DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment on 08/31/2026. Claims 1, 8-12, 15, 17, 18, and 23 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.
Claims 1, 8-12, 15, 17-18, 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 “insufficient sample data” in line 25. This is a relative term that is not defined in the claims or in Applicant’s Specification and therefore renders the claim indefinite. Appropriate correction is required.
Claim 1 recites “are improved” in lines 38-39. However, this is a relative term and it is not defined in the claims or Applicant’s Specification. In other words, “improved” over what standard? 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, 8-12, 15, 17, 18, and 23 are rejected under 35 U.S.C. 101 because the claims are directed to abstract idea.
Step 1 – Claims 1, 8-12 are process claims; claim 15, 18, 23 are apparatus claims; claim 17 is a manufacture. 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);
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));
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));
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 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.”
The target abnormality is an electrocardiogram abnormality with insufficient sample data, 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).
the step of training the multi-task model based on the multi-task learning mechanism comprises adjusting the parameters of each of the related task models (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.), and adjusting parameters of the target task model according to parameters of the at least one related task model (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech); and
the step of adjusting parameters of the target task model according to parameters of the at least one related task model comprises: sharing parameters of the common feature extraction layer in the at least one related task model as parameters of the common feature extraction layer in the target task model (e.g. MPEP 2106.04(a)(2)(III)(A) claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);), and adjusting the parameters of the target task model after the parameters are shared to transfer knowledges learned by the at least one related task model to the target task model, so that classification and recognition performance of the target task model are improved (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech).
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 abstract idea into practical application. For the data that is now “collected by a signal acquisition device,” the examiner finds that the signal acquisition device is used as a tool to collect data, or in other words a classic “apply it” situation, where the tool is applied in a normal manner to collect data. See MPEP 2106.05(f). 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. “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.
Applicant has now added the following underlined limitation(s) to preamble of claim 1:
model training method, applied to a dedicated electrocardiogram monitoring hardware device equipped with a multi-stage electrocardiogram signal filter, the multi-stage electrocardiogram signal filter comprising a band-pass filter to be used to remove power interference in original electrocardio-signals, a low-pass filter to be used to remove power interference in the original electrocardio-signals electromyographic interference and a high-pass filter to be used to remove baseline drift in the original electrocardio-signals, the method comprising:
The examiner finds this limitations as “applied to” the recited electrocardiogram signal falls under the “apply it” rationale as well. See MPEP 2106.05(f) As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Accordingly, when viewed alone and in ordered combination, the additional element(s) do not integrate said abstract idea with practical application, and are found to be directed to abstract idea.
Step 2B – Furthermore, exemplary claim 1 is not found to recite significantly more than the underlying abstract idea. The additional element analysis of Step 2A Prong 2 is equally applied to 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 (WURC) 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 MPEP 2106.05(d)(II).
See claim 1, acquiring data, collecting data via device, inputting data into a model, and sharing data - 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, and therefore exemplary claim 1 is found to be directed to abstract idea.
Dependent claims –Claim 9 recites more abstract idea. See MPEP 2106.04(a)(2)(I). Claim 8 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, 8-12, 15, AND 17-18, 23 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 8/31/2026 have been fully considered but they are not persuasive.
Applicant argues the following:
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Applicant argues that the recited “dedicated electrocardiogram monitoring hardware device . . .” is not a generic computer device, and therefore “apply it” does not apply. The examiner respectfully disagrees. Upon review of Applicant’s Spec at [0142], here is the paragraph:
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The examiner notes that this does not appear to provide support for all of the added limitations relating to the dedicated device. Even if support was fully found here, Applicant has support for “using a band-pass filter,” “using a low-pass filter,” and “using a high-pass filter.” The act of “using” something is simply using a generic device, such as a generic “dedicated electrocardiogram monitoring hardware device” as recited in claim 1. There is nothing in the Specification or claims that indicates that the dedicated electrocardiogram monitoring device” is not a generic monitoring device. There is no indication, at least in [0142-143] or elsewhere, that Applicant invented this “dedicated electrocardiogram . . . device” no matter how complicated the device is. See Applicant’s Spec at:
[0158] Each of devices according to the embodiments of the present disclosure can be implemented by hardware, or implemented by software modules operating on one or more processors, or implemented by the combination thereof. A person skilled in the art should understand that, in practice, a microprocessor or a digital signal processor (DSP) may be used to realize some or all of the functions of some or all of the parts in the electronic device according to the embodiments of the present disclosure. The present disclosure may further be implemented as equipment or device program (for example, computer program and computer program product)
for executing some or all of the methods as described herein. Such program for implementing the present disclosure may be stored in the computer readable medium, or have a form of one or more signals. Such a signal may be downloaded from the Internet websites, or be provided on a carrier signal, or provided in any other form.
[0159] For example, FIG. 9 illustrates an electronic device that may implement the method according to the present disclosure. Traditionally, the electronic device comprises a processor 1010 and a computer program product or a computer readable medium in form of a memory 1020. The memory 1020 may be electronic memories such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk or ROM. The memory 1020 has a memory space 1030 for executing program codes 1031 of any steps in the above methods. For example, the memory space 1030 for program codes may comprise respective program codes 1031 for implementing the respective steps in the method as mentioned above. These program codes may be read from and/or be written into one or more computer program products. These computer program products include program code carriers such as hard disk, compact disk (CD), memory card or floppy disk. These computer program products are usually the portable or stable memory cells as shown in reference FIG. 10. The memory cells may be provided with memory sections, memory spaces, etc., similar to the memory 1020 of the electronic device as shown in FIG. 9. The program codes may be compressed for example in an appropriate form. Usually, the memory cell includes computer readable codes 1031' which can be read for example by processors 1010. When these codes are operated on the electronic device, the electronic device may be caused to execute respective steps in the method as described above.
The examiner has reviewed Applicant’s arguments regarding the 101 rejection. Applicant argues that a relative term (with 112 rejection above) is eligible. The examiner respectfully disagrees for the reasons stated above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Ludwig whose telephone number is (571)270-5599. The examiner can normally be reached Mon-Fri 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd Obeid can be reached at 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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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.
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