DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of November 3, 2026, Applicant, on May 4, 2026, amended claims 1, 5, 17, & 33. Claims 1-51 are now pending in this application and have been rejected below.
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.
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 render moot the 35 USC 103 rejections set forth in the previous action. Therefore, new grounds for rejections necessitated by Applicant’s amendment are set forth 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, for example, Examiner improperly conflates one possible application of what is claimed with what is actually recited in the claims, simplifies claim elements such as simulations to characterize them as "performing evaluation" and "using judgment," ignores the plain meanings of terms such as "hyperparameter," improperly reads limitations of the dependent claims into the independent claims, the claims now recite technical details of how the simulations and sampling are performed, e.g., "iteratively adjusting the safety stock value until, for the service level percentile, a service level metric satisfies a minimum target service level" and "the sampling comprises perturbing at least one of a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement,“ the claims do not fit the definition of a "mathematical concept," i.e., they are not mathematical relationship, mathematical formula, equation, or mathematical calculation (see MPEP 2106.04(a)(2)(I)), the newly added claim elements are not "organizing human activity" because they do not fall under the enumerated sub-groupings of "fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people,” and the newly recited elements could not be reasonably performed in the human mind. 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 comprising one or more of: a demand forecast uncertainty, a supplier time uncertainty, a supplier quantity uncertainty, and a material movement uncertainty; identifying … a plurality of potential values of one or more hyperparameters of … the one or more hyperparameters comprising at least a service level percentile and a safety time percentile; 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; determining, based, at least in part, on results of the multiple simulations, one or more of a safety stock value and a safety time by iteratively adjusting the safety stock value until, for the service level percentile, a service level metric satisfies a minimum target service level; 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 … models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations, and the sampling comprises perturbing at least one of: a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement.” 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 including a demand forecast, a supplier time, a supplier quantity, and a material movement and potential values of hyperparameters including service level and safety time, 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 perturbing demand, delivery delay, delivery or shortage, determining a safety stock value or safety time based on the safety stock value and the service level percentile, 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 including a demand forecast, a supplier time, a supplier quantity, and a material movement and potential values of hyperparameters including service level and safety time, 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 perturbing demand, delivery delay, or deliver shortage, determining a safety stock value or safety time based on the safety stock value and the service level percentile, 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, hyperparameters, and perturbing data and determine a safety stock value or safety time, 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 Examiner simplifies claim elements such as simulations to characterize them as "performing evaluation" and "using judgment," ignores the plain meanings of terms such as "hyperparameter," this appears to reference Applicant’s assertions that hyperparameters are not conceptually separable from AI models and it is therefore not clear how at least the hyperparameter-related elements may be performed mentally and characterized as being directed to a "mental process", Examiner notes, 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.
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, the variables are used in determining inventory that is affected by or involved in a supply chain network, and in these steps, the plurality of future requests represent future order placements, the distributions that of demand, supplier time and quality, and material movement in a supply chain, the hyperparameters include a service level, which is a service level of a supplier in a supply chain, the simulation includes sampling demand, delivery delay, or deliver shortage, and includes determining a safety stock or safety time based on the safety stock and service level, 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, distributions and parameters of future requests for orders of inventory and demand, simulating the effects of future requests for orders of inventory by sampling supply chain activities, determining safety stock, 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 improperly conflates one possible application of what is claimed with what is actually recited in the claims, this assertion appears to be referencing Applicant’s previous argument 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, Examiner notes, 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 that includes sampling data by perturbing and performing Bayesian optimization on re-order parameters, which is a mathematical algorithm using mathematical relationships, equations and calculations; therefore, the claims recite a mathematical concept.
With respect to Applicant’s assertion that Examiner improperly reads limitations of the dependent claims into the independent claims, this appears to refer to Applicants previous assertions that performing Bayesian optimization is not included in the independent claims and characterizing the claims as being directed to an mathematical concept fails to demonstrate that the claim is directed to, rather than merely involves, 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.
Moreover, as amended the claims now recite the sampling of the simulation includes perturbing data, which is a mathematical algorithm using mathematical relationships of numerical values of the perturbed data.
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.
Examiner notes, "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology" and courts have indicated that mere "automation of manual processes, such as using a generic computer" is not sufficient to show an improvement in computer technology. MPEP 2106.05(a).
As in the claims at issue in Electric Power Group, the present claims are not focused on a specific improvement in computers or any other technology, but instead on certain independently abstract ideas that simply invokes computers as tools to implement the abstract idea. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
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 and now moot in view of new grounds for rejection necessitated by Applicant’s amendment.
Applicant argues that the references fail to disclose at least "comprising one or more of: a demand forecast uncertainty, a supplier time uncertainty, a supplier quantity uncertainty, and a material movement uncertainty," "the one or more hyperparameters comprising at least a service level percentile and a safety time percentile," among other limitations, as recited in claim 1, and similarly in claims 17 and 33. Examiner respectfully disagrees.
Contrary to Applicant’s assertions, Misra (US 20100114554 A1), hereinafter Misra, discloses “uncertainty distributions for multiple variables comprising one or more of: a demand forecast uncertainty, a supplier time uncertainty, a supplier quantity uncertainty, and a material movement uncertainty” 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.
In addition, despite Applicant’s assertions, the combined teachings of Misra and Koch, et al. (US 20180240041 A1), hereinafter Koch, the secondary reference, teaches the argued feature of “the one or more hyperparameters comprising at least a service level percentile and a safety time percentile," as detailed below.
Misra teaches “the one or more hyperparameters comprising at least a service level percentile and a safety time percentile” in paragraphs [0070], processor 240 estimates optimum operating variables for corresponding policy wherein an operating variable may be varied within a defined range defined by constraints of effectively operating the policy or meeting a particular service level, etc., [0100], the operating variables may be defined as functions of other operating variables, e.g., optimizing the lead time and shipping and handling cost operating variables may involve identifying the lead time/shipping and handling cost pair that results in the least cost as compared to level of service provided, [0123]-[0124], a service levels may be different service levels a particular business is considering (i.e., multiple service levels regarding provided inventory may be considered by a business and one specific example of a type of service level discussed below is the time it takes for a business to fulfill an order - service level and safety time) offering for a particular product and relates to the type of customer service the business provides with respect to a good or service (i.e., service level and safety time), e.g., a service level may be the time it takes a business to fulfill an order from when the business receives the order (i.e., safety time), e.g., a first service level may be that ninety five percent (i.e. percentile) of orders ship within twenty four hours (i.e., safety time percentile), a second service level may be that ninety percent of orders ship within twenty four hours, and using process 600, processor 240 determines, from among multiple possible strategies for managing the set of data, an optimum strategy for each service level (920) and also determines optimum operating variables for each of the strategy, [0015], multiple service levels (i.e., service level and safety time) for providing a product may be identified and an optimum strategy for managing inventory for the product may be determined based on the service level, from among at least a forecasting strategy and a re-order strategy for managing inventory for the product, for each of the multiple service levels.
Further, Koch also teaches “hyperparameters” in paragraphs [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.
Applicant’s remaining arguments with respect to the 35 USC 103 rejections have been fully considered, but the remaining arguments are now moot in view of new grounds for rejection necessitated by Applicant’s amendment.
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 comprising one or more of: a demand forecast uncertainty, a supplier time uncertainty, a supplier quantity uncertainty, and a material movement uncertainty; identifying … a plurality of potential values of one or more hyperparameters of … the one or more hyperparameters comprising at least a service level percentile and a safety time percentile; 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; determining, based, at least in part, on results of the multiple simulations, one or more of a safety stock value and a safety time by iteratively adjusting the safety stock value until, for the service level percentile, a service level metric satisfies a minimum target service level; 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 … models configured according to different values of the plurality of potential values of the one or more hyperparameters to respectively perform the multiple simulations, and the sampling comprises perturbing at least one of: a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement.” 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 including a demand forecast, a supplier time, a supplier quantity, and a material movement and potential values of hyperparameters including service level and safety time, 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 perturbing demand, delivery delay, delivery or shortage, determining a safety stock value or safety time based on the safety stock value and the service level percentile, 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 including a demand forecast, a supplier time, a supplier quantity, and a material movement and potential values of hyperparameters including service level and safety time, 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 perturbing demand, delivery delay, or deliver shortage, determining a safety stock value or safety time based on the safety stock value and the service level percentile, 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, hyperparameters, and perturbing data and determine a safety stock value or safety time, 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 used in determining inventory that is affected by or involved in a supply chain network, and in these steps, the plurality of future requests represent future order placements, the distributions that of demand, supplier time and quality, and material movement in a supply chain, the hyperparameters include a service level, which is a service level of a supplier in a supply chain, the simulation includes sampling demand, delivery delay, or deliver shortage, and includes determining a safety stock or safety time based on the safety stock and service level, 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, distributions and parameters of future requests for orders of inventory and demand, simulating the effects of future requests for orders of inventory by sampling supply chain activities, determining safety stock, 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 that includes sampling data by perturbing and performing Bayesian optimization on re-order parameters, which is a mathematical algorithm using mathematical relationships, 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, in further view of Ohlsson, et al. (WO 2020092846 A1), hereinafter Ohlsson.
Regarding claim 1, Misra discloses a method comprising ([0003], [0022], [0094], [0129]):
identifying, using at least one processor, uncertainty distributions for multiple variables comprising one or more of: a demand forecast uncertainty, a supplier time uncertainty, a supplier quantity uncertainty, and a material movement uncertainty ([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, wherein the demand pattern is used to simulate demand for the product);
identifying, using the at least one processor, a plurality of potential values of one or more hyperparameters ([0094], [0097]-[0098], 600 performed by the processor 240 to identify the best inventory policy along with the optimum variables includes accessing 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 of a demand pattern for the product, initial inventory and inventory position for the product, lead time for the product, etc., financial/business metrics may of weighted average cost of capital (WACC), price of the product, contribution margin per unit 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)), the one or more hyperparameters comprising at least a service level percentile and a safety time percentile ([0070], processor 240 estimates optimum operating variables for corresponding policy wherein an operating variable may be varied within a defined range defined by constraints of effectively operating the policy or meeting a particular service level, [0100], the operating variables may be defined as functions of other operating variables, e.g., optimizing the lead time and shipping and handling cost operating variables may involve identifying the lead time/shipping and handling cost pair that results in the least cost as compared to level of service provided, [0123]-[0124], a service levels may be different service levels a particular business is considering (i.e., service level and safety time) offering for a particular product and relates to the type of customer service the business provides with respect to a good or service (i.e., service level and safety time), e.g., a service level may be the time it takes a business to fulfill an order from when the business receives the order (i.e., safety time), e.g., a first service level may be that ninety five percent (i.e. percentile) of orders ship within twenty four hours (i.e., safety time percentile), a second service level may be that ninety percent of orders ship within twenty four hours, and using process 600, processor 240 determines, from among multiple possible strategies for managing the set of data, an optimum strategy for each service level (920) and also determines optimum operating variables for each of the strategy, [0015], multiple service levels (i.e., service level and safety time) for providing a product may be identified and an optimum strategy for managing inventory for the product may be determined based on the service level, from among at least a forecasting strategy and a re-order strategy for managing inventory for the product, for each of the multiple service levels);
determining, based, at least in part, on results of the multiple simulations, … until, for the service level percentile, a service level metric satisfies a minimum target service level ([0103], processor 240 optimizes the simulation models to estimate optimum operating variables including first optimum operating variables for the forecasting simulation model and second optimum operating variables (e.g., optimum values for the operating variables) for the re-order simulation model (640), wherein for each of the forecasting and re-order strategies, the processor 240 may perform multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables utilizing techniques described above with respect to reference numeral 340, [0070], processor 240 optimizes the simulation models to estimate optimum operating variables for executing the corresponding policy for managing the set of data (340) by performing repeated simulations attempting to find the optimum combination of operating variables, wherein an operating variable may be varied within a defined range defined by constraints of meeting a particular service level, etc., [0123]-[0124], a service level may be the time it takes a business to fulfill an order from when the business receives the order, e.g., a first service level may be that ninety five percent of orders ship within twenty four hours, a second service level may be that ninety percent of orders ship within twenty four hours, and using process 600, processor 240 determines, from among multiple possible strategies for managing the set of data, an optimum strategy for each service level (920) and also determines optimum operating variables for each of the strategy);
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), [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, wherein the demand pattern is used to simulate demand for the product); 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)); and
the sampling comprises … at least one of ([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): a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement ([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, wherein the demand pattern is used to simulate demand for the product).
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 Misra 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].
Further, While Misra discloses all of the above, including determining, based, at least in part, on results of the multiple simulations, … until, for the service level percentile, a service level metric satisfies a minimum target service level; …
the sampling comprises … at least one of: a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement (as above), Misra does not necessarily require disclose the remaining elements of the following limitations, which however, are taught by further teachings in Ohlsson.
Ohlsson teaches determining, based, at least in part, on results of the multiple simulations, one or more of a safety stock value and a safety time ([0013], [0056]-[0057], the constrained optimization problem includes reorder parameters: safety stock and safety time, solves for the optimal values of reorder parameters (e.g., safety stock)) by iteratively adjusting the safety stock value ([0076]-[0078], simulation module 120 can conduct what-if simulations simulating what would have happened in a particular time period if in the past a different action had been taken during the inventory management process, e.g., if a different safety stock had been used, using history and/or modeled uncertainty distributions to simulate stochasticity, the simulation module 120 can use a materials requirement planning (MRP) algorithm to process this data to simulate the effects of changing different parameters in the inventory management process, e.g., the simulation module 120 can investigate the effect of changing the safety stock of an item while fixing all other parameters) until, for the service level percentile, a service level metric satisfies a minimum target service level ([0013], [0056]-[0057], the optimization problem minimizes a cost function and comprises a constraint selected from the group consisting of an inventory constraint, a service level constraint, an arrival window constraint, etc., the service level constraint comprises a confidence level that an inventory meets a demand requirement, which may be adjustable for each item, e.g., with a 90%, 95%, 96%, 97%, 98%, or 99% confidence, constraints may be defined using reorder parameters: safety stock or safety time, the constrained optimization problem solves for the optimal values of reorder parameters (e.g., safety stock) that minimize the total landed cost while maintaining a level of confidence on the availability of items in stock e.g., a service level constraint); …
the sampling comprises perturbing at least one of: a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement ([0080], wherein the presented approach is one such approach for simulating uncertainty or conducting what-if simulations which looks backward in time, the same approach can also be conducted forward in time wherein the simulation module 120 may rely on predictive models of uncertainty to simulate uncertainty as follows: to run an MRP for a horizon in the future, if there are uncertainties, the simulation module 120 can sample, e.g., a sequence of time and quantity uncertainties from their corresponding predictive distributions can be used for perturbing the scheduled arrivals by the MRP, and the quantity uncertainty sequences can be used for perturbing the demand forecast (consumption in the future) and available inventory, each combination of uncertainty sequences may result in a perturbed inventory profile, and repeating this process for several of such uncertainty sequence combinations may generate several perturbed profiles for different uncertainty pair).
Misra and Ohlsson 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 Misra the ability to determine a safety stock value and/or a safety time by iteratively adjusting the safety stock value and perturbate a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement, as taught by Ohlsson, 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 determining a safety stock value and/or a safety time by iteratively adjusting the safety stock value and perturbing a demand forecast, an order-delivery delay, a delivered-quantity shortage, or a miscellaneous or blocked movement,, 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 Ohlsson in order to produce the added benefit of improved inventory management and optimization by more accurately determining optimal inventory levels. [0003].
Regarding claim 2, the combined teachings of Misra, Koch, and Ohlsson 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, 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, and [0030], the policy for managing a set of data may be an inventory management policy that a company uses to manage inventory of a particular product);
the plurality of future requests represent future order placements ([0102]-[0104], the processor 240 optimizes the simulation models to estimate optimum operating variables for executing the re-order strategy (640)); and
performing the multiple simulations simulates effects of the future order placements on the inventory ([0103], the processor 240 optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) by, e.g., for each of the forecasting and re-order strategies, the processor 240 may perform multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables.).
Regarding claim 3, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein performing the multiple simulations comprises: identifying one or more re-order parameters for optimizing the inventory ([0103], the processor 240 optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) by determining first optimum operating variables (e.g., optimum values for the operating variables) for the forecasting simulation model and second optimum operating variables (e.g., optimum values for the operating variables) for the re-order simulation model, and for each of the forecasting and re-order strategies, the processor 240 may perform multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables); and
placing orders based on the one or more re-order parameters ([0051], a user may consider the output 195 in choosing whether or not to implement the predicted optimum policy, and in some implementations, when the policy for managing the set of data is an inventory management policy, the processor may automatically configure an inventory management system to place orders and/or generate reminders or reports using the predicted optimum inventory management policy).
Regarding claim 5, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein the one or more hyperparameters comprise one of:
the service level percentile representing a percentage of inventory realizations generated by the simulations that meet a minimum target service level and a safety time percentile representing a specified percentile in a supplier time uncertainty distribution ([0123], a service level may be the time it takes a business to fulfill an order from when the business receives the order, e.g., a first service level may be that ninety five percent of orders ship within twenty four hours of receipt and a second service level may be that ninety percent of orders ship within twenty four hours of receipt, [0070], an operating variable may be varied within a defined range, e.g., defined by constraints of effectively operating the policy or meeting a particular service level, [0100], the operating variables may be defined as functions of other operating variables, e.g., optimizing the lead time and shipping and handling cost operating variables may involve identifying the lead time/shipping and handling cost pair that results in the least cost as compared to level of service provided, [0015], multiple service levels for providing a product may be identified and an optimum strategy for managing inventory for the product may be determined, from among at least a forecasting strategy and a re-order strategy for managing inventory for the product, for each of the multiple service levels);
a reorder point percentile representing the percentage of inventory realizations generated by the simulations that meet the minimum target service level and a reorder quantity percentile representing a percentage of inventory realizations generated by the simulations that minimize an overall cost of holding the inventory; and
a minimum inventory level percentile representing the percentage of inventory realizations generated by the simulations that meet the minimum target service level and a maximum inventory level percentile representing the percentage of inventory realizations generated by the simulations that minimize the overall cost of holding the inventory (these claimed alternative members of the group are disclosed by Misra because Misra discloses at least one of the alternatives above).
Regarding claim 6, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein performing the multiple simulations comprises: identifying one or more re-order parameters based on the one or more hyperparameters and the at least one uncertainty distribution ([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)).
Regarding claim 7, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 6 (as above). Further, Misra discloses wherein the one or more re-order parameters comprise one of:
a safety stock value representing an additional quantity of material held in the inventory to reduce a risk that the material will be out of stock and a safety time value representing a time buffer for covering material requirements in cases of future late deliveries of the material (this claimed alternative member of the group are disclosed by Misra because Misra discloses at least one of the alternatives below);
a reorder point value representing an inventory level of material where a new order should be placed to reduce the risk that the material will be out of stock and a reorder quantity value representing an order placement quantity at a reorder point to reduce an overall cost of holding the inventory ([0043], when the simulation model 142 models a re-order inventory management policy, a base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the re-order strategy (e.g., the amount of inventory to order and the minimum threshold of inventory that triggers an order), [0041], the simulation model 142 may model a re-order inventory management policy, wherein a re-order inventory management policy orders additional inventory of a particular product when the inventory of the particular product falls below a minimum threshold (e.g., when all inventory of a product has been depleted or only a few units of the product remain)); and
a minimum inventory level representing the inventory level of material where the new order should be placed to reduce the risk that the material will be out of stock and a maximum inventory level representing an upper bound of the inventory at any order placement to reduce the overall cost of holding the inventory ([0039], the operating variables 131, 132 may be minimum/maximum inventory and inventory to be maintained, and may be a range of possible values with costs corresponding to each of the values within the range assigned and considered when optimizing a simulation model).
Regarding claim 8, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein performing the multiple simulations comprises: performing multiple material requirements planning (MRP) simulations to identify one or more optimal re-order parameters ([0103], the processor 240 optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) including first optimum operating variables for the forecasting simulation model and second optimum operating variables for the re-order simulation mode by, for each of the forecasting and re-order strategies, performing multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables, [0042], when the simulation model 141 models a forecasting inventory management policy, the base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the forecasting strategy (e.g., the amount of inventory to order and how often to order new inventory) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the forecasting strategy, [0043], when the simulation model 142 models a re-order inventory management policy, a base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the re-order strategy (e.g., the amount of inventory to order and the minimum threshold of inventory that triggers an order) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the re-order strategy) using a k-iteration algorithm ([0103], the processor 240 may perform multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables including utilizing techniques similar to those described above with respect to reference numeral 340 shown in FIG. 3, [0070], processor optimizes the simulation models to estimate optimum operating variables for executing the corresponding policy for managing the set of data (340) by performing repeated simulations attempting to find the optimum combination of operating variables (e.g., values of operating variables) that minimize or maximize a target variable or output).
Regarding claim 9, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 8 (as above). Further, Misra discloses wherein the MRP simulations comprise at least one of: safety stock MRP simulations, reorder point MRP simulations, and min-max MRP simulations ([0042], when the simulation model 141 models a forecasting inventory management policy, the base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the forecasting strategy (e.g., the amount of inventory to order and how often to order new inventory) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the forecasting strategy, [0043], when the simulation model 142 models a re-order inventory management policy, a base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the re-order strategy (e.g., the amount of inventory to order and the minimum threshold of inventory that triggers an order) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the re-order strategy).
Regarding claim 10, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein selecting the one or more specified future requests comprises: selecting at least one of the future order placements ([0042], when the simulation model 141 models a forecasting inventory management policy, the base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the forecasting strategy (e.g., the amount of inventory to order and how often to order new inventory) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the forecasting strategy, [0043], when the simulation model 142 models a re-order inventory management policy, a base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the re-order strategy (e.g., the amount of inventory to order and the minimum threshold of inventory that triggers an order) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the re-order strategy) that achieves a target service level while reducing an inventory-related cost ([0070], an operating variable may be varied within a defined range, e.g., defined by constraints of effectively operating the policy or meeting a particular service level, [0100], the operating variables may be defined as functions of other operating variables, e.g., optimizing the lead time and shipping and handling cost operating variables may involve identifying the lead time/shipping and handling cost pair that results in the least cost as compared to level of service provided).
Regarding claim 11, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses further comprising at least one of: displaying the at least one selected future order placement ([0106], the processor 240 generates an output based on the optimum policy for managing inventory for the product (680) by providing a display of the results of the comparison of the forecasting strategy and the re-order strategy including a message indicating which of the strategies achieved better estimated performance (e.g., least cost or highest profits), [0050], output 195 may indicate how much each of the strategies performed better in the simulation and may indicate a probability (e.g., a degree of risk) that the policies will perform at the optimum level); and placing one or more orders based on the at least one selected future order placement ([0051], a user may consider the output 195 in choosing whether or not to implement the predicted optimum policy, and when the policy for managing the set of data is an inventory management policy, the processor may automatically configure an inventory management system to place orders and/or generate reminders or reports using the predicted optimum inventory management policy).
Regarding claim 12, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein the simulations are performed using batched uncertainty realizations of multiple uncertainties associated with the multiple variables generated by perturbing at least one of ([0103], [0044], processor 240 optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) by performing multiple simulations using different combinations of operating variables (e.g., different combinations of operating variable values) and identifies the combination of operating variables/the optimum inventory policy variables that yields the best performance based on the simulation results): a demand forecast, a delay, a quantity shortage of arriving orders, and a miscellaneous order movement ([0098], operating variables may include operation metrics and financial/business metrics, wherein the operation metrics may include a demand pattern for the product, initial inventory and inventory position for the product, lead time for the product, etc., [0039], the operating variables 131, 132 may be supply lead times, order cost, handling costs, retail price, minimum/maximum inventory, inventory to be maintained, ordered quantity, etc.).
Regarding claim 14, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 1 (as above). Further, Misra discloses wherein performing the multiple simulations comprises: performing multiple material requirements planning (MRP) simulations, the MRP simulations ([0103], the processor 240 optimizes the simulation models to estimate optimum operating variables for executing the forecasting strategy and the re-order strategy (640) including first optimum operating variables for the forecasting simulation model and second optimum operating variables for the re-order simulation mode by, for each of the forecasting and re-order strategies, performing multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables, [0042], when the simulation model 141 models a forecasting inventory management policy, the base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the forecasting strategy (e.g., the amount of inventory to order and how often to order new inventory) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the forecasting strategy, [0043], when the simulation model 142 models a re-order inventory management policy, a base model may be adjusted in accordance with the demand pattern and operating variables to determine a simulation model tailored to a particular product in a particular retail environment, and the updated simulation model may include variables for implementing the re-order strategy (e.g., the amount of inventory to order and the minimum threshold of inventory that triggers an order) and output a cost/contribution (e.g., profit) associated with particular values selected for the variables for implementing the re-order strategy) customizable for use with a specific MRP system ([0068], the operating variables themselves and/or values of particular operating variables may be the same or may be different for different policies, and the processor 240 may access operating variables by receiving user input (e.g., inputted by a user into a spreadsheet) that specifies the operating variables, receiving the operating variables from another device (e.g., a customer's computer system) over a network, or retrieving the operating variables from electronic storage).
Regarding claim 15, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 1 (as above). Further, Misra discloses wherein at least performing the multiple simulations and selecting the one or more specified future requests are repeated at a specified optimization frequency ([0103], the processor 240 may perform multiple simulations with multiple possible operating variable combinations to identify the optimum inventory policy variables including utilizing techniques similar to those described above with respect to reference numeral 340 shown in FIG. 3, [0070], processor optimizes the simulation models to estimate optimum operating variables for executing the corresponding policy for managing the set of data (340) by performing repeated simulations attempting to find the optimum combination of operating variables (e.g., values of operating variables) that minimize or maximize a target variable or output).
Regarding claim 16, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 1 (as above). Further, Misra discloses wherein the one or more hyperparameters are based on a trade-off preference associated with a prioritization of multiple objectives, the multiple objectives including at least one of: a risk objective, a cost objective, and a user-defined objective ([0070], the processor 240 performs repeated simulations attempting to find the optimum combination of operating variables that minimize or maximize a target variable or output, e.g., the processor 240 may attempt to minimize cost, and identify the operating variable combination that results in the lowest estimated cost, e.g., an operating variable may be varied within a defined range (e.g., defined by the customer, defined by constraints of effectively operating the policy or meeting a particular service level, etc.) and may have corresponding cost/benefit values associated with changing the operating variable (e.g., a lead time of two days costs ten dollars, and a lead time of one day costs twenty dollars), [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 determining which strategy provides the least cost (or generates the highest profit), [0050], output 195 may indicate how much each of the strategies performed better in the simulation and may indicate a probability (e.g., a degree of risk) that the policies will perform at the optimum level, and thereby, provide the user with an opportunity to factor the degree of risk in a policy selection decision).
Regarding claim 49, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 1 (as above). Further, while Misra discloses wherein identifying the one or more re-order parameters for optimizing the inventory comprises performing … 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 budge ([0102]-[0105], 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 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)), Misra does not necessarily expressly disclose the following remaining limitations, which however, are taught by further teachings in in Koch.
Koch teaches wherein identifying the one or more … parameters for optimizing the inventory comprises performing Bayesian optimization of a grid of … parameters to identify an optimum combination of … parameters within a subset of the grid that satisfies a specified computational time or resources budget ([0167], [0180], in operations 624, iteration manager 314 executes a first tuning search method of the tuning search method specified in operation 522 to determine the first set of hyperparameter configurations, and then in 650, a determination is made concerning whether or not processing should be stopped, e.g., processing is terminated when a maximum number of iterations have been performed, when a maximum number of hyperparameter configuration evaluations have been performed, when a maximum time (computing time or wall clock time) has been exceeded, etc., [0131], [0139], the one or more tuning search methods may be selected from “Grid”, “Bayesian”, wherein the Bayesian search method may be used to generate new hyperparameter configurations (evaluation points) by creating and exploring a kriging surrogate model comprising a weighted average of the known values of the function in the neighborhood of the point to search for improved solutions, and [0051], model tuning application 222 may be implemented using or integrated with an inventory optimization software tool)
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 to identify parameters for optimizing the inventory comprises performing Bayesian optimization of a grid of parameters to identify an optimum combination of parameters within a subset of the grid that satisfies a specified computational time or resources budget, 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 identifying 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, 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 claims 17-19, 21-28, 30-32, 50, these claims are substantially similar to claims 1-3, 5-12, 14-16, 49 respectively, and are, therefore, rejected on the same basis as claims 1-3, 5-12, 14-16. While claims 17-19, 21-28, 30-32, 50 are directed to an apparatus comprising a processor, Misra discloses an apparatus, as claimed. [0003], [0022], [0129].
Regarding claims 33-35, 37-44, 46-48, 51, these claims are substantially similar to claims 1-3, 5-12, 14-16, 49, respectively, and are, therefore, rejected on the same basis as claims 1-3, 5-12, 14-16. While claims 33-35, 37-44, 46-48, 51 are directed to a non-transitory computer readable medium containing instructions, which when executed that cause a processor to perform operations, Misra discloses a non-transitory computer readable medium, as claimed. [0003], [0022], [0129].
Claims 4, 20, & 36 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, in further view of Ohlsson, et al. (WO 2020092846 A1), hereinafter Ohlsson, and Chen, et al. (US 20030126103 A1), hereinafter Chen.
Regarding claim 4, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 2 (as above). Further, Misra discloses wherein the uncertainty distributions for the multiple variables comprise:
a demand forecast uncertainty distribution representing a distribution of differences between demand forecasts and actual consumptions of material in the inventory ([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 by accessing historical sales data for the product and identifies a demand pattern for the product using stochastic processing).
While Misra discloses all of the above, including the uncertainty distributions (as above), Misra does not necessarily expressly disclose the following remaining limitations, which however, are taught by further teachings in Chen.
Chen teaches wherein the uncertainty distributions for the multiple variables comprise:
a demand forecast uncertainty distribution representing a distribution of differences between demand forecasts and actual consumptions of material in the inventory ([0201], a Real Time Demand Forecast Agent takes the point-of-Sales data information immediately when it is generated and evaluates how that will affect future orders. The Real-Time Demand Forecast Agent optionally can evaluate already planned orders to determine whether there are possible changes to the orders. Thus, the Activity Risk Agent uses data obtained from the Real-Time Demand Forecast Agent to forecast orders (or evaluate possibility of changes to planned/fore casted orders) at some future time t', given the current point-of-Sales and other data, Such as inventory levels orders already in transition, etc.);
a material movement uncertainty distribution representing a distribution of movements of the material in the inventory ([0141], to predict order variability, models are extracted, the a model to predict the variability of actual delivery quantity and predict variability on actual delivery date, and the models are extracted from user obtained fields to create decision, wherein the fields include the expected delivery date, the expected delivery quantity, the actual delivered date, and the actual delivered quantity);
a supplier quantity uncertainty distribution representing a distribution of differences between planned quantities of the material in purchase orders and actual quantities of the material received ([0026]-[0028], in an inventory early warning agent (“IEWA”), in a detailed mode of operation using the IEWA predictive models built from detailed activities, potential variation in inventory is estimated for each planned replenishment or consumption activity, such as the IEWA estimates whether a planned truckload of 12 ounce bottles of Bob's bottles will arrive with a quantity that is between 95% and 100% of the requested quantity); and
a supplier time uncertainty distribution representing a distribution of time differences between planned delivery dates of the material and actual delivery dates of the material ([0026]-[0028], in an inventory early warning agent (“IEWA”), in a detailed mode of operation using the IEWA predictive models built from detailed activities, potential variation in inventory is estimated for each planned replenishment or consumption activity, such as the IEWA estimates whether a planned truckload of 12 ounce bottles of Bob's bottles will arrive any time within four hours prior to and six hours after the planned delivery time).
Misra and Chen are analogous fields of invention because both address the problem of monitoring and predicting various data regarding inventory. 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 uncertainty distributions for the multiple variables to comprise a material movement uncertainty distribution, a supplier quantity uncertainty distribution, and a supplier time uncertainty distribution as taught by Chen 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 uncertainty distributions for the multiple variables comprising a demand forecast uncertainty distribution, a material movement uncertainty distribution, a supplier quantity uncertainty distribution, and a supplier time uncertainty distribution, 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 Chen in order to produce the added benefit of improving efficiency, reducing costs, and improving margins. [0004].
Regarding claim 20, this claim is substantially similar to claim 4, and is, therefore, rejected on the same basis as claim 20. While claim 20 is directed to an apparatus comprising a processor, Misra discloses an apparatus, as claimed. [0003], [0022], [0129].
Regarding claim 36, this claim is substantially similar to claim 4, and is, therefore, rejected on the same basis as claim 36. While claim 36 is directed to a non-transitory computer readable medium containing instructions, which when executed that cause a processor to perform operations, Misra discloses a non-transitory computer readable medium, as claimed. [0003], [0022], [0129].
Claims 13, 29, & 45 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, in further view of Ohlsson, et al. (WO 2020092846 A1), hereinafter Ohlsson, and Hubbs, et al. (US 20220027817 A1), hereinafter Hubbs.
Regarding claim 13, the combined teachings of Misra, Koch, and Ohlsson teaches the method of Claim 1 (as above). Further, while Misra discloses wherein the simulations comprise forward-looking simulations using a … horizon ([0066], the pattern may be a model used to estimate future data corresponding to the set of data, [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, and the demand pattern may be used to simulate demand for the product in assessing which inventory management strategy is preferred), Misra does not necessarily expressly disclose the following remaining limitations, which however, are taught by further teachings in Hubbs.
Hubbs teaches wherein the simulations comprise forward-looking simulations using a receding horizon ([0182], both models performed a scheduling process each day throughout the simulation horizon and are utilized to generate schedules that slot products into a production schedule for the simulated production facility for H=7 days in advance, representing a 7 day unchangeable planning horizon within the simulation horizon, [0047], [0173], [0177], in both models, scheduling is performed over a number of time intervals on a receding horizon basis, wherein e.g., Schedule 900 is based on a receding “unchangeable” or fixed planning horizon of H=7 days, and the unchangeable planning horizon (UPH) of 7 days means that, barring a production stoppage, a schedule cannot change during a 7 day interval).
Misra and Hubbs are analogous fields of invention because both address the problem of monitoring and predicting various data for inventory management. 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 simulations to comprise forward-looking simulations using a receding horizon, as taught by Hubbs, 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 simulations comprising forward-looking simulations using a receding horizon, 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 Hubbs in order to produce the added benefit of providing better answers to complex questions regarding which products to produce, how much products to produce, and when to produce the products to help increase profit margins. [0002].
Regarding claim 29, this claim is substantially similar to claim 13, and is, therefore, rejected on the same basis as claim 29. While claim 29 is directed to an apparatus comprising a processor, Misra discloses an apparatus, as claimed. [0003], [0022], [0129].
Regarding claim 45, this claim is substantially similar to claim 13, and is, therefore, rejected on the same basis as claim 45. While claim 45 is directed to a non-transitory computer readable medium containing instructions, which when executed that cause a processor to perform operations, Misra discloses a non-transitory computer readable medium, as claimed. [0003], [0022], [0129].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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