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 1-17 are pending in the instant application. No claims have been added. No claims have been cancelled. No claims have been amended. The rejection of the pending claims is hereby made non-final.
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 1-17 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 storage retrieval management device, 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1,2, 4-7, 9, 10, 12-15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Edgar 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).
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).
Claims 9-10, 12-15, and 17 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
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 3, 8, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over by Edgar et al (US 2023/0101023) 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 supplier to destination (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).
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 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 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).
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 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.
Conclusion
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627
Prosecution Insights