DETAILED ACTION
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 and 13 is/are directed to an abstract idea under the mental process wherein the limitations “processing a set of inputs…to generate a set…on the physical machine”; “optimizing…” and “outputting a recommendation…” are functions that can be reasonably done in the human mind with the aid of pen and paper, through observation evaluation judgement and opinion under Prong I step 2A.
Under Prong II step 2A, the other limitations in the claims including “using a trained machine learning model”; “at least one memory…”; and “at least one processing device…” are mere instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea, thus is not a practical application. See MPEP 2106.05(f).
Under step 2B, these additional elements above either individually or in combination are mere instructions to implement an abstract idea on a generic computer, or merely uses a computer or computer components as a tool to perform the abstract idea, thus is not a practical application. Therefore, these additional elements do not recite an inventive concept, thus, the claimed invention is patent ineligible under 35 USC 101.
Re claims 2-12, the limitations in these claims are analyzed under Prong I step 2A which are functions that can be reasonably done in the human mind with the aid of pen and paper, through observation evaluation judgement and opinion under Prong I step 2A. There is no additional elements that would integrate into the practical application. Therefore, these additional elements do not recite an inventive concept, thus, the claimed invention is patent ineligible under 35 USC 101.
Re claims 14-21, these are system claims having similar limitations cited in claims 2-10, respectively. Thus, claims 14-21 are also rejected under the same rationale as cited in the rejection of claims 2-10 respectively above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-13, and 16-21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Milojicic et al. (U.S. 2024/0211365 A1).
Re claim 1, Milojicic discloses in Figures 1-13 a method, comprising: processing a set of inputs using a trained machine learning model to generate a set of outputs (e.g. Figures 3 and 3A and paragraph [0038 and 0110-0111]), wherein the set of inputs correspond to configuration parameters of a process configured to be performed on a physical machine (e.g. Figures 3, 3A, and 4, and paragraphs [0042-0046]), and wherein the set of outputs includes a plurality of predicted waste metrics resulting from performance of the process on the physical machine (e.g. Figure 2 and paragraphs [0020, 0037, and 0102]); optimizing the set of inputs and the set of outputs for meeting sustainability constraints in view of process constraints (e.g. figure 4 and paragraphs [0026, 0038-0039, and 0046]); and outputting a recommendation for operating the process on the physical machine based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the process, while meeting the sustainability constraints and the process constraints (e.g. Figure 9 and paragraphs [0023-0029 and 0102]).
Re claim 4, Milojicic discloses in Figures 1-13 applying machine learning (ML) regression techniques to one or more input data structures associated with the set of inputs and one or more output data structures associated with the set of outputs to train the ML model, wherein the optimizing the set of inputs and the set of outputs uses a product of the ML regression techniques (e.g. paragraphs [0024, 0038-0039, and 0094]).
Re claim 5, Milojicic discloses in Figures 1-13 determining whether a number of waste metrics included in the one or more output data structures exceeds a first threshold value; and in response to determining that the number of waste metrics exceeds the first threshold value, constructing a reconstructed output data structure to have a lower dimension than the one or more output data structures, wherein the ML regression techniques use the reconstructed output data structure instead of the one or more output data structures (e.g. claim 4 and paragraphs [0020, 0026, 0037 and 0102] wherein optimizing the waste metric would imply producing lower dimension).
Re claim 6, Milojicic discloses in Figures 1-13 determining whether a number of input metrics included in the one or more input data structures exceeds a second threshold value; and in response to determining that the number of input metrics exceeds the second threshold value, constructing a reconstructed input data structure to have a lower dimension than the one or more input data structures, wherein the ML regression techniques use the reconstructed input data structure instead of one or more input data structures (e.g. claim 4 and paragraphs [0020, 0026, 0037 and 0102] wherein optimizing the waste metric would imply producing lower dimension).
Re claim 7, Milojicic discloses in Figures 1-13 reconstructing the waste vector uses at least one method selected from the following methods: single vector decomposition after normalization (e.g. paragraphs [0019, 0097, and 0107]).
Re claim 8, Milojicic discloses in Figures 1-13 performing an outer cross validation to test consistency of a plurality of trained ML model across different test sets and select the trained ML model from a plurality of trained ML models based on the consistency; and performing an inner validation to test consistency of hyper-parameter and/or feature selection for trained ML model and selecting hyper-parameters and/or features for the trained ML model based on the consistency (e.g. paragraphs [0108-0109] cross-validation).
Re claim 9, Milojicic discloses in Figures 1-13 optimizing the set of inputs and the set of outputs includes: using a Jacobian matrix for different waste functions associated with waste outputs of the set of outputs; identifying optimum weights for the Jacobian matrix; and predicting waste output by the process using the optimum weights identified for the Jacobian matrix (e.g. paragraphs [0108-0109] cross-validation).
Re claim 10, Milojicic discloses in Figures 1-13 optimizing the set of inputs and the set of outputs includes: using an ML-based stochastic gradient descent for identifying weights for each waste metric included in a waste vector associated with the set of outputs; and applying a multi-objective waste function for determining the optimized set of inputs and the optimized set of outputs (e.g. paragraphs [0097 and 0111]).
Re claim 11, Milojicic discloses in Figures 1-13 the optimization further minimizes a multi-objective waste function using a Deterministic Optimization process (e.g. paragraphs [0023 and 0037-0038]).
Re claim 12, Milojicic discloses in Figures 1-13 the optimization further minimizes costs associated with operation of the process (e.g. paragraphs [0019 and 0023]).
Re claim 13, it is a system claim having similar limitations cited in claim 1. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Re claim 16, it is a system claim having similar limitations cited in claim 4. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of claim 4 above.
Re claim 17, it is a system claim having similar limitations cited in claim 5 or 6. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 5 or 6 above.
Re claim 18, it is a system claim having similar limitations cited in claim 7. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 7 above.
Re claim 19, it is a system claim having similar limitations cited in claim 8. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 8 above.
Re claim 20, it is a system claim having similar limitations cited in claim 9. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 9 above.
Re claim 21, it is a system claim having similar limitations cited in claim 10. Thus, claim 21 is also rejected under the same rationale as cited in the rejection of claim 10 above.
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, 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 2-3 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Milojicic et al. (U.S. 2024/0211365 A1) in view of Schornig et al. (U.S. 2025/0300937 A1).
Re claim 2, Milojicic fails to disclose in Figures 1-13 the set of inputs are used by a twin or a simulation model of the process as performed on the physical machine to twin or simulate the configuration parameters of the process, and the set of outputs result application of the twin or the simulation model of the process using the set of inputs. However, Schornig et al. disclose the set of inputs are used by a twin or a simulation model of the process as performed on the physical machine to twin or simulate the configuration parameters of the process, and the set of outputs result application of the twin or the simulation model of the process using the set of inputs (e.g. abstract and paragraphs [0116, 0134, and 0148]). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to add the set of inputs are used by a twin or a simulation model of the process as performed on the physical machine to twin or simulate the configuration parameters of the process, and the set of outputs result application of the twin or the simulation model of the process using the set of inputs as conceptually seen in Schornig et al.’s invention into Milojicic’s invention because it would enable to efficiently and effectively predict the optimal configuration.
Re claim 3, Milojicic in view of Schornig et al. disclose the set of outputs includes at least one desired process output and at least one unwanted output, the unwanted output including the plurality of waste metrics resulting from performance of the twin or simulation of the process using the set of inputs (e.g. Schornig et al. - abstract and paragraphs [0116, 0134, and 0148]), and the sustainability constraints are based on the plurality of waste metrics, and wherein the optimizing the set of inputs and the set of outputs includes optimizing both the at least one desired process output and the at least one unwanted output (e.g. Milojicic - figure 4 and paragraphs [0026, 0038-0039, and 0046]).
Re claim 14, it is a system claim having similar limitations cited in claim 2. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 2 above.
Re claim 15, it is a system claim having similar limitations cited in claim 3. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 3 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20250300937-A1
US-20240211365-A1
US-20200327435-A1
US-12639748-B2
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/PHUOC H NGUYEN/Primary Examiner, Art Unit 2451
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