DETAILED ACTION
This Non-Final Office action is in response to Applicant’s RCE filing on 12/01/2025. Claims 1, 3-10, 13-17, 19, and 21-25 are pending. The effective filing date of the claimed invention is 11/08/2022.
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 .
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, 3-10, 13-17, 19, and 21-25 are rejected under 35 U.S.C. 101 because the claims recite abstract idea without inventive concept.
Step 1 – Claims 1, 3-16, and 21 relate to an apparatus/machine; Claims 17, 22, 24 relate to method/process claims; Claims 19, 23, 25 relate to a non-transitory computer product/manufacture claims. Accordingly, step 1 is satisfied for claims 1, 3-17, 19, and 21-25.
Step 2A, Prong 1 – Exemplary claim 1 (and similarly claims 17 and 19) recites the abstract idea of:
train an inventory management engine system, the training comprising: computing, using a first machine learning model, a second demand forecast for an item type obtainable from one or more sources and storable as inventory at one of a first site or a second site after receiving the item type obtainable from the one or more sources at a third site comprising an intermediary location for redirecting items from the one or more sources to one or more sites including the first site and the second site based on a first demand forecast, wherein the first demand forecast is computed prior to a shipment of the item type obtainable from the one or more sources (this limitation relates to training a system by “using machine learning” and computing data; the concept of “training” was found to be ineligible in Recentive, see Step 2A, Prong 2; for the computing data aspect by using machine learning model this relates to abstract idea of e.g. MPEP 2106.04(a)(2)(I) mathematical concepts, and/or MPEP 2106.04(a)(2)(III) compute capable of being performed by pen and paper/in mind),
computing a discrepancy value for the item type for each of the first site and the second site based on the second demand forecast and additional data collected after the item type obtainable from the one or more sources is obtained at the third site, including historical source data associated with the one or more sources, current item order data associated with the item type obtainable from the one or more sources, and forecasted item order data associated with the item type obtainable from the one or more sources, and current item inventory data at the first site and the second site, and item aging data associated with the item type obtainable from the one or more sources using at least a first machine learning algorithm and a second machine learning algorithm of the first machine learning model (for the computing data aspect by using machine learning model and algorithms this relates to abstract idea of e.g. MPEP 2106.04(a)(2)(I) mathematical concepts, and/or MPEP 2106.04(a)(2)(III) compute capable of being performed by pen and paper/in mind);
classifying the first site and the second site, using the first machine learning model based on the computed discrepancy values wherein at least one of the computed discrepancy values exceeds a threshold (see e.g. MPEP 2106.05(f)(2) TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747 (emphasis added));
generating a set of mitigation plans, using a third machine learning algorithm of a second machine learning model, based on the additional data collected at the third site after the item type obtainable from the one or more sources is obtained at the third site and the classification of the first site and the second site based on the computed discrepancy values for each of the first site and the second site exceeding the threshold, and the second demand forecast (see e.g. MPEP 2106.04(a)(2)(II)(B) Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include: ii. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979));
computing a cost associated with each mitigation plan of the set of mitigation plans, using the second machine learning model, based on transportation cost data, labor cost data, and labeling cost data associated with each plan of the set of mitigation plans (see e.g. MPEP 2106.04(a)(2)(I) mathematical concepts, relationships such as cost = ML2 algorithm (a)(b)(c)…(n));
computing a route associated with each mitigation plan of the set of mitigation plans, using a third machine learning model based on locations of the first site, the second site, and the third site and the computed cost associated with each mitigation plan of the set of mitigation plans (see e.g. MPEP 2106.04(a)(2)(I) mathematical concepts, relationships such as route = ML3 algorithm (d)(e)(f)…(n));
selecting, using a fourth machine learning algorithm of the third machine learning model, a recommendation operation from the set of mitigation plans to mitigate the computed discrepancy values for each of the first site and the second site, wherein the recommendation operation selection is based on the associated computed cost and the associated computed route of the set of mitigation plans (this limitation relates to selecting an optimized result by using “a fourth machine learning algorithm”; see e.g. MPEP 2106.04(a)(2)(I)(C) Examples of mathematical calculations recited in a claim include: v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); for the actual selecting see Step 2A, Prong 2); and
re-iterating the training to re-train the first, second and third machine learning models of the inventory management engine system based on an indication of one of acceptance or rejection of the selected recommendation operation (see Step 2A, Prong 2); and
wherein in response to the re-trained first, second and third machine learning models of the inventory management engine system meeting a threshold accuracy, automatically causing the selected recommendation operation to be executed in order to mitigate the computed discrepancy values for each of the first site and the second site, wherein the execution of the recommendation operation comprises one of inventory management engine system: (i) altering one or more shipment plans for the item type obtainable to the first site and the second site, and (ii) placing a new order to another source of the one or more sources for the item type obtainable (see e.g. MPEP 2106.04(a)(2)(I) “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas),” where in the claim datum is selected, and the execution of the selected data is the series of mathematical calculations; for the altering of the shipment plans, this is MPEP 2106.04(a)(2)(II)(A) fundamental economic practice; for the placing an order with a different vendor, this is also MPEP 2106.04(a)(2)(II)(A) fundamental economic practice).
Accordingly, when viewed alone and in ordered combination, these abstract concepts are found to recite abstract idea.
Step 2A, Prong 2 – Claim 1 is not found to integrate the identified abstract idea into practical application. Claim 1 recites the additional elements of “at least one processing device comprising a processor coupled to a memory, the at least one processing device, when executing program code, is configured to [implement abstract idea]; the training and iterative training of machine learning; and “selecting . . . a recommendation operation from the set of mitigation plans. . . .”
For the processor(s) coupled to memory, see “apply it” rationale at MPEP 2106.05(f).
For the training and iterative training aspects, Recentive court, pg 12, found “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”) (emphasis added). See further pg 12 of Recentive, “Recentive argues in its briefs that its application of machine learning is not generic because “Recentive worked out how to make the algorithms function dynamically, so the maps and schedules are automatically customizable and updated with real-time data,” Appellant’s Reply Br. 2, and because “Recentive’s methods unearth ‘useful patterns’ that had previously been buried in the data, unrecognizable to humans,” id. (internal citation omitted). But Recentive also admits that the patents do not claim a specific method for “improving the mathematical algorithm or making machine learning better.” Oral Arg. at 4:40–4:44. Even if Recentive had not conceded the lack of a technological improvement, neither the claims nor the specifications describe how such an improvement was accomplished. That is, the claims do not delineate steps through which the machine learning technology achieves an improvement. See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that “d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way,” because “[s]uch functional claim language, without more, is insufficient for patentability under our law.” (quoting Two-Way Media Ltd v. Comcast Cable Commc’ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). “[T]he patent system represents a carefully crafted bargain that encourages both the creation and the public disclosure of new and useful advances in technology, in return for an exclusive monopoly for a limited period of time.” Pfaff v. Wells Elecs., 525 U.S. 55, 63 (1998); Sanho Corp. v. Kaijet Tech. Int’l Ltd., 108 F.4th 1376, 1382 (Fed. Cir. 2024). Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. In this respect, the patents’ claims are materially different from those in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Koninklijke, the cases on which Recentive relies. Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is event scheduling and the creation of network maps.” Similarly, the current claims use machine learning in the new environment of e.g. creating recommendation relating to altering shipment plans (altering data) and/or placing an order (communicating data)).
For the “selecting . . . a recommendation operation from the set of mitigation plans. . . .” the examiner refers to e.g. MPEP 2106.05(a)(II) Examples that the courts have indicated may not be sufficient to show an improvement to technology include: vii. Selecting one type of content (e.g., FM radio content) from within a range of existing broadcast content types, or selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of well-known, routine, conventional functions performed by the hardware, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016). See also MPEP 2106.05(g) Below are examples of activities that the courts have found to be insignificant extra-solution activity: iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Furthermore, See MPEP 2106.04(II)(A)(2) citing See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”). Therefore, the continued reciting of a first, second, third, fourth, and potentially more, algorithms, models, that are trained and re-iteratively trained, is still just adding abstract idea onto abstract idea, and “does not render the claim non-abstract.”
Another case from the Federal Circuit has recently published. See Recentive Analytics v. Fox Corp., Appeal 2023-2437 (Fed. Cir. 04/18/2025). The claims in Recentive were found to be ineligible as abstract idea. Here is representative claim 1 of the “machine learning training patents”:
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Exemplary claim 1 of Recentive includes, among other things, providing data to machine learning model(s) including first second third model as claimed, iteratively training the model(s) to improve the accuracy of the model(s), detecting a real time change, inputing the change into the model(s), updating the schedule based on the iteratively trained model(s). The court further finds at page 5: “The specification also makes clear that the patented method employs “any suitable machine learning technique[,] . . . such as, for example: a gradient boosted random forest, a regression, a neural network, a decision tree, a support vector machine, a Bayesian network, [or] other type of technique.” Id. col. 6 ll. 1–5. The schedules are generated “dynamically, in response to real-time changes in data,” allowing “input parameters and target features [to] be processed and considered more efficiently and accurately[] compared to prior approaches.” Id. col. 9 ll. 20–25.” See further reasoning in Recentive at pages 11-16. One notable finding made by the court is here:
“Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”); compare McRo, 837 F.3d at 1314– Case: 23-2437 (finding eligibility of claims to use specific computer techniques different from those humans use on their own to produce natural-seeming lip motion for speech).”
Accordingly, claim 1 (and similarly claims 17 and 19), when the limitations above are viewed alone and in ordered combination, is directed to abstract idea.
Step 2B - Claim 1 (and similarly claims 17 and 19) does not include significantly more than the underlying abstract idea. The additional element analysis from Step 2A, Prong 2 is equally applicable at Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. MPEP 2106.05(d).
As for the findings of well-understood, routine, conventional (WURC) activity, the following are relevant findings that the court(s) have made in regards to the limitations that are similar to the claimed limiations. MPEP 2106.05(d)(II): The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
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));
ii. Performing repetitive/iterative calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”);
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
With regard to claim 1, there are aspects of the claim that relate to receiving/transmitting data over a network, such as receiving a selection of data, which is transmitted as a data message; further, the altering the shipment plans and/or placing a new order likely relate to receiving/transmitting data, and are therefore found to be WURC activity.
Furthermore, claim 1 recites training the system and then re-iterating the training to re-train . . .” this is similar to performing repetitive/iterative calculations, which has been found to be WURC activity by the Federal Circuit.
Further, the aspects of the claim that relate to storing data, recordkeeping, retrieving from memory are also found to be WURC activity.
Accordingly, when these additional elements are viewed alone and in ordered combination, they are not found to include significantly more. Therefore, claim 1 is found to be directed to abstract idea.
Dependent claims – Claim 3, 4, 7, 8 recites more abstract idea as shown in July 2024 Subject Matter Eligibility Examples - example 47, claim 2, found ineligible; MPEP 2106.04(a)(2)(I) mathematical concepts. Claims 5-6 further specifies that the “third site is a receiving hub for respective quantities of the item type . . .”, and similarly at claim 6 discussing geographic locations of the hub and the like, where this is a field of use and technological environment type claim where this has not been found to pass the eligibility test at step 2A, prong 2 or Step 2B. See MPEP 2106.05(h). Claim 9 recites more abstract idea of where the discrepancy value(s) data represents certain conditions. See MPEP 2106.04(a)(2)(II) certain methods of organizing human activity. Claim 10 does not pass under step 2A, prong 1 and/or step 2 as automating a manual activity is insufficient to pass under these steps. See MPEP 2106.05(a)(I). Claims 11-13 recite more abstract idea of utilizing cost data. See MPEP 2106.04(a)(2)(II) fundamental economic practice, commercial interaction. Claim 14 recites more abstract idea relating to the type of data in calculations. Claim 15 is more field of use and technological environment subject matter. See MPEP 2106.05(h). Claim 16 is more abstract idea relating to the forecasting. See MPEP 2106.04(a)(2)(II). Claims 21-23 are more abstract idea such as putting input values into an algorithm. See MPEP 2106.04(a)(2)(I)(A). Claims 24-25 recite more abstract idea. See MPEP 2106.04(a)(2). Accordingly, claims 1, 3-17, 19, and 21-23 are found to be directed to abstract idea.
Further, the examiner refers to Applicant’s originally-filed Specification at the Background:
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All of the advantages of the present invention relate to improvements in potentially the underlying abstract idea. However, there is no technical improvement to a technical problem as required under 35 USC 101.
Claims 1, 3-10, 13-17, 19, and 21-25 are Distinguished Over the Prior Art
The examiner has been unable to find the limitations of claims 1, 17, and 19 in the prior art. Accordingly, the examiner has removed the rejections under 35 USC 103.
Response to Arguments
Applicant's arguments filed 12/19/2024 have been fully considered but they are not persuasive.
The examiner has withdrawn the previously made 112(f) invocations and rejections under 112(a-b) based on the amendments provided.
Applicant argues that the claims recite eligible subject matter under 35 USC 101. The examiner respectfully disagrees. The examiner has changed the 101 rejection above to cover the claimed subject matter. The examiner maintains that abstract idea is recited, and that the claims are directed to abstract idea. The reasons are set forth above.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627