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
This action is in response to communications filed on 6/5/2023. Accordingly, claims 1- 20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The following language in the claim(s) below are not clearly understood and as such render the claims indefinite:
As per claim 1: the claim recites “computer-readable memory configured to store a trained model, input data for the inference stage of the ML engine and predictive results data output from the inference stage of the ML engine…and wherein the input data includes at least water injection rate and voidage replacement data for a well field and the output predictive results data includes a cumulative oil production forecast result for the well field”. For purposes of examining the examiner will interpret the claims to read as such: “computer-readable memory configured to store: a trained model, input data for the inference stage of the ML engine, and predictive results data output from the inference stage of the ML engine…and wherein the input data includes at least: water injection rate, and voidage replacement data for a well field, and the output predictive results data includes a cumulative oil production forecast result for the well field”.
As per claims 2-7 & 18 they all depend from claim 1 and as such are rejected for having the same deficiencies as those presented above with respect to claim 1.
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- 13 & 18-19—in particular Independent claims 1 & 8—are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. As per claim 1, it recites storing, providing and outputting data (and storing and applying in claim 8). These limitations, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. But for the processor and memory language, the claims encompass a user simply comparing the collected data to a predetermined/configurable threshold in his/her mind. The mere nominal recitation of a generic bus, processor and memory does not take the claim limitation out of the mental processes grouping. Thus, the claims recite a mental process which is an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the elements of storing, providing and outputting data (and storing and applying in claim 8) and that a generic computer preform these steps. The storing and providing (and/or storing) steps are recited at a high level of generality (i.e., as a general means of receiving/transmitting and storing data for use in the outputting/applying steps), and as such they amount to mere data gathering, which is a form of insignificant extra-solution activity. The processor that performs the comparing and calculating steps is recited at a high level of generality, and merely automates the comparison and calculating steps. Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (the processor). The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer component (the processor). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. 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 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 and does not provide an inventive concept.
For the storing, providing and outputting data (and storing and applying in claim 8) steps were considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The background does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
As per claims 2-7, 8-13 & 18-19 they all depend from claims 1 and 8 and are therefore rejected for having the same deficiencies as those presented above with respect to claims 1 & 8.
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.
Claim 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over CN202110197198A (hereinafter 198) in view of Artificial Neural Network Modeling for the Prediction of Oil Production (hereinafter Elmabrouk).
As per claim 1, 198 discloses: a system, comprising:
a well injection planner implemented on at least one processor, wherein the well-injection planner is coupled to a machine learning (ML) engine having a training stage and an inference stage (see 198 at least fig. 1 and Abstract, Background & Disclosure; machine learning, water injection wells, spatiotemporal graph convolution network); and
wherein the input data includes at least water injection rate and voidage replacement data for a well field and the output predictive results data includes a cumulative oil production forecast result for the well field (see 198 at least fig. 1 and Abstract, Background & Disclosure; water injection wells, water injection rate and pressure data, oil extraction rate, water injection rate, pressure data and predicting oil produciton).
198 discloses the invention as detailed above.
However, 198 does not appear to explicitly discloses a computer-readable memory configured to store a trained model, input data for the inference stage of the ML engine and predictive results data output from the inference stage of the ML engine; wherein the well injection planner is configured to provide the trained model and the input data to the inference stage of the ML engine and to receive the predictive results data output from the inference stage of the ML engine.
Nevertheless, Elmabrouk—who is in the same field of endeavor—discloses a computer-readable memory configured to store a trained model, input data for the inference stage of the ML engine and predictive results data output from the inference stage of the ML engine; wherein the well injection planner is configured to provide the trained model and the input data to the inference stage of the ML engine and to receive the predictive results data output from the inference stage of the ML engine (see Elmabrouk at least fig. 1-6 and Introduction; prediction of oil reservoir production, predict the production performance of oil wells with respect to spatial variations and time series).
One of ordinary skill in the art prior to the effective filing date of the given invention would have been motivated to combine Elmabrouk’s use of artificial neural network modeling for oil prediction with those of 198’s oil production prediction method in order to form a more robust and dynamic system (i.e., by demonstrating that neural network techniques can be used to predict oil reservoir performance).
Motivation to combine 198 with Elmabrouk not only comes from knowledge well known in the art but also from Elmabrouk (see Introduction and conclusion).
Both 198 and Elmabrouk disclose claim 2: further comprising a database configured to store training data, and wherein the well-injection planner is further configured to provide the training data to the training stage of the ML engine to obtain the trained model (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 3: wherein the trained model comprises an artificial neural network model (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 4: wherein the trained artificial neural network model has multiple hidden layers and includes a resilient backprograpation algorithm (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 5: wherein the training stage of the ML engine processes the training data in multiple repetitions starting with an initial artificial neural network model and for each successive repetition uses weight inputs obtained from a prior pass as parameters to the artificial neural network model until a number of repetitions are performed and a final trained model is obtained (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 6: wherein the voidage replacement data comprises a voidage replacement ratio between a volume of injected fluid and a volume of produced fluid for a reservoir in the well field (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 7: wherein the well injection planner is further configured to compare the predictive results output from the inference stage of the ML engine and stored in the computer readable memory with numeric simulator results to obtain a confirmation of the accuracy of the predictive cumulative oil filed forecast (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 8: A computer-implemented method for well field optimization using a machine learning (ML) engine having a training stage and an inference stage, comprising: storing training data in computer-readable memory; applying, with at least one processor, the stored training data to the training stage of the ML engine to obtain a trained model; and applying, with at least one processor, input data for water injection to the inference stage of the ML engine to obtain predictive results data according to the trained model, wherein the input data for water injection includes at least water injection rate and voidage replacement data for a well field and the output predictive results data includes a cumulative oil production forecast result for the well field (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion; see claim 1 above).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 9: wherein the trained model comprises an artificial neural network model (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 10: wherein the trained artificial neural network model has multiple hidden layers and includes a resilient backprograpation algorithm (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 11: further comprising the step of processing the training data in multiple repetitions in the training stage of the ML engine starting with an initial artificial neural network model and for each successive repetition using weight inputs obtained from a prior pass as parameters to the artificial neural network model until a number of repetitions are performed and a final trained model is obtained (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 12: wherein the voidage replacement data comprises a voidage replacement ratio between a volume of injected fluid and a volume of produced fluid for a reservoir in the well field, and the applying input data step includes applying the water injection rate and the voidage replacement ratio for the well field to the inference stage of the ML engine (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 13: further comprising comparing the predictive results output from the inference stage of the ML engine and stored in the computer readable memory with numeric simulator results to obtain a confirmation of the accuracy of the predictive cumulative oil filed forecast (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 14: A computer program product device comprising: non-transitory computer-readable memory having instructions executable by at least one processor to perform the following operations for well field optimization using a machine learning (ML) engine having a training stage and an inference stage: applying, with the at least one processor, training data to the training stage of the ML engine to obtain a trained model; applying, with the at least one processor, input data for water injection to the inference stage of the ML engine to obtain predictive results data according to the trained model, wherein the input data for water injection includes at least water injection rate and voidage replacement data for a well field and the output predictive results data includes a cumulative oil production forecast result for the well field; and receiving the output predictive results data from the ML engine for storage, display or transmission over a data network (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion; see claim 1 above).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 15: wherein the voidage replacement data comprises a voidage replacement ratio between a volume of injected fluid and a volume of produced fluid for a reservoir in the well field, and the applying input data operation includes applying the water injection rate and the voidage replacement ratio for the well field to the inference stage of the ML engine (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 16: wherein the operations further comprise electronically comparing the predictive results output from the inference stage of the ML engine and stored in the computer readable memory with numeric simulator results to obtain a confirmation of the accuracy of the predictive cumulative oil field forecast (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 17: wherein the trained model comprises an artificial neural network model having multiple hidden layers and includes a resilient backprograpation algorithm (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 18: wherein the input data for water injection includes at least water injection rate and voidage replacement data for each water well injector of a well field applied per single well, per region in the field, or per the whole field level (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 19: wherein the input data for water injection includes at least water injection rate and voidage replacement data for each water well injector of a well field applied per single well, per region in the field, or per the whole field level (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Both 198 and Elmabrouk disclose claim 20: wherein the input data for water injection includes at least water injection rate and voidage replacement data for each water well injector of a well field applied per single well, per region in the field, or per the whole field level (see 198 at least fig. 1 and Abstract, Background & Disclosure and see Elmabrouk at least fig. 1-6 and Introduction, Results and Conclusion).
Motivation to combine 198 and Elmabrouk—in the instant claim—for the same reasoning and rationale as that presented above with respect to claims 1 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MACEEH ANWARI whose telephone number is 571-272-7591. The examiner can normally be reached on 9-9:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
MACEEH . ANWARI
Primary Examiner
Art Unit 3663
/MACEEH ANWARI/ Primary Examiner, Art Unit 3663
Prosecution Insights