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
Claims 1, 3-10 are presented for examination.
Claims 1, 9 and 10 were amended.
Claim 2 was cancelled.
This is a Final Action.
Response to Arguments
Applicant's arguments filed 06/24/2026 have been fully considered but they are not persuasive in view of abstract idea. Specifically, applicants argument on page 8 that “…These prediction formulas and the conditions for selecting the prediction formulas allow for the appropriate prediction across different data groups. This improvement to machine learning is similar to the improvement in Ex Parte Des Jardins, and as such, is a practical application of any alleged abstract idea.” However, the specification describes the benefit primarily as helping the user understand the prediction and use it for development planning (Specification, Paragraphs 13-14 and 54). These teachings support improved understanding of information more clearly and displaying it to the user, rather than an improvement in technological operations. Accordingly, 101 abstract idea is maintained.
Claim Rejections - 35 U.S.C. §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, 3-10 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to independent claims, 1, 9 and 10, specifically claim 1 recites “calculating a predicted value of a production volume of the well or a sand return amount of the well based on the feature quantity; conditions for selecting the linear prediction formula”. These limitations could be reasonably and practically performed by the human mind because they constitute mental operations based on observation, evaluation and judgment practically performed by a person. Given a well’s feature values, a person could compare those values against the stated conditions, follow the corresponding branch, select the applicable formula and calculate the estimate with pen and paper. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. At step 2A, prong two, claim(s) 1, 9 and 10 recites the additional elements of “one or more processors, non-transitory computer readable recording medium storing a program, acquire…; output… … machine learning model, Features related to shale-gas or shale-oil well; Using ML model containing multiple linear prediction formulas and feature based selection conditions; outputting the predicted value and displaying the predicted value and formula used; A feature’s weight coefficient indicates its contribution to the prediction” are elements merely invoking a generic computer environment (processor, database, memory), basic data-gathering or data outputting functions (MPEP 21.96.05(f)) and generically applying a technical field (ML/AI) to the abstract idea hence reciting insignificant extra solution activities.
The claims do not recite any specific improvement to computer technology, a particular machine implementing the process in a non-generic manner, a transformation of an article to a different state or thing, or any other meaningful limitation that applies the abstract idea in a manger that imposes a meaningful limit on the claim.
Instead, the additional element simply applies the abstract idea using generic data processing operations, which amounts to implementing the mental processes using a computer environment.
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. The claim is directed to an abstract idea.
The claims, 1, 9 and 10 at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained with respect to Step 2A Prong Two, the additional elements as recited in step 2A prong 2 recite conventional computer executing data gathering and outputting, as well as, applying of generic AI/ML technology. No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself. According, claim 1 is ineligible under 101.
Claims 2-8 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically,
Claim 3. “generating auxiliary information…” recites abstract idea of mental steps (observation & evaluation), These limitations could be reasonably and practically performed by the human mind because they constitute mental process involving evaluating input data and determining an output based on the data, a process that can be performed in the human mind or using pen and paper.
With respect to step 2A prong 2 “one or more processors… machine learning mode…” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 4. With respect to step 2A prong 2 “wherein the auxiliary information is a feature quantity on which the prediction using the machine learning model is based or training data of the machine learning model on which the prediction is based.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which is well-understood, routine and conventional as taught by the prior art of records.
Claim 5. With respect to step 2A prong 2 “wherein the auxiliary information is information representing the machine learning model by a decision tree or a rule model. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 6. With respect to step 2A prong 2 “wherein the feature quantity includes information related to proppant used for the well.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 7. With respect to step 2A prong 2 “wherein the feature quantity includes information related to a fluid used for the well.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 8. With respect to step 2A prong 2 “wherein the machine learning model is trained using training data divided for each region.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claims 9 and 10 are similar to claim 1, hence rejected similarly.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US 2017/0364795) in view of Motohashi et al. (US 2019/0034945) further in view of Smith et al. (US 2011/0125461)
1. Anderson teaches, A prediction device (Fig 1 and 2, Paragraphs 51-52 – teaches PALM which is a prediction system including processor 1100 and a user interface, Anderson) comprising:
a memory configured to store instructions; and one or more processors configured to execute the instructions to (Fig 1, Paragraphs 31 and 51 – teaches processor, memory stored instructions and execution, Anderson);
acquire a feature quantity related to a well of gas or oil (Fig 1, Paragraphs 3 and 5 – teaches “shale zone” as well as shale oil wells, identifying geological, drilling, hydraulic fracturing, completion and production data, including well-specific attributes, Anderson);
calculate a predicted value of a production volume of the well or a sand return amount of the well, based on the feature quantity, using a machine learning model (Fig 11A-11D, Paragraphs 12 and 84-85 – teaches ML prediction of production volumes, based on well attributes, Anderson);
output the predicted value (Fig 1, Paragraphs 12 and 52 – teaches displaying the predicted production information, Anderson);
and wherein the one or more processor display, on a display device, the predict value (Paragraphs 8, 12 and 52, Fig 1 – teaches graphical display of prediction information through the processor’s TotalVU interface, Anderson).
Anderson does not explicitly teach,
wherein the machine learning model includes a plurality of linear prediction formulas for calculating the predicted value, and conditions for selecting the linear prediction formula used to calculate the predicted value based on the feature quantity, and the linear prediction formula used to calculate the predicted value, and wherein a weight coefficient of the feature quantity in the linear prediction formula indicates a contribution degree of the feature quantity to the calculated predicted value.
However, Motohashi teaches,
wherein the machine learning model includes a plurality of linear prediction formulas for calculating the predicted value (Fig 16, Paragraph 48-49, 127-128 – teaches line regression and conditional predictor, disclosing multiple formulas, Motohashi), and conditions for selecting the linear prediction formula used to calculate the predicted value based on the feature quantity (Fig 16, Paragraph 49, 128-129 – teaches input feature conditions determine which formula applies, Motohashi), and wherein a weight coefficient of the feature quantity in the linear prediction formula indicates a contribution degree of the feature quantity to the calculated predicted value (Fig 7, Paragraphs 64-65 – teaches regression coefficients, feature weights and contribution degrees, Motohashi).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to incorporate Motohashi’s conditional linear prediction technique into Anderson’s well production predictor to accommodate differing well conditions and identify influential well attributes through the applicable formula’s coefficients. A reasonable expectation of success would have existed because Anderson already supports linear regression and regression tree compatible with Motohashi’s conditional prediction approach (Fig 3, Paragraph 59, Anderson; Fig 17, Paragraphs 64-65 & 127-129, Motohashi).
However, Smith teaches,
[displaying] the linear prediction formula used to calculate the predicted value (Figs 3 & 8, Paragraph 49 & 58 – teaches displaying linear regression equation, collecting input data and outputting the result, which can be stored to database, Smith).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Anderson’s system, as modified by Motohashi, to employ Smith’s equation display technique to display the predicted value together with the selected linear prediction formula, enabling users to inspect the relationship underlying each forecast. A reasonable expectation of success existed because the combined system already determines the selected formula and prediction, making both available for display (Figs 3 & 8, Paragraphs 49 & 58, Smith).
3. The combination of Anderson, Motohashi and Smith teach, The prediction device according to claim 1, wherein the one or more processors are further configured to execute the instructions to generate auxiliary information indicating a basis of prediction using the machine learning model (Fig 7-8, Paragraphs 64-65, 74 and 77 – teaches generates model derived feature contribution information explaining which feature influence the prediction. The information corresponds to the claimed auxiliary information indicating a basis of prediction, Motohashi), wherein the one or more processors output the auxiliary information as the contribution degree of the feature quantity (Fig 17, 19-20, Paragraphs 142-145 – teaches generated contribution information is output as feature specific contribution degrees, figures 19-20 explicitly show explanatory variables accompanied by numerical values labeled “DEGREE OF CONTRIBUTION”, Motohashi).
4. The combination of Anderson, Motohashi and Smith teach, The prediction device according to claim 3, wherein the auxiliary information is a feature quantity on which the prediction using the machine learning model is based (Fig 19, Paragraphs 142-146 – teaches output identifies the input features underlying the prediction and their contributions, these identified explanatory variables correspond to the claimed feature quantities supplied as auxiliary information, Fig 19 identifies individual explanatory variables “HIGHEST TEMPERATURE OF DAY, alongside their “DEGREE OF CONTRIBUTION” Motohashi) or training data of the machine learning model on which the prediction is based.
5. The combination of Anderson, Motohashi and Smith teach, The prediction device according to claim 3, wherein the auxiliary information is information representing the machine learning model by a decision tree or a rule model (Fig 16, Paragraphs 127-128 teaches the conditional prediction model, Fig 16 contains yes/no branches testing weekday and weather conditions, leading to prediction formulas 1-3. This represents the model through a decision tree structure and corresponding conditional rules, Motohashi).
6. The combination of Anderson, Motohashi and Smith teach, The prediction device according to any one of claim 1,wherein the feature quantity includes information related to proppant used for the well (Abstract, Fig 10, Paragraphs 82-83 & 114, Table 3, attribute 6 – teaches the amount of sand proppant inject into the well is a feature used in predicting production, Anderson).
7. The combination of Anderson, Motohashi and Smith teach, The prediction device according to any one of claim 1, wherein the feature quantity includes information related to a fluid used for the well (Fig 4, Paragraph 63 & 83, Table 3, attribute 98 – Fluid type, slurry volume, and fluid design constitute information concerning fluid used for the well, Anderson).
8. The combination of Anderson, Motohashi and Smith teach, The prediction device according to any one of claim 1, wherein the machine learning model is trained using training data divided for each region (Fig 2 & 4, Paragraph 45, 47, 50 and 54 – teaches learning models for location-specific prediction targets; In combination with Anderson’s region and geological location identifiers (paragraph 11) would be obvious to partition Anderson’s historical well data by geographic regions and apply Motohashi’s target specific model learning approach to those regional datasets).
Claims 9 and 10 are similar to claim 1, hence rejected similarly.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chen et al. (US 2023/0024846) – teaches a crop yield prediction system using regression equations using vegetation index and the measured yield data and meteorological data based on region (Abstract).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMRESH SINGH whose telephone number is (571)270-3560. The examiner can normally be reached Monday-Friday 8am-5pm.
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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