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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mathematical relationships and mental processes) without significantly more. Claim 1 recites:
A method of transforming a model of a first scientific computing language to a surrogate of a second scientific computing language such that the surrogate is trained across a plurality of possible inputs, the method comprising: (this falls within the statutory categories of invention, but intended use in the preamble is not entitled to receiving patentable weight, and in this case amounts to generally linking the use of the exception to the technical field of machine learning as per MPEP 2106.05(h).)
receiving a model input, (insignificant extra-solution activity in the form of mere data gathering, as per MPEP 2106.05(g).)
wherein the model input is of a first scientific computing language; (details of data that is gathered, within the scope of what a person could observe and evaluate mentally)
generating a surrogate based on the received model input; (a person can mentally generate pseudocode and then record it on paper after observing the desired language equivalent)
training the surrogate across a plurality of possible inputs, wherein training the surrogate comprises: (this is done according to mathematical relationships as described throughout the claim, and falls within the scope of mathematical relationships)
selecting an input function representation for each of one or more continuous input functions for the model input, (explicit recitation of mathematical functions, falling within the scope of mathematical relationships)
wherein each input function representation is a finite parameter list; (details of the mathematical functions and what they represent, any link to what the mathematical functions represent here is generally linking the use of the exception to the technical field of machine learning as per MPEP 2106.05(h).)
selecting a parameter space, wherein the parameter space is a cross product of ranges of parameters, and (further mathematical algorithm details for how the numerical or parametric calculations are performed, within the scope of mathematical relationships)
wherein the parameters comprise a concatenation of parameters of the original system and parameters of the input function representations; (further details of the mathematical steps involved in the algorithm)
sampling the parameter space to generate a training set of time series for each parameter set; (further mathematical details that are within the scope of mathematical operations/relationships)
simulating a reservoir; (this simulation is conducted according to the previously defined mathematical algorithms, and falls within the scope of mathematical relationships)
computing projections from the simulated reservoir to each time series in the training set; and (further details of mathematical calculation operations that are performed according to a mathematical algorithm, falling within the scope of mathematical relationships)
fitting an interpolating function between the projections to establish an approximate projection for unknown input functions and parameter values; and (further explicit recitation of mathematical functions that are used to perform numerical/parametric calculations, within the scope of mathematical relationships)
deploying the trained surrogate. (mere instructions to apply an exception as per MPEP 2106.05(f). No details are given as to how this is accomplished, i.e. the claim fails to recite details of how a solution to a problem is accomplished as per MPEP 2106.05(f) consideration (1). Also note that as per consideration (3), this is recited at an extremely high level of generality – just as an undefined deployment operation.)
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 claim 13) and using generic “deployment” (the final claim step, discussed in detail above), 2) generally linking the use of the exception to the technical field of machine learning, and 3) insignificant extra-solution activity in the form of mere data gathering (receiving inputs). 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 generic deployment operation. 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 data is received 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 regarding the generic deployment operation claimed. 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.
The dependent claims 2-12 recite only further features directed to mathematical relationships and the elements discussed above. The rationale applied for claim 1 remains equally applicable to them, and they are ineligible.
Claims 13-24 are substantially similar to claims 1-12, and are rejected under the same rationale.
Allowable Subject Matter
Claims 1-24 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action.
The following is an examiner’s statement of reasons for allowance:
The claims recite highly detailed mathematical algorithms for use in their calculation steps, notably requiring “a cross product of ranges of parameters” in the context of the broader limitations (this being used to define how the parameter space is generated, and the parameter space then being used throughout the claims). There is no teaching equivalent to this in the prior art as a whole – use of cross products for parameter space generation in the context of surrogate model generation is highly limited, and no reference does so in the manner claimed. The closest prior art is:
US 20220156438 A1 discusses using a cross product during iterative training of a surrogate model. (¶98 search dataset geometries created using the randomize cross product function). Its cross product is used for "combinatorial analysis/feature search dataset geometries created using the randomize cross product function" (¶21), which is a completely different use case versus the parameter space generated from cross products of ranges of parameters as claimed. Still, this represents the closest prior art regarding the cross product feature as claimed.
US 20200042659 A1, cited by applicant on the IDS, discusses general surrogate model creation and training, and represents the closest prior art for the other features claimed.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189
