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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 5-6, 9-11, and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor, at the time the application was filed, had possession of the claimed invention. The specification does not explain at all how the machine learning models or deep learning models operate, how they are trained, or how they are implemented to estimate blood pressure. Furthermore, the specification does not explain and/or label what the output is from both the machine learning models and deep learning models. The algorithms, steps, or procedures taken to operate, train, and implement the machine learning models and deep learning models must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended to operate, train, and implement said models. See MPEP §§ 2163.02 and 2181, subsection IV.
Claim 8 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention. The claim recites using software to visually display estimated blood pressure on a mobile device. The specification, however, provides no basis for what type of software is used, or how the software operates to visually display values on the mobile device.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The examiner recommends mentioning that the software instructions are running on an “internal processor” of the mobile device. The examiner also recommends modifying claim 1 to say “a mobile device including an internal processor.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 5-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more (i.e. an Abstract idea under Step 2A, prong 1). Claim 5 recites, “the external processor is configured to estimate a blood pressure…by implementing a machine learning model.” Claim 6 recites, “the external processor is configured to estimate a blood pressure…by implementing a deep learning model.” This judicial exception is not integrated into a practical application (Step 2A, prong 2) because the claim language describes the implementation of machine and deep learning models to perform blood pressure estimation that could be done by hand or in one’s head. Furthermore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B) because the external processor is being used simply as a tool to perform an abstract idea.
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 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Thomson et al. (U.S. PGPub No. 2015/0018660) in view of Goldner et al. (US2022/00361823).
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); 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.
Regarding claim 2, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1 that includes one or more sensors (Paragraph 0200, line 1). Thomson 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.
Regarding claim 3, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 2. 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 2. 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 et al. (U.S. PGPub No. 2015/0018660) in view of Goldner et al. (US2022/00361823) as applied to claim 1 above, and further in view Vule et al. (U.S. PGPub No. 2022/0183569).
Regarding claim 5, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 2 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 2 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) 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 PPG data and time stamp via the PPG device and the accelerometer, respectively; stop recording the data for a set period of time where a motion of a set level is detected by the accelerometer; transmitting the recorded 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 PPG data and time stamps, stopping recording the data when a motion of a set level is detected, transmitting the 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, 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 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 et al. (U.S. PGPub No. 2015/0018660) in view of Goldner et al. (U.S. PGPub No. 2022/0361823) and Vule et al. (U.S. PGPub No. 2022/0183569) as applied to claim 9 above, and further in view of Batra (WIPO Pub. No. 2023/026303).
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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Thomson et al. (U.S. PGPub No. 2015/0018660) in view of Goldner et al. (US2022/00361823) as applied to claim 1 above, and further in view Matichuk et al. (U.S. PGPub No. 2022/0028553).
Regarding claim 7, Thomson teaches the system (Paragraph 0198, lines 1-2) of claim 1. Thomson does not teach that the PPG device, the microcontroller, and the accelerometer are connected on a printed circuit board. Goldner, however, teaches a wearable blood pressure biosensor system (Paragraph 0026, line 1) that includes a PPG device (Paragraph 0052, lines 1-3), a microcontroller (Paragraph 0047, line 3), and an accelerometer (Paragraph 0052, lines 5-6). Goldner does not teach that these components 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. PGPub No. 2024/0312631, U.S. Patent No. 12,527,481, WIPO Pub. No. 2023/214957, U.S. PGPub No. 2023/0277075, U.S. PGPub No. 2023/0036114, U.S. PGPub No. 2021/0378529, U.S. Patent No. 12,347,566, and WIPO Pub. No. 2023/214956.
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/H.A.H./Patent Examiner , Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796