DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/17/2026 has been entered.
Response to Amendment
This Office Action is responsive to the amendment filed on 01/16/2026. As directed by the amendment: claims 1, 4-5, 7, 9, and 12-14, and 16 have been amended, claims 2-3, 6, 8, 10-11, 15, and 17-24 have been cancelled, and no claims have been added. Thus, claims 1, 4-5, 7, 9, and 12-14, and 16 are presently under consideration in this application.
Response to Arguments
Applicant's arguments, see page 7, filed 01/16/2026, regarding 35 U.S.C. 112(b) have been fully considered and are persuasive. The amendments obviate the rejection of record. Therefore, the rejection of the claims is withdrawn.
Applicant's arguments, see pages 7-15, filed 01/16/2026, regarding 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues on pages 8-9 that “Applicant respectfully submits that the portion of the Office Action excerpted above exhibits a piecemeal interpretation of claim 1 that fails to consider the claim as a whole. The various operations described are extracted thereby disconnecting them from the larger context of claim 1 including the various components and device-based operations performed… Operations such as "changing a frequency of the AF monitoring periods" and "reducing a sampling rate of the PPG sensor" (as was recited in dependent claim 2 and now incorporated into claim 1) require changes to the state of physical components in the electronic device and, as such, may not be performed in the human mind. Other operations that involve execution of a first machine learning model, a classification model, and a second model also may not be performed in the human mind… Applicant respectfully submits that while a human mind may decide that an AF monitoring period should be changed or that a given sampling rate should be changed (see, e.g., page 3 in the Response to Arguments portion of the Office Action), the human mind is unable to effectuate any
actual change of the AF monitoring period as performed by the electronic device or effectuate any actual change/reduction of the sampling rate of a PPG sensor in the electronic device as recited in claim 1.” Examiner notes that although the limitations brought forth by Applicant cannot be done in the mind, these limitations are applying/implementing the abstract idea of selecting and changing frequency and compare the physiological attribute signals with estimates to reschedule the length periods, on a generic computing component that do not integrate the judicial exception into practical application.
Applicant then asserts on page 10 that “Applicant respectfully submits that the Office Action's reliance on MPEP 2106.05(f) pertaining to "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" is misplaced. The power efficiency and management features of claim 1 are not analogous to MPEP 2106.05(f) from simply applying an abstract idea on a computer, but rather the improved power management implemented in the claimed electronic device relative to other such electronic devices configured to monitor/detect AF that fail to provide extended A monitoring and detection periods.” Applicant then submits the MPEP changes and memo of Desjardins and argues on pages 10-11 that “independent claim 1 integrates the alleged abstract idea into a practical
application as the functioning of the electronic device is improved. The electronic device is capable
of accurately detecting AF while conserving power so as to extend the time the electronic device is able to monitor for AF in the device user, thereby providing a technological solution to the
technological problems discussed in paragraphs 21-23 of Applicant's disclosure.” Applicant further asserts on page 11 that “”
Examiner disagrees because Desjardins is directed to the improvement of the reducing the use of storage capacity of the memory, reducing system complexing and streamlining, and preservation of performance attributes using machine learning due to “catastrophic forgetting”, which is a practical application. See MPEP 2106.04(d) subsection III. Although the instant claims may improve power management, the claims are not improving the reduction in use of battery capacity using machine learning, but rather, reducing usage of the battery for power efficiency by implementing the abstract idea on a computer to determine when to turn on/off time intervals/periods for AF risk determination.
Applicant then argues on page 11 that “The training processes, unlike other approaches and models, trains the classification model to utilize motion type (e.g., rather than motion intensity as discussed in paragraphs 55-56 of Applicant's disclosure) in generating classifications that are then used as the basis for reducing the sampling rate for one or more of the AF monitoring periods. This process allows power consumption of the electronic device to be reduced through reduction in the sampling rate, thereby extending the monitoring time of the electronic device without sacrificing accuracy in AF detection. See, e.g., paragraphs 52-56 of Applicant's disclosure discussing training of the classification model. Accordingly, claim 1 recites a particular training technique that facilitates power management in terms of reduced sampling rates while maintaining accurate AF detection.” Applicant further argues on page 12 that “submits that the training described in Applicant's disclosure and
referenced in amended claim 1 is analogous to the situation discussed in Ex Parte Desjardins at
least in that complexity of AF monitoring is reduced. Further, sampling rates of a PPG sensor may
be adjusted/reduced while still accurately detecting AF in a user to reduce power consumption
even in the presence of various types of motion that the classification model is, by virtue of the
training, now able to differentiate. Like in Ex Parte Desjardins and as stated by the Desjardins
Memo, "[s]uch improvements were tantamount to how the machine learning model itself would
function in operation and therefore not subsumed in the identified mathematical calculation."”
Unlike Desjardins that uses “the machine learning model [that] is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” that is directly beneficial to reduce both the use of storage capacity and the complexity of the system, the training of a model for generating classifications of AF and adjusting sampling rate has no effect on the battery’s capabilities, but rather, just the controlling of periods of times to monitor AF, which determines the power efficiency. Examiner notes that Desjardins’ protection of knowledge of previous tasks intrinsically would have an effect on memory, due to the problem of “catastrophic forgetting”, which inherently is a storage capacity problem. In the same light, the instant specifications problem of power management cannot be attributed to AF monitoring, because AF monitoring is not the source of the problem, but rather, the management of computing/energy-efficiency done by the electronic device, as seen in [0023] of the instant specification.
Applicant then argues on page 13 that “Claim 1 stands for more than merely the usage of a PPG sensor and/or an accelerometer. This interpretation fails to consider the context in which these elements are recited and exist within claim 1 including, for example, the control and manipulation of these respective elements - particularly the PPG sensor. The control exerted over these elements and operation of the various models recited in claim 1 are not well-understood, routine, or conventional.” Examiner disagrees because PPG sensors and accelerometers are well-understood, routine, and conventional, and the manipulation of the elements are extra-solution activity implemented on a generic computing component.
Applicant then argues on page 14 that “The aforementioned features, e.g., PPG sensors, AF monitoring periods (periods during which the PPG sensor is active), the frequency of such periods, and the sampling rate of the PPG sensor, have a direct bearing on the operation of the electronic device and its ability to provide extended AF monitoring and detection. In addition, the interpretation of these features by the Office Action, as illustrated in the excerpted portions above, demonstrates a piecemeal approach to interpreting claim 1 that fails to consider these features in the larger context of the claim and fails to consider the claim as a whole. While sensors may be considered "data gathering" in some cases, it cannot be said that the control of such sensors by way of adjusting the sampling rate and changing frequency of AF monitoring periods during which such sensors are active is simply data gathering. Further, operations such as powering down the PPG sensor based on the classification that is obtained as recited in claim 7 cannot be said to be mere data gathering steps.” Applicant is asserting the abstract idea itself as the improvement. However, the abstract idea cannot be an “additional element” that shows integration into a practical application. The order of calculations and the particular calculations claimed do not make the abstract idea any less abstract. The claims are currently structured as simply using a generic computer to implement the abstract idea (mental process), which is not enough to show a practical application.
Applicant lastly argues on page 14 “that the aforementioned improvements to the functioning of the electronic device with respect to both changing the time interval between successive AF monitoring periods during which signals are sampled and the ability to change the sampling rate (e.g., dependent claims), the particular training technique recited in connection with the classification model that is operative on the PPG signals and IMU data all contribute to an electronic device that is capable of accurately detecting AF in a user for longer/extended periods of time compared to other such electronic devices.” Examiner disagrees since the processing of data on a microcontroller unit is merely performing this process on a generic computer structure. The transmitting of signals is simply a generic computer function performed by a generic computer structure, wherein implementing the abstract idea with a generic computer is not enough to show integration into a practical application or significantly more than the abstract idea itself. The transmission of data to and from the sensor systems is merely data gathering, which is insignificant extra-solution activity. Therefore, the rejection is maintained.
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, 4-5, 7, 9, and 12-14, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Each of independent claims 1 and 9, recites a step generating an AF risk for the user by processing the physiological attributes including heart rate, respiratory rate, and blood pressure…changing, by the processor of the electronic device, a frequency of subsequent AF monitoring periods by changing, a time interval between successive AF monitoring periods of the subsequent AF monitoring periods, based on the AF risk for the user, wherein a length of the time intervals between the AF monitoring periods is increased in response to detecting a low AF risk for the user … in response to obtaining further signals representing physiological attributes of the user at a start of a selected AF monitoring period, comparing the further signals with the estimates…generating estimates of a heart rhythm of the user using a model that is operative on prior heart rhythms of the user stored in a queue…comparing the further signals with the estimates…resetting a schedule that specifies a length of the time intervals between the AF monitoring periods thereby reducing the length of the time intervals between the AF monitoring periods, which is a mental process. This judicial exception is not integrated into a practical application because the generically recited computer elements (ie. a storage, processor, wearable electronic device), determining AF risk, and scheduling monitoring times do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations are to receiving data, processing data, and scheduling AF monitoring times, which are all well-understood, routine, and conventional computer functions. See MPEP § 2106.05(d).
MPEP 2106(III) outlines steps for determining whether a claim is directed to statutory subject
matter. The stepwise analysis for the instant claim is provided here.
Step 1 – Statutory categories
Claim 9 is directed to a system (i.e. machine) and thus meets the step 1 requirements.
Claims 1 is directed to a method and thus meets the step 1 requirements.
Step 2A – Prong 1 – Judicial exception (j.e.)
Regarding claims 1 and 9 , the following step is an abstract idea:
“generating an AF risk for the user by processing the physiological attributes including heart rate, respiratory rate, and blood pressure…changing, by the processor of the electronic device, a frequency of subsequent AF monitoring periods by changing, a time interval between successive AF monitoring periods of the subsequent AF monitoring periods, based on the AF risk for the user, wherein a length of the time intervals between the AF monitoring periods is increased in response to detecting a low AF risk for the user … in response to obtaining further signals representing physiological attributes of the user at a start of a selected AF monitoring period, comparing the further signals with the estimates…generating estimates of a heart rhythm of the user using a model that is operative on prior heart rhythms of the user stored in a queue…comparing the further signals with the estimates…resetting a schedule that specifies a length of the time intervals between the AF monitoring periods thereby reducing the length of the time intervals between the AF monitoring periods”, which is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(II), the mental process grouping includes observations, evaluations, judgements, and opinions. In this case, a human could select and change frequency of future AF time interval periods based on AF risk. A human can also compare the physiological attribute signals with estimates to reschedule the length periods.
Step 2A – Prong 2 – additional elements to integrate j.e. into a practical application
Regarding claims 1 and 9 , the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “wearable electronic device”, “storage”, and “processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the first machine learning model, second model, neural network, classification model are by definition automating the human thinking process/abstract idea with a computer.
- “PPG sensors” and “IMU” are data gathering structures for the insignificant extra-solution activity of data gathering [MPEP 2106.05(b)];
- “AF risk”, “NSR”, “physiological attributes (heart rate, respiratory, rate, and blood pressure)”, time interval/frequency”, “prior heart rhythms”, “estimates”, “errors”, “sampling rate”, “resetting a schedule”, “AF”, “particular classification”, “plurality of classifications”, “correct AF detection”, “ground truth data”, “time synchronized training ECG signals, training PPG signals, and training IMU data”, and “AF monitoring periods” are data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
Step 2B – significantly more/inventive concept
The following claim elements do not add any meaningful limitation to the abstract idea:
- “wearable electronic device”, “storage”, and “processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the first machine learning model, second model, neural network, classification model are by definition automating the human thinking process/abstract idea with a computer.
- “PPG sensors” and “IMU” are data gathering structures for the insignificant extra-solution activity of data gathering [MPEP 2106.05(b)];
- “AF risk”, “NSR”, “physiological attributes (heart rate, respiratory, rate, and blood pressure)”, time interval/frequency”, “prior heart rhythms”, “estimates”, “errors”, “sampling rate”, “resetting a schedule”, “AF”, “classification”, “correct AF detection”, “ground truth data”, “time synchronized training ECG signals, training PPG signals, and training IMU data”, and “AF monitoring periods” are data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
The additional elements of claims 1 and 9, when considered separately and in combination, do not add significantly more (ie. an inventive concept) to the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the wearable electronic device, processor, and storage, along with their associated functions, are recited at a high level of generality and simply amount to implementing the abstract idea on a computer. These are well-understood, routine and conventional structure since the diagnostic art in McGrath (US 20230346231) teaches the use of a PPG sensor to measure PPG (Abstract) and Roovers et al (US 20170007166) teaches an accelerometer for detecting motion signals ([0008]).
Dependent claims 4-5, 7, 12-14, and 16 do not integrate the abstract idea into a practical application and do not add significantly more to the abstract idea of claim 1, 9, and 21. The dependent claim limitations are directed to further data processing (claims 4-5, 7, 12-14, and 16), which are insignificant extra-solution activity and do not amount to more than what is well-understood, routine, and conventional.
In summary, claims 1, 4-5, 7, 9, and 12-14, and 16 are directed to an abstract idea without significantly more and, therefore, are patent ineligible.
Conclusion
Claims 1, 4-5, 7, 9, and 12-14, and 16 overcome the prior art but are still rejected under 35 U.S.C. 101, 35 U.S.C. 112(b), and claim objections.
The following is a statement of reasons for the indication of the claims overcoming the prior art:
The estimating of heart rhythms using a model based on prior heart rhythms and comparing the estimate to the physiological attributes at a start of a selected AF period to accumulate errors for resetting scheduling of AF monitoring period for reduction in time intervals are not conventionally relied upon in determining risk of AF and are therefore allowable over the prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOUSSA M HADDAD whose telephone number is (571)272-6341. The examiner can normally be reached M-TH 8:00-6:00.
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/MOUSSA HADDAD/Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
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