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 .
Response to Arguments
Applicant’s arguments are persuasive regarding the instant amendment; however, a new ground of rejection is applied regarding user feedback data related to the hydraulic fracturing operation and equipment data from one or more equipment sensors used during the hydraulic fracturing operation. The limitation “related to” encompasses user feedback related to both the hydraulic fracturing operation and equipment data. The limitation “related to” is interpreted as being causally connected to. Edwards teaches user feedback related to hydraulic fracturing and equipment data based on downhole equipment sensors as receiving driller responses to dysfunction regarding downhole equipment in addition to receiving expert feedback during drilling operations. The merger of expert/driller feedback in combination with raw data creates a knowledge base from which automated actions and recommendations are created for adjusting drilling operations. The inclusion of user feedback with raw data provides an improved invention by facilitating the creation of agents for automating otherwise manual activities while enabling the system to improve by acquiring user feedback related to equipment function, drilling processes, and dysfunction such that the system learns using expert knowledge.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6, 9-11, 14, and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by MU et al. (PG/PUB 20220003229) in view over Kisra (PG/PUB 20220372866) in view over Edwards (PG/PUB 20090132458)
Claim 1.
MU et al. teaches a system but does not expressly teach the combination of pre-processing limitations described below, including user feedback and equipment data) Kisra teaches the pre-processing (e.g. combining date) while Edwards teaches user feedback and equipment data described below, comprising:
a processor (0009 e.g. “A system can include one or more processors; memory accessible to at least one of the one or more processors.:)
a memory device that includes instructions executable by the processor for causing the processor to perform operations comprising (0009 e.g. “A system can include one or more processors; memory accessible to at least one of the one or more processors”)
receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation (Figure 2-220 e.g. see remote computer for data processing and control)
pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service (Figure 2-222 e.g. see data filtering)
identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data (Figure 2-226, 224, 228, 229, 232, 234 e.g. application of control parameters), wherein the pre- processing the raw data comprises combining the raw data with pre-existing data, the pre-existing data comprising user feedback data related to the hydraulic fracturing operation (responses to problems) and solutions) and equipment data from one or more equipment sensors used during the hydraulic fracturing operation (Kirsa, see pre-processing as merging multiple data from multiple sources, including equipment data and downhole sensor data 0034, 0037 e.g. “The data gathered by sensors (S) may be collected by surface unit 134 and/or other data collection sources for analysis or other processing. An example of the further processing is the generation of a grid for use in the computation of a juxtaposition diagram as discussed below. The data collected by sensors (S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. 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 stored in separate databases, or combined into a single database.,” see Edwards for user feedback data related to the hydraulic fracturing operation (0009, 0017, 0028-0030, 0058, 0063, 0158 e.g. see reactions and driller input during drilling for improving drilling as well as drilling processes) and equipment data from one or more equipment sensors used during the hydraulic fracturing operation (see user feedback related to equipment data as responses based on received equipment data /dysfunctional related equipment operation e.g. “ This drilling knowledge base suggests solutions to problems based on feedback provided by human experts, learns from experience, represents knowledge, instantiates automated reasoning and argumentation for embodying best drilling practices, see user feedback regarding equipment data from one or more equipment sensors, 0032, 0053-54, 0063 – “reactions to dysfunctions’ based on downhole equipment sensors, 0180-181)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kirsa, namely pre-processing data via combining data further including equipment data, to the teachings of Edwards, namely providing user feedback related to drilling operations and equipment data, to the teachings of MU, namely providing/streaming hydraulic sensor data to the surface for analysis, would achieve an expected and predictable result of further adapting the pre-processing step to integrate additional data including expert feedback and equipment data. The inclusion of metadata enhances the available data for identifying damage patterns, directions, and locations of equipment for providing relevant data to a learning network. Kiras and Edwards are in the same field of endeavor and reasonably pertinent to a problem of combining relevant data as described. Accordingly, merging the streamed hydraulic data MU with the user feedback related to the hydraulic fracturing operation and related to the hydraulic equipment of Edwards based on merging data of Kiras provides at least user reactions and responses related to hydraulic operations and equipment via a knowledge base.
MU, as modified by Kirsa and Edwards, teaches:
determining, via the cloud service, a difference between the at least one parameter (e.g. combined data indicating current performance/evaluation based on metadata. In other words, reference data used against target parameters determined using model predictions based on historical data) and at least one optimized parameter (e.g. see predicted parameters (flow rates, pressures, etc.) via a model trained using history data, see also new target rates, 0115, 0375-0377 – see optimal setting based on model predictions, the model predictions based on historical data), the at least one optimized parameter determined by the cloud service based on historical data or theoretical data (MU et al., (Figure 2-226, 224, 228, 229, 232, 234 , 0073-0085, 0290-0350, 0375 e.g. see adjusting control based on comparison of expected/predicted/simulated/desired parameters/modelled parameters to actual performance/sensor parameters/metadata)
adjusting the hydraulic fracturing operation by controlling a piece of equipment associated with the hydraulic fracturing operation using the difference between the at least one parameter and the at least one optimized parameter (e.g. as interpreted, adjust current parameters to equal the target parameters determined using model predictions based on historic data, Figure 2-236, 0246 , 0256, 0267 , 0274, 0290, 0336-0350 e.g. see adjustment based on comparison for optimization, see also 0073-0085)
wherein the adjusting the hydraulic fracturing operation by controlling the piece of equipment reduces the difference between the at least one parameter and the at least one optimized parameter (MU et al., e.g. see reducing the difference as optimizing the operation based on comparing a current performance to a desired performance, see Figure 2-236, 0246 , 0256, 0267 , 0274, 0290, 0336-0350 e.g. see adjustment based on comparison for optimization, see also 0073-0085 e.g. “The analytics blocks 1824 can use the tagged data to compare one or more performance parameters to predetermined thresholds. If the data have passed a predetermined threshold, the analytics block 1824 can send information to the controller processor 1704 instructing the processor 1704 to take appropriate action to optimize the operation of the fleet of pumps 1740-1 to 1740-N while maintaining the desired pump rate.
[0275] The analytics block 1824, in one or more embodiments, can include computer instructions to use historical data in the health score data block 1826, current real-time data, and performance analytics to update the health score of each of the pumps and to take appropriate action based on the health score of each of the pumps, a risk profile model that uses the real-time data and historical data from the central processing component 1730 to determine a risk profile for each of the pumps and issue a command to the controller 1802 to place pumps with a high risk score in degraded mode for maintenance, reduce the load on the high risk score pumps, or combinations thereof.
Claim 2.
MU teaches the system of claim 1, further comprising displaying the at least one parameter on the cloud-based dashboard (Figure 2-227, 0131, 0181, 0227)
Claim 3.
MU teaches the system of claim 2, wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, pumped rate over time, proppant concentration over time, pressure over time, duration of cavitation per treatment, other diagnostic parameters of hydraulic fracturing equipment, or a combination thereof (Figure 2-226, 229, 0075, 0131, 0181)
Claim 6.
MU teaches the system of claim 1, wherein the operation of adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter further comprises:
adjusting the hydraulic fracturing operation by controlling the piece of equipment associated with the hydraulic fracturing operation using in real-time or near real-time (MU, 0003, 0054, 0070, 0233, Figure 2-254, supra claim 1)
Claim 9.
MU teaches a computer-implemented method comprising:
receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation; supra claim 1
pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service, wherein the pre-processing the raw data comprises combining the raw data with pre-existing data, the pre-existing data comprising user feedback data related to the hydraulic fracturing operation and equipment data from one or more equipment sensors used during the hydraulic fracturing operation supra claim 1
identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data; supra claim 1
determining, via the cloud service, a difference between the at least one parameter and at least one optimized parameter, the at least one optimized parameter determined by the cloud service based on historical data or theoretical data; supra claim 1
adjusting the hydraulic fracturing operation by controlling a piece of equipment associated with the hydraulic fracturing operation using the difference between the at least one parameter and the at least one optimized parameter, wherein the adjusting the hydraulic fracturing operation by controlling the piece of equipment reduces the difference between the at least one parameter and the at least one optimized parameter, supra claim 1
Claim 10.
MU teaches the computer-implemented method of claim 9, further comprising displaying the at least one parameter on the cloud-based dashboard (Figure 2-227, 0131, 0181, 0227)
Claim 11.
MU teaches the computer-implemented method of claim 10, wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, duration of cavitation per treatment, pumped rate, proppant concentration, pressure over time, proppant concentration over time, or any combination thereof ((Figure 2-226, 229, 0075, 0131, 0181)
Claim 14.
MU teaches the computer-implemented method of claim 9, wherein adjusting the hydraulic fracturing operation by controlling the piece of equipment using the difference between the at least one parameter and the at least one optimized parameter further comprises:
adjusting the hydraulic fracturing operation in real-time or near real-time ((MU, 0003, 0054, 0070, 0233, Figure 2-254, supra claim 1
Claim 16.
MU teaches a non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:
receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation, supra claim 1
pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service; wherein the pre-processing the raw data comprises combining the raw data with pre-existing data, the pre-existing data comprising user feedback data and equipment data from one or more equipment sensors; supra claim 1
identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data; supra claim 1
determining, via the cloud service, a difference between the at least one parameter and at least one optimized parameter, the at least one optimized parameter determined by the cloud service based on historical data or theoretical data; supra claim 1
adjusting the hydraulic fracturing operation by controlling a piece of equipment associated with the hydraulic fracturing operation using the difference between the at least one parameter and the at least one optimized parameter, wherein the adjusting the hydraulic fracturing operation by controlling the piece of equipment reduces the difference between the at least one parameter and the at least one optimized parameter, supra claim 1
Claim 17.
MU teaches the non-transitory computer-readable medium of claim 16, further comprising displaying the at least one parameter on the cloud-based dashboard (Figure 2-227, 0131, 0181, 0227)
Claim 18.
MU teaches the non-transitory computer-readable medium of claim 17, wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, duration of cavitation per treatment, pumped rate, proppant concentration, pressure over time, proppant concentration over time, or any combination thereof. (Figure 2-226, 229, 0075, 0131, 0181)
Claim(s) 4, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over MU et al. (PG/PUB 20220003229) in view over Kisra (PG/PUB 20220372866) in view over Edwards (PG/PUB 20090132458) in view over Lopez (USPN 11686192)
Claim 4.
MU teaches the system of claim 1 but does not expressly teach the combining and subset limitations described below. Kisra et al. teaches the combining and Lopez teaches the subset limitations described below
wherein the operation of pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
receiving the raw data at a data lake in a plurality of formats (MU, Figure 2-222, 226, 210, 210, 214, 216)
performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data (Kisra, 0022, 0037)
extracting a subset of the pre-processed data to generate a diagnostic indicator for adjusting the hydraulic fracturing operation (Lopez, Figure 2, Col 7 lines 45-57)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Kisra, namely combining raw data with pre-existing data to provide pre-processed data , to the teachings of MU, namely pre-processing data, would achieve an expected and predictable result via combining said elements using known methods. Kisra is in the same field of endeavor and reasonably pertinent to a problem of pre-processing data as described.
The combination of MU and Kisra does not expressly teach extracting subsets of the pre-processed data to generate diagnostic indicators. Lopez teaches extracting subsets to from processed data to generate diagnostic indicators, Figure 2, Col 7 lines 45-57)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Lopez, namely extracting subsets of data to identify diagnostic indicators , to the teachings of MU as modified, namely pre-processing data comprising combined data, would achieve an expected and predictable result via combining said elements using known methods. Lopez is in the same field of endeavor and pertinent to a problem of identifying abnormal data.
Claim 12.
MU, as modified, teaches the computer-implemented method of claim 9, wherein pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
receiving the raw data at a data lake in a plurality of formats, supra claim 4
performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data; supra claim 4
extracting a subset of the pre-processed data to generate a diagnostic indicator for adjusting the hydraulic fracturing operation, supra claim 4
Claim 19
MU as modified, supra claim 4, teaches the non-transitory computer-readable medium of claim 16, wherein the operation of pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
receiving the raw data at a data lake in a plurality of formats; supra claim 4
performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data; supra claim 4
extracting a subset of the pre-processed data to generate a diagnostic indicator used to adjust the hydraulic fracturing operation, supra claim 4
Claims 5, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over MU et al. (PG/PUB 20220003229) in view over Kisra (PG/PUB 20220372866) in view over Edwards (PG/PUB 20090132458) in view over Lopez (USPN 11686192)
Claim 5.
MU teaches the system of claim 1 but does not expressly teach a subset of data described below. Lopez teaches a subset of data described below
wherein the operation of receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation (Lopez, Figure 2, Col 7 lines 45-57)
generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data (Lopez, Figure 2, Col 7 lines 45-57, Figure 6, see also MU, 0213-0215, 0233)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Lopez, namely extracting subsets of data to identify diagnostic indicators for displaying alerts , to the teachings of MU as modified, namely displaying alarms regarding hydraulic states, would achieve an expected an predictable result via combining said elements using known methods. Lopez is in the same field of endeavor and pertinent to a problem of alerting users to abnormal events as described.
Claim 13.
MU as modified, supra claim 5, teaches the computer-implemented method of claim 9, wherein receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation; supra claim 5
generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data, supra claim 5
Claim 20.
MU as modified, supra claim 5, teaches the non-transitory computer-readable medium of claim 16, wherein the operation of receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation; supra claim 5
generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data, supra claim 5
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over MU et al. (PG/PUB 20220003229) in view over Kisra (PG/PUB 20220372866) in view over Edwards (PG/PUB 20090132458) in view over Straub (PG/PUB 20230382408)
claim 7.
MU teaches the system of claim 1 but does not expressly teach the priority limitations described below. Straub teaches the priority limitations described below
wherein the operation of receiving, by the cloud service, the raw data relating to the hydraulic fracturing operation further comprises:
assigning a first pre-processing priority level to a first subset of raw data from the raw data;
assigning a second pre-processing priority level to a second subset of raw data from the raw data (ABSTRACT, Figure 4, supra claim 1 for processing data)
pre-processing, by the cloud service, the first subset of raw data prior to pre-processing the second subset of raw data based on the first pre-processing priority level exceeding the second pre-processing priority level (ABSTRACT, Figure 4), supra claim 1 for processing data)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Straub, prioritizing sensor processing sets , to the teachings of MU as modified, namely preprocessing data, would achieve an expected and predictable result via combining said elements using known methods. Straub is reasonably pertinent to a problem of sensor processing and would commend itself to prioritizing important sensor sets over others as described for improving awareness within hydraulic operations.
Claim 15.
MU as modified, supra claim 7, teaches the computer-implemented method of claim 9, wherein receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
assigning a first pre-processing priority level to a first subset of raw data from the raw data;
assigning a second pre-processing priority level to a second subset of raw data from the raw data; supra claim 7
pre-processing, by the cloud service, the first subset of raw data prior to pre-processing the second subset of raw data based on the first pre-processing priority level exceeding the second pre-processing priority level, supra claim 7
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over MU et al. (PG/PUB 20220003229) in view over Kisra (PG/PUB 20220372866) in view over Edwards (PG/PUB 20090132458) in view over RHO (PG/PUB 20220067580)
Claim 8.
MU teaches the system of claim 1 but does not expressly teach the impact limitations described below. RHO teaches the impact limitations described below
wherein the operation of pre-processing the raw data or the operation of identifying the at least one parameter further comprises executing a machine learning model that predicts data with a highest impact on the hydraulic fracturing operation (RHO, ABSTRACT, 0017, 0054, 0061, 0091 e.g. see machine learning characterizing the contribution of inputs to outputs)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of RHO, namely learning the contributions of input data to output data, to the teachings of MU, namely obtaining input sensor data, would achieve an expected and predictable result via combining said elements using known methods. RHO is reasonably pertinent to a problem of data processing for machine learning and would commend itself to the prediction models of MU for applying input data known to impact output data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure..
General hydraulic control and data processing
20080164021 8244509 9251276 20190153840 20160208597 20170342808 20160208595 20200370379 11149533 20210396223 20220003229 20220170353 20240003235 20220112796 202200650851 20210255361 20200407625 7516793 20220065085 20210231835 200080164021 20240352839 20220170353 20160273346 20150134258 20200256177 11927087 20220170353 20230287760
20240003235
Claim 1 relevancy to user feedback
20150218914 20210148213 20250129713 20140116776 20150300151 20160053603 10392918 20240062119-0177, 0221 20230116456 20190170898
Claim 4 relevancy
20170161963 11392111 20240103950 20240197177 20230368635 11762371 20220378377 20220277254 20090062933 20080065705 12081418 20230142161 11392111 10289464 20220372866 20240103950 20220277254 10529101 11156998 11443194 20190171187 11686192
Claim 8 relevancy
20240062101 20220351087 20220309359 20240062101 20240272976 20240119300 20220309359
Preprocessing w/expert feedback
20220372866 20240353825 20080097637
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117