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 .
DETAILED ACTION
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on August 14, 2026, has been entered.
2. Claims 1-18 and 22 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-18 and 22 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 15, which is analyzed as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites an article of manufacture for monitoring a distribution of field systems. Thus, the claim is to be a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites an article of manufacture for monitoring a distribution of field systems. The Specification, para [0002] described that “Aspects of the disclosure relate to monitoring of field systems. More specifically, aspects of the disclosure relate to monitoring of a large population of field systems used in hydrocarbon recovery systems to enable predictive maintenance and trends among the systems.” The claim recites the steps: preprocessing a single measurement dataset from the raw data; conducting a feature selection of the single measurement dataset; generating a self-organizing map pertaining to the feature selection of the single measurement dataset; introducing new events to the self-organizing map; regenerating the self-organizing map incorporating the new events to monitor an evolution of the self-organizing map; conducting at least one of a behavior forecast or a predictive risk evaluation…; performing, via the self-organizing time map, exploratory temporal structure analysis to reveal properties of temporal structural changes in the single measurement dataset…; discriminating essential variation of the variations and non-essential variation of the variations; and making a decision based on the discriminating, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of fundamental economic principles or practices including hedging, insurance, mitigating risk. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps of: preprocessing a single measurement dataset from the raw data; conducting a feature selection of the single measurement dataset; generating a self-organizing map pertaining to the feature selection of the single measurement dataset; introducing new events to the self-organizing map; regenerating the self-organizing map incorporating the new events to monitor an evolution of the self-organizing map; conducting at least one of a behavior forecast or a predictive risk evaluation…; performing, via the self-organizing time map, exploratory temporal structure analysis to reveal properties of temporal structural changes in the single measurement dataset…; discriminating essential variation of the variations and non-essential variation of the variations; and making a decision based on the discriminating, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of: a computer, a non-volatile memory, “acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map.”
The additional elements ““acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map” are mere data gathering, transmitting, and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting, and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, transmitting and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the server/the display, they are just merely used as general means for collecting, transmitting, and displaying data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Further, the steps of “acquiring raw data…; transferring the raw data…; preprocessing a single measurement…; conducting a feature selection…; generating a self-organizing map…; displaying the self-organizing map…; detecting new events…; introducing the new events…; displaying the new events…; regenerating the self-organizing map…; conducting at least one of a behavior forecast…; performing, via the self-organizing time map, exploratory temporal structure analysis…; discriminating essential variation…; and making a decision…”, are recited as being performed by the computer having a non-volatile memory. The computer and a non-volatile memory are recited at a high level of generality. In the limitations “acquiring raw data…; transferring the raw data…; displaying the self-organizing map…; displaying the new events…”, the computer is used as a tool to perform the generic computer function of gathering and outputting data. See MPEP 2106.05(f). In limitations “preprocessing a single measurement…; conducting a feature selection…; generating a self-organizing map…; detecting new events…; introducing the new events…;regenerating the self-organizing map…; conducting at least one of a behavior forecast…; performing, via the self-organizing time map, exploratory temporal structure analysis…; discriminating essential variation…; making a decision…; and limiting a run time…”, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the computer, a non-volatile memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, improvements to a plurality of multiphase flowmeters, the data transfer system, the remote server, the display, or other technology. They are just merely used as general means for collecting, transferring, displaying data, and performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The additional elements ““acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map” are recited at a high level of generality. These elements amount to gathering and displaying data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As discussed in Step 2A, Prong Two above, the recitation of the computer having a non-volatile memory to perform limitations “acquiring raw data…; transferring the raw data…; preprocessing a single measurement…; conducting a feature selection…; generating a self-organizing map…; displaying the self-organizing map…; detecting new events…; introducing the new events…; displaying the new events…; regenerating the self-organizing map…; conducting at least one of a behavior forecast…; performing, via the self-organizing time map, exploratory temporal structure analysis…; discriminating essential variation…; making a decision…; and limiting a run time…”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claim 1, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 1 directed to a method, is also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as the independent claim 15.
Regarding dependent claims 2-14, 16-18, and 22, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea, and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2, 13 and 16, the claims recite the additional elements, Claim 2: wherein the raw data acquired from the plurality of multiphase flowmeters is multi-dimensional; Claim 13: wherein the raw data acquired the plurality of multiphase flowmeters has a time-series format; Claim 16: wherein the article of manufacture is at least one of a universal serial device, a compact disk, a solid-state drive or a computer hard disk; which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting, and outputting, and, as such, these limitations do not impose any meaningful limits on the claims. These limitations amount to necessary data gathering, transmitting, and outputting. See MPEP 2106.05 (See claim 15 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3-9, 11-12, and 17, the claims simply refine the abstract idea by further reciting: Claim 3: wherein the preprocessing includes data cleaning; Claim 4: wherein the preprocessing includes labeling; Claim 5: wherein the preprocessing includes feature extraction; Claim 6: wherein the feature selection is related to a user inquiry; Claim 7: wherein the self-organizing map is color coded;
Claim 8: wherein the introducing new events to the self- organizing map is performed on an automated classification; Claim 9: wherein the data pertains to structural health monitoring; Claim 11: wherein the self-organizing map includes a time element;
Claim 12: wherein the data is related to failure risk probability; Claim 17: wherein the raw data relates to structural health monitoring; that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 15. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 10, 14, and 18, the claims simply refine the abstract idea by further reciting: Claims 10 and 18 recite wherein the raw data is formatted in at least one of a two- dimensional matrix or three-dimensional matrix; Claim 14 recites using an algorithm to reduce multi-dimensionality, which is directed to Mathematical Concepts grouping of abstract Ideas (mathematical relationships, mathematical formulas or equations, mathematical calculation). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 22, the claim simply refines the abstract idea by further reciting: monitoring the run time; and calling maintenance to perform maintenance activities based on the run time, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 15. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-18 and 22 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-18 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Graf et al. (hereinafter Graf, US 2009/0020284) in view of Snijder Van Wissenkerke et al. (hereinafter Snijder Van Wissenkerke, US 2024/0360741).
Regarding claim 1, Graf discloses a method to monitor a distribution of field systems, comprising:
acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter of the plurality of multiphase flowmeters comprising a data transfer system and configured to operate in a corresponding hydrocarbon recovery system of a plurality of hydrocarbon recovery systems of the distribution of the field systems (para [0010], Sensors may be positioned about the oilfield to collect data relating to various oilfield operations. For example, sensors in the drilling equipment may monitor drilling conditions, sensors in the wellbore may monitor fluid composition, sensors located along the flow path may monitor flow rates, and sensors at the processing facility may monitor fluids collected. Other sensors may be provided to monitor downhole, surface, equipment or other conditions. The monitored data is often used to make decisions at various locations of the oilfield at various times. Data collected by these sensors may be further analyzed and processed. Examiner notes: Graf’s data collected from sensors are raw data; Graf’s sensors are equivalent to the claimed “multiphase flowmeters”); para [0044], A surface unit (134) is used to communicate with the drilling tool (106b) and offsite operations. The surface unit (134) is capable of communicating with the drilling tool (106b) to send commands to drive the drilling tool (106b), and to receive data therefrom; para [0046], The information gathered by the sensors (S) may be collected by the surface unit (134) and/or other data collection sources for analysis or other processing. The data collected by the sensors (S) may be used alone or in combination with other data. The data may be collected in a database and all or select portions of the data may be selectively used for analyzing and/or predicting oilfield operations of the current and/or other wellbores; para [0049], the data is collected and stored at the surface unit (134). One or more surface units (134) may be located at the oilfield (100), or linked remotely thereto. The surface unit (134) may be a single unit, or a complex network of units used to perform the necessary data management functions throughout the oilfield (100); para [0050], The surface unit (134) may be provided with a transceiver (137) to allow communications between the surface unit (134) and various portions of the oilfield (100) or other locations. The surface unit (134) may also be provided with or functionally linked to a controller for actuating mechanisms at the oilfield. The surface unit (134) may then send command signals to the oilfield (100) in response to data received. The surface unit (134) may receive commands via the transceiver or may itself execute commands to the controller; para [0074], The oilfield data represented by a display unit (526) may be raw data);
transferring, via the data transfer system, the raw data to a remote connected to the data transfer system (para [0044], The surface unit (134) is preferably provided with computer facilities for receiving, storing, processing, and analyzing data from the oilfield (100). The surface unit (134) collects data output (135) generated during the drilling operation. Computer facilities, such as those of the surface unit (134), may be positioned at various locations about the oilfield (100) and/or at remote locations; para [0050], The surface unit (134) may be provided with a transceiver (137) to allow communications between the surface unit (134) and various portions of the oilfield (100) or other locations. The surface unit (134) may also be provided with or functionally linked to a controller for actuating mechanisms at the oilfield. The surface unit (134) may then send command signals to the oilfield (100) in response to data received. The surface unit (134) may receive commands via the transceiver or may itself execute commands to the controller);
preprocessing a single measurement dataset from the raw data (para [0047], Data outputs from the various sensors (S) positioned about the oilfield may be processed for use. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be housed in separate databases, or combined into a single database);
conducting a feature selection of the single measurement dataset (para [0048], the collected data may be used to perform analysis, such as modeling operations. For example, the seismic data output may be used to perform geological, geophysical, reservoir engineering, and/or production simulations. The reservoir, wellbore, surface and/or process data may be used to perform reservoir, wellbore, or other production simulations. The data outputs from the oilfield operation may be generated directly from the sensors (S), or after some preprocessing or modeling. These data outputs may act as inputs for further analysis);
generating a self-organizing map pertaining to the feature selection of the single measurement dataset (para [0080], As an example, initially, available reservoir-level data set is back-populated (gap-filling) and subsequently analyzed using Self-Organizing Maps (SOMs), which are Neural Network algorithms used for multi-dimensional correlation);
displaying the self-organizing map and encoding the self-organizing map based on known data labeling (para [0095], similarities in the oilfield data sets among the collection of oilfield entities (e.g., a large number of reservoirs) may be displayed using a Self-Organizing Map (SOM) (e.g., SOM (711) as shown in FIG. 7B). As is known in the art, a self-organizing map is a type of artificial neural network typically presented as discretized maps (e.g., individual maps of SOM (711)) of training data rendered in color according to a color gradient bar, which maps data values to various colors);
detecting new event via the plurality of multiphase flowmeters (para [0012], Data from one or more wellbores may be analyzed to plan or predict various outcomes at a given wellbore. In some cases, the data from neighboring wellbores or wellbores with similar conditions or equipment may be used to predict how a well will perform. There are usually a large number of variables and large quantities of data to consider in analyzing oilfield operations. It is, therefore, often useful to model the behavior of the oilfield operation to determine the desired course of action. During the ongoing operations, the operating conditions may need adjustment as conditions change and new information is received; para [0084], For certain types of oilfield parameters, (e.g., reservoir depth), inconsistency and incompleteness may be easily detected; but some other parameters (e.g., derived data such as in-place-volumes or ultimate recoveries) are extremely difficult to identify as inconsistent in the database);
introducing the new events to the self-organizing map (para [0025], generate a first artificial neural network of the plurality of oilfield data sets, the first artificial neural network comprising one or more relationships among the plurality of data fields, populate the un-populated data field of the at least one oilfield data set by an estimated data based on the one or more relationships to generate a back-populated oilfield data set, and perform the oilfield operation based on at least the back-populated oilfield data set; para [0105], a second artificial neural network may be generated for the oilfield data sets associated with the KPIs (Step 812). In one or more embodiments of the invention, the second artificial neural network includes all the identified KPIs as outputs such that statistical relationships between these KPIs and other data fields in the oilfield data sets are identified);
displaying the new events on the map (para [0106], As shown in FIG. 9A, clusters (e.g., clusters (910)) may each include multiple SOM locations clustered and enclosed in a boundary indicated by the darkened trace. The exemplary SOM includes approximately 950 locations shown as hexagonal cells, which are clustered into 19 clusters defined by the darkened boundaries);
regenerating the self-organizing map incorporating the new events to monitor an evolution of the self-organizing map into a self-organizing time map (para [0080], As an example, initially, available reservoir-level data set is back-populated (gap-filling) and subsequently analyzed using Self-Organizing Maps (SOMs), which are Neural Network algorithms used for multi-dimensional correlation. Next, a specific number of generic numerical models are built using the stochastic output from the first step. These models are used to create response surfaces to evaluate sensitivities and assess uncertainties of influencing parameters. Further, the reservoir uncertainties are combined with expert knowledge and environmental variables using Bayesian Networks, (i.e., probability reasoning engines). These are used as proxy models and act as objective functions, where the input parameters are assigned in a stochastic manner and the output is represented by a ranking of potential reservoir candidates; para [0081], Once reservoir candidates have been identified, each may undergo a more detailed evaluation to determine whether production and recovery of the reservoir may be improved by performing an oilfield operation on the reservoir (i.e., an enhanced oil recovery operation, a steam flood operation, a waterflood operation, etc.).); and
conducting at least one of a behavior forecast or a predictive risk evaluation of the plurality of hydrocarbon recovery systems of the distribution of the field systems in response to the evolution (para [0012], Data from one or more wellbores may be analyzed to plan or predict various outcomes at a given wellbore. In some cases, the data from neighboring wellbores or wellbores with similar conditions or equipment may be used to predict how a well will perform; para [0046], The data may be collected in a database and all or select portions of the data may be selectively used for analyzing and/or predicting oilfield operations of the current and/or other wellbores);
performing, via the self-organizing time map, exploratory temporal structure analysis to reveal properties of temporal structural changes in the single measurement dataset, the temporal structural changes including variations of multiphase flow in the distribution of the field systems over time (para [0084], these contributing factors may be static or may change with time during the development phase or other phases of oilfield operation. For example, once data are modified, adjusted, or otherwise changed to newer information with time, the changed data may create inconsistencies with other parameters in the database. In general, the indication that certain data have changed during the history is typically lost or not being maintained in the database. For certain types of oilfield parameters, (e.g., reservoir depth), inconsistency and incompleteness may be easily detected; but some other parameters (e.g., derived data such as in-place-volumes or ultimate recoveries) are extremely difficult to identify as inconsistent in the database; para [0109], variations in each KPI parameters for oilfield entities associated with each cluster may be analyzed to derive a statistical distribution for design of experiment in modeling the oilfield operation using the proxy models. The statistical distributions derived for the clusters may also be incorporated into the stochastic database as part of the probability information);
discriminating essential variation of the variations and non-essential variation of the variations (para [0086], Incomplete and inconsistent database are detrimental to portfolio or asset management for a reservoir collection as decisions cannot be made with certainty. For example if a decision needs to be made to identify the reservoirs with the highest impact (e.g., return on the investment) from a water injection (e.g., waterflooding) operation, the large number of reservoirs with missing oil viscosity parameter in the database may not be considered. Furthermore, reservoirs with inconsistent parameters (e.g., "in-place-volume" parameter showing inconsistency to other measured pressure parameters) may not be used in the screening process. Therefore, the resultant ranking from the screening process would only highlight reservoirs with high data completeness and consistency without including other potentially desirable candidate reservoirs with data deficiency); and
making a decision based on the discriminating and a closeness of the new events to a desired position (para [0050], A processor may be provided to analyze the data (locally or remotely) and make the decisions to actuate the controller. In this manner, the oilfield (100) may be selectively adjusted based on the data collected to optimize fluid recovery rates, or to maximize the longevity of the reservoir and its ultimate production capacity. These adjustments may be made automatically based on computer protocol, or manually by an operator. In some cases, well plans may be adjusted to select optimum operating conditions, or to avoid problems; para [0011], The processed data may be used to predict downhole conditions, and make decisions concerning oilfield operations. Such decisions may involve well planning, well targeting, well completions, operating levels, simulation rates and other operations and/or conditions. Often this information is used to determine when to drill new wells, re-complete existing wells, or alter wellbore simulation).
Graf does not disclose, however, Snijder Van Wissenkerke discloses:
limiting a run time of a diesel engine of the plurality of hydrocarbon recovery systems based on the discriminating (para [0055], The AFE (140) includes an AC/DC converter. With approximately 3 MW of power available from the three diesel generators (16) and of the additional discharge capacity available from battery storage (130), an energy supply system (120) according to one or more embodiments may handle the most demanding drilling conditions of a drilling rig (110), while reducing fuel consumption and improving emissions by reducing runtime of the generators (16); para [0061], Therefore, stealth mode operation in accordance with one or more embodiments of the present disclosure improves the capabilities of an energy supply system by reducing the number of active generators to zero and further reducing the overall runtime of generators on the rig (i.e., reducing fuel consumption and emissions).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Graf’s to incorporate the features taught by Snijder Van Wissenkerke above, for the purpose of improving emissions by reducing runtime of the diesel generator. Since Graf discloses monitoring the oilfield operations (see para [0010]), Snijder Van Wissenkerke discloses monitoring the powered equipment installed on the drilling rid including reducing the overall runtime of generators on the rig, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Graf and Snijder Van Wissenkerke would have yield predictable results in monitoring the plurality of hydrocarbon recovery systems.
Regarding claim 2, Graf discloses the method according to claim 1, wherein the raw data acquired from the plurality of multiphase flowmeters is multi-dimensional (para [0093], In one or more embodiments of the invention, for a large collection of oilfield entities (e.g., reservoirs), high order multi-dimensional connections between the various data fields corresponding to these inputs and outputs may be established based on non-linear, multi-layered, parallel regression capabilities inherent in an artificial neural network such as the first artificial neural network).
Regarding claim 3, Graf discloses the method according to claim 1, wherein the preprocessing includes data cleaning (para [0083], data and/or parameters in oilfield development planning phase, production phase, or other phases of oilfield operation may be stored as oilfield data sets in a database or other suitable formats of data storage).
Regarding claim 4, Graf discloses the method according to claim 1, wherein the preprocessing includes labeling (para [0048], the data outputs from the oilfield operation may be generated directly from the sensors (S), or after some preprocessing or modeling. These data outputs may act as inputs for further analysis).
Regarding claim 5, Graf discloses the method according to claim 1, wherein the preprocessing includes feature extraction (para [0046], The information gathered by the sensors (S) may be collected by the surface unit (134) and/or other data collection sources for analysis or other processing).
Regarding claim 6, Graf discloses the method according to claim 1, wherein the feature selection is related to a user inquiry (para [0046], The data may be collected in a database and all or select portions of the data may be selectively used for analyzing and/or predicting oilfield operations of the current and/or other wellbores).
Regarding claim 7, Graf discloses the method according to claim 1, wherein the self-organizing map is color coded (para [0095], a self-organizing map is a type of artificial neural network typically presented as discretized maps (e.g., individual maps of SOM (711)) of training data rendered in color according to a color gradient bar, which maps data values to various colors).
Regarding claim 8, Graf discloses the method according to claim 1, wherein the introducing the new events to the self-organizing map is performed on an automated classification (para [0106], clusters may be identified based on the second artificial neural network (e.g., the SOM of FIG, 9A) (Step 813). As shown in FIG. 9A, clusters (e.g., clusters (910)) may each include multiple SOM locations clustered and enclosed in a boundary indicated by the darkened trace. The exemplary SOM includes approximately 950 locations shown as hexagonal cells, which are clustered into 19 clusters defined by the darkened boundaries).
Regarding claim 9, Graf discloses the method according to claim 1, wherein the raw data pertains to structural health monitoring (para [0010], Sensors may be positioned about the oilfield to collect data relating to various oilfield operations. For example, sensors in the drilling equipment may monitor drilling conditions, sensors in the wellbore may monitor fluid composition, sensors located along the flow path may monitor flow rates, and sensors at the processing facility may monitor fluids collected. Other sensors may be provided to monitor downhole, surface, equipment or other conditions. The monitored data is often used to make decisions at various locations of the oilfield at various times).
Regarding claim 10, Graf discloses the method according to claim 1, wherein the raw data is formatted in at least one of a two-dimensional matrix or a three-dimensional matrix (para [0096], multi-dimensional cross-plots and blind tests may be performed to control the quality of the back-populated oilfield data sets).
Regarding claim 11, Graf discloses the method according to claim 1, wherein the self-organizing map includes a time element (para [0098], Time and information constraints can limit the depth and rigor of such a screening evaluation. Time is reflected by the effort of screening a vast number of reservoirs for the applicability of implementing a waterflood, whereas information is reflected by the availability of data (consistency of measured and modeled data) with which to extract significant knowledge necessary to make good development decision).
Regarding claim 12, Graf discloses the method according to claim 1, wherein the raw data is related to failure risk probability (para [0096], Moreover, probability information of both the originally populated data fields and the back-populated data fields may also be analyzed to identify outliers that may indicate inconsistency of members in the oilfield data sets. Accordingly, validation ranges for data fields may be established against which originally populated data fields and/or back-populated data fields may be validated).
Regarding claim 13, Graf discloses the method according to claim 1, wherein the raw data acquired from the plurality of multiphase flowmeters comprises a time-series format (para [0084], these contributing factors may be static or may change with time during the development phase or other phases of oilfield operation. For example, once data are modified, adjusted, or otherwise changed to newer information with time, the changed data may create inconsistencies with other parameters in the database).
Regarding claim 14, Graf discloses the method according to claim 2, further comprising using an algorithm to reduce multi-dimensionality (para [0107], In one or more embodiments of the invention, the clusters may be generated automatically by the SOM algorithm. In one or more embodiments of the invention, the automatic cluster generation by the SOM algorithm may be guided by user inputs).
Regarding claims 15 and 17-18, Graf discloses an article of manufacture having a non-volatile memory storing a set of instructions to be executed by a processor to perform a method for monitoring (para [0072], As shown in FIG. 5, the surface unit (534) has computer facilities, such as memory (520), controller (522), processor (524), and display unit (526), for managing the data. The data is collected in memory (520), and processed by the processor (524) for analysis. Data may be collected from the oilfield sensors (S) and/or by other sources), the method comprising the steps described in claims 1, 22, and 9-10 above, therefore, are rejected by the same rationale.
Regarding claim 16, Graf discloses the article of manufacture according to claim 15, wherein the article of manufacture is at least one of a universal serial device, a compact disk, a solid-state drive or a computer hard disk (para [0083], In one or more embodiments of the invention, data and/or parameters in oilfield development planning phase, production phase, or other phases of oilfield operation may be stored as oilfield data sets in a database or other suitable formats of data storage).
Regarding claim 22, Graf does not disclose, however, Snijder Van Wissenkerke discloses the method according to claim 1, further comprising:
monitoring the run time (para [0058], During drilling operations, power demand increases over the course of drilling a well, from about 1 MW up to 3 MW or higher, as the bit travels further from the surface. Using an energy supply system (120) in accordance with embodiments of the disclosure, the BEMS (150) may monitor rig power demand and automatically start and stop generators (16) as needed based on the demand profile and the state of charge of the battery storage (130). When demand is consistently over 2.5 MW during active drilling operations (e.g., top drive rotating and mud pumps set for high pressure output), running all three generators (16) may be required. However, when the three generators become inefficient (i.e., a low average load), the BEMS (150) may disconnect one generator (16) (e.g., deactivate and/or open a breaker) to create a more efficient load on the remaining active and connected generators (16), supported by additional output from the battery storage (130)); and
calling maintenance to perform maintenance activities based on the run time (para [0149], When a drilling rig (110) takes a short break for maintenance (e.g., scheduled daily maintenance, equipment repair, generator repair), the BEMS (150) may exclusively power the electrical bus (14) via the battery storage (130) during the period of low demand. For example, the drilling rig (110) (e.g., a controller or equipment on the drilling rig (110)) may send schedule information of the maintenance period (e.g., a schedule, a time duration, a timer, a status flag) to inform the BEMS (150) of the maintenance. In one or more embodiments, the schedule information may be included in a power demand or load information reported from the drilling rig (110). Alternatively, the BEMS (150) may detect the maintenance period by monitoring a load information of the electrically powered equipment on the drilling rig (110)).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Graf’s to incorporate the features taught by Snijder Van Wissenkerke above, for the purpose of monitoring the run time for maintenance purposes. Since Graf discloses monitoring the oilfield operations (see para [0010]), Snijder Van Wissenkerke discloses monitoring the runtime, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Graf and Snijder Van Wissenkerke would have yield predictable results in monitoring the runtime of the plurality of hydrocarbon recovery systems.
Response to Arguments/Amendment
7. Applicant's arguments with respect to claims 1-18 and 22 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-18 and 22 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more. (See details above).
In response to the Applicant’s arguments, the Examiner respectfully submits that the amended claims do not overcome the rejection because:
Step 2A, Prong One: In response to the Applicant’s arguments “Claims do not recite a judicial exception”, the Examiner respectfully disagrees and submits that:
The claims recite an article of manufacture for monitoring a distribution of field systems. The Specification, para [0002] described that “Aspects of the disclosure relate to monitoring of field systems. More specifically, aspects of the disclosure relate to monitoring of a large population of field systems used in hydrocarbon recovery systems to enable predictive maintenance and trends among the systems.” The claim recites the steps: preprocessing a single measurement dataset from the raw data; conducting a feature selection of the single measurement dataset; generating a self-organizing map pertaining to the feature selection of the single measurement dataset; introducing new events to the self-organizing map; regenerating the self-organizing map incorporating the new events to monitor an evolution of the self-organizing map; conducting at least one of a behavior forecast or a predictive risk evaluation…; performing, via the self-organizing time map, exploratory temporal structure analysis to reveal properties of temporal structural changes in the single measurement dataset…; discriminating essential variation of the variations and non-essential variation of the variations; making a decision based on the discriminating and a closeness of the new events; and limiting a runtime of a diesel engine of the plurality of hydrocarbon recovery systems based on the discriminating, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of fundamental economic principles or practices including hedging, insurance, mitigating risk. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps of: preprocessing a single measurement dataset from the raw data; conducting a feature selection of the single measurement dataset; generating a self-organizing map pertaining to the feature selection of the single measurement dataset; introducing new events to the self-organizing map; regenerating the self-organizing map incorporating the new events to monitor an evolution of the self-organizing map; conducting at least one of a behavior forecast or a predictive risk evaluation…; performing, via the self-organizing time map, exploratory temporal structure analysis to reveal properties of temporal structural changes in the single measurement dataset…; discriminating essential variation of the variations and non-essential variation of the variations; making a decision based on the discriminating and a closeness of the new events; and limiting a runtime of a diesel engine of the plurality of hydrocarbon recovery systems based on the discriminating, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III. Therefore, the claims recite an abstract idea.
The limitations "transferring, via the data transfer system [of the multiphase flowmeter], the raw data," "displaying the self-organizing map and encoding the self-organizing map based on known data labeling," "displaying the new events on the self-organizing map" are additional elements and are analyzed under Step 2A, Prong Two.
Step 2A, Prong Two: In response to the Applicant’s arguments “Claims recite features that integrate any alleged judicial exception into a practical application and recites an inventive concept outside of any alleged judicial exception”, the Examiner respectfully disagrees and submits that:
The claims recite the additional elements of: a computer, a non-volatile memory, “acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map.”
The additional elements ““acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map” are mere data gathering, transmitting, and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting, and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, transmitting and outputting. See MPEP 2106.05.
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, improvements to a plurality of multiphase flowmeters, the data transfer system, the remote server, the display, or other technology. They are just merely used as general means for collecting, transferring, displaying data, and performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: In response to the Applicant’s arguments “Claims provide an inventive concept”, the Examiner respectfully disagrees and submits that:
As discussed in Step 2A, Prong Two above, the additional elements of “acquiring raw data from a plurality of multiphase flowmeters, each multiphase flowmeter comprising a data transfer system”, “transferring the data, via the data transfer system, the raw data to a remote server connected to the data transfer system”; “displaying the self-organizing map and encoding the self-organizing map based on known data labeling”; “detecting new events via the plurality of multiphase flowmeters”; and “displaying the new events on the self-organizing map” are recited at a high level of generality. These elements amount to gathering and displaying data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As discussed in Step 2A, Prong Two above, the recitation of the computer having a non-volatile memory to perform limitations “transferring the data from each of the distribution of the hydrocarbon recovery systems…; displaying the self-organizing map and encoding the map…”; “displaying the new events on the map…; preprocessing datasets…; conducting a feature selection of the data; generating a self-organizing map…; introducing new events to the self-organizing map; tracking the new event from measurement unit by a path on the map; regenerating the self-organizing map…; and conducting at least one of a behavior forecast and a predictive risk …”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
Accordingly, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 102
Applicants’ arguments with respect to claim 1-18 and 22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the arguments. The new ground of 103 rejection is described above.
Conclusion
8. Claims 1-18 and 22 are rejected.
9. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Ba et al. (US 2022/0290550) disclose a system may implement an intelligent power management framework, which can be a predictive power management system that reduces emissions, fuel consumption, and engine run time on a rig through a combination of automated software, energy storage system, and hydrogen (see para [0319]).
Snijder Van Wissenkerke et al. (US 2025/0105644) disclose with approximately 3 MW of power available from the three diesel generators (160) and of the additional discharge capacity available from battery storage (130), an energy supply system (120) according to one or more embodiments may handle the most demanding drilling conditions of a drilling rig (110), while reducing fuel consumption and improving emissions by reducing runtime of the generators (160) (see para [0057]).
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 September 19, 2026
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