DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment filed on 01/28/2026. Claims 1, 2, 7-12, 15, and 17-19 are pending. The effective filing date of the claimed invention is 07/27/2021.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 2, 7-12, 15, 17-19, 22-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the common features” in line 22. There is insufficient antecedent basis for this limitation as ‘common features’ by itself has not been recited prior to this. Accordingly, this renders the claim indefinite. Appropriate correction is required.
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 1-2, 7-12, 15, 17-19, 22, and 23 are rejected under 35 U.S.C. 101 because the claims are directed to abstract idea.
Step 1 – Claims 1-2, 7-12 are process claims; claim 15 is an apparatus claim; claim 17 is a manufacture; claims 18, 19, 22, 23 are apparatus claims. These claims satisfy step 1.
Step 2A, Prong 1 – Exemplary claim 1 recites the following abstract idea:
A model training method, comprising:
Acquiring, by a model training device1, a training sample set, wherein the training sample set comprises sample electrocardio-signals and abnormal labels of the sample electrocardio-signals, and the abnormal labels comprise target abnormal labels and at least one related abnormal label (see Recentive v. Fox, Appeal No. 2023-2437 (Fed. Cir. 2025)(attached), where for the “Machine Learning Training Patents” the Court found the following to be abstract idea:
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Where in a later limitation starting with “iteratively training. . . .” this data received is used to train the ML model(s); MPEP 2106.04(a)(2)(III)(C)(1-3) mental process performed by a computer);
inputting the sample electrocardio-signals into a multi-task model, and training the multi-task model based on a multi-task learning mechanism according to an output of the multi-task model and the abnormal labels (see Recentive, where the limitation of training the model based on the input data was found to be abstract idea; MPEP 2106.04(a)(2)(I); MPEP 2106.04(a)(2)(II)(C));
by using the filter, removing noise interference from the training sample set (see e.g. MPEP 2106.04(a)(2)(II)(C) Other examples of managing personal behavior recited in a claim include:
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));
inputting, by the model training device, the sample electrocardio-signals into a multi-task model, and training, by the model training device, the multi-task model based on a multi-task learning mechanism according to an output of the multi-task model and the abnormal labels (See Recentive page 3 - Claim 1 of the ’367 patent is representative of the Machine Learning Training patents and recites a method containing: (i) a collecting step (receiving event parameters and target features); (ii) an iterative training step for the machine learning model (identifying relationships within the data); (iii) an output step (generating an optimized schedule); and (iv) an updating step (detecting changes to the data inputs and iteratively generating new, further optimized schedules); see Recentive, page 14, We have long recognized that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC);
wherein, the multi-task model comprises a target task model and at least one related task model, a target output of the target task model is target abnormality labels of the inputted sample electrocardio-signals, and a target output of the related task model is the related abnormal labels of the inputted sample electrocardio-signals (see Recentive, where the limitation of training the model based on the input data was found to be abstract idea; MPEP 2106.04(a)(2)(I); MPEP 2106.04(a)(2)(II)(C), see also Step 2A Prong 2, Step 2B); and
determining, by the model training device, the target task model after trained as a target-abnormality-recognition model (see e.g. MPEP 2106.04(a)(2)(III)), wherein the target-abnormality-recognition model is configured for recognizing a target abnormality in the electrocardio-signals inputted into the target-abnormality-recognition model (see e.g. MPEP 2106.04(a)(2)(III); See Recentive, limitation of,
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where target features are taken into account). See Recentive, “In operating the machine learning model, users enter “target features,” which are a user’s selected results, such as maximizing event attendance, revenue, or ticket sales. Id. col. 6 ll. 12–15. The machine learning model may “be trained to recognize how to optimize, maximize, or minimize one or more of the target features based on a given set of input parameters.” Id. Eventually, the machine learning model will “generate the optimized schedule[] and provide the schedule . . . as output.” Id. col. 6 ll. 16–17.”
wherein the target task model and each of the at least one related task models comprise a private feature extraction layer and a common feature extraction layer, the private feature extraction layer is used to extract private features, and the common feature extraction layer is configured to extract the common features of the target abnormality and a related abnormality (See July 2024 Subject Matter Eligibility Examples, claim 2, where the ANN with various layers is training and outputs data, is found be abstract ideal see also MPEP 2106.04(a)(2)(III)(D) Examples of product claims reciting mental processes include: An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356).
When viewed alone and in ordered combination (as a whole), these abstract idea limitations are found to recite abstract idea.
Step 2A, Prong 2 – Exemplary claim 1 is not found to integrate the found abstract idea into practical application. For the training limitation, see Recentive, “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). “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))).” “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.” “We see no merit to Recentive’s argument that its patents are eligible because they apply machine learning to this new field of use. We have long recognized that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015); see also Alice, 573 U.S. at 222; Parker v. Flook, 437 U.S. 584, 593 (1978); Stanford, 989 F.3d at 1373 (rejecting argument that a claim was not abstract where patentee contended “the specific application of the steps [was] novel and enable[d] scientists to ascertain more haplotype information than was previously possible”). We have also held the application of existing technology to a novel database does not create patent eligibility.” Similarly, the present claims use machine learning techniques in the electrocardio-signal technological area (i.e. new environment), and further the how is not recited in the claim. Accordingly, when viewed alone and in ordered combination, the additional element(s) do not integrate said abstract idea with practical application.
Step 2B – Furthermore, exemplary claim 1 is not found to recite significantly more than the underlying abstract idea. 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.
“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.” The Court did not find this persuasive.
The courts have recognized the following computer functions as well‐understood, routine, and conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
See claim 1, acquiring data, inputting data into a model - i. Receiving or transmitting data over a network (see in claim 1), 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));
See claim 1, iterative training - 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.”);
See claim 1, extracting data - v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
As a result, the examiner does not find that exemplary claim 1 recites significantly more than the underlying abstract idea.
Dependent claims – Claim 2, 19 is not eligible as this was addressed in Recentive, “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.” Claim 3, 20 recites more abstract idea as shown in MPEP 2106.04(a)(2)(III) and Recentive. Claim 4 recites more abstract idea see MPEP 2106.04(a)(2)(I). Claim 5, 21 recites more abstract idea as shown in Recentive and MPEP 2106.04(a)(2)(III). Claim 6, 9 recites more abstract idea. See MPEP 2106.04(a)(2)(I). Claim 7-8, 22 recites more abstract idea under e.g. MPEP 2106.04(a)(2)(II)(C). See also Recentive. Claim 10-12, 15, 17, 18, 23 recites abstract idea as shown above, and WURC at MPEP 2106.05(d) 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.”).
CLAIMS 1, 2, 7-12, 15, AND 17-19 DISTINGUISHED OVER PRIOR ART
The examiner has been unable to find each and every limitation, in ordered combination, as claimed. Accordingly, the examiner has withdrawn the previously-made rejections under 35 USC 103.
Response to Arguments
Applicant's arguments filed 01/28/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims recite eligible subject matter. The examiner respectfully disagrees. In reviewing the claim set, the examiner does not find any technological improvement to the underlying technology. Applicant argues that their own Spec at [0087-88] provides for the improvement in the underlying technology. These paragraphs appear to indicate that the improvement lies in generalization, classifying and recognizing large amounts of data, and that these “are improved.” The examiner refers Applicant to MPEP 2106.05(f)(2) Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
As for the 103 rejections and related arguments, the examiner has withdrawn the prior art rejections. In reviewing the arguments and the claim amendments, the examiner agrees that the prior art does not teach the recited limitations.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
1 See Applicant’s Spec at e.g. [0066] where the model training device can be a smart phone, tablet computer, etc.