DETAILED ACTION
This Non-Final Office action is in response to Applicant’s Amendment filed on 03/16/2026. Claims 17-36 are pending. The effective filing date of the claimed invention is 08/11/2023.
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 17-36 are rejected under 35 U.S.C. 101 because the claims are found to be directed to abstract idea.
Step 1 – Claims 24-30 relate to process claims; and, claims 17-23 and 31-36 relate to machine claims. Step 1 is satisfied.
Step 2A, Prong 1 – Exemplary claim 17 (and similarly claims 24, 31) recites abstract idea of:
maintaining product profiles of a plurality of products, wherein the plurality of products includes respective components, and wherein the product profiles indicate updated configurations of the plurality of products, including updated configurations of the respective components of the plurality of products (see e.g. MPEP 2106.04(a)(2)(III) mental process of maintaining profiles in mind, or with pen and paper; for the updating aspects, see MPEP 2106.04(a)(2)(II)(C) Another example of a claim reciting social activities is Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of “’providing information to a person without interfering with the person’s primary activity.’” 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)). The patentee claimed an attention manager for acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content. 896 F.3d at 1339-40, 127 USPQ2d at 1555. The Federal Circuit concluded that “[s]tanding alone, the act of providing someone an additional set of information without disrupting the ongoing provision of an initial set of information is an abstract idea,” observing that the district court “pointed to the nontechnical human activity of passing a note to a person who is in the middle of a meeting or conversation as further illustrating the basic, longstanding practice that is the focus of the [patent ineligible] claimed invention.” 896 F.3d at 1344-45, 127 USPQ2d at 1559.);
building and training a set of models based on at least some of the maintained product profiles, the set of models configured to perform (see e.g. MPEP 2106.04(a)(2)(I); Recentive1 v. Fox. Corp., Appeal No. 2023-2437 (Fed. Cir. 04/18/2025) building and training models found to include “using known generic mathematical techniques” page 92; Recentive page 11, “Considering the focus of the disputed claims, Alice, 573 U.S. at 217, it is clear that they are directed to ineligible, abstract subject matter.” Recentive at page 13 - 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.):
identifying products that are expected to be returned in the future (see MPEP 2106.04(a)(2)(II)(b); MPEP 2106.04(a)(2)(III));
predicting retention times for products that are expected to be returned in the future (see MPEP 2106.04(a)(2)(II)(b); MPEP 2106.04(a)(2)(III)); and
determining, based on quality indicators of products that are expected to be returned in the future and on requirements for reusability of components, reusable components of products that are expected to be returned in the future (see MPEP 2106.04(a)(2)(II)(b); MPEP 2106.04(a)(2)(III)); and
using the set of models and a product profile, of the maintained product profiles, of a product in use, the product in use comprising a set of components and the product profile indicating a current configuration of the product in use, including a current configuration of the set of components (see e.g. MPEP 2106.04(a)(2)(I); Recentive v. Fox. Corp., Appeal No. 2023-2437 (Fed. Cir. 04/18/2025) building and training and using models found to include mathematical concepts, and in any case found to be ineligible abstract idea):
determining that the product in use is expected to be returned in the future and an expected timeframe of the return of the product in use (see e.g. MPEP 2106.04(a)(2)(III)); and
determining one or more components, of the set of components of the product in use, that are reusable upon return of the product in the future (see e.g. MPEP 2106.04(a)(2)(III));
repeating, for a collection of other computer hardware products in use, the using the set of models, together with maintained product profiles of the other computer hardware products in use, wherein the repeating determines one or more other computer hardware products in use that are expected to be returned in the future, and determines computer hardware components, of the one or more other computer hardware products, that are reusable upon return of the one or more other computer hardware products in the future (see MPEP 2106.04(a)(2)(II)(b); MPEP 2106.04(a)(2)(III));
aggregating results of the using the set of models to determine a collection of reusable computer hardware components, timing when each reusable computer hardware component of the collection will be available in the future, configuration of each reusable computer hardware component of the collection, and quality of each reusable computer hardware component of the collection (see MPEP 2106.04(A)(2)(C) i. filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis); MPEP 2106.04(a)(2)(II)(B) citing Fort Properties, Inc. v. American Master Lease, LLC, 671 F.3d 1317, 101 USPQ2d 1785 (Fed Cir. 2012)); and
based on the aggregating, identifying at least one computer hardware product to remanufacture for quality improvement by replacing one or more computer hardware components of the at least one computer hardware product (see MPEP 2106.04(a)(2)(III) mental process).
When viewed alone and in ordered combination, these abstract ideas are found to recite abstract idea.
Step 2A, Prong 2 – Exemplary claim 17 (and similarly claim 24, 31) is not found to integrate the abstract idea into practical application. Claim 17 recites the additional limitations of a memory and a processor in communication with the memory wherein the computer system is configured to perform the method, a CRM readable by a processing circuit and storing instructions for execution by the processing circuit. For the memory and processor in communication with the memory, the examiner refers to abstract idea findings above, and MPEP 2106.04(a)(2)(III) Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C. MPEP 2106.04(a)(2)(III)(C). See also MPEP 2106.04(d). 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).
See Recentive page 12 – “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).
When viewed alone and in ordered combination, the examiner does not find the claimed additional limitations to integrate the abstract idea into practical application. The examiner finds claim 12 (and similarly claim 1 and 17) to be directed to abstract idea.
Step 2B – Exemplary claim 12 (and similarly claims 1 and 17) is not found to integrate the abstract idea into practical application. The additional element analysis of Step 2A, Prong 2 is equally applied in 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. See MPEP 2106.05(d). 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.
For the claimed limitations relating to computing communicating - 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));
For the iterative calculation such as iterative building and training a set of models - ii. Performing repetitive 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.”);
For the claimed limitations relating to maintaining product profiles and storing data - 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.
When viewed alone and in ordered combination, the examiner does not find the claimed additional limitations to include significantly more. Accordingly, the examiner finds claim 12 (and similarly claim 1 and 17) to be directed to abstract idea.
As found in Recentive, page 10:
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Dependent Claims – Claims 18, 25, 32 recite more abstract idea. See MPEP 2106.04(a)(2)(III); see also MPEP 2106.05(d)(II) Below are examples of other types of activity that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016). Claim 19, 29, 35 recites a retention model. See Recentive, finding models to be abstract idea. Claims 20, 30, 36 recite more abstract idea. See MPEP 2106.04(a)(2)(III). Claim 21, 26, 33 recite a prediction model. See MPEP 2106.04(a)(2)(I) and (III). Claim 22, 27, 34 more about the prediction model which is abstract idea. See MPEP 2106.04(a)(2)(I) and (III); see WURC finding with regard to independent claim. Claim 23 recites more about the set of models, which is abstract idea. See MPEP 2106.04(a)(2)(I) and Recentive. Claim 28 recites maintaining a database, which is abstract idea. See MPEP 2106.04(a)(2)(I) and also MPEP 2106.05(d)(II)(C).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 17-36 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2021/0312388 to Li et al. (“Li”) in view of U.S. Pat. Pub. No. 2023/0307117 to Kumar (“Kumar”).
With regard to claims 17, 24, 31, Li discloses the claimed computer-implemented method comprising:
memory and processor (see Li e.g. [0059]);
maintaining product profiles of a plurality of products, wherein the plurality of products includes respective components (see Li e.g. Fig. 1, 104, [0022] where information on various early lifecycle products are stored in a profile), and wherein the product profiles indicate updated configurations of the plurality of products, including updated configurations of the respective components of the plurality of products (see Li e.g. [0013-14]; See also Kumar, Fig. 2 (combination below));
building and training a set of models based on at least some of the maintained product profiles, the set of models configured to perform (see e.g. Li [0013] building the trained return prediction model; [0014] leveraging machine learning to weight and quantify the impact and importance of these characteristics for predicting return rates; [0015] The early lifecycle product management system exploits these new return prediction ideas (addressing the case of predicting for new products) and uses machine learning to automatically predict a expected return rate for a new product.; [0020-21] [0030-31]; See also Kumar Figs. 9-10):
identifying products that are expected to be returned in the future (see e.g. Li [0015] [0020] [0028]; see also Kumar Fig. 7, blocks 705-715);
predicting retention times for products that are expected to be returned in the future (see e.g. Li [0030]; Fig. 2; see also Kumar Fig. 8 length of use output 810);
determining that the product in use is expected to be returned in the future and an expected timeframe of the return of the product in use (see e.g. Li Fig. 2).
Li does not disclose the other limitations of claim 17, 24, 31. However, Kumar teaches at e.g. Fig. 2, Fig. 5, Fig. 11 [0061-62] [0067] that it would have been obvious to one of ordinary skill in the reusability determination art to include the following:
determining, based on quality indicators of products that are expected to be returned in the future and on requirements for reusability of components, reusable components of products that are expected to be returned in the future (see e.g. Kumar Fig. 11, 1105, 1110, 1115, 1120, where the quality indicators could be the tracked usage of the medical device with the various components, the components shown in Fig. 2 where Medical Device A has Face Apparatus Component, Electric Motor Component, Ventilator Component, Oxygen Supply Component); and
using the set of models and a product profile, of the maintained product profiles, of a product in use, the product in use comprising a set of components and the product profile indicating a current configuration of the product in use, including a current configuration of the set of components (see e.g. Kumar Fig. 2, current configuration:
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):
determining one or more components, of the set of components of the product in use, that are reusable upon return of the product in the future (see e.g. Kumar, Fig. 5, where the remaining life of a particular component is adjusted based on quality indicators such as use, sterilization of said item, and the like; [0061-62] [0067]).
repeating, for a collection of other computer hardware products in use, the using the set of models, together with maintained product profiles of the other computer hardware products in use, wherein the repeating determines one or more other computer hardware products in use that are expected to be returned in the future, and determines computer hardware components, of the one or more other computer hardware products, that are reusable upon return of the one or more other computer hardware products in the future (Kumar Figs. 6-8);
aggregating results of the using the set of models to determine a collection of reusable computer hardware components, timing when each reusable computer hardware component of the collection will be available in the future, configuration of each reusable computer hardware component of the collection, and quality of each reusable computer hardware component of the collection (Kumar, Fig 4, 6); and
based on the aggregating, identifying at least one computer hardware product to remanufacture for quality improvement by replacing one or more computer hardware components of the at least one computer hardware product (Kumar Fig. 3, elements 330-340).
Therefore, it would have been obvious to one of ordinary skill in the reuse art before the effective filing date of the claimed invention to modify Li to include the ability to determine the reusability of specific components of the device as a whole, as shown in Kumar, where this is beneficial in that the reuse components data can be placed into an ML model and the model can identify specific devices where the component(s) can be placed into in order to reuse the components, as shown in Kumar at e.g. [0090-100]. This combination allows for further benefits of using recycled components. See Kumar, [0056].
With regard to claims 18, 25, 32, Li further discloses where maintaining the product profile of the product in use comprises: obtaining order data associated with the product in use, the order data including details of product components at a time of provision of the product in use to a client (e.g. [0003] Fig. 1); and maintaining a current configuration of the product in use by tracking and analyzing changes in configuration of the product over time, including alterations to the set of components of the product (e.g. [0030] different variations of the product).
With regard to claims 19, 29, 35, Li further discloses where the set of models comprises a retention model configured to predict an amount of time the product in use will be retained from an initial date before it is returned, and wherein the determining the expected timeframe of the return of the product in use comprises adding the predicted amount of time to the initial date (e.g. Fig. 2).
With regard to claims 20, 30, 36, Li does not teach the limitations of claims 10, 11 16. However, Kumar teaches at e.g. [0023] that it would have been obvious to one of ordinary skill in the reuse art to include the ability to maintaining the product profile of the product in use comprises: obtaining quality indicators from field quality data associated with the product in use (e.g. [0023] usage); and obtaining maintenance data indicating maintenance performed on the product in use (e.g. [0023] sterilization maintenance); wherein the determining the one or more components that are reusable upon return of the product in use comprises comparing the quality indicators and the maintenance data to the requirements for reusability of components to determine which components of the product in use satisfy the requirements of reusability thereof (e.g. [0023] where the electric motor can be reused, but the ventilator and face mask, based on usage and sterilization cannot be reused). For safety concerns at Kumar [0023] Therefore, it would have been obvious to one of ordinary skill in the reuse art before the effective filing date of the claimed invention to modify Li to include these various quality indicators for analysis, as shown in Kumar, where this is beneficial as the combination of usage and maintenance (e.g. sterilization) is used to better assess whether the various components can be reused, or recycled, or discarded, as shown in Kumar.
With regard to claim 21, 26, 33, Li further discloses where the set of models comprises a prediction model configured to make a prediction that the product in use is expected to be returned in the future, the prediction based, at least in part, on the maintained current configuration (e.g. [0003]).
With regard to claims 22, 27, 34, Li further discloses where the prediction by the prediction model is based further in part on indications as to (i) whether the product in use is current technology (e.g. [0029]), (ii) whether previous technology remains available (e.g. [0029] substitute items), and (iii) whether newer technology is available to replace the product in use ([0013] where the product is new and different thing are taken into account).
With regard to claim 23, 28, Li further discloses where the maintaining the product profiles of the plurality of products further comprises maintaining return statuses of the plurality of products, and wherein the building and training the set of models uses the maintained return statuses in building and training the prediction model (e.g. [0028]).
Response to Arguments
Applicant's arguments filed 03/16/2026 have been fully considered but they are not persuasive.
The examiner has reviewed the arguments under 35 USC 101 and does not find them persuasive. The examiner has updated the 101 rejection above in relation to the current amendments.
As for the prior art rejections, the examiner does not find them persuasive. The examiner has referred to the prior art to teach the added limitations, as shown above. The examiner suggests Applicant review the rejection above and make amendments accordingly.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
1 Recentive ruling – “This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.” Page 10.
2 Recentive, page 16, “The district court correctly concluded that the Machine Learning Training and Network Map patents are directed to abstract ideas at step one of Alice.”
