DETAILED ACTION
Status of the Application
The following is a non-Final Office Action. In response to Examiner's communication of March 27, 2025, Applicant, on September 26, 2025, amended claims 1, 17, 33, & 43. Claims 1-51 are now pending in this application and have been rejected below.
The Information Disclosure Statement (IDS) filed on October 6, 2025 has been acknowledged.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 26, 2025 has been entered.
Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are maintained and updated below.
Applicant's amendments are not sufficient to overcome the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections are maintained and updated below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant argues that “[t]he examiner asserts that the claims are directed to a mental process, methods of organizing human activity, and a mathematical concept,” and “[a]ll of these characterizations are improper” because, with regard to "mathematical concepts," "performing Bayesian optimization” is not even included in the independent claims and characterizing the claims as being directed to an mathematical concept is improper because it fails to demonstrate that the claim is directed to, rather than merely involves, Bayesian optimization, with regard to "organizing human activity," Examiner’s characterization of the claims as "instructions or rules to follow or manage business interactions and sales and marketing activity of a business managing and selecting future requests,” at most, describes one possible application of what is recited, and the characterization as "organizing human activity" does not address the claim elements as actually written, with regard to "mental processes," “[h]yperparameters are not conceptually separable from AI models,” “[i]t is therefore not clear, nor does the examiner even attempt to show (beyond bare assertion), how at least the hyperparameter-related elements may possibly, let alone practically, be performed mentally, and therefore the characterization of the claims as being directed to a "mental process" is improper.” Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
Claim 1, and similarly claims 2-51, recites “identifying … uncertainty distributions for multiple variables; identifying … a plurality of potential values of one or more hyperparameters of; performing, …, multiple simulations to obtain simulated effects of a plurality of future requests, the multiple simulations involving sampling of at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the plurality of future requests; and selecting, … from the plurality of future requests, one or more specified future requests based on the multiple simulations; wherein the multiple simulations are performed using at least the one or more … model,” and further, claim 2, and similarly claims 18 & 34, recites “wherein: the multiple variables are associated with an inventory that is affected by or involved in a supply chain network; the plurality of future requests represent future order placements; and performing the multiple simulations simulates effects of the future order placements on the inventory.” In addition, claim 49, and similarly claims 50 & 51, recites “wherein identifying the one or more re-order parameters for optimizing the inventory comprises performing Bayesian optimization of a grid of re-order parameters to identify an optimum combination of re-order parameters within a subset of the grid that satisfies a specified computational time or resources budget.” Claims 1-51, in view of the claim limitations, recite the abstract idea of identifying distributions of multiple variables associated with inventory involved in a supply chain and potential values of hyperparameters, performing a simulation of effects of future requests for future order placements on the inventory using the hyperparameters and distributions by performing Bayesian optimization on re-order parameters, and selecting one of the simulated future requests for future order placements on the inventory.
As noted above, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes).
A claim recites mental processes when the claim recites concepts performed in the human mind (including an observation, evaluation, judgment, opinion), wherein if the claim contains limitations that, under the broadest reasonable interpretation, can practically be performed in the mind but for the recitation of generic computer components, then the claim recites a mental process. 84 Fed. Reg. 52 n.14; MPEP 2106.04(a), 2106.04(a)(2), subsect. III.A. Here, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited of identifying distributions of multiple variables associated with inventory involved in a supply chain and potential values of hyperparameters, performing simulation of effects of future requests for future order placements on the inventory using the hyperparameters and distributions by performing Bayesian optimization on re-order parameters, and selecting one of the simulated future requests for future order placements on the inventory could all be reasonably interpreted as a human making observations of information and using judgment to identify distributions and potential values of hyperparameters, a human using judgement and performing evaluations to simulate effects of future requests based on the identified distributions and hyperparameters, and a human using judgement to select one of the simulated future requests mentally and/or with a pen and paper; therefore, the claims recite mental processes.
In response to Applicant’s assertion that “[h]yperparameters are not conceptually separable from AI models,” and therefore, “[i]t is therefore not clear, nor does the examiner even attempt to show (beyond bare assertion), how at least the hyperparameter-related elements may possibly, be performed mentally, and therefore the characterization of the claims as being directed to a "mental process" is improper,” claims can recite a mental process even if they are claimed as requiring or being performed on a computer. MPEP 2106.04(a)(2), subsect. III.C. Simply because the potential values identified are potential values of “hyperparameters of one or more artificial intelligence models” and the simulations are performed “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more identified hyperparameters” does not mean a human cannot identify potential values of hyperparameters or perform a simulation without using AI models. Similar to “a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register” that the Supreme Court concluded, in Gottschalk v. Benson, 409 U.S. 63, 67 (1972), could be performed purely mentally, as noted above, a human can make observations and using judgment to identify potential values of hyperparameters and using judgement and a human can perform evaluations to simulate effects of future requests based on the hyperparameters, even though the claimed procedures can be carried out in existing computers. MPEP 2106.04(a)(2), subsect. III.C.
Applicant’s assertion that the “characterization of the claims as being directed to a "mental process" is improper” since “[h]yperparameters are not conceptually separable from AI models” and the claim recites “hyperparameter-related elements” ignores test set forth in the first prong of Step 2A, which asks whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas. The elements that are “the hyperparameter-related elements” referred to by Applicant are only the recitations of “hyperparameters of one or more artificial intelligence models” and requiring that the simulations are performed “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more identified hyperparameters,” which are are additional elements beyond the recited abstract idea addressed in the second prong of Step 2A, and when analyzed under Prong 2 of Step 2A, these are nothing more than generic computer components applying the abstract idea and also generally link the abstract idea to technical environment, namely a generic computer environment, which is not sufficient to integrate an abstract idea into a practical Application.
In addition, a claim recites certain methods of organizing human activity when the claim recites fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). 84 Fed. Reg. at 52. Here, as set forth in the claims and specification, the variables are associated with an inventory that is affected by or involved in a supply chain network, in these steps, the plurality of future requests represent future order placements, and performing the multiple simulations simulates effects of the future order placements on the inventory, so these limitations provide instructions or rules to follow to manage business interactions and sales and marketing activity of a business managing and selecting future requests, such as orders for inventory, by identifying variables and parameters of future requests for orders of inventory, simulating the effects of future requests for orders of inventory, and selecting future requests for orders of inventory including requests for orders of inventory by optimizing re-order parameters; thus, the claims recite certain methods of organizing human activity.
With respect to Applicant’s assertions, that Examiner’s characterization of the claims as "instructions or rules to follow or manage business interactions and sales and marketing activity of a business managing and selecting future requests,” at most, describes one possible application of what is recited, and the characterization as "organizing human activity" does not address the claim elements as actually written,” as required by the MPEP, evaluating whether a claim is directed to a judicial exception “should be made after determining what the inventor has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation.” MPEP 2106.04, subsect. II. As noted above, for example, claim 2, recites “wherein: the multiple variables are associated with an inventory that is affected by or involved in a supply chain network; the plurality of future requests represent future order placements; and performing the multiple simulations simulates effects of the future order placements on the inventory.” Therefore, after “reviewing the entire application disclosure” as required by the MPEP, the variables are associated with an inventory that is affected by or involved in a supply chain network, in these steps, the plurality of future requests represent future order placements, and performing the multiple simulations simulates effects of the future order placements on the inventory, and thus, pursuant to the broadest reasonable interpretation, these limitations provide instructions or rules to follow to manage business interactions and sales and marketing activity of a business managing and selecting future requests, such as orders for inventory, by identifying variables and parameters of future requests for orders of inventory, simulating the effects of future requests for orders of inventory, and selecting future requests for orders of inventory including requests for orders of inventory by optimizing re-order parameters.
Additionally, the abstract idea of “mathematical concepts” is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 2106.04(a)(2). Further, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. MPEP 2106.04(a)(2); October 2019 Update: Subject Matter Eligibility at p. 4. Here, as set forth in the claims and specification, performing the simulations includes performing Bayesian optimization on re-order parameters, which is a mathematical algorithm using mathematical equations and calculations; therefore, the claims recite a mathematical concept.
With respect to Applicant’s assertion that "performing Bayesian optimization” is not even included in the independent claims and characterizing the claims as being directed to an mathematical concept is improper because it fails to demonstrate that the claim is directed to, rather than merely involves, Bayesian optimization, as noted above, the MPEP requires that evaluating whether a claim is directed to a judicial exception “should be made after determining what the inventor has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation.” MPEP 2106.04, subsect. II. Further, with respect to mathematical calculations particularly, the MPEP sets forth “[a] claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” MPEP 2106.04(a)(2), subsect. I.C. Here, since the claims and specification disclose that “performing the simulations includes performing Bayesian optimization on re-order parameters and performing Bayesian optimization is a mathematical algorithm using mathematical equations and calculations, the recited performing a simulation of effects of future requests is a recitation of a mathematical calculation.
Furthermore, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-16, 18-32, & 34-51 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and the sales and marketing activity businesses managing requests.
Accordingly, since the claims recite a certain method of organizing human activity, mental processes, and mathematical concepts, the claims recite an abstract idea under the first prong of Step 2A.
Applicant argues the claims recite a practical application because the recited elements improve computer technology by, inter alia, improving the determination hyperparameters for an artificial intelligence model, in contrast to computationally expensive techniques such as random or grid searches. Examiner respectfully disagrees.
The recitations “hyperparameters of one or more artificial intelligence models” and the simulations are performed “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more identified hyperparameters” do not make the claims directed to an improvement in computer technology, but rather, these additional elements beyond the recited abstract idea are nothing more than generic computer components applying the abstract idea and also generally link the abstract idea to technical environment, namely a generic computer environment using generic AI, which is not sufficient to integrate an abstract idea into a practical Application.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] method comprising,” “using at least one processor,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 1, “[a]n apparatus comprising: at least one processor configured to,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 17, and “[a] non-transitory computer readable medium containing instructions that when executed cause at least one processor to,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 33; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. In addition, these additional elements, including the recitations of ““using at least one artificial intelligence model configured according to the one or more identified hyperparameters” generally link the abstract idea to a field of use, which is not sufficient to integrate an abstract idea into a practical application. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16, 18-32, and 34-51 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0298] (describing the use of terms such as (but not limited to) "mechanism,” “module,” “device," "unit," "component," "element," "member," "apparatus," "machine," "system," "processor," or "controller" within a claim is understood and intended to refer to structures known to those skilled in the relevant art). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16, 18-32, and 34-51 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself.
Response to Arguments - Prior Art
Applicant’s arguments with respect to the 35 USC 103 rejections have been fully considered, but they are not persuasive.
Applicant argues that no combination of the references discloses at least "multiple simulations are performed using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations" as recited in claim 1, and similarly in claims 17 and 33. Examiner respectfully disagrees.
Contrary to Applicant’s assertion, Koch, et al. (US 20180240041 A1), hereinafter Koch, the secondary reference, teaches the argued feature as detailed below.
Further, initially, while Misra (US 20100114554 A1), hereinafter Misra, discloses “multiple simulations are performed using the at least the one or more … models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations in paragraphs [0102]-[0104], wherein the processor 240 determines simulation models that estimate performance of the forecasting strategy and the re-order strategy (630) by adapting the simulation models including inputting the identified pattern for the product and the accessed operating variables to the predetermined simulation models, optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) because y accessing and optimizing operating variables for the simulation models, which is referred to in the plural form of “simulation models,” Misra discloses performing multiple simulations using one or more models configured according to different values of the plurality of potential values of the one or more hyperparameters, Misra does not disclose using one or more artificial intelligence models configured according to different values of the plurality of potential values of the hyperparameters. However, contrary to applicant’s these additional features are taught by Koch as detailed below.
Koch teaches “wherein the multiple simulations are performed using at least the one or more artificial intelligence models” in paragraph, [0005], wherein hyperparameter values are selected based for training a predictive model includes [0075], wherein a model type may be selected from “Decision Tree”, “Factorization Machine”, “Forest”, “Gradient Boosting Tree”, “Neural Network”, “Support Vector Machine” (i.e. using AI models) and these multiple prediction models that generate predictions taught in Koch are multiple simulation models using AI models that are “configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations” as taught by Koch in [0089], [0146]-[0149], after, at 516, an indicator is received that defines values for one or more of the hyperparameters to evaluate based on the selected model type, e.g., values for one or more of the hyperparameters may be defined using a default value, a lower bound value, an upper bound value, and an iteration value and/or a specific value instead of the default value, a list of possible values instead of a range of default values, using default bounds and initial values (i.e. values the plurality of potential values of the hyperparameters), at 528-534, a tuning evaluation to select hyperparameters is requested, tuning evaluation results are accessed, hyperparameters from the accessed tuning evaluation results are selected, including the hyperparameters included in the “Best Configuration” output table, and the selected hyperparameters may be used to train the selected model type (i.e. the multiple simulations using AI are configured according to different values of the plurality of potential values of the hyperparameters), [0005], automatically selecting hyperparameter values based on objective criteria for training a predictive model includes a plurality of tuning evaluation parameters that include a model type, a search method type, and values to evaluate for each hyperparameter of a plurality of hyperparameters associated with the model type are accessed, the model is trained using the assigned hyperparameter configuration and a training dataset that is a first portion of the input dataset, the trained model is scored using the assigned hyperparameter configuration and a validation dataset that is a second portion of the input dataset, and a best hyperparameter configuration is identified based on an extreme value of the stored objective function values, [0262], Model tuning application 222, selection manager application 312, and model train/score manager application 412 automatically tune hyperparameters of modeling algorithms that include decision tree models, forest models, gradient boosting models, neural network models, support vector machine models, factorization machine models, etc.
Accordingly, Koch teaches "multiple simulations are performed using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations," as claimed.
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 1-51 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1, and similarly claims 2-51, recites “identifying … uncertainty distributions for multiple variables; identifying … a plurality of potential values of one or more hyperparameters of; performing, …, multiple simulations to obtain simulated effects of a plurality of future requests, the multiple simulations involving sampling of at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the plurality of future requests; and selecting, … from the plurality of future requests, one or more specified future requests based on the multiple simulations; wherein the multiple simulations are performed using at least the one or more … model,” and further, claim 2, and similarly claims 18 & 34, recites “wherein: the multiple variables are associated with an inventory that is affected by or involved in a supply chain network; the plurality of future requests represent future order placements; and performing the multiple simulations simulates effects of the future order placements on the inventory.” In addition, claim 49, and similarly claims 50 & 51, recites “wherein identifying the one or more re-order parameters for optimizing the inventory comprises performing Bayesian optimization of a grid of re-order parameters to identify an optimum combination of re-order parameters within a subset of the grid that satisfies a specified computational time or resources budget.” Claims 1-51, in view of the claim limitations, recite the abstract idea of identifying distributions of multiple variables associated with inventory involved in a supply chain and potential values of hyperparameters, performing simulation of effects of future requests for future order placements on the inventory using the hyperparameters and distributions by performing Bayesian optimization on re-order parameters, and selecting one of the simulated future requests for future order placements on the inventory.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited of identifying distributions of multiple variables associated with inventory involved in a supply chain and hyperparameters, performing simulation of effects of future requests for future order placements on the inventory using the hyperparameters and distributions by performing Bayesian optimization on re-order parameters, and selecting one of the simulated future requests for future order placements on the inventory could all be reasonably interpreted as a human making observations of information and using judgment to identify distributions and potential values of hyperparameters, a human using judgement and performing evaluations to simulate effects of future requests based on the identified distributions and hyperparameters, and a human using judgement to select one of the simulated future requests mentally and/or with a pen and paper; therefore, the claims recite mental processes; therefore, the claims recite mental processes. In addition, as set forth in the claims and specification, the variables are associated with an inventory that is affected by or involved in a supply chain network, in these steps, the plurality of future requests represent future order placements, and performing the multiple simulations simulates effects of the future order placements on the inventory, and these limitations provide instructions or rules to follow to manage business interactions and sales and marketing activity of a business managing and selecting future requests, such as orders for inventory, by identifying variables and parameters of future requests for orders of inventory, simulating the effects of future requests for orders of inventory, and selecting future requests for orders of inventory including requests for orders of inventory by optimizing re-order parameters; thus, the claims recite certain methods of organizing human activity. Additionally, as set forth in the claims and specification, performing the simulations includes performing Bayesian optimization on re-order parameters, which is a mathematical algorithm using mathematical equations and calculations; therefore, the claims recite a mathematical concept. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-16, 18-32, & 34-51 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and the sales and marketing activity businesses managing requests. Accordingly, since the claims recite a certain method of organizing human activity, mental processes, and mathematical concepts, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] method comprising,” “using at least one processor,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 1, “[a]n apparatus comprising: at least one processor configured to,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 17, and “[a] non-transitory computer readable medium containing instructions that when executed cause at least one processor to,” “one or more artificial intelligence models” and “using at least the one or more artificial intelligence models configured according to different values of the plurality of potential values of the one or more hyperparameters” in claim 33; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. In addition, these additional elements, including the recitations of ““using at least one artificial intelligence model configured according to the one or more identified hyperparameters” generally link the abstract idea to a field of use, which is not sufficient to integrate an abstract idea into a practical application. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16, 18-32, and 34-51 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0298] (describing the use of terms such as (but not limited to) "mechanism,” “module,” “device," "unit," "component," "element," "member," "apparatus," "machine," "system," "processor," or "controller" within a claim is understood and intended to refer to structures known to those skilled in the relevant art). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-16, 18-32, and 34-51 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-51 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 1-3, 5-12, 14-19, 21-28, 30-35, 37-44, & 46-51 are rejected under 35 U.S.C. 103 as being unpatentable over Misra (US 20100114554 A1), hereinafter Misra, in view of Koch, et al. (US 20180240041 A1), hereinafter Koch.
Regarding claim 1, Misra discloses a method comprising ([0003], [0022], [0094], [0129]):
identifying, using at least one processor, uncertainty distributions for multiple variables ([0095], the processor 240 identifies a demand pattern for a product using stochastic processing (610) by identifying the best-fit stochastic probability distribution that replicates the consumption of the inventory for the product);
identifying, using the at least one processor, a plurality of potential values of one or more hyperparameters ([0097]-[0098], the processor 240 accesses first operating variables for managing inventory for the product using a forecasting strategy and second operating variables for managing inventory for the product using a re-order strategy (620) comprising, e.g., a user may identify, quantify, and key in operating and financial variables relevant to implementing each of a forecasting inventory management strategy and a re-order inventory management strategy (i.e. potential values for one more hyperparameters), the operating variables may include operation metrics and financial/business metrics including operation metrics of a demand pattern for the product, initial inventory and inventory position for the product, lead time for the product, order cost for the product, inventory holding cost for the product, inventory handling cost for the product, mean forecast error for the product, and other operation costs and attributes, financial/business metrics may of weighted average cost of capital (WACC), price of the product, contribution margin per unit of the product, cost of lost sale of the product, etc.) of one or more … models ([0102]-[0104], the processor 240 determines simulation models that estimate performance of the forecasting strategy and the re-order strategy (630) by adapting the simulation models including inputting the identified pattern for the product and the accessed operating variables to the predetermined simulation models, optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640));
performing, using the at least one processor, multiple simulations to obtain simulated effects of a plurality of future requests, the multiple simulations involving sampling of at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the plurality of future requests ([0102]-[0104], the processor 240 determines simulation models that estimate performance of the forecasting strategy and the re-order strategy (630) by adapting the simulation models including inputting the identified pattern for the product and the accessed operating variables to the predetermined simulation models, optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640), and computes, using the optimized simulation models, cost data that estimates the cost of executing the forecasting strategy and the re-order strategy (650)); and
selecting, using the at least one processor from the plurality of future requests, one or more specified future requests based on the multiple simulations ([0105], the processor 240 compares the computed cost data (660) and determines an optimum policy for managing the inventory for the product based on the comparison (670) by, e.g., the processor 240 compares the cost data computed for the forecasting strategy with the cost data computed for the re-order strategy, and determines which inventory management strategy provides the least cost (or generates the highest profit));
wherein the multiple simulations are performed using the at least the one or more … models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations ([0102]-[0104], the processor 240 determines simulation models that estimate performance of the forecasting strategy and the re-order strategy (630) by adapting the simulation models including inputting the identified pattern for the product and the accessed operating variables to the predetermined simulation models, optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640)).
While Misra discloses all of the above, including identifying, using the at least one processor, a plurality of potential values of one or more hyperparameters of one or more … models; …
wherein the multiple simulations are performed using the at least the one or more … models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations (as above), Misra does not necessarily require disclose the remaining elements of the following limitations, which however, are taught by further teachings in Koch.
Koch teaches identifying, using the at least one processor, a plurality of potential values of one or more hyperparameters of one or more artificial intelligence models ([0089], at 516, an indicator is received that defines values for one or more of the hyperparameters to evaluate based on the selected model type, e.g., using a default value, a lower bound value, an upper bound value, and an iteration value and/or a specific value instead of the default value, a list of possible values instead of a range of default values, using default bounds and initial values. [0075], wherein in 512, a sixth a model type for which to identify a best hyperparameter configuration may be received, e.g., a model type may be selected from “Decision Tree”, “Factorization Machine”, “Forest”, “Gradient Boosting Tree”, “Neural Network”, “Support Vector Machine”); …
wherein the multiple simulations are performed using at least the one or more artificial intelligence models ([0075], a model type may be selected from “Decision Tree”, “Factorization Machine”, “Forest”, “Gradient Boosting Tree”, “Neural Network”, “Support Vector Machine”);) configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations ([0089], [0146]-[0149], after, at 516, an indicator is received that defines values for one or more of the hyperparameters to evaluate based on the selected model type, at 528-534, a tuning evaluation to select hyperparameters is requested, tuning evaluation results are accessed, hyperparameters from the accessed tuning evaluation results are selected, including the hyperparameters included in the “Best Configuration” output table, and the selected hyperparameters may be used to train the selected model type, [0005], automatically selecting hyperparameter values based on objective criteria for training a predictive model includes a plurality of tuning evaluation parameters that include a model type, a search method type, and values to evaluate for each hyperparameter of a plurality of hyperparameters associated with the model type are accessed, the model is trained using the assigned hyperparameter configuration and a training dataset that is a first portion of the input dataset, the trained model is scored using the assigned hyperparameter configuration and a validation dataset that is a second portion of the input dataset, and a best hyperparameter configuration is identified based on an extreme value of the stored objective function values, [0262], Model tuning application 222, selection manager application 312, and model train/score manager application 412 automatically tune hyperparameters of modeling algorithms that include decision tree models, forest models, gradient boosting models, neural network models, support vector machine models, factorization machine models, etc.).
Misra and Koch are analogous fields of invention because both address the problem of generating predictive models implemented for inventory optimization. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Mirsa the ability for the multiple simulations to be performed using at least one artificial intelligence model configured according to the one or more identified hyperparameters, as taught by Koch, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of the multiple simulations being performed using at least one artificial intelligence model configured according to the one or more identified hyperparameters, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Misra with the aforementioned teachings of Koch in order to produce the added benefit of providing artificial intelligence models with improved accuracy and reduced error. [0240].
Regarding claim 2, the combined teachings of Misra and Koch teaches the method of Claim 1 (as above). Further, Misra discloses wherein: the multiple variables are associated with an inventory that is affected by or involved in a supply chain network ([0094]-[0095], in the process 600 to identify the best inventory policy for a particular product along with the optimum variables for the identified inventory policy,