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 with respect to the rejections of claims 1-20 under 35 U.S.C. 103 have been fully 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.
Applicant argues on page 10 that the prior art of record fail to teach generating an alert based on a heart rate or respiratory rate above or below a predetermined threshold, and a motion of a patient.
The current office action introduces newly found prior art, Klap, et al., US 20110046498 A1, which discloses that system 10 alerts on a change in HR vs. baseline for a beta blocker patient by combining the information about the HR and the motion information. For example, system 10 will alert upon a change of 10% in heart rate that is not correlated with a significant increase in the patient's overall body movements.
Hence, the claims stand rejected.
Withdrawn Rejections
Pursuant of Applicant’s amendments filed 03/24/2025, the rejection of claims 1-20 under 35 U.S.C. 101 have been withdrawn.
Withdrawn Objections
The objections made to claims 1-2, 6, 8 and 15 have been withdrawn in view of Applicant’s amendments filed 11/03/2025.
Withdrawn Rejections
Pursuant of Applicant’s amendments filed 11/03/2025, the rejection of claims 1-20 under 35 U.S.C. 112(b) have been withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Niemeyer, E.A., US 20170055877 A1 in view of Sackner, et al., US 20080082018 A1 and Klap, et al., US 20110046498 A1.
Regarding claim 1, Niemeyer teaches a system for processing images ([0027] discloses “a 3D camera-based infant monitoring system 101”) comprising:
an infrared camera ([0036] discloses an RGBNIR camera sensor, which may be any known CMOS sensor without a hot mirror in the optical path, or an enhanced NIR sensor including a specific NIR sensitive pixels) configured to generate an infrared camera image ([0037] states that “Method 201 continues at operation 215, where scene information is collected in the form of consecutive image data frames (i.e., video frames)”);
a processor ([0023] discloses image processors and graphic processors for implementing the functions of the system) configured to: obtain the infrared camera image ([0033] states “method 201 is performed by computer processor(s) integrated into 3D camera platform 120”, which includes the data collection step at step 215 of fig. 2);
extract one or more movement indicators from the infrared camera image, the movement indicators measure intensity pixel values indicating a movement within a pixel of the infrared camera image ([0037] specifies that the consecutive image data frames include “depth map correlated with a plurality of pixels p.sub.i, each having an image coordinate x.sub.i,y.sub.i associated with the input image frame”. Figs. 5A and 5B and corresponding [0046]-[0048] describe analysis of the depth data with respect to time, based on the pixel positions and displacements within a region of interest (ROI), the ROI being an abnormal cavity of a patient. Here, the measured depth, in figs. 5A and 5B, comprises the movement indicators);
provide a processed output based on the physiologic signal to a user interface based on the determined peaks ([0031] states that “In some embodiments, one or more of 3D image data, logged respiratory data, or alerts is communicated by 3D camera platform 120 over a wide area network 152 to mobile computing platform 160 (e.g., smart phone, tablet computer, etc.) associated with system user 165”, where the respiratory data is generated based on peak inhale and peak exhale information. [0040] further states that “FIG. 3 is a schematic of infant abdominal cavity distance measurement with a 3D camera system, in accordance with some embodiments. As shown, over three points in time t.sub.1, t.sub.2, t.sub.3 representing a full respiratory cycle, a distance between a baseline associated with 3D camera platform 120 and a surface associated with the abdominal cavity of infant 105 varies from z.sub.1 at peak exhale, to z.sub.2 at peak inhale, and back to z.sub.1”).
Niemeyer fails to teach use wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion of a patient; process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient.
However, within the same field of endeavor, Sackner teaches improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors (abstract) and [0009] states that while respiratory inductive plethysmography (RIP) is the preferred measurement technology, the systems and methods of the invention are readily adapted to other sensor technologies such as movement analysis by optical reflection. Sackner then teaches use wavelet decomposition to determine three data streams from the one or more movement indicators ([0075] states that “Wavelet filtering, also known as wavelet de-noising, processes the measured signal, T.sub.T, by retaining signal components that represent the desired respiratory signal, T.sub.R, but suppressing signal components that do not represent T.sub.R, i.e., that represent other signals, T.sub.PA+T.sub.MA+N. Generally, this method proceeds by decomposing T.sub.T into components along a wavelet basis, and then suppressing the wavelet coefficients representing the other signals while retaining the wavelet coefficients representing the desired signal”, the data streams comprising respiratory components, motion artifacts, noise signals, and cardiac signals according to [0052]. According to [0043], the various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies),
wherein each of the three data streams represents a different motion artifact of a patient ([0052] discloses the different streams of data into which the input signal is decomposed. The different data streams comprise respiratory components, motion artifacts, noise signals, and cardiac signals. Of note, the system and method of Sackner highlights suppressing the cardiac and motion artifact signals, extracting those signals occurs first and then according to [0075], wavelet coefficients associated with those signals are suppressed);
process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient ([0062] states “Turning first to state space filtering, in this approach, the total signal, T.sub.T, is represented as a linear combination of components, in particular, T.sub.R, T.sub.PA, T.sub.MA, and N, that are produced by deterministic, discrete-time processes occurring in state spaces which are described by linear stochastic difference equations. In particular, respiration itself is represented as a deterministic process occurring in a state space that includes a sufficient set of states to adequately represent respiration”, [0063] stats “Physiological artifact signals, e.g. cardiac signals, can be similarly represented as a deterministic, discrete-time process in a state space along with a linear relation between the states and the artifact measurements, T.sub.PA.”, and [0064] states “Motion artifacts, T.sub.MA, often occur randomly with respect to the respiratory process, and therefore may not be easily or usefully represented as a deterministic, discrete-time process”. The deterministic, discrete-time process includes extracting various physiological parameters that depend on extracting maxima and peaks in the physiological signal, as noted in [0098], which discloses examplary extraction of various respiratory parameters including peak inspiration and peak expiration. Also in fig. 4A, an examplary respiratory waveform signa is shown with maxima identified. Meaning that the cardiac signal (heart rate according to []0052]) and motion artifact signal are also extracted with characteristic maxima and peaks of the physiological signal); and
the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts ([0052] states that “the torso excursion signals are measured plethysmographic means, in particular by RIP (respiratory inductive plethysmography). FIG. 3A schematically illustrates an exemplary RIP signal spectrum. The respiratory components, T.sub.R or 50, in the RIP signal usually include RC (rib cage) and AB (abdomen) size signals…Other components 52 can include cardiac signals (depending on the placement of the RIP bands), motion artifacts, noise, and the like. Cardiac signals (and signals for other physiological processes), T.sub.PA, usually have amplitudes less than about 10% of the respiratory amplitudes and fundamental frequencies of about 0.75 to about 1 and to about 2 Hz (i.e., heart rates of about 45 to about 60 and to about 120 beats/min with harmonics to about 3 Hz and higher. Motion artifacts, T.sub.MA, are usually present during ambulatory monitoring and have widely varying amplitudes and widely varying frequency spectra. The respiratory and cardiac signals, included in the torso excursion signals are plethysmographic data and all signals, including the motion artifacts, are represented as deterministic, discrete-time process, as outlined above), and
wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient (see figs. 3A-3C that show graphs of physiological signal, total torso-measurement signal TT, which comprises cardiac signals (heart rate), respiratory rate and movement artifacts, which are general and local body movements. [0052] describe the different signals as outlined above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer to use wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion artifact of a patient; process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Niemeyer in view of Sackner fails to teach generate an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
However, within the same field of endeavor, Klap teaches apparatus and methods for use with a subject who is undergoing respiration. A motion sensor senses motion of a subject. A breathing pattern analysis unit analyzes components of the sensed motion that result from the subject's respiration (abstract). [0188] states that “system 10 alerts on a change in HR vs. baseline for a beta blocker patient by combining the information about the HR and the motion information. For example, system 10 will alert upon a change of 10% in heart rate that is not correlated with a significant increase in the patient's overall body movements”, and hence teaching generating an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, to generate an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position, as taught by Klap, to improve the sensitivity of detecting abnormalities in the patient, as [0188] goes on to state that “In general, for patients who are treated by drugs that reduce heart rate variability, system 10 is configured to reduce by at least 30% the threshold amount of change for generating an alert upon detecting a change in heart rate, in response to receiving an indication that the patient is taking such a drug, compared to the threshold used for patients who are not taking such a drug”.
Regarding claim 8, Niemeyer teaches a method ([0033] states that “FIG. 2 is a flow diagram of a method 201 for infant monitoring with a 3D camera system”) comprising:
generate an infrared camera image using an infrared camera ([0028] states that “3D camera platform 120 includes one or more digital cameras operable to collect information from which depth (i.e., range) information may be determined for an image frame. Any cameras known to be capable of generating 3D image frame data may be employed”. According to [0036] the camera is a red/green/blue near infrared (RGBNIR) camera sensor).
obtaining the infrared camera image from the infrared camera ([0037] states that “Method 201 continues at operation 215, where scene information is collected in the form of consecutive image data frames (i.e., video frames)” using a red/green/blue near infrared (RGBNIR) camera sensor according [0036]);
extracting one or more movement indicators from the infrared camera image, the movement indicators measure intensity pixel values indicating a movement within a pixel of the infrared camera image ([0037] specifies that the consecutive image data frames include “depth map correlated with a plurality of pixels p.sub.i, each having an image coordinate x.sub.i,y.sub.i associated with the input image frame”. Figs. 5A and 5B and corresponding [0046]-[0048] describe analysis of the depth data with respect to time, based on the pixel positions and displacements within a region of interest (ROI), the ROI being an abnormal cavity of a patient. Here, the measured depth, in figs. 5A and 5B, comprises the movement indicators);
providing a processed output based on the physiologic signal to a user interface based on the determined peaks ([0031] states that “In some embodiments, one or more of 3D image data, logged respiratory data, or alerts is communicated by 3D camera platform 120 over a wide area network 152 to mobile computing platform 160 (e.g., smart phone, tablet computer, etc.) associated with system user 165”, where the respiratory data is generated based on peak inhale and peak exhale information. [0040] further states that “FIG. 3 is a schematic of infant abdominal cavity distance measurement with a 3D camera system, in accordance with some embodiments. As shown, over three points in time t.sub.1, t.sub.2, t.sub.3 representing a full respiratory cycle, a distance between a baseline associated with 3D camera platform 120 and a surface associated with the abdominal cavity of infant 105 varies from z.sub.1 at peak exhale, to z.sub.2 at peak inhale, and back to z.sub.1”), and
Niemeyer fails to teach using wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion of a patient; processing the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient.
However, within the same field of endeavor, Sackner teaches improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors (abstract) and [0009] states that while respiratory inductive plethysmography (RIP) is the preferred measurement technology, the systems and methods of the invention are readily adapted to other sensor technologies such as movement analysis by optical reflection. Sackner then teaches using wavelet decomposition to determine three data streams from the one or more movement indicators ([0075] states that “Wavelet filtering, also known as wavelet de-noising, processes the measured signal, T.sub.T, by retaining signal components that represent the desired respiratory signal, T.sub.R, but suppressing signal components that do not represent T.sub.R, i.e., that represent other signals, T.sub.PA+T.sub.MA+N. Generally, this method proceeds by decomposing T.sub.T into components along a wavelet basis, and then suppressing the wavelet coefficients representing the other signals while retaining the wavelet coefficients representing the desired signal”, the data streams comprising respiratory components, motion artifacts, noise signals, and cardiac signals according to [0052]. According to [0043], the various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies),
wherein each of the three data streams represents a different motion artifact of a patient ([0052] discloses the different streams of data into which the input signal is decomposed. The different data streams comprise respiratory components, motion artifacts, noise signals, and cardiac signals. Of note, the system and method of Sackner highlights suppressing the cardiac and motion artifact signals, extracting those signals occurs first and then according to [0075], wavelet coefficients associated with those signals are suppressed);
processing the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient ([0062] states “Turning first to state space filtering, in this approach, the total signal, T.sub.T, is represented as a linear combination of components, in particular, T.sub.R, T.sub.PA, T.sub.MA, and N, that are produced by deterministic, discrete-time processes occurring in state spaces which are described by linear stochastic difference equations. In particular, respiration itself is represented as a deterministic process occurring in a state space that includes a sufficient set of states to adequately represent respiration”, [0063] stats “Physiological artifact signals, e.g. cardiac signals, can be similarly represented as a deterministic, discrete-time process in a state space along with a linear relation between the states and the artifact measurements, T.sub.PA.”, and [0064] states “Motion artifacts, T.sub.MA, often occur randomly with respect to the respiratory process, and therefore may not be easily or usefully represented as a deterministic, discrete-time process”. The deterministic, discrete-time process includes extracting various physiological parameters that depend on extracting maxima and peaks in the physiological signal, as noted in [0098], which discloses examplary extraction of various respiratory parameters including peak inspiration and peak expiration. Also in fig. 4A, an examplary respiratory waveform signa is shown with maxima identified. Meaning that the cardiac signal (heart rate according to []0052]) and motion artifact signal are also extracted with characteristic maxima and peaks of the physiological signal); and
the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts ([0052] states that “the torso excursion signals are measured plethysmographic means, in particular by RIP (respiratory inductive plethysmography). FIG. 3A schematically illustrates an exemplary RIP signal spectrum. The respiratory components, T.sub.R or 50, in the RIP signal usually include RC (rib cage) and AB (abdomen) size signals…Other components 52 can include cardiac signals (depending on the placement of the RIP bands), motion artifacts, noise, and the like. Cardiac signals (and signals for other physiological processes), T.sub.PA, usually have amplitudes less than about 10% of the respiratory amplitudes and fundamental frequencies of about 0.75 to about 1 and to about 2 Hz (i.e., heart rates of about 45 to about 60 and to about 120 beats/min with harmonics to about 3 Hz and higher. Motion artifacts, T.sub.MA, are usually present during ambulatory monitoring and have widely varying amplitudes and widely varying frequency spectra. The respiratory and cardiac signals, included in the torso excursion signals are plethysmographic data and all signals, including the motion artifacts, are represented as deterministic, discrete-time process, as outlined above), and
wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient (see figs. 3A-3C that show graphs of physiological signal, total torso-measurement signal TT, which comprises cardiac signals (heart rate), respiratory rate and movement artifacts, which are general and local body movements. [0052] describe the different signals as outlined above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer to using wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion artifact of a patient; processing the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Niemeyer in view of Sackner fails to teach generating an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
However, within the same field of endeavor, Klap teaches apparatus and methods for use with a subject who is undergoing respiration. A motion sensor senses motion of a subject. A breathing pattern analysis unit analyzes components of the sensed motion that result from the subject's respiration (abstract). [0188] states that “system 10 alerts on a change in HR vs. baseline for a beta blocker patient by combining the information about the HR and the motion information. For example, system 10 will alert upon a change of 10% in heart rate that is not correlated with a significant increase in the patient's overall body movements”, and hence teaching generating an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, to generate an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position, as taught by Klap, to improve the sensitivity of detecting abnormalities in the patient, as [0188] goes on to state that “In general, for patients who are treated by drugs that reduce heart rate variability, system 10 is configured to reduce by at least 30% the threshold amount of change for generating an alert upon detecting a change in heart rate, in response to receiving an indication that the patient is taking such a drug, compared to the threshold used for patients who are not taking such a drug”.
Regarding claim 15, Niemeyer teaches a non-transitory machine-executable media, the non-transitory machine-executable media comprising a plurality of instructions that, ([0023] discloses that “At least some of the material disclosed herein may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors (graphics processors and/or central processors)”) in response to execution by a processor cause the processor to:
obtain an infrared camera image ([0037] states that “Method 201 continues at operation 215, where scene information is collected in the form of consecutive image data frames (i.e., video frames)” using a red/green/blue near infrared (RGBNIR) camera sensor according [0036]);
extract one or more movement indicators from the infrared camera image, the movement indicators measure intensity pixel values indicating a movement within a pixel of the infrared camera image ([0037] specifies that the consecutive image data frames include “depth map correlated with a plurality of pixels p.sub.i, each having an image coordinate x.sub.i,y.sub.i associated with the input image frame”. Figs. 5A and 5B and corresponding [0046]-[0048] describe analysis of the depth data with respect to time, based on the pixel positions and displacements within a region of interest (ROI), the ROI being an abnormal cavity of a patient. Here, the measured depth, in figs. 5A and 5B, comprises the movement indicators);
provide a processed output based on the physiologic signal to a user interface based on the determined peaks ([0031] states that “In some embodiments, one or more of 3D image data, logged respiratory data, or alerts is communicated by 3D camera platform 120 over a wide area network 152 to mobile computing platform 160 (e.g., smart phone, tablet computer, etc.) associated with system user 165”, where the respiratory data is generated based on peak inhale and peak exhale information. [0040] further states that “FIG. 3 is a schematic of infant abdominal cavity distance measurement with a 3D camera system, in accordance with some embodiments. As shown, over three points in time t.sub.1, t.sub.2, t.sub.3 representing a full respiratory cycle, a distance between a baseline associated with 3D camera platform 120 and a surface associated with the abdominal cavity of infant 105 varies from z.sub.1 at peak exhale, to z.sub.2 at peak inhale, and back to z.sub.1”), and
Niemeyer fails to teach use wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion artifact of a patient; process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient.
However, within the same field of endeavor, Sackner teaches improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors (abstract) and [0009] states that while respiratory inductive plethysmography (RIP) is the preferred measurement technology, the systems and methods of the invention are readily adapted to other sensor technologies such as movement analysis by optical reflection. Sackner then teaches use wavelet decomposition to determine three data streams from the one or more movement indicators ([0075] states that “Wavelet filtering, also known as wavelet de-noising, processes the measured signal, T.sub.T, by retaining signal components that represent the desired respiratory signal, T.sub.R, but suppressing signal components that do not represent T.sub.R, i.e., that represent other signals, T.sub.PA+T.sub.MA+N. Generally, this method proceeds by decomposing T.sub.T into components along a wavelet basis, and then suppressing the wavelet coefficients representing the other signals while retaining the wavelet coefficients representing the desired signal”, the data streams comprising respiratory components, motion artifacts, noise signals, and cardiac signals according to [0052]. According to [0043], the various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies),
wherein each of the three data streams represents a different motion artifact of a patient ([0052] discloses the different streams of data into which the input signal is decomposed. The different data streams comprise respiratory components, motion artifacts, noise signals, and cardiac signals. Of note, the system and method of Sackner highlights suppressing the cardiac and motion artifact signals, extracting those signals occurs first and then according to [0075], wavelet coefficients associated with those signals are suppressed);
process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient ([0062] states “Turning first to state space filtering, in this approach, the total signal, T.sub.T, is represented as a linear combination of components, in particular, T.sub.R, T.sub.PA, T.sub.MA, and N, that are produced by deterministic, discrete-time processes occurring in state spaces which are described by linear stochastic difference equations. In particular, respiration itself is represented as a deterministic process occurring in a state space that includes a sufficient set of states to adequately represent respiration”, [0063] stats “Physiological artifact signals, e.g. cardiac signals, can be similarly represented as a deterministic, discrete-time process in a state space along with a linear relation between the states and the artifact measurements, T.sub.PA.”, and [0064] states “Motion artifacts, T.sub.MA, often occur randomly with respect to the respiratory process, and therefore may not be easily or usefully represented as a deterministic, discrete-time process”. The deterministic, discrete-time process includes extracting various physiological parameters that depend on extracting maxima and peaks in the physiological signal, as noted in [0098], which discloses examplary extraction of various respiratory parameters including peak inspiration and peak expiration. Also in fig. 4A, an examplary respiratory waveform signa is shown with maxima identified. Meaning that the cardiac signal (heart rate according to []0052]) and motion artifact signal are also extracted with characteristic maxima and peaks of the physiological signal); and
the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts ([0052] states that “the torso excursion signals are measured plethysmographic means, in particular by RIP (respiratory inductive plethysmography). FIG. 3A schematically illustrates an exemplary RIP signal spectrum. The respiratory components, T.sub.R or 50, in the RIP signal usually include RC (rib cage) and AB (abdomen) size signals…Other components 52 can include cardiac signals (depending on the placement of the RIP bands), motion artifacts, noise, and the like. Cardiac signals (and signals for other physiological processes), T.sub.PA, usually have amplitudes less than about 10% of the respiratory amplitudes and fundamental frequencies of about 0.75 to about 1 and to about 2 Hz (i.e., heart rates of about 45 to about 60 and to about 120 beats/min with harmonics to about 3 Hz and higher. Motion artifacts, T.sub.MA, are usually present during ambulatory monitoring and have widely varying amplitudes and widely varying frequency spectra. The respiratory and cardiac signals, included in the torso excursion signals are plethysmographic data and all signals, including the motion artifacts, are represented as deterministic, discrete-time process, as outlined above), and
wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient (see figs. 3A-3C that show graphs of physiological signal, total torso-measurement signal TT, which comprises cardiac signals (heart rate), respiratory rate and movement artifacts, which are general and local body movements. [0052] describe the different signals as outlined above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer to use wavelet decomposition to determine three data streams from the one or more movement indicators, wherein each of the three data streams represents a different motion artifact of a patient; process the three data streams from the wavelet decomposition to determine any number of peaks that indicate a physiologic signal comprising a heart rate, respiratory rate, and a motion of the patient; and the processed output including a pulse plethysmograph, a respiration rate plethysmograph, and a time series of motion artifacts, and wherein the processed output indicates a heart rate, a respiratory rate, and movement patterns of the patient, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Niemeyer in view of Sackner fails to teach generate an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
However, within the same field of endeavor, Klap teaches apparatus and methods for use with a subject who is undergoing respiration. A motion sensor senses motion of a subject. A breathing pattern analysis unit analyzes components of the sensed motion that result from the subject's respiration (abstract). [0188] states that “system 10 alerts on a change in HR vs. baseline for a beta blocker patient by combining the information about the HR and the motion information. For example, system 10 will alert upon a change of 10% in heart rate that is not correlated with a significant increase in the patient's overall body movements”, and hence teaching generating an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, to generate an alert via the user interface indicating a condition of the patient, and wherein the alert is based on the heart rate or the respiratory rate being above a first predetermined threshold or below a second predetermined threshold, and the motion of the patient indicating a body position, as taught by Klap, to improve the sensitivity of detecting abnormalities in the patient, as [0188] goes on to state that “In general, for patients who are treated by drugs that reduce heart rate variability, system 10 is configured to reduce by at least 30% the threshold amount of change for generating an alert upon detecting a change in heart rate, in response to receiving an indication that the patient is taking such a drug, compared to the threshold used for patients who are not taking such a drug”.
Claims 2-4, 9-11 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Niemeyer, E.A., US 20170055877 A1 in view of Sackner, et al., US 20080082018 A1, and Klap, et al., US 20110046498 A1, as applied to claims 1, 8 and 15, respectively above, and further in view of Addison, et al., US 20170238805 A1.
Regarding claim 2, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 1.
Niemeyer further teaches wherein the processor ([0069] discloses “a data processing system 800” comprising one or more processors 750) is configured to: calculate a plurality of pixel values for each of the at least two data streams, the plurality of pixel values comprising intensity values ([0037] states, with respect to step 215 of method 201 of fig. 2, that “depth information collected at operation 215 is in the form of a depth map correlated with a plurality of pixels p.sub.i, each having an image coordinate x.sub.i,y.sub.i associated with the input image frame”, where each pixel is assigned a depth value, tantamount to the pixel intensity or amplitude).
Niemeyer in view of Sackner and Klap fails to teach wherein the processor is to: perform a computation based on the plurality of pixel values.
However, within the same field of endeavor, Addison teaches non-contact, video-based medical monitoring of pulse rate, respiration rate, motion, and oxygen saturation (see abstract) wherein a processor (processor of [0047]) is to: perform a computation based on the plurality of pixel values ([0053] states that “a method for video-based monitoring of a patient's pulse rate includes generating a video signal from a video camera having a field of view exposed to a patient, the video signal comprising a time-varying intensity signal for each of a plurality of pixels in the field of view; combining the intensity signals within a region of the field of view to produce a regional intensity signal; transforming the regional intensity signal into the frequency domain to produce a regional frequency signal; over a sliding time window, identifying peaks in the regional frequency signal; over a period of time, accumulating the identified peaks; selecting a median frequency from the accumulated peaks; updating a running average pulse rate of a patient, wherein updating comprises: converting the median frequency into a measured pulse rate”. The transformation of the signal into frequency domain comprises the recited computation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer as modified by Sackner and Klap, wherein the processor is to: perform a computation based on the plurality of pixel values, as taught by Addison, as such modification would allow a more robust way to enhance the physiologic signal of interest while reducing or rejecting unwanted contributions and noise (see [0096] and [0112]).
Regarding claim 3, Niemeyer in view of Sackner, Klap and Addison teaches all the limitations of claim 2.
Niemeyer in view of Sackner and Klap fail to teach wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams.
However, Addison further teaches wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams ([0053] states that “a method for video-based monitoring of a patient's pulse rate includes generating a video signal from a video camera having a field of view exposed to a patient, the video signal comprising a time-varying intensity signal for each of a plurality of pixels in the field of view; combining the intensity signals within a region of the field of view to produce a regional intensity signal; transforming the regional intensity signal into the frequency domain to produce a regional frequency signal; over a sliding time window, identifying peaks in the regional frequency signal; over a period of time, accumulating the identified peaks; selecting a median frequency from the accumulated peaks; updating a running average pulse rate of a patient, wherein updating comprises: converting the median frequency into a measured pulse rate”. The transformation of the signal into frequency domain comprises the recited computation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer as modified by Sackner and Klap, wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams, as taught by Addison, as such modification would allow a more robust way to enhance the physiologic signal of interest while reducing or rejecting unwanted contributions and noise (see [0096] and [0112]).
Regarding claim 4, Niemeyer in view of Sackner, Klap and Addison teaches all the limitations of claim 3.
Niemeyer fails to teach wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams.
However, Sackner further teaches wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams ([0043] states “various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies” and [0050] states that “the torso measurement signal can be represented as a combination of the several components of torso excursions preferably grouped as follows: T.sub.T=T.sub.R+T.sub.PA+T.sub.MA+N”, where “T.sub.T” is the input signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, Klap, and Addison, wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Regarding claim 9, Niemeyer in view of Sackner, Klap, and Addison teaches all the limitations of claim 8.
Niemeyer further teaches calculating a plurality of pixel values for each of the three data streams, the plurality of pixel values comprising intensity values ([0037] states, with respect to step 215 of method 201 of fig. 2, that “depth information collected at operation 215 is in the form of a depth map correlated with a plurality of pixels p.sub.i, each having an image coordinate x.sub.i,y.sub.i associated with the input image frame”, where each pixel is assigned a depth value, tantamount to the pixel intensity or amplitude).
Niemeyer in view of Sackner and Klap fail to teach to teach performing a computation based on the plurality of pixel values.
However, Addison further teaches performing a computation based on the plurality of pixel values ([0053] states that “a method for video-based monitoring of a patient's pulse rate includes generating a video signal from a video camera having a field of view exposed to a patient, the video signal comprising a time-varying intensity signal for each of a plurality of pixels in the field of view; combining the intensity signals within a region of the field of view to produce a regional intensity signal; transforming the regional intensity signal into the frequency domain to produce a regional frequency signal; over a sliding time window, identifying peaks in the regional frequency signal; over a period of time, accumulating the identified peaks; selecting a median frequency from the accumulated peaks; updating a running average pulse rate of a patient, wherein updating comprises: converting the median frequency into a measured pulse rate”. The transformation of the signal into frequency domain comprises the recited computation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer as modified by Sackner and Klap, performing a computation based on the plurality of pixel values, as taught by Addison, as such modification would allow a more robust way to enhance the physiologic signal of interest while reducing or rejecting unwanted contributions and noise (see [0096] and [0112]).
Regarding claim 10, Niemeyer in view of Sackner, Klap, and Addison teaches all the limitations of claim 9.
Niemeyer in view of Sackner and Klap fail to teach wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams.
However, Addison further teaches wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the at least two data streams ([0053] states that “a method for video-based monitoring of a patient's pulse rate includes generating a video signal from a video camera having a field of view exposed to a patient, the video signal comprising a time-varying intensity signal for each of a plurality of pixels in the field of view; combining the intensity signals within a region of the field of view to produce a regional intensity signal; transforming the regional intensity signal into the frequency domain to produce a regional frequency signal; over a sliding time window, identifying peaks in the regional frequency signal; over a period of time, accumulating the identified peaks; selecting a median frequency from the accumulated peaks; updating a running average pulse rate of a patient, wherein updating comprises: converting the median frequency into a measured pulse rate”. The transformation of the signal into frequency domain comprises the recited computation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer as modified by Sackner and Klap, wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams, as taught by Addison, as such modification would allow a more robust way to enhance the physiologic signal of interest while reducing or rejecting unwanted contributions and noise (see [0096] and [0112]).
Regarding claim 11, Niemeyer in view of Sackner, Klap, and Addison teaches all the limitations of claim 10.
Niemeyer fails to teach wherein the using the wavelet decomposition comprises reconstructing an input signal based on the at least two data streams.
However, Sackner further teaches wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams ([0043] states “various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies” and [0050] states that “the torso measurement signal can be represented as a combination of the several components of torso excursions preferably grouped as follows: T.sub.T=T.sub.R+T.sub.PA+T.sub.MA+N”, where “T.sub.T” is the input signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, Klap, and Addison, wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Regarding claim 16, Niemeyer in view of Sackner, Klap, and Addison teaches all the limitations of claim 9.
Niemeyer in view of Sackner and Klap fail to teach wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the at least two data streams.
However, Addison further teaches wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the three data streams ([0053] states that “a method for video-based monitoring of a patient's pulse rate includes generating a video signal from a video camera having a field of view exposed to a patient, the video signal comprising a time-varying intensity signal for each of a plurality of pixels in the field of view; combining the intensity signals within a region of the field of view to produce a regional intensity signal; transforming the regional intensity signal into the frequency domain to produce a regional frequency signal; over a sliding time window, identifying peaks in the regional frequency signal; over a period of time, accumulating the identified peaks; selecting a median frequency from the accumulated peaks; updating a running average pulse rate of a patient, wherein updating comprises: converting the median frequency into a measured pulse rate”. The transformation of the signal into frequency domain comprises the recited computation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, wherein the computation comprises transforming the plurality of pixel values to a frequency domain to obtain a spectrum of frequencies for each of the at least two data streams, as taught by Addison, as such modification would allow a more robust way to enhance the physiologic signal of interest while reducing or rejecting unwanted contributions and noise (see [0096] and [0112]).
Regarding claim 17, Niemeyer in view of Sackner, Klap, and Addison teaches all the limitations of claim 10.
Niemeyer fails to teach wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams.
However, Sackner further teaches wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams ([0043] states “various input signals reflecting torso dimensions, movements, or shapes can be generated by various technologies” and [0050] states that “the torso measurement signal can be represented as a combination of the several components of torso excursions preferably grouped as follows: T.sub.T=T.sub.R+T.sub.PA+T.sub.MA+N”, where “T.sub.T” is the input signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner, Klap, and Addison, wherein the using the wavelet decomposition comprises reconstructing an input signal based on the three data streams, as taught by Sackner, hence providing an efficient way to adequately discriminate the signal of interest from the raw data ([0076]), with a reasonable expectation of success, since Niemeyer also strives to solve the same problem of extracting the data of interest while reducing noise ([0048]).
Claims 5, 12 and 18 rejected under 35 U.S.C. 103 as being unpatentable over Niemeyer, E.A., US 20170055877 A1 in view of Sackner, et al., US 20080082018 A1, Klap, et al., US 20110046498 A1, as applied to claims 1, 8 and 15, respectively above, and further in view of Causevic, et al., US 20030185408 A1.
Regarding claim 5, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 1.
Niemeyer in view of Sackner and Klap fails to teach wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the three data streams.
However, Causevic teaches a method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm using a digital processor (abstract) wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams (see claim 1 which recites “a memory for storing said input signal as an array A1 of a plurality of component frames, and a processor configured to: (a) perform a wavelet transform on each of said component frames to thereby create a corresponding array of wavelet coefficients; (b) iteratively for at least one repetition (i) combine multiple numbers of said wavelet coefficients into a plurality of resultant wavelet coefficients forming an array A2 of a like number 2.sup.N of wavelet coefficients”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the three data streams, as taught by Causevic, for accurate and fast signal detection and hence improved outcomes in diagnosis (paragraph 198).
Regarding claim 12, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 8.
Niemeyer in view of Sackner and Klap fails to teach wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams.
However, Causevic teaches a method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm using a digital processor (abstract) wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams (see claim 1 which recites “a memory for storing said input signal as an array A1 of a plurality of component frames, and a processor configured to: (a) perform a wavelet transform on each of said component frames to thereby create a corresponding array of wavelet coefficients; (b) iteratively for at least one repetition (i) combine multiple numbers of said wavelet coefficients into a plurality of resultant wavelet coefficients forming an array A2 of a like number 2.sup.N of wavelet coefficients”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams, as taught by Causevic, for accurate and fast signal detection and hence improved outcomes in diagnosis (paragraph 198).
Regarding claim 18, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 15.
Niemeyer in view of Sackner and Klap fails to teach wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the three data streams.
However, Causevic teaches a method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm using a digital processor (abstract) wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams (see claim 1 which recites “a memory for storing said input signal as an array A1 of a plurality of component frames, and a processor configured to: (a) perform a wavelet transform on each of said component frames to thereby create a corresponding array of wavelet coefficients; (b) iteratively for at least one repetition (i) combine multiple numbers of said wavelet coefficients into a plurality of resultant wavelet coefficients forming an array A2 of a like number 2.sup.N of wavelet coefficients”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, wherein the wavelet decomposition comprises generating a data structure based on a sum of components of the at least two data streams, as taught by Causevic, for accurate and fast signal detection and hence improved outcomes in diagnosis (paragraph 198).
Claims 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Niemeyer, E.A., US 20170055877 A1 in view of Sackner, et al., US 20080082018 A1, Klap, et al., US 20110046498 A1, as applied to claims 1, 8 and 15, respectively above, and further in view of Wang, et al., US 20210247483 A1.
Regarding claim 6, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 1.
Niemeyer in view of Sackner and Klap fails to teach wherein the processor is to further provide a heart rate variability based on the wavelet decomposition.
However, Wang teaches methods, apparatus and systems for wireless vital monitoring (see abstract and steps s1 to s8 of [0298]) wherein the processor (abstract for the processor) is to further provide a heart rate variability based on the wavelet decomposition ([0317] states that “At step s8: identify an exact time of each heartbeat and then calculate the inter-beat intervals to estimate heart rate variability (HRV) and/or other statistics of inter-beat intervals, wherein the exact time of each heartbeat can be identified by several ways, e.g., by identifying the peaks of the heartbeat waves, identifying the zero-crossing points, or finding some feature points after taking continuous wavelet transform”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, to further provide heart rate variability based on the wave decomposition, as taught by Wang, as such modification would improve the overall monitoring of the health of the patient through robust and accurate isolation of the signals of interest for the monitoring ([0042]-[0043]), which is a shared goal with Niemeyer (see [0002]-[0004]).
Regarding claim 13 Niemeyer in view of Sackner and Klap teaches all the limitations of claim 8.
Niemeyer in view of Sackner and Klap fails to teach wherein the processor is to further provide a heart rate variability based on the wavelet decomposition.
However, Wang teaches methods, apparatus and systems for wireless vital monitoring (see abstract and steps s1 to s8 of [0298]) wherein the processor (abstract for the processor) is to further provide a heart rate variability based on the wavelet decomposition ([0317] states that “At step s8: identify an exact time of each heartbeat and then calculate the inter-beat intervals to estimate heart rate variability (HRV) and/or other statistics of inter-beat intervals, wherein the exact time of each heartbeat can be identified by several ways, e.g., by identifying the peaks of the heartbeat waves, identifying the zero-crossing points, or finding some feature points after taking continuous wavelet transform”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, to further provide heart rate variability based on the wave decomposition, as taught by Wang, as such modification would improve the overall monitoring of the health of the patient through robust and accurate isolation of the signals of interest for the monitoring ([0042]-[0043]), which is a shared goal with Niemeyer (see [0002]-[0004]).
Regarding claim 19, Niemeyer in view of Sackner and Klap teaches all the limitations of claim 15.
Niemeyer in view of Sackner and Klap fails to teach wherein the processor is to further provide a heart rate variability based on the wavelet decomposition.
However, Wang teaches methods, apparatus and systems for wireless vital monitoring (see abstract and steps s1 to s8 of [0298]) wherein the processor (abstract for the processor) is to further provide a heart rate variability based on the wavelet decomposition ([0317] states that “At step s8: identify an exact time of each heartbeat and then calculate the inter-beat intervals to estimate heart rate variability (HRV) and/or other statistics of inter-beat intervals, wherein the exact time of each heartbeat can be identified by several ways, e.g., by identifying the peaks of the heartbeat waves, identifying the zero-crossing points, or finding some feature points after taking continuous wavelet transform”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Niemeyer, as modified by Sackner and Klap, to further provide heart rate variability based on the wave decomposition, as taught by Wang, as such modification would improve the overall monitoring of the health of the patient through robust and accurate isolation of the signals of interest for the monitoring ([0042]-[0043]), which is a shared goal with Niemeyer (see [0002]-[0004]).
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.
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/FAROUK A BRUCE/ Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/ Supervisory Patent Examiner, Art Unit 3797