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 Amendment
2. This action is responsive to the amendments filed 2/24/2026. Claims 1-4, 6, 8-12, and 14, and 16-19 have been amended. No claims were newly added. Claims 5, 7, 13, and 15 have been canceled.
Response to Arguments
3. Applicant’s response with respect to the rejections under 35 USC 112(b) have been considered are withdrawn in light of the amendments. Applicant’s response with respect to the art rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-4, 6, 8-12, 14, and 16-19 are rejected under 35 U.S.C 103 as being unpatentable over Guidoboni et al. (US Pub.: 2022/0031220 A1, – Previously Cited) and further in view of Parchani et al. (US Pub.: 2021/0059539 A1) and further in view of Rissacher (US Pub.: 2015/0157239 A1) and further in view of Al-Ali (US Pub.: 2014/0371632 A1) and further in view of Venkatraman (US Pub.: 2021/0259560 A1).
Regarding claim 1, Guidoboni teaches a system for non-intrusive monitoring and prediction of body functions of a user (e.g. abstract, paragraph 0095), comprising:
a sensor device (e.g. Fig. 9A – force detection sensor 90 (i.e. accelerometer)) placed in the vicinity of the user such that the sensor device is able to capture micro-vibrations of one or more physiological parameters of the user and convert the captured micro-vibrations to a digital data signal (e.g. paragraphs 0093, 0101, – an accelerometer 90 strapped around the chest of the subject 26 to capture at least one vibrations of the heart walls contracting (physiological parameters) generating a seismo-cardiogram “SCG” signal);
a data capturing device (e.g. Fig. 9A – integrated circuited (IC) 92) placed in the vicinity of the sensor device (e.g. Fig. 9A – accelerometer 90; paragraph 0093), the data capturing device configured to record the digital data signal from the sensor device in a predefined chronological format (e.g. paragraphs 0093, 0101-0103, – the programmed IC 92 records the signals from the accelerometer 90 which includes the SCG signal including synchronous recordings thus in a predefined chronological format),
a data receiver module (e.g. Fig. 9A – computing terminal 94) configured to receive digital data signal from the data capturing device (e.g. paragraph 0093);
and an energy spectrum processing platform (e.g. Fig. 11 – model 110) comprising an energy spectrum engine (e.g. paragraph 0093).
However, Guidoboni does not explicitly teach amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal;
a data receiver module configured to receive the optimized digital data signal from the data capturing device;
and an energy spectrum processing platform comprising an energy spectrum engine, configured to:
(a) convert a segment of the optimized digital data signal into a spectrogram;
(b) crop the spectrogram to obtain a cropped spectrogram, wherein the cropped spectrogram retains frequencies comprising information related to ejection fraction of the user; and
(c) process the cropped spectrogram to determine a quantifiable measurement of a current ejection fraction of the user and compare the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames.
Parchani, in a same field of endeavor of noninvasive micro-vibration capturing systems, discloses amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal (e.g. paragraphs 0029, 0032); a data receiver module configured to receive the optimized digital data signal from the data capturing device (e.g. paragraph 0031).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Guidoboni to include amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal, as taught and suggested by Parchani, in order accurately process the micro-voltage digital signal for more efficient detection of physiological parameters and to aid the sensor to operate with any thickness and construction of medium between the sensor and the subject (Parchani, paragraph 0032).
However, Guidoboni in view of Parchani does not explicitly teach that the energy spectrum processing platform comprising an energy spectrum engine is configured to:
(a) convert a segment of the optimized digital data signal into a spectrogram;
(b) crop the spectrogram to obtain a cropped spectrogram, wherein the cropped spectrogram retains frequencies comprising information related to ejection fraction of the user; and
(c) process the cropped spectrogram to determine a quantifiable measurement of a current ejection fraction of the user and compare the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames.
Rissacher, in a same field of endeavor of noninvasive physiological monitoring systems, discloses (a) converting a segment of the optimized digital data signal into a spectrogram (e.g. paragraphs 0039, 0050 – performing spectrogram transformation of the sampled data);
(b) wherein the spectrogram retains frequencies comprising information related to ejection fraction of the user (e.g. paragraphs 0039, 0050); and
(c) processing the spectrogram to determine a quantifiable measurement of a current ejection fraction of the user (e.g. paragraphs 0039, 0054).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni and Parchani to incorporate converting a segment of the optimized digital data signal into a spectrogram; wherein the spectrogram retains frequencies comprising information related to ejection fraction of the user; and processing the spectrogram to determine a quantifiable measurement of a current ejection fraction of the user, as taught and suggested by Rissacher, because it provides additional information of how frequency content of the measured signal changes over time and allows for more tailored biometric identification in both healthy individuals and those with cardiac conditions/defects (Rissacher, paragraph 0055).
However, Guidoboni in view of Parchani in view of Rissacher does not explicitly teach cropping of the spectrogram.
Al-Ali, in a same field of endeavor of noninvasive physiological monitoring systems, discloses cropping of the spectrogram (e.g. paragraph 0031).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni, Parchani, and Rissacher to incorporate cropping of the spectrogram, as taught and suggested by Al-Ali, in order to provide the predictable results of emphasizing and isolating a specific part of the spectrogram for further processing (Al-Ali, paragraph 0031).
However, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali does not explicitly teach comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames.
Venkatraman, in a same field of endeavor of noninvasive heart acoustic measuring systems, discloses comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames (e.g. paragraphs 0108, 0155).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni, Parchani, Rissacher, and Al-Ali to incorporate comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames, as taught and suggested by Venkatraman, in order to provide the predictable results of providing healthcare providers with reference points for adverse cardiac conditions (i.e. low ejection fraction, congestive heart failure, etc) when diagnosing a patient (Venkatraman, paragraph 0155).
Regarding claim 2, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the micro-vibrations comprise at least one or more mechanical or force-base signals including ballistocardiograph (BCG), seismo-cardiographs, and impedance signals associated with the one or more physiological parameters of the user (e.g. paragraphs 0002, 0101, – seismocardiograph (SCG), ballistocardiograph (BCG), etc).
Regarding claim 3, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the data capturing device comprises a conditioning unit which is configured to obtain the optimized digital data signals for efficient detection of user body functions (e.g. Fig. 11 – model 110; paragraph 0105).
Regarding claim 4, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 3 as discussed above, and Guidoboni further teaches wherein the optimized digital data signals are communicated to the data receiver module at regular intervals (e.g. paragraphs 0093, 0101, – synchronous recordings of SCG signals were acquired and then processed and analyzed and is communicated to the computer terminal 94 at regular intervals).
Regarding claim 6, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the energy spectrum processing platform is configured to generate one or more reports and alerts based on determined current and future body functions, cardiovascular functions, cardiac functions including cardiopulmonary functions (e.g. paragraphs 0096, 0100-0101).
Regarding claim 8, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the energy spectrum engine comprises:
an ejection fraction computation unit configured to compute the ejection fraction of the user based on an energy spectrum of the user’s body functions (e.g. paragraphs 0063-0064, 0101-0103; an algorithm which computes ejection fraction based on the SCG signal of the heart valves closing); an energy spectrum features unit configured to process the cropped spectrogram by adding one or more layers of filtering and analysis (e.g. paragraph 0105); and the database configured to store the digital data signals in a predetermined data storage format (e.g. paragraphs 0091, 0093, 0141).
Regarding claim 9, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the digital data signals comprise micro-voltages in a range from 0.1V to 3.5V (e.g. Fig. 13; paragraph 0002).
Regarding claim 10, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the system comprises a user device configured to continuously view and monitor user body functions, cardiovascular functions, and cardiac functions including cardiopulmonary functions (e.g. Fig. 13; paragraphs 0093, 0101, 0105), and receive one or more reports, and alerts (e.g. paragraphs 0096, 0100-0101).
Regarding claim 11, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the system as claimed in claim 1 as discussed above, and Guidoboni further teaches wherein the system comprises a communication network configured to facilitate communication between the data capturing device and the data receiver module (e.g. paragraphs 0093, 0138, – wireless networks include communication to communicate the sensor signal which include the programmed IC 92 and the computer terminal 94).
Regarding claim 12, Guidoboni teaches a method for non-intrusive monitoring and prediction of body functions of a user (e.g. abstract, paragraph 0095), comprising:
capturing micro-vibrations of one or more physiological parameters of the user and converting the captured micro-vibrations to a digital data signal by a sensor device, wherein the sensor device is placed in the vicinity of the user (e.g. Fig. 9A – force detection sensor 90 (i.e. accelerometer); paragraphs 0093, 0101, – an accelerometer 90 strapped around the chest of the subject 26 to capture at least one vibrations of the heart walls contracting (physiological parameters) generating a seismo-cardiogram “SCG” signal);
recording the digital data signal from the sensor device in a predefined chronological format by a data capturing device, wherein the data capturing device is placed in the vicinity of the sensor device (e.g. Fig. 9A – accelerometer 90, integrated circuited (IC) 92; paragraphs 0093, 0101-0103, – the programmed IC 92 records the signals from the accelerometer 90 which includes the SCG signal including synchronous recordings thus in a predefined chronological format);
a data receiver module (e.g. Fig. 9A – computing terminal 94) receiving digital data signal from the data capturing device (e.g. paragraph 0093);
and an energy spectrum processing platform (e.g. Fig. 11 – model 110; paragraph 0093).
However, Guidoboni does not explicitly teach amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal by the data capturing device; receiving the optimized digital data from the data capturing device by a data receiver module;
converting a segment of the optimized digital data signal into a spectrogram by an energy spectrum processing platform;
cropping the spectrogram to obtain a cropped spectrogram by the energy spectrum processing platform, wherein the cropped spectrogram retains frequencies comprising information related to ejection fraction of the user;
and processing, by the energy spectrum processing platform, the cropped spectrogram to determine a quantifiable measurement of a current ejection fraction of the user and comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time frames.
Parchani, in a same field of endeavor of noninvasive micro-vibration capturing methods, discloses amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal by the data capturing device (e.g. paragraphs 0029, 0032); receiving the optimized digital data from the data capturing device by a data receiver module (e.g. paragraph 0031).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Guidoboni to include amplifying the recorded digital data signal and maximizing resolution of the recorded digital data signal to obtain an optimized digital data signal, as taught and suggested by Parchani, in order accurately process the micro-voltage digital signal for more efficient detection of physiological parameters and to aid the sensor to operate with any thickness and construction of medium between the sensor and the subject (Parchani, paragraph 0032).
However, Guidoboni in view of Parchani does not explicitly teach converting a segment of the optimized digital data signal into a spectrogram by an energy spectrum processing platform;
cropping the spectrogram to obtain a cropped spectrogram by the energy spectrum processing platform, wherein the cropped spectrogram retains frequencies comprising information related to ejection fraction of the user;
and processing, by the energy spectrum processing platform, the cropped spectrogram to determine a quantifiable measurement of a current ejection fraction of the user and comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time frames.
Rissacher, in a same field of endeavor of noninvasive physiological monitoring methods, discloses converting a segment of the optimized digital data signal into a spectrogram by an energy spectrum processing platform (e.g. paragraphs 0039, 0050 – performing spectrogram transformation of the sampled data);
wherein the spectrogram retains frequencies comprising information related to ejection fraction of the user (e.g. paragraphs 0039, 0050);
and processing, by the energy spectrum processing platform, the spectrogram to determine a quantifiable measurement of a current ejection fraction of the user (e.g. paragraphs 0039, 0054).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni and Parchani to incorporate converting a segment of the optimized digital data signal into a spectrogram; wherein the spectrogram retains frequencies comprising information related to ejection fraction of the user; and processing, by the energy spectrum processing platform, the spectrogram to determine a quantifiable measurement of a current ejection fraction of the user, as taught and suggested by Rissacher, because it provides additional information of how frequency content of the measured signal changes over time and allows for more tailored biometric identification in both healthy individuals and those with cardiac conditions/defects (Rissacher, paragraph 0055).
However, Guidoboni in view of Parchani in view of Rissacher does not explicitly teach cropping of the spectrogram.
Al-Ali, in a same field of endeavor of noninvasive physiological monitoring methods, discloses cropping of the spectrogram (e.g. paragraph 0031).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni, Parchani, and Rissacher to incorporate cropping of the spectrogram, as taught and suggested by Al-Ali, in order to provide the predictable results of emphasizing and isolating a specific part of the spectrogram for further processing (Al-Ali, paragraph 0031).
However, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali does not explicitly teach comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames.
Venkatraman, in a same field of endeavor of noninvasive heart acoustic measuring methods, discloses comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames (e.g. paragraphs 0108, 0155).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Guidoboni, Parchani, Rissacher, and Al-Ali to incorporate comparing the determined ejection fraction with previously stored spectrograms in a database for predicting the one of more body functions of the user across one or more time-frames, as taught and suggested by Venkatraman, in order to provide the predictable results of providing healthcare providers with reference points for adverse cardiac conditions (i.e. low ejection fraction, congestive heart failure, etc) when diagnosing a patient (Venkatraman, paragraph 0155).
Regarding claim 14, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the method as claimed in claim 12 as discussed above, and Guidoboni further teaches wherein the method comprises communicating the optimized digital data signal to the data receiver module at regular intervals (e.g. paragraphs 0093, 0101, – synchronous recordings of SCG signals were acquired and then processed and analyzed and is communicated to the computer terminal 94 at regular intervals).
Regarding claim 16, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the method as claimed in claim 12 as discussed above, and Guidoboni further teaches the method comprising configuring the energy spectrum processing platform to collect data from the data capturing device and the sensor device to analyze received data by an energy spectrum engine (e.g. paragraphs 0093, 0101-101, 0105; model 110 includes an algorithm which receives data from the programmed IC 92 and the accelerometer 90 to analyze received data).
Regarding claim 17, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the method as claimed in claim 12 as discussed above, and Guidoboni further teaches wherein the method comprises: computing the ejection fraction of the user based on an energy spectrum of the user’s body function by an ejection fraction computation unit (e.g. paragraphs 0063-0064, 0101-0103; an algorithm which computes ejection fraction based on the SCG signal of the heart valves closing); processing the spectrogram by adding one or more layers of filtering and analysis by an energy spectrum features unit (e.g. paragraph 0105); and storing the digital data signal in a predetermined data storage format in the database (e.g. paragraphs 0091, 0093, 0141).
Regarding claim 18, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the method as claimed in claim 12 as discussed above, and Guidoboni further teaches wherein the comprises viewing continuously and monitoring the user body functions, cardiovascular functions, and cardiac functions including the ejection fraction (e.g. Fig. 13; paragraphs 0063, 0093, 0101, 0105), and receiving one or more reports, and alerts by a user device (e.g. paragraphs 0096, 0100-0101).
Regarding claim 19, Guidoboni in view of Parchani in view of Rissacher in view of Al-Ali in view of Venkatraman teaches the method as claimed in claim 12 as discussed above, and Guidoboni further teaches wherein the method comprises facilitating communication between the data capturing device and the data receiver module by a communication network (e.g. paragraphs 0093, 0138, – wireless networks include communication to communicate the sensor signal which include the programmed IC 92 and the computer terminal 94).
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 DANIEL TEHRANI whose telephone number is (571)270-0697. The examiner can normally be reached 9:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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