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 .
Election/Restrictions
Applicant’s election with traverse of Group I, claims 1-14, in the reply filed on 21 July 2026 is acknowledged. Claims 15-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Group II . Applicant timely traversed the restriction (election) requirement in the reply, however, because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement the requirement is still deemed proper and is therefore made FINAL.
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 1-14 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-14 are directed to determining a planned consumption of a specific component, retrieving historical lifetime data associated with the specific component, determining an associated normalized current life consumed, a probability of survival distribution, a probability of future failure and a projected number of specific component failures, generating at least one acquisition request and increasing the usable inventory of the specific component which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
As indicated in the preamble of the claims, the examiner finds the claims are directed to a process.
Step 2A – Prong One - Abstract Idea Analysis
Exemplary claim 1 recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
An automated method to generate at least one acquisition request of a specific component of a machine having a plurality of components, using a probabilistic model and based on a current life consumed and a planned consumption, to maintain a usable inventory of the specific component, each acquisition request being configured to maintain the usable inventory, the method comprising, for each specific component:
determining a planned consumption of the specific component, the planned consumption comprising an amount of work the specific component is expected to complete during a job assigned to the machine;
retrieving historical lifetime data associated with the specific component,
the historical lifetime data comprising historical specimens of the specific component consumed in a set time range, and
consumption comprising a period between installation and failure;
based on the historical lifetime data, determining an associated normalized current life consumed for each individual historical specimen at the time of failure based on a power law equivalency model tailored to the specific component;
based on the normalized current life consumed for each individual historical specimen at the time of failure, determining a probability of survival distribution associated with the specific component;
based on the probability of survival distribution, the associated normalized current life consumed for each individual specimen of the specific component in a current inventory, and the planned consumption of the specific component, determine a probability of future failure of each individual specimen of the specific component in the current inventory given the planned consumption of the specific component;
based on the determined probability of future failure of each individual specimen of the specific component in the current inventory, and the planned consumption of the specific component during the job assigned to the machine, determine a projected number of specific component failures prior to completion of the job associated with the specific component;
generate the at least one acquisition request, the at least one acquisition request configured to acquire a quantity of new specimens of the specific component, the quantity of new specimens at least equal to the projected number of specific component failures prior to completion of the job assigned to the machine; and
increase the usable inventory of the specific component to include at least the quantity of new specimens.
The claim features in italics above as drafted, under its broadest reasonable interpretation, are mental processes and/or certain methods of organizing human activity performed by generic computer components. While the claim recites “a machine having a plurality of components”, none of the steps are positively recited as being performed by the these components. Therefore, nothing in the claim element precludes the step from practically being performed in the mind or a method of organized human activity. The steps of “determining a planned consumption of the specific component, the planned consumption comprising an amount of work the specific component is expected to complete during a job assigned to the machine; based on the historical lifetime data, determining an associated normalized current life consumed for each individual historical specimen at the time of failure based on a power law equivalency model tailored to the specific component; based on the normalized current life consumed for each individual historical specimen at the time of failure, determining a probability of survival distribution associated with the specific component; based on the probability of survival distribution, the associated normalized current life consumed for each individual specimen of the specific component in a current inventory, and the planned consumption of the specific component, determine a probability of future failure of each individual specimen of the specific component in the current inventory given the planned consumption of the specific component; based on the determined probability of future failure of each individual specimen of the specific component in the current inventory, and the planned consumption of the specific component during the job assigned to the machine, determine a projected number of specific component failures prior to completion of the job associated with the specific component” in the context of this claim encompasses mental processes. If the claim limitations, under its broadest reasonable interpretation, covers steps which could be performed in the human mind including an observation, evaluation, judgement of opinion but for the recitation of generic computer components, then it falls within the “mental process” grouping of abstract ideas. Further, “retrieving historical lifetime data associated with the specific component, the historical lifetime data comprising historical specimens of the specific component consumed in a set time range, and consumption comprising a period between installation and failure; generate the at least one acquisition request, the at least one acquisition request configured to acquire a quantity of new specimens of the specific component, the quantity of new specimens at least equal to the projected number of specific component failures prior to completion of the job assigned to the machine; and increase the usable inventory of the specific component to include at least the quantity of new specimens” in the context of this claim encompasses certain methods of organizing human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the “certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claims only recite a few additional elements – “a machine having a plurality of components” and “the machine is a pumping unit configured for wellbore servicing operations, and the plurality of components is selected from the group consisting of an engine, a transmission, a speed reducer, a power end, a fluid end, and combinations thereof” (which are not positively recited as performing any of the above steps. The “machine having a plurality of components” and “pumping unit configured for wellbore servicing operations, and …group consisting of an engine, a transmission, a speed reducer, a power end, a fluid end, and combinations thereof” are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f), i.e. the determining, generating and increasing steps), data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g), i.e. the retrieving step) and linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h), i.e. pumping unit configured for wellbore servicing operations, and a plurality of components selected from a group consisting of an engine, a transmission, a speed reducer, a power end, a fluid end, and combinations thereof). Accordingly, these 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. The claim is directed to an abstract idea.
Step 2B - Significantly More Analysis
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a machine having a plurality of components” and “the machine is a pumping unit configured for wellbore servicing operations, and the plurality of components is selected from the group consisting of an engine, a transmission, a speed reducer, a power end, a fluid end, and combinations thereof” amount to no more than mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity and linking the use of the judicial exception to a particular technological environment or field of use. Mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity and linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the background does not provide any indication that the “machine having a plurality of components” and “machine is a pumping unit configured for wellbore servicing operations, and the plurality of components is selected from the group consisting of an engine, a transmission, a speed reducer, a power end, a fluid end, and combinations thereof” are anything other than generic, off-the-shelf computer components. For these reasons, there is no inventive concept. The claim is not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
United States Patent Application Publication No. 2017/0308802 A1 to Ramsoy (“Ramsoy”) teaches and discloses “Methods and accompanying systems are provided for predicting outcomes, such as industrial asset failures, in heavy industries. The predicted outcomes can be used by owners and operators of oil rigs, mines, factories, and other operational sites to identify potential failures and take preventive and/or remedial action with respect to industrial assets. In one implementation, historical data associated with a plurality of outcomes is received at one or more central site servers from one or more data sources, and datasets are generated from the historical data. Using the datasets, a set of models is trained to predict an outcome. A particular model includes sub-models corresponding to a hierarchy of components of an industrial asset. The set of models is combined into an ensemble model, which is transmitted to remote sites” (Ramsoy: Abstract and ¶¶ 0006, 0008, 0025-0033, 0052-0080, 0232 and Figs. 10 and 22).
United States Patent Application Publication No. 2003/0216888 A1 to Ridolfo (“Ridolfo”) teaches and discloses a “display system in which subsequent failures of plant equipment and plant systems are predicted to occur and in which the probability of failure before a specified date and the probability of failure after a specified date is determined and displayed and in which the calendar date is determined and displayed when the desired probability that the equipment not fail prior to the calendar date is specified. The system includes an Equipment Failure And Degradation Module that determines the remaining equipment/system life; a Probability-of-Failure Predictor Module that determines the probability of the equipment/system failing prior to a specified date and the probability of the equipment/system failing after a specified date; and a Date-of-Failure Predictor Module that determines the calendar date that corresponds to a specified probability that equipment not fail prior to the date” (Ridolfo: Abstract).
United States Patent Application Publication No. 2019/0188584 A1 to Rao et al. (“Rao”) teaches and discloses a “system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations” (Rao: Abstract).
United States Patent Application Publication No. 2023/0082374 A1 to Vahid et al. (“Vahid”) teaches and discloses a “Method, system, apparatus, and/or device for predicting the failure of a shipper. The failure prediction system includes a first sensor configured to detect or measure first sensor data. The failure prediction system includes a memory configured to store a dewar failure model that models a failure of various shippers given one or more constraints. The failure prediction system includes a processor coupled to the memory and the first sensor. The processor is configured to estimate or predict a probability or a likelihood that a shipper will fail before or during a subsequent shipment of the shipper based on the first sensor data and the dewar failure model. The processor is configured to provide the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper” (Vahid: Abstract).
United States Patent Application Publication No. 2023/0260332 A1 to K et al. (“K”) teaches and discloses a “prognostic and health monitoring system for a device with a rotating component is provided. The system includes a plurality of sensors. Each sensor is configured to sense a parameter of the device. A controller is in communication output sensor signals. The controller, based on instructions stored in a memory, is configured to filter the output sensor signals based on operational speed data of the rotating component to obtain normalized sensor data, construct multivariate gaussian distribution parameters from the normalized sensor data using a central limit theorem, compare a model generated with a learning algorithm applied to previous constructed multivariate gaussian distribution parameters with the constructed multivariate gaussian distribution parameters, and determine a state of the device based at least in part on the comparison of model with the constructed multivariate gaussian distribution parameters. A communication system communicates the determined state of the device to a remote location” (K: Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hunter Wilder whose telephone number is (571)270-7948. The examiner can normally be reached Monday-Friday 8:30AM-5:30PM.
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/A. Hunter Wilder/Primary Examiner, Art Unit 3627
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