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 .
DETAILED ACTION
This office action is in response to communication filed on 5/7/2025.
Claim 1 is presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/11/2025, 11/07/2025 are being considered by the examiner.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claim 1, under Step 2A claim 7 recites a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites
receiving an inventor dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time, wherein the plurality of inventory variables are characterized by having one or more future uncertainty levels; processing the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables that are characterized by having one or more future uncertainty levels; and providing the processed inventory dataset to an optimization algorithm, wherein the optimization algorithm is used to predict a target inventory level for optimizing an inventory holding cost, and wherein the optimization algorithm comprises one or more constraint conditions that require the target inventory level to at least satisfy a present, incoming or expected demand requirement.
These limitations recite organizing human activity, such as by performing commercial interactions (see: MPEP 2106.04(a)(2)(II)). This is because the limitations above recite receiving inventory data, processing, optimizing inventory based on target level, which represents the performance of a marketing and/or sales activity, which are commercial interactions. Each of these falls under organizing human activity. Accordingly, under step 2A (prong 1) the claim recites an abstract idea because the claim recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas. The steps include concepts performed by algorithm which are considered Mathematical Concepts. The claimed steps include receiving training data, processing data, optimizing represents mathematical or data manipulation steps that could be performed in a person’s mind or by pen and paper.
Alternatively, the limitations also recite the abstract idea exception of mental processes. MPEP § 2106.04(a)(2)(III). These limitations, as drafted, recite a simple mental process that under the broadest reasonable interpretation, cover performance with pen and paper but for the recitation of the generic computer components For example, the claim encompasses a prediction of the plurality inventory variables that are characterized by having one or more future uncertainty levels; and providing the processed inventory dataset to an optimization algorithm using pen and paper and manually by hand. This would involve writing down the data and analyzing the optimization information which may be performed in the human mind or with pen and paper. If a claim limitation under BRI, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract idea exception. MPEP § 2106.04(a)(2)(III).
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including: computer, storage devices.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks). The claims recites a “trained machine learning model” but the use of machine learning algorithms does not, by itself, make an abstract data processing idea eligible.
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application (see again: 2019 Revised Patent Subject Matter Eligibility Guidance).
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data
storing or retrieving information
With specific reference to computing system, the Examiner finds that automation within claims stems primarily from “the computer system” (e.g. see spec [96-103]).
Further, see MPEP 2106.05(f), “Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);”.
See MPEP 2106.05(d), “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually. In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Sustrova (“A suitable artificial intelligence model for inventory level optimization”), in view of Carbonneau (“Application of machine learning techniques for supply chain demand forecasting”).
Regarding claim 1, Sustrova teaches receiving an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time, wherein the plurality of inventory variables are characterized by having one or more future uncertainty levels; (model of neural network to determine the optimum amount of ordered goods to optimize the current inventory amount, purchase prices and transport costs are used as input parameters, pg 51);
processing the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables; (ANN model which can be used to optimize inventory level and thus improve inventory management and order system of an enterprise. The variables of current demand, demand in the next 3 months, demand in 3 months following after the 3-month or der cycle (3-month delay), current inventory level, purchase prices and transport costs are used as input parameters. Output data is the ordered quantity, pg. 51., A feed-forward backpropagation network model with one hidden layer and output layer is used, while the number of hidden neurons can be defined, pg. 52, The model was tested with a manufacturing industry data and the results indicated that the model can be used to forecast finished goods inventory level in response to the model parameters, pg 50, The future order amount can be planned based on predicated demand and thus the inventory management can be improved as a part of supply chain management, pg 54),
providing the processed inventory dataset to an optimization algorithm, wherein the optimization algorithm is used to predict a target inventory level for optimizing an inventory holding cost, (A feed-forward backpropagation network model with one hidden layer and output layer is used, while the number of hidden neurons can be defined, pg 52, see Fig. 1 and 2.
Sustrova does not explicitly teach variables that are characterized by having one or more future uncertainty levels and wherein the optimization algorithm comprises one or more constraint conditions that require the target inventory level to at least satisfy a present, incoming or expected demand requirement.
However, Carbonneau teaches the simulated demand signal processing is introduced as the source of demand distortion. In essence, demand signal processing is modeled by a simple linear regression that calculates the trend over the past 10 days, and, which is then used to forecast the demand in 2 days demand signal processing sufficient to cause significant distortion at the end of the extended supply chain, pg. 1144, 1145, machine learning techniques to demand forecasting in supply chains, pg. 1142.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Sustrova’s optimization approach with Carbonneau’s uncertainity characterizing forecasting approach, in order to enable effective extended supply chain collaboration, (Carbonneau, pg. 1141).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MILENA RACIC whose telephone number is (571)270-5933. The examiner can normally be reached M-F 7:30am-4pm EST.
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/MILENA RACIC/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627
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