DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings as submitted by Applicant on 01/17/2024 have been accepted.
Disposition of Claims
Claims 1-20 are pending in the instant application. Claims 18-20 have been added. No claims have been cancelled. Claims 1, 3, 8, 9, 11, 16, and 17 have been amended. The rejection of pending claims 1-20 is hereby made final.
Response to Remarks
The examiner has considered Applicant’s arguments pertaining to the rejection of the pending claims under 35 USC 101 but does not find said arguments to be persuasive. The examiner submits that the pending claims recited at least a method, non-transitory computer readable medium, and apparatus for supply management, which both fall into one of the four statutory categories. The claims must then qualify as patent eligible subject matter. i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014) (citing Ass'n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 589, 106 USPQ2d 1972, 1979 (2013). See MPEP § 2106.04 for detailed information on the judicial exceptions. Because abstract ideas, laws of nature, and natural phenomenon "are the basic tools of scientific and technological work", the Supreme Court has expressed concern that monopolizing these tools by granting patent rights may impede innovation rather than promote it. See Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980; Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012). However, the Court has also emphasized that an invention is not considered to be ineligible for patenting simply because it involves a judicial exception. Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1980-81 (citing Diamond v. Diehr, 450 U.S. 175, 187, 209 USPQ 1, 8 (1981)). See also Thales Visionix Inc. v. United States, 850 F.3d. 1343, 1349, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017) ("That a mathematical equation is required to complete the claimed method and system does not doom the claims to abstraction."). Accordingly, the Court has said that integration of an abstract idea, law of nature or natural phenomenon into a practical application may be eligible for patent protection. See, e.g., Alice, 573 U.S. at 217, 110 USPQ2d at 1981 (explaining that "in applying the §101 exception, we must distinguish between patents that claim the ‘buildin[g] block[s]’ of human ingenuity and those that integrate the building blocks into something more" (quoting Mayo, 566 U.S. at 89, 110 USPQ2d at 1971) and stating that Mayo "set forth a framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts"); Mayo, 566 U.S. at 80, 84, 101 USPQ2d at 1969, 1971. The Supreme Court in Mayo laid out a framework for determining whether an applicant is seeking to patent a judicial exception itself, or a patent-eligible application of the judicial exception. See Alice Corp., 573 U.S. at 217-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961). This framework, which is referred to as the Mayo test or the Alice/Mayo test, is discussed in further detail in subsection III, below. The first part of the Mayo test is to determine whether the claims are directed to an abstract idea, a law of nature or a natural phenomenon (i.e., a judicial exception). Id. If the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). The Supreme Court has described the second part of the test as the "search for an 'inventive concept'". Alice Corp., 573 U.S. at 217-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). The examiner does agree that the broadest reasonable interpretation of the claim must be established and considered in the determination of patent eligibility under 35 USC 101.
In considering the pending claims, the examiner submits the following: Claim 1 of the pending application (as well as the claims that depend therefrom) recite: A method, comprising: for an input of supplier features associated with one or more suppliers, supply chain network features and predicted demand features: processing the input through a trained deep learning model trained using solutions from two-stage stochastic programming instances, wherein a first stage of the two-stage stochastic programming contracts primary and backup suppliers and allocates orders to the primary suppliers and reserves capacity from backup suppliers, and wherein a second stage of the two-stage stochastic programming adapts operational plans after disruption, the trained deep learning model configured to intake the input and output a primary supplier from the one or more suppliers, a backup supplier from the one or more suppliers, order quantity for the primary supplier, and reservation capacity from the backup supplier simultaneously based on supplier capacity transition probability matrices modeling disruption and recovery processes of the one or more suppliers; and executing a contract with the primary supplier and the backup supplier based on the order quantity and the reservation capacity.
The language of at least claim 1 is reproduced above, with the significant structural language and method steps emboldened. In considering the claims, the examiner has taken into consideration the state of the art at the time of filing consistent with Applicant’s specification, as originally filed. The examiner further submits that the language of the computer readable storage medium and system as recited in independent claims 9 and 17, respectively, recite language that is similar to that recited in the method of claim 1, so that the 101 analysis as applied to claim 1, similarly applies to claims 9 and 17, at least based on their resemblance to claim 1. Based on the broadest reasonable interpretation of claim 1, it is clear that the pending claim 1 and similarly recited independent claims 9 and 17 fall into the judicial exception category of certain methods of organizing human activity, which includes 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) (see MPEP § 2106.04(a)(2), subsection II). The aforementioned categorization is evidenced at least in the steps of: “executing a contract with the primary supplier and the backup supplier based on the order quantity and the reservation capacity” (claim 1 of the pending application) which constitutes a commercial interaction, and therefore places the pending claim into the category of certain methods of organizing human activity. If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application, see MPEP § 2106.04(d). The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In the aforementioned claims, supply chain data is being used to implement contracts and place inventory orders based on reservation capacity, and is being implemented on a computer processor. An example that would be considered analogous to the pending claims would be manual processing of a purchase order, Inventor Holdings, LLC v. Bed Bath Beyond, 876 F.3d 1372, 1378-79, 125 USPQ2d 1019, 1023 (Fed. Cir. 2017).
The Supreme Court has long distinguished between principles themselves (which are not patent eligible) and the integration of those principles into practical applications (which are patent eligible). See, e.g., Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 80, 84, 101 USPQ2d 1961, 1968-69, 1970 (2012). The Federal Circuit has distinguished between claims that are ‘‘directed to’’ a judicial exception (which require further analysis to determine their eligibility) and those that are not (which are therefore patent eligible), e.g., claims that improve the functioning of a computer or other technology or technological field. See Diamond v. Diehr, 450 U.S. 175, 209 USPQ 1 (1981); Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972), MPEP 2106.06(b) (summarizing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 118 USPQ2d 1684 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 120 USPQ2d 1091 (Fed. Cir. 2016), and other cases that were eligible as improvements to technology or computer functionality instead of being directed to abstract ideas). Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
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• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
In determining if the language of the pending claims integrate the judicial exception into a practical application, the examiner identifies the method steps and the system components of exemplary claim 1, which are provided in bold below:
Claim 1. : A method, comprising: for an input of supplier features associated with one or more suppliers, supply chain network features and predicted demand features: processing the input through a trained deep learning model trained using solutions from two-stage stochastic programming instances, wherein a first stage of the two-stage stochastic programming contracts primary and backup suppliers and allocates orders to the primary suppliers and reserves capacity from backup suppliers, and wherein a second stage of the two-stage stochastic programming adapts operational plans after disruption, the trained deep learning model configured to intake the input and output a primary supplier from the one or more suppliers, a backup supplier from the one or more suppliers, order quantity for the primary supplier, and reservation capacity from the backup supplier simultaneously based on supplier capacity transition probability matrices modeling disruption and recovery processes of the one or more suppliers; and executing a contract with the primary supplier and the backup supplier based on the order quantity and the reservation capacity.
A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not "directed to" the recited judicial exception.
The courts have not provided an explicit test for this consideration but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").
One of ordinary skill in the art would recognize that Applicant’s specification fails to identify or explicitly disclose any improvement in the function of a computer or any other technology or technological field, as evidenced by the aforementioned citations from Applicant’s specification. Applicant’s specification merely combines generic, well understood computing elements in the performance of the well understood, routine, and conventional steps. Claims 2-8, 1-16, and 18-20 fail to cure the deficiencies under 35 USC 101 of the claims from which they depend and are similarly rejected for the same reasoning as applied to independent claim 1, and similarly recited claims 9 and 17.
For the aforementioned reasoning, the claims are found to be ineligible, and the rejection of the pending claims in view of 35 USC 101 is hereby maintained and made final.
Regarding Applicant’s remarks pertaining to the rejection of the pending claims under 35 USC 102 and 103 in view of the applied prior art of record, the examiner submits that said arguments are found to be moot in view of the new grounds of rejection presented below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under 2106.03 Eligibility step 1, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claims 1-8 are directed to a method, claims *9-16 are directed to a non-transitory computer readable storage medium, and claim 12 is directed to a system for supplier selection and order allocation, each of which falls within one of the four statutory categories of inventions (process/apparatus). Accordingly, the claims will be further analyzed under 2106.04 Eligibility step 2A:
Under 2106.04 Eligibility step 2A, it must be considered whether the claims are “directed to” a judicial exception by referring to the groupings of subject matter. 2106.04, certain methods of organizing human activity include 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).
Regarding representative independent claim 1, the claim sets forth a supply management method, in the following limitations:
Processing an input;
Executing a contract;
The above-recited limitations set forth an arrangement to determine suppliers and order allocation associated with said suppliers based on demand, supply, and other historical data input and analysis. This arrangement amounts to certain methods of organizing human activity associated with sales activities and commercial interactions. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts (See 2019 Revised Patent Subject Matter Eligibility Guidance).
Under 2106.04 Eligibility step 2A (prong 2), the next step in the eligibility analysis looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements such as:
A processor (claim 17)
However, this elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, the recitations above are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims 9 and 17 and dependent claims 2-8, and 10-16 also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A processor (claim 17)
This additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101. Appropriate correction and/or clarification is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the
manner in which the invention was made.
Claims 1,2, 4-7, 9, 10, 12-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Edgar et al (US 2023/0101023) in view of Jin et al (US 2023/0101023).
Regarding claim 1, the prior art discloses a method, comprising: for an input of supplier features associated with one or more suppliers (see at least paragraph [0064] to Edgar et al, wherein the SSF component inputs a supply catalog that includes supply units of the IT resource. Each supply unit specifies an IT resource (e.g., core) and quantity (e.g., 1K cores), an asset (e.g., servers that hold the cores), a cost model (e.g., rent, depreciation, or option), a lead time (e.g., 3 months), a lead time uncertainty (e.g., 2 weeks), and a range of order dates for future analysis. The supply catalog may represent the supply available from different suppliers), supply chain network features and predicted demand features (see at least paragraph [0073] to Edgar et al, wherein D represents IT resource demand): processing the input through a trained deep learning model configured to intake the input and output a primary supplier from the one or more suppliers (see at least paragraph [0067] to Edgar et al, wherein SSF component employs a content-supplier classifier that inputs text content and outputs a supplier in a region, such as Company X in Country Y), a backup supplier from the one or more suppliers, order quantity for the primary supplier, and reservation capacity from the backup supplier (see at least paragraph [0064] to Edgar et al, wherein each supply unit specifies an IT resource (e.g., core) and quantity (e.g., 1K cores), an asset (e.g., servers that hold the cores), a cost model (e.g., rent, depreciation, or option), a lead time (e.g., 3 months), a lead time uncertainty (e.g., 2 weeks), and a range of order dates for future analysis. The supply catalog may represent the supply available from different suppliers); and executing a contract with the primary supplier and the backup supplier based on the order quantity and the reservation capacity (see at least paragraph [0093] to Edgar et al, wherein the method further sends orders to one or more suppliers of the resource).
Edgar et al does not appear to explicitly disclose wherein the machine learning models are trained using solutions from two-stage stochastic programming instances, wherein a first stage of the two-stage stochastic programming contracts primary and backup suppliers and allocates orders to the primary suppliers and reserves capacity from backup suppliers, and wherein a second stage of the two-stage stochastic programming adapts operational plans after disruption, the trained deep learning model simultaneously based on supplier capacity transition probability matrices modeling disruption and recovery processes of the one or more suppliers.
However, Jin et al discloses an AI based hyperparameter tuning in simulation based optimization system and method, wherein the machine learning models are trained using solutions from two-stage stochastic programming instances, wherein a first stage of the two-stage stochastic programming contracts primary and backup suppliers and allocates orders to the primary suppliers and reserves capacity from backup suppliers, and wherein a second stage of the two-stage stochastic programming adapts operational plans after disruption, the trained deep learning model simultaneously based on supplier capacity transition probability matrices modeling disruption and recovery processes of the one or more suppliers (see at least paragraphs [0111] and [0112] to Jin et al).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the resource planning system and method, as disclosed by Edgar et al and the AI based hyperparameter tuning in simulation based optimization system and method as taught by Jin et al, in order to reduce fulfillment delays based on disruptions and inadequate supply within the supply chain, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the method of claim 1, further comprising training the deep learning model, the training the deep learning model comprising: building and solving a two-stage stochastic programming with an objective of minimizing a total expected cost over a plurality of disruption scenarios generated based on the one or more suppliers, supply chain network, demand features extracted from historical data (see at least paragraph [0027] to Edgar et al, wherein e RP system employs a set of stochastic demand models to model demand for an IT resource. The stochastic demand models may be based on forward-looking revenue plans and backward-looking historical data. The stochastic demand models may include top-down and/or bottom-up models); wherein a first stage of the two-stage stochastic programming is configured to contract primary and backup suppliers, allocate orders to the primary suppliers and reserve capacity from backup suppliers (see at least paragraph [0093] to Edgar et al, wherein the resource is supplied via a supply chain with multiple suppliers in the supply chain), wherein a second stage of the two-stage stochastic programming is configured to adapt operational plans after disruption (see at least paragraph [0092] to Edgar et al, wherein the supply collection is generated based on a stochastic process that factors in factors relating to quantity of a supply unit and uncertainty in order lead time); executing a number of two-stage stochastic programming instances with different suppliers, different supply chain network, and different demand features to generate training data (see at least paragraph [0064] to Edgar et al); and training the deep learning model from the training data (see at least paragraph [0047] to Edgar et al).
Regarding claim 4, the prior art discloses the method of claim 1, wherein the supplier features comprise a probability transition matrix indicative of capacity transition that results from one or more disruption events and recovery process of the supplier (see at least paragraph [0018] to Edgar et al, wherein The cost function may be customized to the company based on, for example, how the company values overcapacity and undercapacity. Because the computational resources needed to exhaustively evaluate every possible combination of demand and supply may be prohibitively large, the RP system employs probability-based distribution samplings that factor in the uncertainties in demand and supply and evaluates the costs based on those samplings. The RP system may employ various distribution models such as a triangular probability distribution and a Geometric Brownian Motion distribution).
Regarding claim 5, the prior art discloses the method of claim 1, further comprising: periodically providing subsequent input to the trained deep learning model; and based on the output from the trained deep learning model from the subsequent input, updating a prediction of supplier cohort, necessity of changing the primary supplier, and a necessity of increase of the reservation capacity (see at least paragraph [0078] to Edgar et al, wherein the supply models component 102 additionally inputs the supply catalog 103 and outputs a supply collection. The simulation engine 105 inputs the demand collection and supply collection, accesses the cost function 104, and outputs a capacity plan).
Regarding claim 6, the prior art discloses the method of claim 1, wherein for receipt of another input of a parameter of interest: generating a plurality of values for the parameter of interest (see at least paragraph [0064] to Edgar et al, wherein the SSF component inputs a supply catalog that includes supply units of the IT resource. Each supply unit specifies an IT resource (e.g., core) and quantity (e.g., 1K cores), an asset (e.g., servers that hold the cores), a cost model (e.g., rent, depreciation, or option), a lead time (e.g., 3 months), a lead time uncertainty (e.g., 2 weeks), and a range of order dates for future analysis); processing the input with each of the plurality of values for the parameter of interest in the trained deep learning model to generate a plurality of the output for display (see at least paragraph [0066] to Edgar et al, wherein e SSF component trains a content-shortage classifier (e.g., a support vector machine or a neural network) using the feature vectors and labels the training data. The content-shortage classifier may be a binary classifier. The content-shortage classifier inputs text content and outputs whether the text content represents no capacity shortage or a capacity shortage).
Regarding claim 7, the prior art discloses the method of claim 1, wherein for receipt of another input of a parameter of interest: forecasting future values of the parameter of interest for an upcoming period of interest based on historical data (see at least paragraph [0017] to Edgar et al, wherein RP system may forecast demand based on historical data of the company); processing the future values of the parameter of interest through the trained deep learning model (see at least paragraph [0046] to Edgar et al) to determine another optimal primary supplier (see at least paragraph [0030] to Edgar et al, wherein RP system may be employed to generate a resource plan for the manufacturer based on its demand, the supply of direct suppliers, and resource plans for suppliers in the supply chain. The resource plans of the manufacturer and the suppliers may be developed using an optimization technique to identify optimal resource plans for the supply chain).
Regarding claim 19, the prior art discloses the method of claim 1, wherein the supplier capacity transition probability matrices are generated using Discrete Time Markov Chain modeling, wherein states of the Discrete Time Markov Chain represent capacity levels of each supplier, and wherein transitions between states capture disruption processes transitioning from higher capacity to lower capacity and recovery processes transitioning from lower capacity to higher capacity (see at least paragraph [0109] to Jin et al).
Regarding claim 20, the prior art discloses the method of claim 1, wherein the predicted demand features comprise demand vectors modeled using a Gaussian process with a multivariate normal distribution, and wherein demand scenario generation for training the deep learning model comprises applying Cholesky decomposition to a demand covariance matrix to generate correlated demand realizations across multiple time periods (see at least paragraph [0090] to Jin et al).
Claims 9-10, 12-15, and 17 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
Claims 3, 8, 11, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over by Edgar et al (US 2023/0101023) in view of Jin et al (US 2023/0101023) and further in view of Williams et al (US 2016/0350837).
Regarding claims 3 and 11, the prior art discloses the method of claim 1, wherein the supply chain network features comprise an adjacency matrix pairing each supplier to each distribution center destination with associated transportation lead times and costs for optimization of the two-stage stochastic programming (see at least paragraph [0101] to Williams et al, wherein local fulfillment center workstations of a pizza provider may be located in delivery vehicles of the pizza provider and used by pizza deliverers to determine where to deliver each pizza or a delivery route for the pizzas. The delivery route may be dynamically planned based on types of orders and locations to which the orders will be delivered and paragraph [0082] to Jin et al).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the resource planning system and method, as disclosed by Edgar et al, the AI based hyperparameter tuning in simulation based optimization system and method as taught by Jin et al, and the intelligent delivery queuing system and method as taught by Williams et al, in order to reduce fulfillment delays based on disruptions and inadequate supply within the supply chain, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claims 8 and 16, the prior art discloses the method of claim 1, wherein the input of the supplier features associated with the one or more suppliers, the supply chain network features and the predicted demand features are received through a user interface configured to display supplier capacity levels, disruption probability parameters, and backup reservation options alongside locations of the one or more suppliers through a map (see at least paragraph [0014] to Williams et al, wherein a location module that is programmed to (i) determine current locations of the mobile computing devices and (ii) identify locations of the particular providers and at least paragraph [0031] wherein an electronic map may be accessed by the computer system 104 and paragraph [0070] to Jin et al).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the resource planning system and method, as disclosed by Edgar et al, the AI based hyperparameter tuning in simulation based optimization system and method as taught by Jin et al, and the intelligent delivery queuing system and method as taught by Williams et al, in order to reduce fulfillment delays based on disruptions and inadequate supply within the supply chain, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 18, the prior art discloses the method of claim 3, wherein the transportation lead times in the adjacency matrix distinguish between domestic supplier-to-distribution-center routes and offshore supplier-to-distribution-center routes (see at least paragraph [0151] to Jin et al).
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627
