DETAILED ACTION
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, see remarks, filed 11/11/2025, with respect to the claim rejections under 35 USC 112(a) have been fully considered and are persuasive. The rejections of claims 1-9,11-14,16,18,19, and 21-24 has been withdrawn. The amendments made distinctly claim and clarify the subject matter.
Applicant’s arguments, see remarks, filed 11/11/2025, with respect to the claim rejections under 35 USC 101 have been fully considered and are persuasive. The rejections of claims 13-16,18 and 19 has been withdrawn. The additional limitations provided in the amendment specify how the proposed invention is being used to perform the abstract idea of identifying indicia thereby integrating it into a practical application at step 2a prong 1 of the subject matter eligibility test.
Applicant's arguments filed 11/11/2025 with respect to the claim rejections under 35 USC 103 have been fully considered but they are not persuasive. The applicant argues that Kaib combines the physiological data and user input data retroactively rather than in real time and Schnell does not remedy this deficiency and does not teach “temporally correlate the input corresponding to the health assessment rating from the user interface with the portion of patient parameter data from the sensed data capture module”. However, while the intended use of Schnell is to analyze and/or combine the data sets retroactively, Schnell also teaches “In some examples, the user interface may be configured to present stored and/or real-time brain signal data alone or together with one or more stimulation parameter values defining stimulation delivered when the brain signal data is sensed” ([0006]). This indicates that real-time data can also be shown and analyzed on the user interface.
Claim Rejections - 35 USC § 103
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) 1-9, 11,21, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaib et. al (US 20130231711 A1); hereinafter Kaib in view of Schnell et. al (US 20210196964 A1); hereinafter Schnell, and Cronin et al (US20180008191A1); hereinafter Cronin (all cited previously).
Regarding claim 1, Kaib discloses a user interface configured to receive an input from the patient, the input corresponding to a health assessment rating indicative of a current health of the patient ([0073] user interface to receive quality of life data), wherein the user interface is further configured to prompt the patient to provide the input periodically ([0071] the delivery of treatment can be delayed to allow time for audio voice messages to prompt the subject to respond by pressing the response button);
a sensed data capture module ([0009] sensor) configured to record at least a portion of patient parameter data, the patient parameter data including at least physiological data of the patient ([0140] physiological monitoring), a patient monitor module configured to receive the input from the user interface, wherein the patient monitor module is configured to cause the sensed data capture module to capture at least the portion of patient parameter data based on the received input from the user interface ([0073] treatment device 100), and receive the portion of patient parameter data from the sensed data capture module ([0009] sensor). Kaib further teaches to transmit at least the correlated portion of the patient parameter data and the health assessment rating to a remote server ([0070]), to identify indicia in the patient parameter data that indicates the presence of a heightened risk of an adverse health event ([0084] risk assessment and monitoring of heart failure indicators and onset of symptoms). Kaib discloses the ability to make correlations between different data types ([0116] making correlations) but fails to disclose that that correlation is made between input from the user interface with the portion of the patient parameter data. Schnell discloses temporally correlate the input according to the health assessment rating from the user interface with the portion of patient parameter data ([0211] correlated stimulation parameter data) from the sensed data capture module based on a timestamp associated with the input and the portion of the patient parameter data ([0006] in some examples, the user interface may be configured to present stored and/or real-time brain signal data alone or together with one or more stimulation parameter values defining stimulation delivered when the brain signal data is sensed). It would have been obvious to a person having ordinary skill in the art before the filing date of this invention to modify Kaib with the teachings of Schnell because there is a teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Schnell discloses that the system may correlate patient condition, events, and sensed signals to monitor stimulation efficacy and adjust stimulation over time to reduce patient symptoms ([0046]). Therefore, there is a reasonable expectation of success for a patient monitor module to include temporally correlating user input data and sensed data as described in the current invention.
The combination of Kaib and Schnell fails to teach temporally correlating the input health assessment rating and the sensed data only when a baseline threshold is met but is still below the alert threshold. Cronin teaches determine whether the at least physiological data of the portion of patient parameter data from the sensed data capture module comprises information indicating a threshold is crossed, and wherein the threshold is below a predefined threshold level associated with triggering an automated therapy or alert ([0092] provide an alert to the user (e.g., vibration using the vibrator) when a predicted occurrence of pain is above a pre-defined threshold), perform an action that is not an alert responsive to the indication that the threshold is crossed ([0154] for measurements below zero and five there may be no action. If measurements are going five to six, a single vibration may be provided to the user with a corresponding comment that a pain may occur – this kind of graded response with different thresholds for different actions make it obvious to trigger a correlation when one threshold is crossed and to trigger an alert when a higher threshold is crossed).
Regarding claim 2, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further discloses manually input data further comprising the health assessment rating that represents how the patient subjectively feels ([0073]) on a scale ([0083]).
Regarding claim 3, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further discloses the input comprises audibly input data ([0151] interface devices can include microphones) that represents how the patient subjectively feels ([0073]).
Regarding claim 4, the combination of Kaib, Schnell, and Cronin teaches the device of claim 3. Kaib further discloses audibly input data ([0151] interface devices can include microphones) comprises a direct indication of how the patient subjectively feels ([0073]) on a scale ([0083]).
Regarding claim 5, the combination of Kaib, Schnell, and Cronin teaches the device of claim 3. Kaib further discloses the audibly input data comprises an audio recording of the patient after the patient has been prompted to provide the audibly input data ([0083] prompting the subject to provide entries).
Regarding claim 6, the combination of Kaib, Schnell, and Cronin teaches the device of claim 3. Cronin further teaches the audibly input data ([0109] acoustic events are segmented from the digitized microphone signal) comprises an audio recording of the patient passively captured ([0109] background sound level).
Regarding claim 7, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further discloses a communication module ([0076] wireless communication over a network) configured to enable bidirectional communication ([0045] provide output information and receive input information) between the medical monitoring device and the remote server (0045] remote computer server), the patient monitor module being configured to transmit at least the correlated portion of patient parameter data ([0076] heart failure information) to the remote server using the communication module.
Regarding claim 8, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further discloses the sensed data capture module ([0066] serial communication bus) is further configured to receive, via the communication module, the patient parameter data from a wearable medical device ([0066] data) that includes sensors ([0066] activity sensors 135) to capture the patient parameter data.
Regarding claim 9, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further discloses one or more sensors configured to capture the patient parameter data ([0009] cardiac sensing electrode).
Regarding claim 11, the combination of Kaib, Schnell, and Cronin teaches the device of claim 3 Kaib further discloses the medical monitoring device ([0084] treatment device) further comprises a sentiment analysis module to analyze the audibly input data ([0084] information is aggregated) to identify the health assessment rating for how the patient subjectively feels ([0084] omnibus quality of life score).
Regarding claim 21, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Kaib further teaches the adverse health event is related to a cardiac event ([0116] probability of a cardiac event), and wherein the input corresponding to the health assessment rating of the patient is received at the same time as the portion of patient parameter data ([0120]).
Regarding claim 23, the combination of Kaib, Schnell, and Cronin teaches the device of claim 1. Cronin further teaches the medical monitoring device is customized based on the physiological data of the patient ([0068] the pain management GUI may be used by the user to manage and customize operation of the pain management device) , the physiological data including values measured during periods of rest and during periods of motion ([0010] motion sensor detects both activity and lack thereof).
Claim 12 is rejected under 35 USC 103 as being unpatentable over the combination of Kaib, Schnell, and Cronin further in view of Lurie et al. (US 20170020444 A1); hereinafter Lurie (cited previously). The combination of Kaib, Schnell, and Cronin teaches the device of claim 3. The combination of Kaib, Schnell, and Cronin fails to teach using the audibly collected data to identify a stress level of the patient. Lurie discloses the patient monitor module ([0083] sensors integrated in the smartwatch) is further configured to analyze the audibly input data to identify a stress level of the patient ([0083] calculation of stress level). It would have been obvious to a person having ordinary skill in the art before the time of the filing date of this invention to modify the combination of Kaib, Schnell, and Cronin with the teachings of Lurie because there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Lurie discloses many diseases are a result of, or are precipitated and influenced by, stress. Research shows biofeedback and auto-regulation methods such as deep breathing help manage disease conditions such as hypertension, depression, anxiety, and pain. The difficulty in the current practice lies in providing relevant real-time feedback to the user when in a stressful condition that contains relevance to their physiology so they can do something about it in the moment ([0066]).
Claim(s) 13,14,18,19,22, and 24 is/are rejected under 35 USC 103 as being unpatentable over Kaib, Schnell, and Cronin and further in view of Freeman et. al (US 20190385744 A1); hereinafter Freeman and Virtanen et al (US20190362819A1); hereinafter Virtanen (both cited previously).
Regarding claim 13, Kaib teaches a medical monitoring device system for a patient, comprising:
a medical monitoring device, comprising:
a user interface ([0009] user interface) configured to receive an input corresponding to a health assessment rating from the patient ([0009] receive quality of life information from the subject), wherein the user interface is further configured to prompt the patient to provide the input periodically ([0071] the delivery of treatment can be delayed to allow time for audio voice messages to prompt the subject to respond by pressing the response button);
a sensed data capture module configured to record at least a portion of patient parameter data ([0011] detected cardiac information) ,
and a patient monitor module configured to:
receive the input from the user interface and the portion of patient parameter data from the sensed data capture module (Fig 6 act 615 receive quality of life information),
and transmit the health assessment rating ([0074] controller receives inputted quality of life information – it must be transmitted to be received by another component) and the portion of patient parameter data to a remote server ([0070] transmit ECG signals) ;
and [[a]]the remote server configured to:
receive data over a communication link to a wide area network ([0076] wireless communication),
Kaib fails to teach temporal correlation of the health assessment rating the sensed data. Schnell teaches that system will temporally correlate the input corresponding to the health assessment rating from the user interface with the portion of patient parameter data from the sensed data capture module ([0211] patient reported events such as feeling well) based on a timestamp associated with the input and the portion of the patient parameter data ([0006] in some examples, the user interface may be configured to present stored and/or real-time brain signal data alone or together with one or more stimulation parameter values defining stimulation delivered when the brain signal data is sensed). It would have been obvious to a person having ordinary skill in the art before the filing date of this invention to modify Kaib with the teachings of Schnell because there is a teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Schnell discloses that the system may correlate patient condition, events, and sensed signals to monitor efficacy and adjust stimulation over time to reduce patient symptoms ([0046]). Therefore, there is a reasonable expectation of success for a patient monitor module to include temporally correlating user input data and sensed data as described in the current invention.
The combination of Kaib and Schnell fails to teach temporally correlating the input health assessment rating and the sensed data only when a baseline threshold is met but is still below the alert threshold. Cronin teaches determine whether the at least physiological data of the portion of patient parameter data from the sensed data capture module comprises information indicating a threshold is crossed, and wherein the threshold is below a predefined threshold level associated with triggering an automated therapy or alert ([0092] provide an alert to the user (e.g., vibration using the vibrator) when a predicted occurrence of pain is above a pre-defined threshold), perform an action that is not an alert responsive to the indication that the threshold is crossed ([0154] for measurements below zero and five there may be no action. If measurements are going five to six, a single vibration may be provided to the user with a corresponding comment that a pain may occur – this kind of graded response with different thresholds for different actions make it obvious to trigger a correlation when one threshold is crossed and to trigger an alert when a higher threshold is crossed).
The combination of Kaib, Schnell, and Cronin fails to teach aggregating data across multiple patients. Virtanen teaches aggregate patient data for multiple different patients ([0007] aggregate patient data from multiple independent sources) to learn trends in patient evaluation and adverse health events, train a machine learning classifier using the aggregated patient data ([0024] creating tailored monitoring plans based on the aggregated data requires learning trends, [0060] analysis of data reads on machine learning as machine learning in and of itself is a very general application). It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify the combination of Kaib and Schnell with the teachings of Virtanen because there is some teaching, suggestion, or motivation to do so. Virtanen teaches that aggregating patient data allows for tailored treatment and monitoring plans since patterns can be identified ([0008]).
Kaib further teaches receiving health assessment rating and the patient parameter data of the patient from the medical monitoring device over the communication link (Fig 6 act 615 receive quality of life information). Schnell further teaches the health assessment rating being temporally correlated to the patient parameter data ([0211] correlated stimulation parameter data).
The combination of Kaib, Schnell, and Cronin fails to teach using machine learning to identify indicia that indicates the presence of a heightened risk that an adverse event will occur. Freeman teaches analyze the correlated health assessment rating ([0010] patient assessment identifies a second clinical condition) and the patient parameter data, using the trained machine learning classifier ([0010] identify indicia) to identify indicia in the patient parameter data that indicates the presence of a heightened risk of an adverse health event ([0010] risk assessment process). It would have been obvious to a person having ordinary skill in the art before the time of the filing date of this invention to modify the combination of Kaib, Schnell, and Virtanen to include the teachings of Freeman because there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Freeman discloses that traditional monitoring systems provide no way of consolidating information to provide warnings before it is too late ([0003]-[0005]) because the victim has no perceptible warning of the impending fibrillation ([0004]). Therefore, there is a suggestion to consolidate information using an algorithm to provide an indication of an impending fibrillation.
Kaib further teaches the analyzed health assessment rating and the patient parameter data are accessible by a physician of the patient to modify a treatment plan for the patient and improve a quality of shock therapy ([0018] display – what the physician chooses to do with the data is not patentably significant and this section of Kaib teaches that the device has the ability to display the necessary information to the physician and then they can decide what to do) and provide a notification indicating the presence of the heightened risk of the adverse health event ([0076] heart failure indicators).
Regarding claim 14, the combination of Kaib, Schnell, Virtanen, and Freeman teaches the system according to claim 13. Cronin further teaches the remote server ([0125] client and server are typically remote from each other) receives the data from a companion device ([0005] wearable device – worn over or attached to a body part, or embedded into an item of clothing or footwear and configured for sensing of various physiological parameters) associated with the patient.
Regarding claim 18, Kaib discloses receiving patient input by prompting the patient ([0071] the delivery of treatment can be delayed to allow time for audio voice messages to prompt the subject to respond by pressing the response button) tending to indicate a subjective quality of health for the patient ([0073] how the patient feels [0083] on a scale), receiving patient parameter data, that shows objective physiological criteria for the patient, the physiological criteria including an electrocardiogram waveform ([0051] subjects’ cardiac information (e.g., ECG)), the patient parameter data being captured based on the received patient input ([0051] the clinician may gradually increase the magnitude of one or more parameters defining the electrical stimulation therapy and determine the point at which further increase to the magnitude of one or more parameters defining the electrical stimulation therapy causes a perceptible side effect for patient 112 – the stimulation is being adjusted based on an external factor that can as an obvious variation be inputted by the patient). Kaib discloses the ability to make correlations between different data types ([0116] making correlations) but fails to disclose that that correlation is made between the patient input with the portion of the patient parameter data. Schnell discloses correlating the patient input with the patient parameter data ([0211]). It would have been obvious to a person having ordinary skill in the art before the filing date of this invention to modify Kaib with the teachings of Schnell because there is a teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Schnell discloses that the system may correlate patient condition, events, and sensed signals to monitor stimulation efficacy and adjust stimulation over time to reduce patient symptoms ([0046]). Therefore, there is a reasonable expectation of success for a patient monitor module to include temporally correlating user input data and sensed data as described in the current invention.
The combination of Kaib and Schnell fails to teach temporally correlating the input health assessment rating and the sensed data only when a baseline threshold is met but is still below the alert threshold. Cronin teaches determine whether the at least physiological data of the portion of patient parameter data from the sensed data capture module comprises information indicating a threshold is crossed, and wherein the threshold is below a predefined threshold level associated with triggering an automated therapy or alert ([0092] provide an alert to the user (e.g., vibration using the vibrator) when a predicted occurrence of pain is above a pre-defined threshold), perform an action that is not an alert responsive to the indication that the threshold is crossed ([0154] for measurements below zero and five there may be no action. If measurements are going five to six, a single vibration may be provided to the user with a corresponding comment that a pain may occur – this kind of graded response with different thresholds for different actions make it obvious to trigger a correlation when one threshold is crossed and to trigger an alert when a higher threshold is crossed).
The combination of Kaib, Schnell, and Cronin fails to teach aggregating data across multiple patients. Virtanen teaches aggregate patient data for multiple different patients ([0007] aggregate patient data from multiple independent sources) to learn trends in patient evaluation and adverse health events, train a machine learning classifier using the aggregated patient data ([0024] creating tailored monitoring plans based on the aggregated data requires learning trends, [0060] analysis of data reads on machine learning as machine learning in and of itself is a very general application). It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify the combination of Kaib and Schnell with the teachings of Virtanen because there is some teaching, suggestion, or motivation to do so. Virtanen teaches that aggregating patient data allows for tailored treatment and monitoring plans since patterns can be identified ([0008]).
The combination of Kaib, Schnell, Cronin, and Virtanen fails to disclose analyzing the (additional) parameter data to identify indicia of the adverse health event. Freeman teaches analyzing the patient parameter data to identify indicia of the adverse health event and analyzing additional patient parameter data to identify the indicia of the adverse health event ([0010] patient assessment as a second clinical condition). It would have been obvious to a person having ordinary skill in the art before the time of the filing date of this invention to modify the combination of Kaib, Schnell, and Cronin to include the teachings of Freeman because there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention (MPEP 2143). Freeman discloses that traditional monitoring systems provide no way of consolidating information to provide warnings before it is too late ([0003]-[0005]) because the victim has no perceptible warning of the impending fibrillation ([0004]). Therefore, there is a suggestion to consolidate information to provide an indication of an impending fibrillation (adverse health event).
Kaib further teaches the analyzed health assessment rating and the patient parameter data are accessible by a physician of the patient to modify a treatment plan for the patient and improve a quality of shock therapy ([0018] display – what the physician chooses to do with the data is not patentably significant and this section of Kaib teaches that the device has the ability to display the necessary information to the physician and then they can decide what to do) and provide a notification indicating the presence of the heightened risk of the adverse health event ([0076] heart failure indicators).
Regarding claim 19, the combination of Kaib, Schnell, Cronin, Virtanen, and Freeman teaches the method according to claim 18. Cronin further teaches analyzing additional patient parameter data associated with an occurrence of the adverse health event to investigate whether the indicia of the adverse health event was present prior to the adverse health event ([0173] the predictive model may take the cycling activity as an indicator of the future pain level of 9).
Regarding claim 22, the combination of Kaib, Schnell, Cronin, Virtanen, and Freeman teaches the method of claim 18. Freeman further teaches training the machine learning classifier comprises utilizing patient outcomes in form of episodes ([0010] discusses updating the machine learning algorithm with the patient outcomes) corresponding to the multiple patients, to predict a likelihood of the adverse health event for the patient ([0010] risk of deterioration of the patient).
Regarding claim 24, the combination of Kaib, Schnell, Cronin, Freeman, and Virtanen teaches the system of claim 13. Freeman further teaches the remote server is further configured to:
identify the adverse health event as a shockable or non-shockable event ([0008] identify the first clinical condition based on initial patient assessment - identifying the condition means that it can be classified as shockable or non-shockable),
and perform evaluations to learn relationships between trends in the patient data and the adverse health event to predict at least one of future adverse health event and imminent adverse health event ([0088] predicting and/or detecting a deteriorating clinical condition in a patient, [0219] to obtain an improved a posteriori estimate).
Claim(s) 16 is rejected under 35 USC 103 as being unpatentable over Kaib et. al (US 20130231711 A1); hereinafter Kaib, Schnell, and Cronin and further in view of Virtanen and Freeman et. al (US 20190385744 A1); hereinafter Freeman, and further in view of Schulhauser et. al (US 20200038671 A1); hereinafter Schulhauser (cited previously).
Regarding claim 16, the combination of Kaib, Schnell, Cronin, Virtanen, and Freeman teaches the system of claim 13. The combination fails to teach that the remote server receives the data from a Wearable Cardioverter Defibrillator and from an external monitoring device associated with the wearable cardioverter defibrillator system. Schulhauser teaches the remote server receives the data ([0043] external device 170 receives patient data from apparatus 110) from a Wearable Cardioverter Defibrillator ([0039] apparatus 110 is a WAED). Schulhauser further teaches the remote server ([0043] remote patient monitoring device) receives the data from an external monitoring device ([0043] external device 170) associated with a Wearable Cardioverter Defibrillator system ([0043] receives data from apparatus 110 – which has been stated to include a WAED [0039]). It would have been obvious to a person having ordinary skill in the art before the time of the filing date of this invention to modify the combination of Kaib, Schnell, and Freeman with the teachings of Schulhauser because combining prior art elements according to known methods to yield predictable results. Patients who have risk factors for adverse events (especially cardiac) but do not have implantable systems yet will often be wearing a WAED so combining the sensors and health assessment analysis into a device that already has the required sensors would have a predictable result as the sensors in the WAED are similar to those necessitated by the claims of this invention.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Dhrasti SNEHAL Dalal whose telephone number is (571)272-0780. The examiner can normally be reached Monday - Thursday 8:30 am - 6:00 pm, Alternate Friday off, 8:30 am - 5:00 pm.
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/D.S.D./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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