DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-20 are pending. 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 non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 11, and 19 recite an abstract idea. Claims 1, 11, and 19 include “determining a maximum possible value of a criteria for a solution; splitting, that employs a combinational optimization algorithm, a search space into a number of bins based on the maximum possible value of the criteria, wherein each bin covers a different range of values of the criteria; generating, a possible solution for each of one or more of the bins that places one or more types of supports at one or more possible locations along members of the infrastructure model, wherein each possible solution has a value of the criteria that is within the range of values covered by the possible solution's respective bin; testing, each possible solution to verify the possible solution satisfies design requirements and is thereby a valid solution; updating, the search space by if the possible solution for a bin is a valid solution, decreasing the maximum possible value of the criteria to a value no greater than the value of the criteria for the valid solution, and if the possible solution for the bin is not a valid solution, adding the possible solution for the bin to a set of tested invalid solutions excluded from future generating; repeating the steps of splitting, generating, testing, and updating until a stopping condition is met and a final valid solution is returned; and adding, supports of the types at the locations indicated by the final valid solution to the infrastructure model and outputting the infrastructure model”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for adding supports to an infrastructure model. As a result, claims 1, 11, and 19 recite an abstract idea under Step 2A Prong One.
Claims 2-10, 12-18, and 20 further describe the process for adding supports to an infrastructure model. As a result, claims 2-10, 12-18, and 20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 11, and 19.
With respect to Step 2A Prong Two of the framework, claims 1, 11, and 19 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 11, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 11, and 19 include a software, computing devices, a non-transitory computer readable medium, processors, and memories. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 11, and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-10, 12-18, and 20 do not include any additional elements beyond those recited with respect to claims 1, 11, and 19. As a result, claims 2-10, 12-18, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 11, and 19.
With respect to Step 2B of the framework, claims 1, 11, and 19 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 11, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 11, and 19 include a software, computing devices, a non-transitory computer readable medium, processors, and memories. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 11, and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-10, 12-18, and 20 do not include any additional elements beyond those recited with respect to claims 1, 11, and 19. As a result, claims 2-10, 12-18, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 11, and 19.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
Claims 1-20 appear to be allowable if rewritten to overcome the 35 USC § 101 rejection.
The prior art references most closely resembling the Applicant’s claimed invention Cao et al. (US Pub No. 2022/0292303) (hereinafter Cao et al.) in view of Wu et al. (US Pub No. 7,593,839) (hereinafter Wu et al.).
Cao et al. discloses determining a maximum possible value of a criteria for a solution (see Cao, para [0057], wherein the stopping criterion can be a maximum number of resource configuration explored; and para [0063], wherein a resource configuration that results in the best performance metrics (e.g., shortest training time) compared to previous resource configurations can be selected as an optimal resource configuration).
Cao et al. discloses splitting, by software that employs a combinational optimization algorithm executing on one or more computing devices, a search space into a number of bins based on the maximum possible value of the criteria, wherein each bin covers a different range of values of the criteria (see Cao, para [0027], wherein a search space of resource configurations splitting out a problem of finding optimal node arrangement and a problem of allocating resources. For example, a distributed training loop (e.g., a DT-loop) can explore different combinations of node arrangements to determine an optimal node arrangement; para [0082], wherein executable software codes that are executed by the computing device(s); para [0030], wherein hyperparameter or deep learning parameter optimization/tuning involves choosing a set of optimal hyperparameter values for an ML algorithm (i.e., a parameter whose value is used to control the learning process); para [0043], wherein segmented into different parts that can be concurrently trained using the same data (subset of data) at different nodes, while in some cases, the training data is divided into multiple subsets (data parallelism); para [0057], wherein the stopping criterion can be a maximum number of resource configuration explored; and para [0063], wherein a resource configuration that results in the best performance metrics (e.g., shortest training time) compared to previous resource configurations can be selected as an optimal resource configuration).
However the system in Cao does not explicitly disclose generating, by the software, a possible solution for each of one or more of the bins that places one or more types of supports at one or more possible locations along members of the infrastructure model, wherein each possible solution has a value of the criteria that is within the range of values covered by the possible solution's respective bin; testing, by the software, each possible solution to verify the possible solution satisfies design requirements and is thereby a valid solution; updating, by the software, the search space by if the possible solution for a bin is a valid solution, decreasing the maximum possible value of the criteria to a value no greater than the value of the criteria for the valid solution, and if the possible solution for the bin is not a valid solution, adding the possible solution for the bin to a set of tested invalid solutions excluded from future generating; repeating the steps of splitting, generating, testing, and updating until a stopping condition is met and a final valid solution is returned; and adding, by the software, supports of the types at the locations indicated by the final valid solution to the infrastructure model and outputting the infrastructure model.
Moreover, neither Cao et al. and Wu et al et al. disclose generating, by the software, a possible solution for each of one or more of the bins that places one or more types of supports at one or more possible locations along members of the infrastructure model, wherein each possible solution has a value of the criteria that is within the range of values covered by the possible solution's respective bin; updating, by the software, the search space by if the possible solution for a bin is a valid solution, decreasing the maximum possible value of the criteria to a value no greater than the value of the criteria for the valid solution, and if the possible solution for the bin is not a valid solution, adding the possible solution for the bin to a set of tested invalid solutions excluded from future generating; repeating the steps of splitting, generating, testing, and updating until a stopping condition is met and a final valid solution is returned; and adding, by the software, supports of the types at the locations indicated by the final valid solution to the infrastructure model and outputting the infrastructure model.
Wu et al. discloses testing, by the software, each possible solution to verify the possible solution satisfies design requirements and is thereby a valid solution (see Wu, column 1, lines 19-20, wherein software tools for designing such networks; column 9, lines 25-28, wherein an engineer will analyze each design trial solution by a number of hydraulic simulation runs corresponding to the multiple demand conditions, the system responses, such as junction pressures, flow velocities and hydraulic gradients).
Moreover, since the specific combination of claim elements generating, by the software, a possible solution for each of one or more of the bins that places one or more types of supports at one or more possible locations along members of the infrastructure model, wherein each possible solution has a value of the criteria that is within the range of values covered by the possible solution's respective bin; updating, by the software, the search space by if the possible solution for a bin is a valid solution, decreasing the maximum possible value of the criteria to a value no greater than the value of the criteria for the valid solution, and if the possible solution for the bin is not a valid solution, adding the possible solution for the bin to a set of tested invalid solutions excluded from future generating; repeating the steps of splitting, generating, testing, and updating until a stopping condition is met and a final valid solution is returned; and adding, by the software, supports of the types at the locations indicated by the final valid solution to the infrastructure model and outputting the infrastructure model recited in claims 1, 11, and 19 cannot be found in the cited prior art and can only be found as recited in Applicant’s Specification, any combination of the cited references and/or additional references(s) to teach all the claim elements, including the aforementioned features not taught by the cited prior art, would be the result of impermissible hindsight reconstruction. Accordingly, a combination of Cao et al., Wu et al., and/or any other additional reference(s) would be improper to teach the claimed invention.
While the teachings of Cao et al., and Wu et al. separately address different parts of the claimed invention, these teachings would not be combinable by one of ordinary skill in the art at the time of the invention with a reasonable expectation of success to provide a predictable combination that would render the claimed invention obvious. Thus, the novelty of the claimed invention is in the combination of limitations rather than any single limitation.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure.
JJ Grefenstette (Optimization of control parameters for genetic algorithms) IEEE Transactions on systems, man, and cybernetics, 1986•ieeexplore.ieee.org discloses a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency.
J Toutouh, D Rossit, S Nesmachnow (Soft computing methods for multiobjective location of garbage accumulation points in smart cities)- Annals of Mathematics and Artificial …, 2020 - Springer discloses locations for waste bins is a variation of the Facility Location Prob… compare to the potential benefits that smart solutions in this … to rank the potential locations to install infrastructure.
Raghunathan et al. US Pub No. 2020/0377331 discloses estimation of the lower and upper bounds of regions/branches of the search space.
Sirgany et al. US Pub No. 2002/0016785 discloses a number of techniques which the inventor has discovered will substantially increase the efficiency of the bin packing algorithm as well as the quality of the result.
Narzisi et al. US Pub No. 2008/0215512 discloses optimization of the resultant search space can be utilized.
Grefenstette et al. US Pub No. 2005/0038762 discloses objective function is capable of mapping structures in a search space.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 08/28/2026
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