DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/17/2026 has been entered.
Claims 1, 8, 14 and 15 have been amended. Claims 1-3, 7-10, 14-17 and 21-23 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The previously pending objection to claims 1 and 15 has been withdrawn.
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 1-3, 7-10, 14-17 and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 1-3, 7 and 21 are directed to a series of steps, data gathering and analysis system claims 8-10, 14 and 22 are directed to a computer processor, and memory storing instructions, and system claims 15-17 and 23 is directed to a data gathering and analysis system. Thus the claims are directed to a process, machine, and machine, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite performing a field operation, including drilling, storing, obtaining, generating, determining, facilitating, and adjusting steps.
The limitations of generating, determining, facilitating, and adjusting, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite obtaining well log data files, wherein the well log data files comprise one or more rock and fluid properties including irradiation, density, electrical, and acoustic properties; storing the well log data files; obtaining metadata records of the wells, wherein the metadata records comprise well coordinates, well surveys, inter-well spacing, graph interconnections, development scenario decisions, and historical drilling sequences; generating the well log data files of the wells, a geology related similarity score for each pair of wells with respect to a well log depth range of a plurality of well log depth ranges; obtaining and adjusting production data files based on aggregation parameters; generating, based on the production data files of the wells, a production related similarity score for said each pair of wells with respect to a production time stamp range of a plurality of production time stamp ranges; generating, by combining the geology related similarity score and the production related similarity score, a combined similarity score of said each pair of wells with respect to the well log depth range and the production time stamp range; determining, by aggregating the combined similarity score of said each pair of wells with respect to the plurality of well log depth ranges and the plurality of production time stamp ranges, an aggregate similarity score of said each pair of wells; facilitating, based at least on the aggregate similarity score of said each pair of wells, the field operation in the field; generating, based at least on the aggregate similarity score of said each pair of wells, a plurality of well clusters; generating a production performance evaluation result of the wells and respective contributions to overall performance of the field; and adjusting the field operation based on the production performance evaluation result, wherein the field operation is selected from the group consisting of a drilling operation, a production operation, an injection operation, a logging operation, a maintenance operation, and combinations thereof; wherein each of the plurality of well clusters corresponds to an analogous portion of the field, wherein the plurality of well clusters is further generated based on inter-well spacing and well connectivity estimate.
That is, other than reciting a wireline measurement tool, a computer processor and memory storing instructions, a deep learning model, and a machine learning model, the claim limitations merely cover managing behavior, including following rules or instructions, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a wireline measurement tool, a computer processor and memory storing instructions, a deep learning model, and a machine learning model. The wireline measurement tool, computer processor and memory storing instructions, deep learning model, and machine learning model in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a wireline measurement tool, a computer processor and memory storing instructions, a deep learning model, and a machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally, drilling, using a rig, a borehole to form a wellbore, wherein the wellbore extends from a surface towards a target zone of the formation recited in the claims is merely a field of use or technological environment in which to apply a judicial exception. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP §2106.05(h).
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2 and 3 recite additional generating steps, and further describe the geology and the production related similarity score. Claim 7 recites additional initiating or adjusting steps. Similarly, dependent claims 9, 10, 14, 16, 17 and 21-23 recite additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a wireline measurement tool, a computer processor and memory storing instructions, a deep learning model, and a machine learning model.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0055 of the specification, “Embodiments may be implemented on a computing system. FIG. 4 depicts a block diagram) of a computing system (400) including a computer (402) used to provide computational functionalities associated with described machine learning networks, algorithms, methods, functions, processes, flows, and procedures as described in this disclosure, according to one or more embodiments. The illustrated computer (402) is intended to encompass any computing device such as a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, or any other suitable processing device, including both physical or virtual instances (or both) of the computing device.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
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.
Claims 1-3, 7-10, 14-17 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al (US 20240411041 A1), in view of Fahham et al (US 20240152831 A1).
As per claim 1, Kulkarni et al disclose a method to perform a field operation based on similarity of wells in a field (i.e., visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations, ¶ 0042), comprising:
drilling, using a rig, a borehole to form a wellbore, wherein the wellbore extends from a surface towards a target zone of the formation (i.e., a borehole 332 is formed in subsurface formations 330 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc., ¶ 0066);
obtaining, using a wireline measurement tool lowered into the wellbore, well log data files, wherein the well log data files comprise one or more rock and fluid properties including irradiation, density, electrical, and acoustic properties (i.e., In the example of FIG. 4, various wirelines services equipment can be operated to perform one or more wirelines services including, for example, acquisition of data from one or more positions within the bore 420, ¶ 0102, wherein );
storing, using a computer processor and memory storing instructions, the well log data files (i.e., FIG. 1, the GUI 120 can include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization 123, one or more other features 124, data access 125, and data storage 126, ¶ 0023, wherein FIGS. 3 and 4 illustrate various examples of equipment that may be utilized in one or more workflows that include, at least in part, acquiring well log data, ¶ 0064);
obtaining metadata records of the wells, wherein the metadata records comprise well coordinates, well surveys, inter-well spacing, graph interconnections, development scenario decisions, and historical drilling sequences (i.e., Various features can be included for processing various types of data such as, for example, one or more of: land, marine, and transition zone data; time and depth data; 2D, 3D, and 4D surveys; isotropic and anisotropic (TTI and VTI) velocity fields; and multicomponent data, ¶ 0038. One or more well logs may be accessed where data are given as a function of MD. In such an example, where coordinates of corresponding well trajectories are known (e.g., accessible), a method can include transforming the well logs from being a function of MD to being a function of TVD, ¶ 0119. A dimension may be added such as through shading, hatching, color coding, etc., to indicate a numeric value of each of the wells. As shown, a group of wells in the mid-to upper right of the two dimension space 1400 are highly correlated according to a correlation metric; whereas, in the mid-left of the two dimension space 1400, various wells are less correlated, ¶ 0148. Providing visual feedback to an interpreter where a low dimensional visualization of marker signature similarity can be generated (see, e.g., the plot 1400 of FIG. 14). As explained, a BERT model can provide vector representations for log sequences, which can be used along with appropriate dimension reduction techniques for marker similarity visualization. In various examples, a method can generate a normalized probability score of well log window similarity through uses of an SNN, ¶ 0161);
generating, using a deep learning model trained using well log data files and metadata records of the wells, a geology related similarity score for each pair of wells with respect to a well log depth range of a plurality of well log depth ranges (i.e., A well log can be a record (e.g., a recording) of well log signals and/or signal-based output. A well log can be a record of results of electronic measurements of physical quantities acquired in a continuum fashion (e.g., time series and/or depth series), which may be at one or more different well depths, ¶ 0058. FIGS. 9 and 10 show series of well logs 900 and 1000 with associated scores and rank with respect to a query well log. As shown, a Siamese-BERT approach can be effective for well log similarity learning where a method can include using different query log windows as a reference, ¶ 0140. The assessment block 1342 may utilize one or more features of the process block 1322. For example, consider using a machine learning model or models for assessing the well logs correlation, which may provide for rendering a result thereof in a low dimension space where quality may be determined as to the well logs correlation, ¶ 0146);
obtaining and adjusting production data files based on aggregation parameters (i.e., Data-based interpretation may aim to identify and/or classify one or more subsurface boundaries based at least in part on one or more parameters, which can include one or more dip parameters (e.g., angle or magnitude, azimuth, etc.), ¶ 0056);
generating, based on the production data files of the wells, a production related similarity score for said each pair of wells with respect to a production time stamp range of a plurality of production time stamp ranges (i.e., a workflow may utilize geochemical data, and optionally other data, for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.), ¶ 0115, wherein FIGS. 9 and 10 show series of well logs 900 and 1000 with associated scores and rank with respect to a query well log. As shown, a Siamese-BERT approach can be effective for well log similarity learning where a method can include using different query log windows as a reference, ¶ 0140);
generating, by combining the geology related similarity score and the production related similarity score, a combined similarity score of said each pair of wells with respect to the well log depth range and the production time stamp range (i.e., FIGS. 9 and 10 show series of well logs 900 and 1000 with associated scores and rank with respect to a query well log. As shown, a Siamese-BERT approach can be effective for well log similarity learning where a method can include using different query log windows as a reference, ¶ 0140);
determining, by aggregating the combined similarity score of said each pair of wells with respect to the plurality of well log depth ranges and the plurality of production time stamp ranges, an aggregate similarity score of said each pair of wells (i.e., a method 700 for a Siamese Neural Network (SNN) architecture that can be utilized for learning log similarity. As shown, log window blocks 714 and 718 can receive a first portion of log data of a first well log (e.g., log window 1) and can receive a second portion of log data of a second well log (e.g., log window 2). In such an example, each portion of log data can encompass a marker and/or a suspected marker. In such an example, each portion of log data can be series data, which may include a number of 1D data points with respect to time and/or depth. As an example, a number of data points (e.g., window size, etc.) may be selectable and range from approximately three to several hundred or more, ¶ 0135);
facilitating, based at least on the aggregate similarity score of said each pair of wells, the field operation in the field (i.e., visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations, ¶ 0042);
generating, based at least on the aggregate similarity score of said each pair of wells, a plurality of well clusters (i.e., a single cluster for a number of wells where those wells may be utilized in one or more workflows. As an example, a single cluster of wells may be considered the “ground truth” for purposes of training, which may involve splitting data in to training and testing data. As an example, a group of marked well logs may be utilized in a well log correlation workflow, ¶ 0156);
adjusting the field operation based on the production performance evaluation result, wherein the field operation is selected from the group consisting of a drilling operation, a production operation, an injection operation, a logging operation, a maintenance operation, and combinations thereof (i.e., As shown in FIG. 1, outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.), ¶ 0039, wherein visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.), ¶ 0042);
wherein each of the plurality of well clusters corresponds to an analogous portion of the field (i.e., In the example of FIG. 14, the reduced dimension space 1400 provides an indication as to grouping or similarity between the wells, ¶ 0148),
wherein the plurality of well clusters is further generated based on inter-well spacing and well connectivity estimate (i.e., performing one or more types of well correlation workflows, which can include connection of points from well to well, for example, where data indicate that the points (e.g., locations) are likely to have been deposited at a common chronostratigraphic time and/or possess similar and/or related characteristics. A framework can include well correlation features that can display logs, core images, seismic data, grid data, and completions and simulation results, which may be played through time, ¶ 0052).
Kulkarni et al does not disclose generating, based at least on the plurality of well clusters, a machine learning model of the field performance; and generating, using at least the machine learning model and a regional clustering model based on the plurality of well clusters, a production performance evaluation result of the wells and respective contributions to overall performance of the field.
Fahham et al disclose well data is obtained from a set of wells. In one or more embodiments, the well data is obtained as described in FIG. 1 and collected by an E&P computer system, which may be a cloud-based computing system (¶ 0040). The machine learning models are applied to the data to generate indications of issues with the wells. Different machine learning models may identify different types of issues with the wells. As an example, a first machine language model predicts issues related to high water production from features generated from the well data received from a well, as further described below. As another example, a second machine learning model predicts issues related to production shortfall from features generated from the well data received from a well (¶ 0041).
Kulkarni et al and Fahham et al are concerned with effective well management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating, based at least on the plurality of well clusters, a machine learning model of the field performance; and generating, using at least the machine learning model and a regional clustering model based on the plurality of well clusters, a production performance evaluation result of the wells and respective contributions to overall performance of the field in Kulkarni et al, as seen in Fahham et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Kulkarni et al disclose generating, using a feature engineering technique, a plurality of well log feature vectors from the well log data files, wherein the geology related similarity score is generated by applying a machine learning algorithm to the plurality of well log feature vectors (i.e., one or more machine learning models can provide for assessing well logs such as, for example, assessing log similarity, ¶ 0129).
As per claim 3, Kulkarni et al disclose generating, using the feature engineering technique, a plurality of production data feature vectors from the production data files, wherein the production related similarity score is generated by applying the machine learning algorithm to the plurality of production data feature vectors (i.e., representation blocks 724 and 728 can generate vector representations of the two log windows of blocks 714 and 718. These vector representations can then be compared via a feature difference block 730 where output thereof may be a probability score per a probability score block 740, ¶ 0135).
As per claim 7, Kulkarni et al disclose initiating or adjusting, in response to a user viewing the production performance evaluation result, the field operation (i.e., As shown in FIG. 1, outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.), ¶ 0039, wherein visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations, ¶ 0042).
As per claim 21, Kulkarni et al disclose the well connectivity estimate for each pair of wells in the plurality of well clusters exceeds a pre-determined connectivity threshold (i.e., In an ideal scenario, a workflow may lead to a marker QC plot that includes a single cluster, where wells for a marker are closely correlated with each other and there are no more outliers in the plot, ¶ 0153. As an example, an inlier and/or outlier determination may occur in a higher dimension space where, for example, a line, a plane, a curve, a surface, etc., may be automatically determined that provides for demarcation of inliers and outliers, ¶ 0154. A full dimensional approach may utilize the 128 dimensions; noting that the 128 dimensions may be subjected to multidimensional scaling (MDS) to reduce dimensionality, for example, to a number of dimensions that may explain or otherwise represent similarity or dissimilarity between wells. As to the aforementioned MDS approach using the metric, Mu, a threshold may be utilized as a cutoff that determines outliers and/or inliers, ¶ 0155).
Claims 8-10, 14 and 22 are rejected based upon the same rationale as the rejection of claims 1-3, 7 and 21, respectively, since they are the data gathering and analysis system claims corresponding to the method claims.
Claims 15-17 and 23 are rejected based upon the same rationale as the rejection of claims 1-3 and 21, respectively, since they are the system claims corresponding to the method claims.
Response to Arguments
In the Remarks, Applicant argues asserts that a determination that the proposed amendments amount to a practical application of the alleged abstract idea is supported by Ex Parte Liu, PTAB decision in Serial No. 14/699,291, Appeal No. 2019-004955, April 15, 2020. In Ex Parte Liu, the PTAB concluded that a limitation reciting "sending a vehicle update request to the users of the joint rental group indicating that the better-matched vehicle is now available" amounts to a practical application of the alleged abstract idea by improving the field of vehicle rental systems. The PTAB's conclusion hinged on the Appellant's Specification describing that "the ride-sharing server determining whether there is a change in the composition of the rental group and changing the needs for the size and other characteristics of the vehicle accordingly. Spec. 104-106. If a better matched vehicle is available, a vehicle update request is sent to the rental group. Spec. 106-107." Based on the aforementioned portions of the Appellant's Specification, the PTAB concluded that the "claim as a whole reflects a practical application that is an improvement to existing vehicle rental systems." The PTAB's decision is thus necessarily rooted in a determination that "vehicle rental systems" are a technology or technical field.
The amended claims are directed towards an improvement to a technology or technical field. Applicant respectfully asserts that the claimed invention is confined to the technical field of well planning and operations. Turning to the improvement, Applicant asserts that the amended limitations improve the technical field of well planning and operations by allowing for the field operations, such as drilling, production, injection, logging, and other maintenance and management operations (see paragraph [0020] of the specification as filed), to be adjusted based on the production performance evaluation result generated by the machine learning model. This results in an improved field operation process achieved by the machine learning model based on specific criteria of the wells such as inter-well spacing and well connectivity.
As noted above, independent claim 1 has been amended to recite that a rig is configured to drill a borehole to form a wellbore extending from a surface towards a target zone of the formation and that a wireline measurement tool is configured to obtain well log data files by lowering into the wellbore. Additionally, claim 1 is amended to recite that a computer processor stores the well log data files. Applicant respectfully asserts that the rig, wireline measurement tool, and computer processor as claimed are integral to the process of performing a field operation based on similarity of wells in a field. Furthermore, the rig, wireline measurement tool, and computer processor qualify as particular machines by virtue of their functions of drilling a well, obtaining data from the well, and storing the data obtained from the well, which cannot possibly be performed by the human mind. See M.P.E.P. § 2106.05(b)(II) and M.P.E.P. 2106. 04(a)(2)(III)(A) ("[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations.")
Thus, even assuming, arguendo, that the Examiner maintains the opinion that the amended claims should still be rejected under Step 2A Prong 2 of the Alice/Mayo test, Applicant respectfully asserts that the amended claims are patentable under Step 2B of the Alice/Mayo test by requiring the use of a particular machine (i.e., the controller) that is integral to the alleged mental process/abstract idea (i.e., a method for performing a field operation based on similarity of wells in a field). The Examiner respectfully disagrees.
As an initial note, and as Applicant is likely aware, Ex Parte Liu is not a precedential decision, and was decided on the specific facts and claim language of that Application, and has no bearing on the analysis of this Application.
Moreover, Applicant’s amended claimed use of a deep learning model and a machine learning model does not seem to involve anything other than the application of a known technique in its normal, routine, and ordinary capacity. Following, and contrary to Applicant’s assertion, there is no improvement to a technology or technical field. Additionally, as amended, the claims do not include any feedback into the machine learning model, re-training of the machine learning model, and/or input into the machine learning model, etc., that would indicate an improvement in the processing of the machine learning model.
Additionally, drilling, using a rig, a borehole to form a wellbore, wherein the wellbore extends from a surface towards a target zone of the formation; recited in the claims is merely a field of use or technological environment in which to apply a judicial exception. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP §2106.05(h).
Moreover, the wireline measurement tool merely functions as an obvious data gathering mechanism. This type of data gathering is similar to that seen in Content Extraction v. Wells Fargo Bank (Fed. Cir. Dec. 23, 2014), which found that a scanner using known OCR technology was not significantly more than the abstract idea.
Also, the Examiner also notes that while amended method claim 1 recites “storing, using a computer processor and memory storing instructions, the well log data files”, method claims 1-3, 7 and 21 fail to recite the computer processor implementing each of the method steps, as seen in independent system claim 8.
Lastly, the claims are directed to a method or system to perform a field operation. As described in the paragraph 0020 of the specification, “The well control system (126) may control various operations of the well system (106), such as well production operations, well drilling operation, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations.” Moreover, paragraph 0023 recites “Field operations performed throughout the field includes, drilling operation, production operation, injection operation, logging operation, and other maintenance and management operations.”
Additionally, paragraph 0054 recites “For example, a field operation, such as a drilling operation, production operation, injection operation, logging operation, and other maintenance and management operations may be initiated and/or adjusted in response to a user viewing the production performance evaluation results from Block 25. For example, the user may choose a location to drill a new well based on the well locations of a well cluster that is indicated in the production evaluation results as having the highest production performance.”
Following, the claim limitations merely cover managing behavior, including following rules or instructions, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a wireline measurement tool, a computer processor and memory storing instructions, a deep learning model, and a machine learning model. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
Applicant also argues, as neither Kulkarni nor Fahham disclose obtaining metadata records of the wells, using a deep learning model trained using the well log data files and metadata records to generate a geology related similarity score, and generating a production performance evaluation result using the machine learning model and a regional clustering model, Kulkarni in combination with Fahham is unable to arrive at the amended claims. The Examiner respectfully disagrees.
Contrary to Applicant’s assertion, as discussed in the updated rejection, Kulkarni et al, in view of Fahham et al, indeed disclose Applicant’s amended claim language.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 June 10, 2026
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