled to a computing device of the system, the computing device including the processor, the memory, and the instructions (AlSinan [0054], [0152], Duckworth F2A); and the instructions are further executable by the processor for causing the computing device to receive the pressure data samples from the conduit monitoring system (AlSinan [0056]-[0059]).
Regarding claims 4, 10, Duckworth as modified teaches the system and the method, wherein the conduit pressure dataset comprises pressure data samples recorded only during a time period of interest (Duckworth C6L36-39, AlSinan [0057], [0083]).
Regarding claims 5, 11 and 18, Duckworth as modified teaches the system, the method and the medium, wherein the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset on a frequency range of interest of
Duckworth as modified does not explicitly teach a frequency range of interest of between 6 Hz to 7 Hz. However, the particular elements are obvious, and any particular frequency range would be an obvious to try combination of elements in order to achieve a predictable results. See MPEP 2143.
Regarding claims 6, 12 and 19, Duckworth as modified teaches the system, the method and the medium, wherein the instructions are further executable by the processor for causing the processor to:
after filtering of the pressure data samples of the conduit pressure dataset, calculate at least a first derivative of each of the pressure data samples of the conduit pressure dataset (Duckworth C10L31-45, C15L1-17, C19L8-67, AlSinan [0061], [0096], [0111], [0114]); and identify the plurality of key attributes in each of the pressure data samples of the conduit pressure dataset from the first derivative of each of the pressure data samples of the conduit pressure dataset (Duckworth C14L5-55).
Claims 5-6, 11-12 and 18-19 is/are additionally or alternatively rejected under 35 U.S.C. 103 as being unpatentable over Duckworth as modified and in further view of Pilt et al. “New Photoplethysmographic Signal Analysis Algorithm for Arterial Stiffness Estimation”
Regarding claims 5, 11 and 18, Duckworth as modified teaches the system, the method and the medium, wherein the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset on a frequency range of interest
Duckworth as modified does not explicitly teach, however, Pilt discloses a frequency range of interest of between 6 Hz to 7 Hz (Abstract, p.8 C1 last par).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Duckworth to include a frequency range of interest of between 6 Hz to 7 Hz as disclosed by Pilt. Doing so provides improved signal analysis (p.5 C2L1-5 2.3).
Regarding claims 6, 12 and 19, Duckworth as modified does not explicitly teach, however, Pilt discloses the system, the method and the medium, wherein the instructions are further executable by the processor for causing the processor to: after filtering of the pressure data samples of the conduit pressure dataset, calculate at least a first derivative of each of the pressure data samples of the conduit pressure dataset; and identify the plurality of key attributes in each of the pressure data samples of the conduit pressure dataset from the first derivative of each of the pressure data samples of the conduit pressure dataset (Abstract, F1-2, 4, p.4 C2L1-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Duckworth to include a first derivative of each of the pressure data samples as disclosed by Pilt. Doing so provides improved signal analysis (p.5 C2L1-5 2.3).
NOTE in analogous prior art (US 20180100948) likewise discloses claims 6, 12 and 19 and further obviates the teachings of Duckworth.
Claims 3, 7, 9, 13-14, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duckworth as modified and in view of THIRUVENKATANATHAN et al. (US 20230043381) and Hill (US 20110149688).
Regarding claims 3, 9 and 17, Duckworth as modified teaches the system and the method, wherein the conduit pressure dataset includes a first set of pressure data associated with a conduit known to include a transient object
Duckworth does not explicitly teach, however THIRUVENKATANATHAN and Hill discloses the conduit pressure dataset includes a first set of pressure data associated with a conduit known to include a transient object that is a pig, a second set of pressure data associated with a conduit known to include a blockage other than a pig, and a third set of pressure data associated with an ideal conduit (THIRUVENKATANATHAN [0018]-[0019], [0032], Hill [0006], [0040]-[0041]); and the pressure data in the conduit pressure dataset is pressure data recorded only during a time period of interest (THIRUVENKATANATHAN [0113], [0107], [0122], Hill [0008], [0011]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Duckworth to include transient object that is a pig as disclosed by THIRUVENKATANATHAN and Hill. Doing so would provide improved conduit monitoring (Hill [0005]).
Regarding claims 7, 13 and 20, Duckworth does not explicitly teach, however THIRUVENKATANATHAN and Hill discloses the system, the method and the medium, further comprising: predicting, by the processor, a location and movement of a transient object in a conduit of interest by applying the predictive model to datasets comprising measured pressure data associated with the conduit of interest and iterated over time, and analyzing pressure profiles defined by the measured pressure data (THIRUVENKATANATHAN [0018]-[0019], [0032], Hill [0006], [0040]-[0041]); and in response to predicting a location and movement of a transient object in the conduit of interest, outputting by the processor, a command to execute an action selected from the group consisting of generating a notification indicating at least a location of the transient object, scheduling a removal of the transient object when the transient object is a pig, initiating a remediation action relative to the transient object when the transient object is a blockage, and combinations thereof (THIRUVENKATANATHAN [0022], [0050] Hill [0010]-[0012]).
NOTE although THIRUVENKATANATHAN and Hill does not explicitly teach “scheduling” such functionality is well-known to those skilled in the art and would be obvious to implement to achieve a desired action.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Duckworth to include remediation action as disclosed by THIRUVENKATANATHAN and Hill. Doing so would consequently aid in improving the accuracy of the identification of the movement of fluids and/or solids in real time (THIRUVENKATANATHAN [0020]).
Regarding claim 14, Duckworth as modified teaches the computer-implemented method of claim 13, wherein predicting the location and movement of the transient object in the conduit of interest includes providing information selected from the group consisting of transient object speed, transient object direction of movement, transient object distance to travel, and combinations thereof (Duckworth C24L27-32, THIRUVENKATANATHAN [0028], [0049], [0052], [0057], [0064], Hill [0035], [0037]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/Primary Examiner, Art Unit 2165 July 27, 2026
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