Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A computer-implemented method that enables real time and autonomous petrophysical formation evaluation and machine learning deployment, comprising:
streaming, using at least one hardware processor, data comprising petrophysical data associated with at least one subsurface formation obtained in real time;
analyzing, using the at least one hardware processor, the stream of data to determine at least one model configured to evaluate the at least one subsurface formation;
executing, using the at least one hardware processor, the at least one model to evaluate the at least one subsurface formation using the stream of data as input; and
outputting, using the at least one hardware processor, a representation of formation characteristics in real time“.
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process for claim 1, and apparatus for claims 8 and 15).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the step of “analyzing the stream of data to determine at least one model configured to evaluate the at least one subsurface formation” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
The step of “executing the at least one model to evaluate the at least one subsurface formation using the stream of data as input” is a mathematical concept, therefore, it is considered to be an abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
streaming, using at least one hardware processor, data comprising petrophysical data associated with at least one subsurface formation obtained in real time; and outputting, using the at least one hardware processor, a representation of formation characteristics in real time.
The additional element “streaming data comprising petrophysical data associated with at least one subsurface formation obtained in real time” represents necessary data gathering and does not integrate the limitation into a practical application. The additional element “outputting a representation of formation characteristics in real time” is not sufficient to integrate the abstract idea into a practical application because it only adds an insignificant extra-solution activity to the judicial exception. In addition, a generic processor is generally recited and therefore, not qualified as a particular machine.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, streaming data comprising petrophysical data associated with at least one subsurface formation obtained in real time is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, outputting a representation of formation characteristics in real time is disclosed by “Hong US 20230041525”, [0040], [0056], [0101], [0108], [0128], [0240]; and “Ozgen US 20070016389”, [0005], [0084], [0090], [0125], [0142], [0146].
The claim, therefore, is not patent eligible.
Independent claims 8 and 15 recite subject matter that are similar or analogous to that of claim 1, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 2-7, 9-14, and 16-20 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form
the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-6, 8, 12-13, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hong et al. (US 20230041525, hereinafter Hong).
As to claims 1, 8, and 15, Hong teaches streaming, using at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), data comprising petrophysical data associated with at least one subsurface formation obtained in real time ([0110] discloses real-time measurements of downhole from a bottom hole assembly (BHA); [0128] discloses “a method operates in real-time responsive to receipt of data during acquisition using a downhole tool”);
analyzing, using the at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), the stream of data to determine at least one model configured to evaluate the at least one subsurface formation ([0122] discloses acquire data in real-time at various depths in the borehole, and the data may be fed to a trained machine learning model that can output a model; [0265] discloses “receiving data for a geologic region; based at least in part on the data, selecting a model from a plurality of models using a trained machine learning mode”);
executing, using the at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), the at least one model to evaluate the at least one subsurface formation using the stream of data as input ([0055] and [0135] discloses a model that is selected is utilized for purposes of evaluation, drilling, placement, fracturing, perforation number, perforation spacing, perforation location, etc.; and borehole image data may be utilized to identify one or more types of features such as, for example, horizons, fractures, faults, etc.; [0136]); and
outputting, using the at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), a representation of formation characteristics in real time ([0108] discloses various types of information that can be rendered to a display, for example, during one or more real-time operations (e.g., field operations, etc.); [0040], [0128], and [0240] disclose a method operates in real-time responsive to receipt of data during acquisition using a downhole tool; and formation evaluation is performed for interpreting data acquired from a drilled borehole to provide information about the geological formations and/or in-situ fluid(s) that can be used for assessing the producibility of reservoir rocks penetrated by the borehole. Output rock properties (i.e., formation characteristics - emphasis added by Examiner) based at least in part on processing of seismic data).
As to claims 5, 12, and 19, Hong teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches analyzing the stream of data to determine at least one model configured to evaluate the at least one subsurface formation in view of a context extracted from the petrophysical data and drilling data ([0055], [0135], and [0214] disclose a model that is selected is utilized for purposes of evaluation, drilling, placement, fracturing, perforation number, perforation spacing, perforation location, etc.; and borehole image data may be utilized to identify one or more types of features such as, for example, horizons, fractures, faults, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the petrophysical data - emphasis added by Examiner). MWD equipment provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the drilling data - emphasis added by Examiner)), and LWD equipment sends to the surface various types of data of interest, including for example, geological data ( e.g., gamma ray log, resistivity, density and sonic logs, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the petrophysical data - emphasis added by Examiner); [0265] discloses “receiving data for a geologic region; based at least in part on the data, selecting a model from a plurality of models using a trained machine learning mode”).
As to claims 6, 13, and 20, Hong teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches wherein the at least one model is a trained machine learning model deployed based on a type of inputs to the trained machine learning model being found in the petrophysical data ([0122] discloses as tool is moved in a borehole to acquire data at various depths in the borehole, the data may be fed to a trained machine learning model (i.e., the petrophysical data is used to train a machine leaning or model - emphasis added by Examiner); [0188] discloses generating a relatively large amount of training data by varying one or more model parameters such as resistivity, anisotropy, dip and invasion, etc. As an example, modeled measurements together with model type can be used as input to supervised machine learning workflow (i.e., the petrophysical data is used to train a machine learning or model - emphasis added by Examiner)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3, 9-10, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20230041525, hereinafter Hong) in view of Imhof et al. (US 20110048731, hereinafter Imhof).
As to claims 2, 9, and 16, Hong teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches analyzing the stream of data to determine models configured to evaluate data associated with multiple subsurface formations obtained in real time ([0110] and [00240] disclose measurements are obtained in real-time from a bottom hole assembly (BHA); and formation evaluation is performed for interpreting data acquired from a drilled borehole to provide information about the geological formations and/or in-situ fluid(s) that can be used for assessing the producibility of reservoir rocks penetrated by the borehole).
Hong does not explicitly teach analyzing the stream of data to determine models configured to simultaneously evaluate data associated with multiple subsurface formations obtained.
Imhof teaches analyzing the stream of data to determine models configured to simultaneously evaluate data associated with multiple subsurface formations obtained ([0057] discloses “The ability to pick many surfaces simultaneously (i.e., the ability to skeletonize seismic data) enables a pattern recognition or machine learning method to search geological or geophysical data for direct indications of hydrocarbons or elements of the hydrocarbon system such as reservoir, seal, source, maturation and migration to determine and delineate potential accumulations of hydrocarbons (i.e., simultaneously evaluate geological or geophysical data associated with multiple subsurface formations obtained - emphasis added by Examiner)”; [0127]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Imhof into Hong for the purpose of obtaining a seismic data volume representing the subsurface region and hydrocarbon potential of subterranean regions in order to simultaneously analyzing them for hydrocarbon indications. This combination would optimize in creation and analysis of many stratigraphically consistent surfaces from seismic data volumes so that objects such as surfaces and geobodies of regions with a potential to contain hydrocarbons can be created, and a potential for hydrocarbon accumulations in the subsurface region can be predicted.
As to claims 3, 10, and 17, Hong teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches wherein outputting the formation characterization in real time comprises rendering the formation characterization for multiple instances of a visualization system ([0101] and [0116] disclose in FIG. 5, the method 500 is illustrated graphically where information for a subsurface region 510 (e.g., logs, etc.) is received and analyzed to identify three sub-regions 522, 524 and 526 and where results 532, 534 and 536 are generated for the three subregions 522, 524 and 526, individually. The results 532, 534 and 536 (i.e., rendering the formation characterization for multiple instances of a visualization system - emphasis added by Examiner) are then consolidated to output results 540 for the subsurface region 510. A user can visually analyze measurement of a subsurface region or logs; [0108] discloses various types of information that can be rendered to a display during one or more real-time field operations (i.e., the formation characterization would be outputted in real time - emphasis added by Examiner)).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hong in view of Moreau et al. (US 20220268392, hereinafter Moreau).
As to claims 4, 11, and 18, Hong teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches does not explicitly teach wherein the stream of data is converted to a standardized format in real time.
Moreau teaches wherein the stream of data is converted to a standardized format in real time ([0048] discloses data from the image processor 20 can be recorded for logging and/or review by an operator, and the data from the image processor 20 can be transmitted in a wide range of formats (e.g. DICONDE standard formats and/or TIFF) to an operator for real-time observation).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Moreau into Hong for the purpose of recording data or image signal of pipe filled with static or dynamic fluids, such as oil, gas, and/or water in order to convert and transmit the data in standard formats data. This combination would improve in recording, converting, and transmitting the data in a wide range of formats including standard formats to ensure consistency, quality, and compatibility, which enables accurate analysis, seamless integration across systems, efficient operations.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hong in view of Song et al. (US 20230281544, hereinafter Song).
As to claims 7 and 17, Hong teaches the claimed limitations as discussed in claims 1 and 8, respectively.
Hong teaches does not explicitly teach wherein the at least one model is deployed using a custom deployer configured for deployment in an environment where data is obtained in differing formats.
Song teaches wherein the at least one model is deployed using a custom deployer configured for deployment in an environment where data is obtained in differing formats ([0046] discloses oil-and-gas multi-source heterogeneous data volumes, thereby realizing integrated data management in an oil and gas field with various types of data (i.e., in an environment where data is obtained in differing formats - emphasis added by Examiner). The intelligence algorithm component library 103 integrates the multi-scenario production control and prediction technology based on big data and artificial intelligence (AI); and forms a knowledge map of oil and gas data and a customized pattern featuring end-to-end no-code development of models in different scenarios, and meanwhile provides basic algorithms and intelligence algorithms customized based on specific scenarios (i.e., model is deployed using a custom deployer configured for deployment in an environment - emphasis added by Examiner); [0113] discloses FIG. 9 shows a model building interface diagram of a custom algorithm editing module; and FIG. 10 shows a visual model result display interface of a custom algorithm editing module (i.e., FIGs. 9 and 10 show model that is deployed in an environment, and thus, would have been deployed by using a custom deployer - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Song into Hong for the purpose of realizing integrated data management in an oil and gas field in order to increase correlation of business scenarios in the production of oil and gas industry. This combination would improve in realizing the digital and intelligent transformation of the oil and gas industry, and achieving the purpose of cost decreasing and benefit increasing oil and gas enterprises.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Eberlein US 20190356737” teaches “An IoT data packet of IoT data in a first data format is received over a distributed system from an Internet of Things (IoT) device. A determination is made that a second different data format of at least some of the IoT data is being used by at least one application by using an IoT data model of an IoT persistency service storing the IoT data in different data formats supporting different applications. A transformation rule in the IoT data model is used to transform the IoT data packet in the first data format to the second different data format. The IoT data packet in the second different data format is stored in the IoT persistency service”.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAL CE MANG/Examiner, Art Unit 2863