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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/20/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to prior art rejections of the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new 102 rejection is made under Thakur et al.
Applicant's arguments filed 10/20/2025 in regards to the rejections under U.S.C. 101 have been fully considered but are not persuasive.
Applicant’s argument, that the claims are directed to an improvement that integrates the judicial exception into a practical application, is unpersuasive. While the improvement of a device is not required to be explicitly stated in the claims, the claims are required to provide explicit disclosure of any necessary limitations that cause said improvement. Applicant’s claims as currently written broadly describe the collection of physiological signals and associated parameters, to broadly predict a symptom. This cannot capture the claimed improvement by Applicant, i.e. the fall prediction disclosed on Pg. 9 of Applicant’s response. Even if it were possible to read this improvement into the current claim language, the prediction of future symptoms based on physiological data is the abstract thought process of any doctor, such as determining that someone with AFib and Hypertension is likely to develop a stroke, or hypertension and decreased respiratory volume (shortness of breath) could predict a heart attack. Further, as disclosed below in the adjusted 101 rejection below, this type of prediction is likewise merely routine and conventional, particularly at the breadth of the current claims (which do not specify either the symptoms or physiological signals detected).
For these reasons, the claims remain rejected under U.S.C. 101, and the rejection has been updated to account for the newly amended claim limitations.
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 5-6, 13-15, and 20-23 are rejected under U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
STEP 1: Independent Claims 5, 13, and 20 recite a device, a method and a non-transitory computer-readable medium, respectively. Thus, they are directed to statutory categories of invention.
STEP 2A, Prong 1:
Claims 5, 13, and 20 recite the following claim limitations:
determine a plurality of parameter values of the one or more physiological parameters of the patient during a time period based on the one or more signals from the one or more sensors
compare the determined plurality of parameter values to reference data sets associated with one or more symptoms from a database in memory on the comparison satisfying a threshold
based on the comparison satisfying a threshold, generate a prediction that the patient will experience a symptom in an impending period of time and notify the patient of the prediction
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e. using pen and paper. A person is capable receiving a physiological signal, determining a parameter value from the signal, and making a prediction of a future symptom and providing the patient with said prediction, such as the steps taken by a doctor or nurse monitoring a patient.
STEP 2A, Prong 2:
Claims 5, 13, and 20 recite the following additional elements:
a medical device
one or more sensors configured to continuously sense one or more signals
memory/databases/processing circuitry to perform the above steps
notifying the patient of the prediction in a user interface
Electronically receiving information over an unspecified time span is merely insignificant pre-solution activity (See MPEP 2106.05(g)).
The recitation of one or more processors, one or more sensors, and computer storage media with computer-usable instructions that, when executed by the one or more processors, implement a method are merely reciting both the processors and computer storage media at a high-level of generality, and the computer readable storage media merely instructs the processors to carry out the steps of the method. In other words, the computer components are being used as a tool to carry out the method (See MPEP 2106.05(f)).
Thus, the abstract idea is not integrated into a practical application. The combination of these additional elements is no more than insignificant extra solution activity, and mere instructions to apply the exception using generic computer components (the processors and computer readable storage media). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
STEP 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
For the "electrically receiving..." step that was considered insignificant extra-solution activity in Step 2A Prong Two, it has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The following evidence supports such a determination:
electronically receiving information for a patient being acquired over a time span
(See MPEP 2106.05(d) II. Receiving or transmitting data over a network, 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).
Predicting a symptom based on physiological signals is likewise merely routine and conventional in the art, see U.S. Patent Publication 20200297230 awarded to Thakur et al (Para. 0068) and U.S. Patent 5995868 to Dorfmeister (abstract), see MPEP 2106.05(d).
DEPENDENT CLAIMS:
Claims 6, 14-15, and 21-13 merely further limit the abstract idea by elaborating on method steps in a way that does not provide significantly more to the abstract idea, even when viewed as a whole in combination with the independent claims.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 5-6, 13-15, and 20-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Publication 20200297230 awarded to Thakur et al.
Regarding Claims 5, 13, and 20, Thakur teaches a medical device system and method of use (abstract) stored using non-transitory computer-readable medium comprising instructions for causing one or more processors to perform the method (Para. 0104), comprising: a medical device (arrhythmic risk stratification system 200, Para. 0062) comprising one or more sensors configured to continuously sense one or more signals that indicate one or more physiological parameters of a patient (sensors 211-215 of sensor circuit 210, Fig. 2 Para. 0062), and processing circuitry configured to: determine a plurality of parameter values of the one or more physiological parameters of the patient during a time period based on the one or more signals from the one or more sensors (Para. 0062, “The sensor circuit 210 may include one or more other sub-circuits to digitize, filter, or perform other signal conditioning operations on the sensed physiologic signal. In some examples, the physiologic signals may be stored in a storage device such as an electronic medical record system. The sensor circuit 210 may retrieve a physiologic signal from the storage device in response to a command signal that is provided by a system user, or automatically generated in response to occurrence of a specific event”); compare the determined plurality of parameter values to reference data sets (Para. 0063-0067 discloses all the different parameter forms determined from the different sensors 211-215) associated with one or more symptoms from a database in memory on the comparison satisfying a threshold (Para. 0068 lists the various types of symptoms determined from the various parameters listed in Paras. 0063-0067), based on the comparison satisfying a threshold, generate a prediction that the patient will experience a symptom in an impending period of time (Para. 0068, “One or more of those additional sensors discussed above may be included in the system 200, in addition to or in lieu of any of the sensors 211-215, and used for arrhythmia risk stratification and AF event prediction. In some examples, the physiologic information received by the sensor circuit 210 may include information from the sensor(s) as discussed above excluding electrocardiograph or electrogram information”); and notify, via a user interface, the patient of the prediction (Para. 0079, “The arrhythmia risk indication may be represented by a categorical value (e.g., “high”, “medium”, or “low” risk) or a numerical value (e.g., on a scale of 1-5, where “1” indicates the lowest risk and “5” indicates the highest risk), which indicate various degrees of arrhythmic risk. Categorization of various arrhythmic risk degrees may be carried out based on a deviation of the measured value of the signal metric from a threshold (e.g., a reference or baseline value of the signal metric). In an example, the arrhythmic risk degrees may be proportional to said deviation, such that the more the signal metric deviates from the threshold, the higher the arrhythmic risk degree is. The atrial tachyarrhythmia risk assessor 234 may predict an onset timing or timeframe of future atrial tachyarrhythmia based on the deviation of the measured signal metric from the threshold”).
Regarding Claim 6, Thakur teaches the medical device system of Claim 5, wherein the processing circuitry is further configured to: receive, via the user interface, a patient confirmation or denial of the notified prediction (Para. 0055, “The server may process the device-generated event episodes to verify that a specific medical event (e.g., a cardiac arrhythmia type) is detected such that the device-detected event is a true positive (TP) detection; or that no such medical event is detected such that the device-detected event is a false positive (FP) detection. The processing of the device-generated medical event episodes may be based on a stored association. In an example, a first event episode may be presented to a user (e.g., a clinician), who would provide an adjudication decision and a first episode characterization. If the adjudication decision indicates that the first event episode is a FP detection, then the server may identify from the stored association a detection algorithm corresponding to the first episode characterization, and process a second event episode using at least the identified detection algorithm to determine that the second event episode is either a TP or a FP detection”); responsive to receiving the patient confirmation of the notified prediction, prioritize the reference data set associated with the notified prediction and save, to the database in memory, the data set including the determined plurality of patient parameter values in a log associated with the notified prediction; and responsive to receiving a denial of the notified symptom, deprioritize the reference data set associated with the notified prediction (Para. 0055, “The processing of the device-generated medical event episodes may be based on a stored association. In an example, a first event episode may be presented to a user (e.g., a clinician), who would provide an adjudication decision and a first episode characterization. If the adjudication decision indicates that the first event episode is a FP detection, then the server may identify from the stored association a detection algorithm corresponding to the first episode characterization, and process a second event episode using at least the identified detection algorithm to determine that the second event episode is either a TP or a FP detection. The server may schedule a presentation of at least a portion of the second episode using the processing result of the second episode. By using the detection algorithms tailored for recognizing episode with an episode characterization associated with a FP episode, more FP episodes having the same or similar episode characterization may be identified, and therefore avoided from being reviewed and adjudicated by the user. If the second event episode is determined to be a TP episode, then an alert is generated indicating further user review may be warranted”).
Regarding Claim 14, Thakur teaches the method of Claim 13, further comprising: receiving, by the processing circuitry of the medical device system via the user interface, a patient confirmation or denial of the notified prediction (Para. 0055, “The processing of the device-generated medical event episodes may be based on a stored association. In an example, a first event episode may be presented to a user (e.g., a clinician), who would provide an adjudication decision and a first episode characterization); responsive to receiving the patient confirmation of the notified prediction, prioritizing, by the processing circuitry of the medical device system, the reference data set associated with the notified prediction and saving, by the processing circuitry and to the database in memory, the data set including the determined first plurality of parameter values in the log associated with the notified prediction (Para. 0055, “If the adjudication decision indicates that the first event episode is a FP detection, then the server may identify from the stored association a detection algorithm corresponding to the first episode characterization, and process a second event episode using at least the identified detection algorithm to determine that the second event episode is either a TP or a FP detection. The server may schedule a presentation of at least a portion of the second episode using the processing result of the second episode. By using the detection algorithms tailored for recognizing episode with an episode characterization associated with a FP episode, more FP episodes having the same or similar episode characterization may be identified, and therefore avoided from being reviewed and adjudicated by the user”).
Regarding Claim 15, Thakur teaches the method of Claim 13, further comprising: responsive to receiving a denial of the notified prediction, deprioritizing, by the processing circuitry of the medical device system, the reference data set associated with the notified prediction (Para. 0055, “The processing of the device-generated medical event episodes may be based on a stored association. In an example, a first event episode may be presented to a user (e.g., a clinician), who would provide an adjudication decision and a first episode characterization. If the adjudication decision indicates that the first event episode is a FP detection, then the server may identify from the stored association a detection algorithm corresponding to the first episode characterization, and process a second event episode using at least the identified detection algorithm to determine that the second event episode is either a TP or a FP detection. The server may schedule a presentation of at least a portion of the second episode using the processing result of the second episode. By using the detection algorithms tailored for recognizing episode with an episode characterization associated with a FP episode, more FP episodes having the same or similar episode characterization may be identified, and therefore avoided from being reviewed and adjudicated by the user. If the second event episode is determined to be a TP episode, then an alert is generated indicating further user review may be warranted”).
Regarding Claim 21, Thakur teaches the non-transitory computer-readable storage medium of Claim 20, further comprising instructions for causing the one or more processors to: receive, via the user interface, a patient confirmation or denial of the notified prediction; responsive to receiving the patient confirmation of the notified prediction, prioritize the data set associated with the notified prediction and save, to the database in memory, the data set including the determined first plurality of patient parameter values in a log associated with the notified prediction; and responsive to receiving a denial of the notified prediction, deprioritize the reference data set associated with the notified prediction (Para. 0055, “The processing of the device-generated medical event episodes may be based on a stored association. In an example, a first event episode may be presented to a user (e.g., a clinician), who would provide an adjudication decision and a first episode characterization. If the adjudication decision indicates that the first event episode is a FP detection, then the server may identify from the stored association a detection algorithm corresponding to the first episode characterization, and process a second event episode using at least the identified detection algorithm to determine that the second event episode is either a TP or a FP detection. The server may schedule a presentation of at least a portion of the second episode using the processing result of the second episode. By using the detection algorithms tailored for recognizing episode with an episode characterization associated with a FP episode, more FP episodes having the same or similar episode characterization may be identified, and therefore avoided from being reviewed and adjudicated by the user. If the second event episode is determined to be a TP episode, then an alert is generated indicating further user review may be warranted”).
Regarding Claim 22, Thakur teaches the medical device of Claim 5, wherein the processing circuitry is further configured to: based on the comparison satisfying the threshold, determine the patient has one or more first symptoms (rapid shallow-breathing index (RSBI) and respiration rate increase, Para. 0090); and based on the determination the patient has one or more first symptoms, generate a prediction that the patient will experience a second symptom (Para. 0090, future atrial fibrillation) in the impending period of time, wherein the second symptom is different that the one or more first symptoms (Para. 0090, “FIG. 5D illustrates a respiration rate (RR) trend (trend 541 for AF group and trend 542 for control group), and FIG. 5E illustrates a trend of rapid-shallow breathing index (RSBI) defined as the ratio of respiratory frequency to tidal volume (trend 551 for AF group and trend 552 for control group). As shown in FIGS. 5D-5E, both the RR trend and the RSBI trend of the AF group are higher than the respective trends of the control group as far as 90 days prior to AF onset. The distinctly higher RR and higher RSBI become more prominent as time goes and approaches AF onset time TO. Thus, the patients with elevated RR or elevated RSBI may be at a higher risk of developing future AF than those with lower RR or lower RSBI levels, even as far as 90 days prior to AF event actually occurs. The arrhythmia stratifier 230 may compare RR or RSBI to their respective thresholds, and determine an AF risk in the patient, or predict a future AF event, based at least on a deviation of the measured RR or RSBI level to their respective thresholds”).
Regarding Claim 23, Thakur teaches the medical device of Claim 5, wherein the plurality of parameter values is a first plurality of parameter values, the time period is a first time period and wherein the prediction is a first prediction, and wherein the processing circuitry is further configured to: determine a second plurality of parameter values of the one or more physiological parameters of the patient during a second time period based on the one or more signals from the one or more sensors, the second time period being after the first time period and compare the determined second plurality of parameter values to the data set including the determined first plurality of patient parameter values (Para. 0098, “Alternatively or additionally, at 644, arrhythmia risk stratification may be updated according to the arrhythmic risk indication, such as using the monitor adjustment circuit 440. This may include, for example, selective activating or de-activating certain sensors, adjusting sensor data acquisition time, schedule, frequency, duration, sampling rate, or data precision, modifying the signal metrics (e.g., adding a new signal metric, or removing a previously used signal metric) included in the determination of the arrhythmia risk indication, tuning one or more arrhythmia risk stratification parameters such as respective thresholds for various physiologic signal metrics, the threshold for the composite signal metric, weight factors for respective signal metrics for generating the arrhythmia risk indication, a parameter of the ML model for generating the arrhythmic risk indication, or selecting a different ML model, etc. With the updated arrhythmia risk stratification at 644, more aggressive arrhythmia monitoring may be performed if a high arrhythmia risk is indicated. This accordingly may improve timely detection of AF as soon as it first occurs, as well as improving the identification of silent AF before patients become symptomatic”); based on the comparison of the determined second plurality of parameter values to the data set including the determined first plurality of patient parameter values satisfying a threshold, generate a prediction that the patient will experience the symptom in a second impending time period (Para. 0079, “In an example, the arrhythmic risk degrees may be proportional to said deviation, such that the more the signal metric deviates from the threshold, the higher the arrhythmic risk degree is. The atrial tachyarrhythmia risk assessor 234 may predict an onset timing or timeframe of future atrial tachyarrhythmia based on the deviation of the measured signal metric from the threshold. In an example, a correspondence (e.g., a mapping) between said deviation and projected atrial tachyarrhythmia onset time (e.g., in days, weeks, or months from the time of risk assessment) may be created and updated as needed using patient population data, and stored in a memory. The atrial tachyarrhythmia risk assessor 234 may then map a measured deviation associated with a signal metric to a projected AF onset time or timeframe using the stored correspondence”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: see U.S. Patent Publication 20180192894 awarded to An et al (abstract, Para. 0066).
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/JLM/
Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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