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 .
Response to Arguments
Applicant's arguments filed 12/4/2025 have been fully considered but they are not persuasive. Regarding the 101 rejection, the Applicant stated that the claims recite processing circuitry configured to “derive one or more features…” which are significant enough to integrate a judicial exception into a practical application. The Examiner respectfully disagrees. The processing circuitry is considered a generic computer in view of the Applicant’s specification Para. 60 and 208, which is merely used as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). The sensing devices collecting data are generically recited to amount to an extrasolution activity of necessary data gathering (see MPEP 2106.05(g)). Genetic Technologies Limited v. Merial LLC (Fed Cir., 2016) tells us that the inventive concept of step 2 of the Alice/Mayo analysis cannot be supplied by the abstract idea. The inventive concept necessary at step two of the Mayo/Alice analysis cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. That is, under the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility; instead, the application must provide something inventive, beyond mere “well-understood, routine, conventional activity.” Mayo, 132 S. Ct. at 1294; see also Myriad, 133 S. Ct. at 2117; Ariosa, 788 F.3d at 1379. The 101 rejection is maintained below.
Applicant’s arguments, see pages 7-8, filed 12/4/2025, with respect to USC 112 have been fully considered and are persuasive. The 112 rejection of claims 1-20 has been withdrawn.
Applicant's arguments filed 12/4/2025 have been fully considered but they are not persuasive. The Applicant argued that the references fail to disclose “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”, stating the reference does not derive any feature from the determined AF burden, much less an AF burden feature (eg. Remarks Page 9). The Examiner respectfully disagrees. To reiterate, the Sarkar reference shows monitoring AF burdens such as how long AF lasts and the pattern feature is the worsening of heart failure and providing an indication of worsening heart failure (eg. Para. 91, 93, 120-123, 141-142). One of ordinary skill would have thought to combine the references since both are related to arrhythmia detection such as atrial fibrillation by monitoring cardiac features and risk stratification of detected arrhythmias. Using the monitoring of parameters as taught by Sarkar would provide a predictable result of expanding that risk stratification to heart failure. Regarding the arguments of claim 2, Chakravarthy uses parameter averages for 30-60 minute windows. Sarkar also compares AF burdens from each AF incident to a threshold to determine if those values satisfy a predetermined condition corresponding to worsening heart failure and various values can be used such as average daily/weekly values of the patient (eg. Para. 116, 143-148, Fig, 11-12). Using an instantaneous/current value to compare with an average is known in the art for determining if that current value is significantly different from an average to show if a metric is worsening or improving. Regarding the use of machine learning from populations, the Examiner interprets that population data can include a patient’s data within that data since it can be typically from particular patients (eg. Chakravarthy, Para. 10 and 71-72). Although other patients can typically be used, one of ordinary skill could have used the actual patient as part of the group of particular patients to train as meta data for the machine learning algorithm since the art does not preclude/discredit using the same patient’s data as the trained data (see MPEP 2123). The 103 rejection is maintained below.
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 1-20 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 1-11, 13, and 15 and 16-17 and claims 12, 14, and 18-20 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 16-19 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).
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.
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).
Regarding claims 2 and 18, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan discloses the AF burden pattern feature comprises a comparison between a current AF burden value and an average of AF burden values (eg. Chakravarthy, Para. 54, 59, 81, one of ordinary skill would have been able to use comparison of a parameter to an average since it is a known calculation in statistical analysis).
Regarding claims 3 and 19, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan discloses the current AF burden value comprises a shorter-term average of AF burden values and the average of AF burden values comprises a longer-term average of AF burden values (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).
Regarding claim 4, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan 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 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 discloses the health event comprises a symptomatic event (eg. Chakravarthy, Para. 47).
Regarding claim 8, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan 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 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 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 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 Ziegler (US 2011/0106200 A1).
Regarding claims 5 and 20, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan 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, and Gopalakrishnan 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 Sarkar I (US 2012/0253207 A1).
Regarding claim 9, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan 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, and Gopalakrishnan 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 Shah (US 2017/0325749 A1).
Regarding claim 11, the combined invention of Chakravarthy, Sarkar, and Gopalakrishnan 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, and Gopalakrishnan 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 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).
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, and Gopalakrishnan 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, 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,. 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).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J LAU whose telephone number is (571)272-2317. The examiner can normally be reached 8-5:30 PM.
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/MICHAEL J LAU/Examiner, Art Unit 3796