DETAILED ACTION
Response to Amendment
Claims 1, 3-6, 8-9, and 17 have been amended, and claims 2 and 7 have been cancelled. All other claims remain as originally or previously presented. Rejections of amended claims have been updated or addressed below.
Response to Arguments
Applicant’s arguments, see pages 5-6, filed on 7/16/2026, with respect to the previous 35 U.S.C. 112(a) rejections have been fully considered and are persuasive. The previous 112(a) rejections of claims 5-6, 8-11, and 17 have been withdrawn.
Applicant’s arguments, see page 6, filed on 7/16/26, with respect to the previous 35 U.S.C. 112(b) rejection have been fully considered and are persuasive. The previous 112(b) rejection of claim 8 has been withdrawn.
Applicant’s arguments, see page 7, filed on 7/16/26, with respect to the previous 35 U.S.C. 101 rejections have been fully considered and are persuasive. The previous 101 rejections of claims 5-6 have been withdrawn.
Applicant's arguments, see pages 7-11, filed on 7/16/2026, with respect to the previous 35 U.S.C. 103 rejections have been fully considered but they are not persuasive.
The Applicant argues that the combination of Thomson and Goldner does not
disclose that the “PPG device and the accelerometer are configured to concurrently record the PPG data and time stamp, respectively”. The Examiner respectfully disagrees. Goldner discloses a user device/biosensor device (104/200) that can comprise one or more sensors, such as a PPG device (Paragraph 0052, line 3) and an accelerometer (Paragraph 0052, lines 5-6). Goldner teaches that the sensors can be used at the same time to record one or more measurements of health parameters (Paragraph 0047, lines 5-24). Goldner later specifies that both PPG data and activity data can be recorded by the device (Paragraph 0057, lines 5-7). Goldner teaches that the data can be timestamped (Paragraph 0062, lines 3-7). Therefore, it is still understood that the combination of Thomson and Goldner teach that the “PPG device and the accelerometer are configured to concurrently record the PPG data and time stamp, respectively”.
The Applicant also argues that the previously cited prior art references do not disclose the method step to “stop recording the PPG data for a set period of time where a motion of a set level is detected by the accelerometer”, as recited in claim 9. The Examiner respectfully disagrees. Vule teaches an apparatus (100) that may be worn by a user to sense, collect, monitor, analyze, and/or display one or more physiological parameters (Paragraph 0038, lines 1-6). Vule teaches that the apparatus may comprise at least one of an upper module (110) and/or a lower module (150), where each comprises sensors for collecting physiological parameters of the user (Paragraph 0039, lines 1-7).
Vule also teaches that each of the upper and lower modules may comprise a PPG sensor and an accelerometer (Paragraph 0026, lines 1-5). Vule teaches that neither the upper and/or lower modules may continuously collect data, but rather may wait to collect information when conditions are such that accurate readings are most likely (Paragraph 0082, lines 8-11). Therefore, it is understood that Vule teaches that the PPG data recording may be stopped for a set period of time, i.e. the modules may wait to collect information.
Furthermore, Vule teaches that accelerometers can be used to determine whether the user is still, at rest, or sleeping, and then the upper and/or lower modules may collect information from the user (Paragraph 0082, lines 11-16). Vule teaches that artifacts resulting from physical movements are absent when collecting information from the user (Paragraph 0082, lines 11-16). Although it is not explicitly stated, Vule teaches that when motion of a set level is detected, i.e. when the user is physically moving versus resting, data collection does not occur. Therefore, it is understood that Vule discloses “stopping recordings of PPG data for a set period of time where a motion of a set level is detected by the accelerometer”.
Claim Objections
Claim 8 is objected to because of the following informality: in lines 1-2, “a software runs on the internal processor” should read “a software that runs on the internal processor”. Appropriate correction is required.
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 non-obviousness.
Claims 1 and 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Thomson et al. (U.S. PGPub No. 2015/0018660) (previously cited) in view of Goldner et al. (US2022/00361823) (previously cited) and Matichuk et al. (U.S. PGPub No. 2022/0028553) (previously cited).
Regarding claim 1, Thomson teaches a system (Paragraph 0198, lines 1-2) for monitoring blood pressure (Paragraph 0181, lines 5-8), comprising: a mobile device (Fig. 2-3, paragraph 0198, line 3) including an internal processor (Fig. 1-5C, Paragraph 0179, lines 6-15 and Paragraph 0180, lines 1-2); and an apparatus (Fig. 2-3, paragraph 0198, lines 3-4) configured to be attached on the mobile device (Fig. 2-3, paragraph 0198, lines 8-11), wherein the apparatus comprises a photoplethysmography (PPG) device (Paragraph 0249, lines 4-7 and 18-22) and an external processor (Paragraph 0181, line 2).
Thomson does not teach that the apparatus comprises a microcontroller and an accelerometer. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) used to predict blood pressure (Paragraph 0037, line 1) that includes a user device (Paragraph 0026, line 2) with PPG sensors (Paragraph 0052, line 3), a microcontroller (Paragraph 0047, line 3), and an accelerometer (Paragraph 0052, lines 5-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner to include that the apparatus comprises a microcontroller and an accelerometer. Doing so would ensure that the apparatus has a means for operating said apparatus and for detecting motion of the apparatus, as recognized by Goldner.
Thomson also does not teach that the PPG device, the microcontroller, and the accelerometer are connected on a printed circuit board. Matichuk, however, teaches a wearable apparatus (Fig. 1, paragraph 0084, line 2) that calculates and predicts blood pressure (Paragraph 0101, lines 8-10) that includes a PPG device (Paragraph 0093, lines 13-15), a microcontroller (Paragraph 0092, line 3), and an accelerometer (Paragraph 0095, lines 1-2) that are connected on a printed circuit board (Fig. 1, paragraph 0084, lines 7-10). The use of printed circuit boards to connect different sensors and electronic components would also be well known by a person of ordinary skill in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner and Matichuk to include that the PPG device, the microcontroller, and the accelerometer are connected on a printed circuit board. Doing so would ensure that the electronic components are organized and closely connected on a PCB within the apparatus, as recognized by Goldner and Matichuk.
Thomson also does not teach that the PPG device is configured to generate PPG data from one or more sensors. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that is configured to generate PPG data (Paragraph 0057, lines 5-7) with one or more PPG sensors (Paragraph 0052, lines 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner to include that the PPG device is configured to generate PPG data from one or more sensors. Doing so would ensure that the PPG device has a means to generate PPG data for blood pressure monitoring, as recognized by Goldner.
Thompson also does not teach that the PPG device and the accelerometer are configured to concurrently record the PPG data and time stamp, respectively. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that includes a user device (Paragraph 0026, line 2) that concurrently records (Paragraph 0047, lines 11-13) PPG data (Paragraph 0057, lines 5-7) and time stamps (Paragraph 0062, lines 3-7) via the PPG device (Paragraph 0052, line 3) and the accelerometer (Paragraph 0052, lines 5-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner to include that the PPG device and the accelerometer are configured to concurrently record the PPG data and time stamp, respectively. Doing so would ensure that the PPG data can be grouped and/or aggregated over time, as recognized by Goldner.
Regarding claim 3, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1. Thomson does not teach that the microcontroller is programmed to collect, process, and store the PPG data. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that includes a microcontroller (Paragraph 0047, line 3) that is programmed to collect (Paragraph 0047, lines 11-13), process (Paragraph 0047, lines 11-13), and store (Paragraph 0047, lines 34-37 and paragraph 0048, lines 3-4) the PPG data (Paragraph 0057, lines 5-7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner to include that the microcontroller is programmed to collect, process, and store the PPG data. Doing so would ensure that the system has a means to collect, process, and store collected data that is used to monitor blood pressure, as recognized by Goldner.
Regarding claim 4, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1. Thomson does not teach that the microcontroller is configured to transmit the PPG data to the external processor. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that includes a microcontroller (Paragraph 0047, line 3) that is configured to transmit (Paragraph 0047, lines 34-37 and paragraph 0051, lines 1-3) the PPG data (Paragraph 0057, lines 5-7) to an external processor (Paragraph 0051, line 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner to include that the microcontroller is configured to transmit the PPG data to the external processor. Doing so would ensure that the system has a means to transmit the collected data to the external processor to be used for blood pressure monitoring, as recognized by Goldner.
Claims 5-6, 8-10, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Thomson (previously cited) in view of Goldner (previously cited) and Matichuk (previously cited) as applied to claim 1 above, and further in view Vule et al. (U.S. PGPub No. 2022/0183569) (previously cited).
Regarding claim 5, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1 that includes an external processor (Paragraph 0181, line 2). Thomson does not teach that the external processor is configured to estimate a blood pressure from the PPG data by implementing a machine learning model. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) that includes a processor (Paragraph 0120, lines 8-11) configured to estimate blood pressure (Paragraph 0031, lines 1-2) from PPG data (Paragraph 0048, lines 4-6) by implementing a machine learning model (Paragraph 0031, lines 1-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the external processor is configured to estimate a blood pressure from the PPG data by implementing a machine learning model. Doing so would ensure that blood pressure estimates are more accurately determined by using machine learning models, as recognized by Vule.
Regarding claim 6, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1 that includes an external processor (Paragraph 0181, line 2). Thomson does not teach that the external processor is configured to estimate a blood pressure from the PPG data by implementing a deep learning model. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) that includes a processor (Paragraph 0120, lines 8-11) configured to estimate blood pressure (Paragraph 0031, lines 1-2) from PPG data (Paragraph 0048, lines 4-6) by implementing a deep learning model (Paragraph 0031, lines 6-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the external processor is configured to estimate a blood pressure from the PPG data by implementing a deep learning model. Doing so would ensure that blood pressure estimates are more accurately determined by using deep learning models, as recognized by Vule.
Regarding claim 8, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1, further comprising a software (Paragraph 0180, lines 10-14 and paragraph 0182, lines 14-16) runs on (Paragraph 0180, line 10) the internal processor (Fig. 1-5C, Paragraph 0179, lines 6-15 and Paragraph 0180, lines 1-2) to visually display (Paragraph 0199 lines 1-3) measured parameters (Paragraph 0201, lines 7-8) on the mobile device (Fig. 2-3, paragraph 0198, line 3). Thomson does not teach that the system is configured to estimate blood pressure. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) that includes a processor (Paragraph 0120, lines 8-11) configured to estimate blood pressure (Paragraph 0031, lines 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that software visually displays estimated blood pressure on the mobile device. Doing so would ensure that the user of said system would have a means for being notified of their personal estimated blood pressure, as recognized by Vule.
Regarding claim 9, Thomson teaches a method (Fig. 6, paragraph 0212, line 1) for monitoring blood pressure (Paragraph 0181, lines 5-8) of a subject (Paragraph 0201, line 1), comprising: detecting a contact (Paragraph 0201, lines 1-5) between the subject and the apparatus (Fig. 2-3, paragraph 0198, lines 3-4) of claim 1. Thomson does not teach that the method includes concurrently recording the PPG data and the time stamp via the PPG device and the accelerometer, respectively; stop recording the PPG data for a set period of time where a motion of a set level is detected by the accelerometer; transmitting the recorded PPG data to the external processor; and estimating a blood pressure by implementing at least one of a machine learning model and a deep learning model at the external processor.
Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that includes a user device (Paragraph 0026, line 2) that concurrently records (Paragraph 0047, lines 11-13) PPG data (Paragraph 0057, lines 5-7) and time stamps (Paragraph 0062, lines 3-7) via the PPG device (Paragraph 0052, line 3) and the accelerometer (Paragraph 0052, lines 5-6). Goldner also teaches transmitting (Paragraph 0047, lines 34-37 and paragraph 0051, lines 1-3) the recorded data to the external processor (Paragraph 0051, line 3). Although Goldner teaches using machine and deep learning models to predict blood pressure, Goldner does not teach using said models to estimate blood pressure.
Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) that stops recording the data for a set period of time where a motion of a set level (Paragraph 0082, lines 12-16) is detected by the accelerometer (Paragraph 0082, line 11). Vule also teaches that the apparatus is configured to estimate blood pressure (Paragraph 0031, lines 1-2) by implementing at least one of a machine learning model (Paragraph 0031, lines 1-4) and a deep learning model (Paragraph 0031, lines 6-8) at the external processor (Paragraph 0120, lines 8-11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner and Vule to include that the method comprises recording the PPG data and the time stamps, stopping recording the PPG data when a motion of a set level is detected, transmitting the PPG data to the processor, and estimating a blood pressure by implementing at least one of a machine learning model and a deep learning model. Doing so would ensure that the PPG data is not recorded when the user is moving at a certain level, in order to most accurately estimate blood pressure of the user, as recognized by Goldner and Vule.
Regarding claim 10, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9. Thomson does not teach that the machine learning model is Random Forest regressor or XGBoost regressor. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) configured to estimate blood pressure (Paragraph 0031, lines 1-2) by implementing a machine learning model (Paragraph 0031, lines 1-4). Furthermore, Vule discloses that the machine learning model is Random Forest regressor (Paragraph 0035, lines 5-6) or XGBoost regressor (Paragraph 0035, lines 4-5).
Although it is not explicitly stated in Vule that the machine learning model is XGBoost regressor, Vule discloses that the “machine learning model can be a gradient boosting model” [0035]. It would be well known by a person of ordinary skill in the art that an XGBoost regressor is a type of gradient boosting machine learning model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the machine learning model is Random Forest regressor or XGBoost regressor. Doing so would ensure that specific machine learning models are used to improve blood pressure estimation accuracy, as recognized by Vule.
Regarding claim 17, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9. Thomson does not teach that the deep learning model is a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer model, a model incorporating attention mechanism, a hybrid model, an autoencoder, a generative adversarial network (GAN), a graph neural network (GNN), a WaveNet model, a deep belief network (DBN), a model incorporating sparse coding, or any combination thereof.
Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that predicts blood pressure (Paragraph 0037, line 1) by using a deep learning model (Paragraph 0035, line 29). Furthermore, Goldner discloses that the deep learning model can be a recurrent neural network (RNN) (Paragraph 0035, lines 30-31), a convolutional neural network (CNN) (Paragraph 0035, line 30), or a deep belief network (DBN) (Paragraph 0035, line 32).
Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) configured to estimate blood pressure (Paragraph 0031, lines 1-2) by implementing a deep learning model (Paragraph 0031, lines 6-8). Furthermore, Vule discloses that the deep learning model can be a recurrent neural network (RNN) (Paragraph 0096, lines 4-5), a convolutional neural network (CNN) (Paragraph 0095, line 6), and an autoencoder (Paragraph 0095, lines 7-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Goldner and Vule to include that the deep learning model could be a RNN, a CNN, a DBN, or an autoencoder. Doing so would ensure that a variety of deep learning model types can be used by said system to properly estimate blood pressure, as recognized by Goldner and Vule. Although not every listed model/network is disclosed by said references, it would be well understood by a person of ordinary skill in the art that any such models could be used for blood pressure estimation.
Regarding claim 18, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9, further comprising displaying (Paragraph 0199 lines 1-3) measured parameters (Paragraph 0201, lines 7-8) on the mobile device (Fig. 2-3, paragraph 0198, line 3). Thomson does not teach that the method includes displaying estimated blood pressure on the mobile device. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) that includes a processor (Paragraph 0120, lines 8-11) configured to estimate blood pressure (Paragraph 0031, lines 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the method includes displaying estimated blood pressure on the mobile device. Doing so would ensure that the user of said system would have a means for being notified of their personal estimated blood pressure, as recognized by Vule.
Regarding claim 19, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9. Thomson does not teach that the PPG data are recorded from an analog channel. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) for monitoring blood pressure (Paragraph 0030, lines 1-4) that includes a PPG device (Fig. 1, paragraph 0026, line 3) that is configured to generate PPG data (Paragraph 0048, lines 4-6) from an analog channel (Paragraph 0099, lines 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the PPG data are recorded from an analog channel. Doing so would ensure that PPG data collection is smooth and continuous, as recognized by Vule.
Regarding claim 20, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9. Thomson does not teach that the PPG data are raw signals. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) for monitoring blood pressure (Paragraph 0030, lines 1-4) that includes a PPG device (Fig. 1, paragraph 0026, line 3) that is configured to generate PPG data (Paragraph 0048, lines 4-6) that are raw signals (Paragraph 0099, lines 6-7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the PPG data are raw signals. Doing so would ensure that PPG data collected is able to be used repeatedly with maximum flexibility, as recognized by Vule.
Claims 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Thomson (previously cited) in view of Goldner (previously cited), Matichuk (previously cited), and Vule (previously cited) as applied to claim 9 above, and further in view Batra (WIPO Pub. No. 2023/026303) (previously cited).
Regarding claim 11, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 9. Thomson does not teach that the machine learning model is trained and tested with a dataset having PPG data and arterial blood pressure (ABP) data from a same heartbeat. Batra, however, teaches a method for continuous estimation of arterial blood pressure (Paragraph 0059, lines 3-7) that uses a machine learning model (Paragraph 0068, line 2) that is trained (Paragraph 0068, line 2) and tested (Paragraph 0092, line 1) with a dataset having PPG data (Paragraph 0060, line 3) and ABP data (Paragraph 0069, lines 1-3) from a same heartbeat (Paragraph 0049, lines 4-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Batra to include that the machine learning model is trained and tested with a dataset having PPG data and arterial blood pressure (ABP) data from a same heartbeat. Doing so would ensure that the machine learning model can be accurately trained and tested with relevant data to improve blood pressure estimation, as recognized by Batra.
Regarding claim 12, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 11. Thomson does not teach that the PPG data and the ABP data are preprocessed by filtering and normalization. Batra, however, teaches a method for continuous estimation of arterial blood pressure (Paragraph 0059, lines 3-7) by using PPG data (Paragraph 0060, line 3), where the PPG data and the ABP data (Paragraph 0069, lines 1-3) are preprocessed (Paragraph 0062, lines 1-2) by filtering (Paragraph 0064, line 1) and normalization (Paragraph 0065, line 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Batra to include that the PPG data and the ABP data are preprocessed by filtering and normalization. Doing so would improve data quality and accuracy for the machine learning model to use to properly estimate blood pressure, as recognized by Batra.
Regarding claim 13, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 11. Thomson does not teach that at least one extracted feature is extracted from a waveform contour of the PPG data. Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) for monitoring blood pressure (Paragraph 0030, lines 1-4) that extracts at least one extracted feature (Fig. 11, paragraph 0117, lines 3-7) from a waveform contour (Fig. 11, paragraph 0117, line 2) of the PPG data (Paragraph 0048, lines 4-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that at least one extracted feature is extracted from a waveform contour of the PPG data. Doing so would ensure that additional metrics can be determined from the PPG data in order to improve prediction results, as recognized by Vule.
Regarding claim 14, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 13. Thomson does not teach that the at least one extracted feature is a systolic phase, a diastolic phase, a distance from a diastolic peak to a systolic peak, a distance from an onset to a tip of signal, a distance from a tip to a peak of a diastole, a ratio of diastolic time over systolic time, or any combination thereof.
Vule, however, teaches an apparatus (Fig. 1, paragraph 0038, line 2) for monitoring blood pressure (Paragraph 0030, lines 1-4) that extracts at least one extracted feature (Fig. 11, paragraph 0117, lines 3-7) from PPG data (Paragraph 0048, lines 4-6). Furthermore, Vule teaches that the feature is a systolic phase (Fig. 11, paragraph 0118, lines 1-3), a diastolic phase (Fig. 11, paragraph 0118, lines 2-5), a distance from a diastolic peak to a systolic peak (Fig. 11, paragraph 0118, lines 14-15), a distance from an onset to a tip of signal (Fig. 11, paragraph 0118, lines 14-15), a distance from a tip to a peak of a diastole (Fig. 11, paragraph 0118, lines 14-15), a ratio of diastolic time over systolic time (Fig. 11, paragraph 0118, lines 14-15), or any combination thereof.
Although the feature being a distance from a diastolic peak to a systolic peak, a distance from an onset to a tip of signal, a distance from a tip to a peak of a diastole, or a ratio of diastolic time over systolic time is not explicitly disclosed by Vule, Vule does teach that “the horizontal distance between the onset (O) and the pulse wave end (PWE) is the pulse wave duration (PWD)” [0118]. It would be well known by a person of ordinary skill in the art that distance from a diastolic peak to a systolic peak, a distance from an onset to a tip of signal, a distance from a tip to a peak of a diastole, and a ratio of diastolic time over systolic time could be all be determined from the pulse wave duration and known locations of onset, a tip, diastolic peak, and systolic peak.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Vule to include that the at least one extracted feature is a systolic phase, a diastolic phase, a distance from a diastolic peak to a systolic peak, a distance from an onset to a tip of signal, a distance from a tip to a peak of a diastole, a ratio of diastolic time over systolic time, or any combination thereof. Doing so would ensure that a variety of metrics obtained from PPG waveforms can be used to improve blood pressure estimations, as recognized by Vule.
Regarding claim 15, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 11. Thomson does not teach that at least one feature is extracted from the arterial blood pressure data. Batra, however, teaches a method for continuous estimation of arterial blood pressure (Paragraph 0059, lines 3-7) that extracts at least one feature (Fig. 5-7) from the ABP data (Paragraph 0069, lines 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Batra to include that at least one feature is extracted from the arterial blood pressure data. Doing so would ensure that additional metrics can be determined from the ABP data in order to improve prediction results, as recognized by Batra.
Regarding claim 16, Thomson teaches the method (Fig. 6, paragraph 0212, line 1) of claim 15. Thomson does not teach that the at least one feature is systolic blood pressure of diastolic blood pressure. Batra, however, teaches a method for continuous estimation of arterial blood pressure (Paragraph 0059, lines 3-7) that extracts at least one feature that is systolic blood pressure (Fig. 5-7) or diastolic blood pressure (Fig. 5-7).
Although it is not explicitly stated in Batra that systolic blood pressure or diastolic blood pressure are determined from the ABP data, Batra does disclose that ABP waveforms are generated from ABP data, as seen in Fig. 5-7. Furthermore, it would be well known by a person of ordinary skill in the art that the maximum values of ABP in Fig. 5-7 correlate to systolic blood pressure, and the minimum values of ABP in Fig. 5-7 correlate to diastolic blood pressure.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomson to incorporate the teachings of Batra to include that the at least one feature is systolic blood pressure or diastolic blood pressure. Doing so would ensure that systolic blood pressure and diastolic blood pressure values can be used to improve blood pressure estimations, as recognized by Batra.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.A.H./Patent Examiner , Art Unit 3796
/NIKETA PATEL/Supervisory Patent Examiner, Art Unit 3792
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