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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/7/2026 has been entered.
Response to Arguments
Applicant's arguments and amendments regarding the 101 rejection filed 5/7/2026 have been fully considered but they are not persuasive. The Applicant amended the claims to recite the parametric data comprises AF burden data and deriving a feature comprises one or more offsets between moving averages of the AF burden for different time periods and argued that the data was specialized and the derivation is a technique that improves the functioning of a computer or other technology. The Examiner respectfully disagrees. The claim language does not indicate that the parametric data is specialized, AF burden is typically characterized by counting the length of relative periods of atrial fibrillation, which a trained physician can observe on cardiac data. The claimed invention merely receives sensor data for multiple parameters, which falls under an extrasolution activity of necessary data gathering (see MPEP 2106.05(g)). The derivation as claimed involves calculating averages on collected data for multiple time periods and comparing the differences/offsets between those averages, which is able to be performed in the mind of a trained physician. If the derivation cannot be performed in the mind, the Applicant should file a declaration stating that it cannot be performed in the human mind. Additionally, the applying of one or more features to a model is merely instructions to perform an abstract idea (using mathematical models and making determinations) using a computer as a tool (see MPEP 2106.05(f)). The Examiner recommends adding in claim language that provides a technological improvement to how the model works.
Regarding the other independent claims, the claims recite the same ideas as listed above. Additionally, the claims do not recite any technological improvements to training a model, merely just applying a generic model to be trained with data inputs (see MPEP 2106.05(a)).
Applicant’s arguments and amendments regarding the 103 rejection, see pages 8-11, filed 5/7/2026, with respect to the rejection(s) of claim(s) 1-2, 4-18, and 20-21 under USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1).
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, 4-18, and 20-21 are rejected under 35 U.S.C. 101 because of the following analysis:
Step 1: Do the claims recite one of the statutory categories of matter (i.e. method, apparatus, etc.)? YES, claims 1-2, 4-11, 13, 15-17, and 21 and claims 12, 14, 18, and 20 recite a method.
Step 2A Prong 1: Is there an abstract idea involved? YES, the claim language recites deriving one or more features (making determinations/calculations/analysis), applying the one or more features to a model (observing and analyzing data) and determine a risk level (determination). These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in mind or by a person using a pen and paper.
Step 2a Prong 2: Do the claims recite additional elements that integrate the exception into a practical application? NO, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims recite a processing circuitry and sensing devices, which are recited at a high level of generality and is recited as performing generic computer functions. i.e., data processing and display. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. The sensing devices are generically recited to amount to no more than necessary data gathering (see MPEP 2106.05(g)).
The dependent claims do not recite additional elements to bring the abstract ideas into practical applications.
Step 2B: Do the additional elements amount to “Significantly More” than the judicial exception? NO, The emphasized elements cited above do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’I, 110 USPQ2d 1976 (2014)). In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
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.
Claim(s) 1-4, 6-8, 10, 12-14, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1).
Regarding claims 1 and 12-14, Chakravarthy discloses a system (Eg. Para. 6 IMD 10) comprising processing circuitry (eg. Para. 36, processing circuitry 50) configured to: receive parametric data for a plurality of parameters of a patient (eg. Para. 10 and 26), wherein the parametric data is generated by one or more sensing devices of the patient based on physiological signals of the patient (eg. Para. 59) sensed by the one or more sensing devices (eg. Para. 59, sensors 58), and determine a risk level of a health event for the patient based on the application of the one or more features to the model (eg. Para. 77), but does not disclose and wherein the plurality of parameters comprises AF burden; derive one or more features based on the parametric data for the plurality of parameters, wherein the one or more features comprise at least one AF burden pattern feature; apply the one or more features to a model, and offsets between moving averages of AF burden data for different time periods.
Sarkar teaches a method (eg. Para. 13) of applying the system (eg. Para. 14), wherein the plurality of parameters comprise AF burden (eg. Para. 33) and the one or more features comprise at least one AF burden pattern feature (eg. Para. 91).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy with the AF burden tracking as taught by Sarkar to provide the predictable result of allowing more tracking of heart conditions (eg. Sarkar, Para. 33).
Gopalakrishnan teaches using a machine learning system (eg. Para.9, 16-17) that trains the model using parametric data (eg. Para. 16), classify a training set of parametric data based on classification data collected automatically in response to detection of a trigger (eg. Para. 63), and train the model with classified training set of parametric data (eg. Para. 16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning algorithm of Chakravarthy and Sarkarto with the classification and risk score calculations as taught by Gopalakrishnan to provide the predictable result of provide the predictable result of improving diagnosis and recommendations for heart conditions (eg. Gopalakrishnan Para. 17-19).
Stadler teaches heart monitoring using a running average to compare to differences across multiple periods to identify a cardiac cycle being highly variable which would indicate atrial fibrillation (eg. Para. 58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkarto, and Gopalakrishnan with using Stadler’s method of classifying atrial fibrillation using running average comparisons since the inventions are all related to monitoring and classifying atrial fibrillation and would provide the predictable result of improving classification of atrial fibrillation involving highly variable cardiac cycles (eg. Stadler, Para. 58).
Regarding claims 2 and 18, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses one or more offsets comprise an offset comparison between a current AF burden value comprising a shorter-term moving average of the AF burden data and longer- term moving average of the AF burden data (eg. Chakravarthy, Para. 54, 59, 81, can be average measurements over longer- and shorter-term time periods, one of ordinary skill would have been able to use comparison of a parameter over different time segments to an average since it is a known calculation in statistical analysis, Stadler, Para. 58-62, the use of multiple lengths of cardiac cycle periods).
Regarding claim 4, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the one or more features comprise a patient activity feature (eg. Chakravarthy, Para. 59).
Regarding claim 6, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the health event comprises a health care utilization event (eg. Sarkar, Para. 169).
Regarding claim 7, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the health event comprises a symptomatic event (eg. Chakravarthy, Para. 47).
Regarding claim 8, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses to determine the risk level of the health event, the processing circuitry is configured to determine a probability of occurrence of the health event (eg. Chakravarthy, Para. 80).
Regarding claim 10, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the processing circuitry is configured to determine whether the risk level of the health event satisfies a criterion (eg. Gopalakrishnan, Para. 78).
Regarding claim 16, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses processing circuitry comprises processing circuitry of at least one of:a patient computing device configured for wireless communication with the one or more sensing devices; and a computing system configured for network communication with the patient computing device (eg. Chakravarthy, Para. 29, 34-37).
Regarding claim 17, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the system comprises the one or more sensing devices comprising an implantable medical device (eg. Chakravarthy, Fig. 1, implant 10) and an external sensing device that is a peripheral device for the patient computing device (eg. Chakravarthy, Para. 40).
Claim(s) 5 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1), further in view of Ziegler (US 2011/0106200 A1).
Regarding claims 5 and 20, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the invention of claim 1, but does not disclose the health event comprises stroke.
Ziegler teaches a system that detects atrial fibrillation burden exceeding a threshold and presents a stroke risk factor (Eg. Para. 85).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler with the stroke risk factor as taught by Ziegler to provide the predictable result of having additional diagnoses for different conditions such as stroke since atrial fibrillation is a known risk factor for strokes in the art (eg. Ziegler, Para. 85).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1), further in view of Sarkar I (US 2012/0253207 A1).
Regarding claim 9, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the invention of claim 1, but does not disclose the risk level comprises a risk that the health event will occur within a predetermined time period.
Sarkar I teaches a heart monitoring system (eg. Fig. 1) that can generate a risk level that indicates the likelihood that a patient will be hospitalized within a predetermined time period (eg. Para. 58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler with the likelihood indication as taught by Sarkar I to provide the predictable result of having a more detailed risk classification for monitoring cardiac health.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1), further in view of Shah (US 2017/0325749 A1).
Regarding claim 11, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan, and Stadler discloses the invention of claim 1, but does not disclose the processing circuitry is configured to change a sensing configuration of at least one of the one or more sensing devices based on the risk of the health event satisfying the criterion.
Shah teaches a sensor device that can change the sample rate based on whether evaluated data is trending towards an alarm threshold (eg. Fig. 2B and Para. 67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler with the changing sensor sample rate as taught by Shah to provide the predictable result of providing more accurate data when an alarm level is reached (eg. Shah, Para. 67).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1), further in view of further in view of Bouguerra (US 2020/0093388 A1), further in view of Zhang (US 10182768 B2), further in view of Sarkar I (US 2012/0253207 A1).
Regarding claim 15, Chakravarthy discloses a system (Eg. Chakravarthy Para. 6 IMD 10) comprising processing circuitry (eg. Chakravarthy Para. 36, processing circuitry 50) configured to: derive one or more features based on parametric data of a patient generated by one or more sensing devices of the patient based on one or more signals of the patient sensed by the one or more sensing devices (eg. Chakravarthy Para. 10 and 26), but does not disclose wherein the parametric data comprises AF burden data, the one or more features comprise one or more offsets between moving averages of the AF burden data for different time periods; apply the one or more features to a rules-based model; and determine a risk level of a health care utilization event for the patient based on the application of the one or more features to the rules-based model.
Sarkar teaches a method (eg. Para. 13) of applying the system (eg. Para. 14), wherein the plurality of parameters comprise AF burden (eg. Para. 33) and the one or more features comprise at least one AF burden pattern feature (eg. Para. 91).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy with the AF burden tracking as taught by Sarkar to provide the predictable result of allowing more tracking of heart conditions (eg. Sarkar, Para. 33).
Gopalakrishnan teaches using a machine learning system (eg. Para.9, 16-17) that trains the model using parametric data (eg. Para. 16), classify a training set of parametric data based on classification data collected automatically in response to detection of a trigger (eg. Para. 63), and train the model with classified training set of parametric data (eg. Para. 16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning algorithm of Chakravarthy and Sarkar to with the classification and risk score calculations as taught by Gopalakrishnan to provide the predictable result of provide the predictable result of improving diagnosis and recommendations for heart conditions (eg. Gopalakrishnan Para. 17-19).
Stadler teaches heart monitoring using a running average to compare to differences across multiple periods to identify a cardiac cycle being highly variable which would indicate atrial fibrillation (eg. Para. 58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkarto, and Gopalakrishnan with using Stadler’s method of classifying atrial fibrillation using running average comparisons since the inventions are all related to monitoring and classifying atrial fibrillation and would provide the predictable result of improving classification of atrial fibrillation involving highly variable cardiac cycles (eg. Stadler, Para. 58).
Bouguerra teaches a system that comprises one or more offsets of moving averages of the AF burden data for different time periods (eg. Para. 48, Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler with the moving averages of AF burden data as taught by Bouguerra to provide the predictable result of removing false positives in diagnoses (Eg. Bourguerra, Para. 2-3).
Zhang teaches a heart failure detection system using rules based model (eg. Col. 10, Ln. 33-45 and Col. 19, Ln. 23-44).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the model of Chakravarthy, Sarkar, Gopalakrishnan, Stadler, and Bouguerra to have rules as taught by Zhang to provide the predictable result of using a known alternative equivalent model to provide a risk index (eg. Zhang, Col. 10, Ln. 33-45 and Col. 19, Ln. 23-44).
Sarkar I teaches determining a risk level of a human care utilization event for the patient based on application of one or more features to the model (eg. Para. 62).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, Stadler, Bouguerra, and Zhang with the risk of utilization as taught by Sarkar I to provide the predictable result of adding another diagnostic metric of a risk of hospitalization to help determine the next steps following a diagnosis (eg. Sarkar I, Para. 62).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy (US 2020/0352466 A1) in view of Sarkar (US 2015/0230722 A1), further in view of Gopalakrishnan (US 2015/0164349 A1), further in view of Stadler (US 2020/0155742 A1), further in view of Chintakindi (US 2020/0104876 A1).
Regarding claim 21, the combined invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler discloses the invention of claim 13, but does not disclose trigger comprises the patient being within a geofenced area for at least a threshold time, and the processing circuitry is configured to classify the training set of parametric data based on classification data collected automatically in response to the patient being within the geofenced area for at least the threshold time.
Chintakindi teaches the collection of data of a person in a geofenced location over a threshold amount of time (eg. Para. 46, 48).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Chakravarthy, Sarkar, Gopalakrishnan, and Stadler with the geofenced data collection as taught by Chintakindi because it is known in the art that certain locations have different risks associated with the location and being in a certain location above a threshold amount of time can impact outputs (eg. Chintakindi, Para. 48).
Conclusion
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/MICHAEL J LAU/Examiner, Art Unit 3796
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