DETAILED ACTION
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 an abstract idea (mental processes and mathematical relationships) without significantly more. Claim 1 recites:
As a preliminary matter, the claims use the terms "predictive model" and "weights". The definition and scope of these terms are important when considering whether the claims are directed to mathematical relationships or merely include them. Interpreting the predictive models in light of ¶15 and ¶18-19 of the specification, they are mathematical in nature and operate via numerical probability calculations using numerical coefficient/weight values as specified by the claim to perform mathematical calculations. Specifically, the "weights" are coefficients in an equation (the beta coefficients discussed in ¶19 and used in equation 3 of the specification), and the predictive model is equations 1 or 2 (mathematical in nature), which is then chosen between by equation 3 (also mathematical).
In view of the above analysis, claim 1 is analyzed as follows:
A method for generating a predicted value, the method comprising: (this falls within the statutory categories of invention)
receiving one or more input signals; (insignificant extra-solution activity in the form of mere data gathering as per MPEP 2106.05(g) - no detail is provided on how or with what the data is gathered)
generating a first predicted value by applying a first set of weights from a first predictive model to the one or more input signals; (the "first predicted value" is a numerical value. It is obtained by "applying a first set of weights", which are numerical coefficients used in an equation to perform mathematical calculations upon the "one or more input signals", which are a series of numerical values. The generation of the weights by the predictive model is not described or claimed here, rather, this claim language is directed to generating the predicted value by applying the weights, which is a mathematical operation)
generating a second predicted value by applying a second set of weights from a second predictive model to the one or more input signals; (the same as the above step, but for a different set of numerical values)
calculating a preference probability for one of the first predictive model or the second predictive model through a preference regression model comprising a third set of weights, the third set of weights applied to the one or more input signals and model identification value; (calculating a probability is a mathematical operation, and here is accomplished again with the "weights", which are numerical coefficients in an equation)
receiving an observed value corresponding to the one or more input signals, (insignificant extra-solution activity in the form of mere data gathering as per MPEP 2106.05(g) - no detail is provided on how or with what the data is gathered)
and updating a list of sample points with the one or more input signals and the corresponding observed value; (updating sample points here is effectively appending numerical data to a vector or matrix of numerical values, which is within the scope of mathematical relationships)
generating a first set of optimum controllable values with the first predictive model; (the operation of the predictive model is never described, nor is how it can generate the optimum values. The values themselves are numerical. Interpreting this in light of the specification, specifically ¶14 "a model selection pool includes a linear model and a non-linear model", it appears that these are mathematical models that perform calculations (otherwise they could not be classified as linear vs. non-linear). See also Equations 1 and 2 in the specification, along with ¶18 that calls them out as examples of "models" - showing that the specification is defining models to be equivalent to equations. As such, this limitation is reciting the calculation of a set of numerical values by either linear or nonlinear mathematical calculations, and is therefore directed to mathematical relationships)
generating a second set of optimum controllable values with the second predictive model; (the same as above, but with a different set of linear or nonlinear mathematical calculations)
receiving a preference value indicating a preference for the first predictive model or the second predictive model based on a comparison between one of the first predicted value and the second predicted value or the first set of optimum controllable values and the second set of optimum controllable values; (the receiving portion is insignificant extra-solution activity in the form of mere data gathering as per MPEP 2106.05(g) - no detail is provided on how or with what the data is gathered. The preference is determined by mathematically comparing the series of numerical values using an equation, for example by equation 3 of the specification, which is described in ¶18 as one way of determining the preference.)
updating the first set of weights based on the list of sample points using a first naïve optimizer associated with the first predictive model; (updating weights with sample points is altering one set of numerical values based on another set of mathematical values. The predictive model, as set forth above, is a mathematical equation. The use of a naïve optimizer is not mathematical in nature, however, no detail is provided on how the naïve optimizer is used or functions. The claim recites only the idea of a solution or outcome, in this case the idea that weights are somehow updated with sample points. The claim fails to recite details of how a solution to a problem is accomplished, in this case how the naïve optimizer functions or generates updated weights given the old weights and the list of sample points. In view of this, reciting "using a first naïve optimizer" is equivalent to mere instructions to apply an exception, as per MPEP 2106.05(f).
updating the second set of weights based on the list of sample points using a second naïve optimizer associated with the second predictive model; and (the same as above, but with a different set of weights and naïve optimizer)
updating the third set of weights based on received preference value using a preference value optimizer associated with the preference regression model. (the same as above, but with a different set of weights and optimizer)
Note that the above limitations could equally be performed mentally with aid of pencil and paper by a person performing the above discussed calculations on a paper worksheet and manually solving them via mental evaluations.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the following additional elements: 1) mere instructions to apply the exception using generic computer components (the processor/memory of claims 8 and 15) and generic machine learning models (the naïve optimizers), 2) generally linking the use of the exception to the technical field of well drilling, and 3) insignificant extra-solution activity in the form of mere data gathering (receiving observed values). The processor/memory is recited at a high-level of generality (i.e., as a generic processor/memory performing a generic computer function of executing instructions and storing data) 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. The same is true for the recitation of merely “using” naïve optimizers. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application. The specification that a report is generated is only tangentially linked to the calculation and analysis steps, and does not meaningfully limit the claim. The claim is directed to an abstract idea.
The claim does 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 using a processor/memory to perform the claimed steps 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. The same is true for the recitation of merely “using” naïve optimizers (equivalent to “apply it” as discussed above). 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. The addition of insignificant extra-solution activity does not amount to an inventive concept. The claim is not patent eligible.
Claims 8 and 15 are substantially similar to claim 1, and are rejected under the same grounds as those set forth above for claim 1.
Claims 2, 9, and 16 additionally recite features that display the predicted values and receive preference values as input. These limitations are merely further insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated (the display or results, which discusses only what data is displayed with no detail on how that display is accomplished or structured) and mere data gathering (receiving values as input), as per MPEP 2106.05(g). They remain ineligible.
Claims 3-7, 10-14, and 17-20 recite only further details that fall within the scope of the judicial exception as analyzed and set forth for claim 1, and further details that amount merely to generally linking the use of the exception to the field of well drilling as analyzed above for claim 1. They remain ineligible for the above reasons.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101 set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: The independent claims recite “calculating a preference probability for one of the first predictive model or the second predictive model through a preference regression model comprising a third set of weights, the third set of weights applied to the one or more input signals and model identification value” and then later use “a preference value indicating a preference for the first predictive model or the second predictive model based on a comparison between one of the first predicted value and the second predicted value or the first set of optimum controllable values and the second set of optimum controllable values”. This is then used to update all weights with a naïve optimizer (for the first two sets of weights) or a preference value optimizer (for the third set of weights). This is a highly detailed methodology for determining and updating weights, which is not taught or suggested by the prior art. The closest prior art is discussed below, in order of relevance:
Gonzalez (González, J., Dai, Z., Damianou, A., & Lawrence, N. D. (2017, July). Preferential bayesian optimization. In International Conference on Machine Learning (pp. 1282-1291). PMLR.) discusses preferential Bayesian optimization where the model weights are chosen based on probabilities to optimize a preference function (but not a separate preference regression model).
US 20170343969 A1 discloses weighted cost functions that are minimized (i.e. optimized) and modified in the context of model based real-time control for a drilling system.
Cavagnaro (Cavagnaro, D. R., Pitt, M. A., Gonzalez, R., & Myung, J. I. (2013). Discriminating among probability weighting functions using adaptive design optimization. Journal of risk and uncertainty, 47(3), 255-289.) discusses how to use probabilities and predicted preferences to optimize the weighting functions of models.
Snoek (Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., ... & Adams, R. (2015, June). Scalable bayesian optimization using deep neural networks. In International conference on machine learning (pp. 2171-2180). PMLR.) uses Bayesian optimization to model distributions and use them to optimize preference, but doesn't adjust weighting with it in a manner equivalent to that claimed.
Conclusion
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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189