DETAILED ACTION
Applicant’s preliminary amendment filed 10/22/2024 has been entered.
Claims 43-61 are hereby the present claims under consideration.
Examiner’s Note: all references to Applicant’s specification are made using the paragraph numbers set forth in the US publication of the present application US 20250040892 A1.
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 Objections
Claims 43 and 52 are objected to because of the following informalities:
Claim 43 line 2, it appears that “A circuit board” should read “a circuit board”
Claim 52 line 2 it appears that “the wearable device” should read “the wearable electronic device”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 44-47, 52, and 55-61 are 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.
Claim 44 recites “the system of claim 43 further including a classification model” but the “classification model” is recited as a physical element of the system rather than a model implemented onto the processor. It is thus unclear what physical element “a classification model” is meant to add to the system. For the purposes of this examination, the limitation will be interpreted as the processor configured to execute a classification model.
Claim 44 recites “the classification model is configured to indicate one or more of …” but it is unclear what the inputs of the classification model are. The claim only sets forth the outputs of the model and it is thus unclear from what inputs these outputs are generated from and how the outputs are generated. For the purposes of this examination, this limitation is interpreted as the classifier using at least the cardiac data signals of claim 43.
Claims 45-46 depend from claim 44 and are rejected by virtue of their dependency.
Claim 46 recites “wherein the classification model uses machine learning to analyze the cardiac health of the user” but it is unclear if the recited “analyze the cardiac health of the user” is the same as, related to, or different from “indicate one or more of …” as recited in claim 44. It is unclear if the conditions recited in claim 44 constitute the analysis of the cardiac health of the user or if the indications of these conditions are separate from the analysis of the cardiac health of the user. For the purposes of this examination, the indication of any of the recited conditions in claim 44 will be interpreted as an “analysis of the cardiac health of the user”.
Claim 47 recites “the system is configured to transmit a message” but it is unclear what element of the system carries out this recited function of the system. For the purposes of this examination, the limitation is interpreted as the system including wireless transmission circuitry.
Claim 52 is rejected as it recites that the wearable electronic device is a smart ring however claim 51 from which it depends sets forth that the wearable electronic device is a smartwatch. It is unclear how the device can be both a watch and a ring. For the purposes of this examination, the limitations are interpreted as requiring sensors in either a ring or a watch.
Claim 55 recites “wherein the cardiac monitoring instructions are configured to identify a plurality of unique physiological markers of the user based on historical data signals” but it is unclear what such “unique physiological markers of the user” entail. It is unclear if the “unique physiological markers” are meant to refer to any plurality of different, or “unique” features, or if the unique markers are meant to refer to user identifiable biometrics that can be used to distinguish one person’s heartbeat from another person’s heartbeat. For the purposes of this examination, the limitation will be interpreted as any extraction of features.
Claim 56 recites “wherein the cardiac monitoring instructions are configured to identify a plurality of unique physiological markers of the user based on historical cardiac sound wave data signals captured by the microphone sensor” but it is unclear what such “unique physiological markers of the user” entail. It is unclear if the “unique physiological markers” are meant to refer to any plurality of features of the cardiac soundwave data such that different features are “unique” from other features (i.e. amplitude, frequency, etc. ) or if it is meant to refer to user identifiable biometrics that can be used to distinguish one person’s heartbeat sound data from another person’s heartbeat sound data. For the purposes of this examination, the limitation will be interpreted as any extraction of features from the sound signals.
Claims 57-61 each recite that the cardiac monitoring instructions are configured to estimate a parameter but it is unclear what the inputs to the recited estimations are. It is unclear if the parameters are being estimated based on previously measured data or some other form of estimation. For the purposes of this examination, each of these claims will be interpreted as the estimation being based on the cardiac signals of claim 43.
Claim Rejections - 35 USC § 112(a)
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 44 and 55-61 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 claim(s) 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 inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 44 recites “the classification model is configured to indicate one or more of …” but the specification does not describe how the classification algorithm considers or converts the recited inputs (assumed to be the cardiac data signals of claim 43) into the recited outputs. MPEP 2161.01 recites that claims may lack written description when the claims define the invention in functional languagespecifying a desired result but the specification does not sufficiently describe how the function isperformed or the result is achieved. For software, this can occur when the algorithm orsteps/procedure for performing the computer function are not explained at all or are notexplained in sufficient detail (simply restating the function recited in the claim is not necessarilysufficient). In other words, the algorithm or steps/procedure taken to perform the function mustbe described with sufficient detail so that one of ordinary skill in the art would understand howthe inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsectionIV.”. With respect to claim 44 this claim is rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function or particular details on how such as classification algorithm is trained. In particular, paragraph 0074 of the specification sets forth that it is well-known in the art to detect hypertension from visual based-analysis and that it is well-known to detect ischemic cardiomyopathy, aortic and mitral stenosis and regurgitation from acoustic patterns. However the specification does not indicate that the detection of hypertension from acoustic patterns and/or seismic cardiac signals is well known and no method of determining such a parameter from the recited inputs or specific method of classifier training it set forth. The specification fails to provide the particular algorithm or steps taken in order to carry out the recited determinations and thus is considered to lack sufficient written description support.
Claims 55 and 56 recite that the instructions are configured to identify a plurality of unique physiological markers of the user based on historical signals. The specification does not appear to describe what such markers entail or how they are identified from the historical signals. Paragraphs 0072, and 0077 recite such a functionality in purely functional language but do not define the meets and bounds of “unique physiological markers” and further do not describe the particular steps taken or algorithm used to identify such markers from historical data. MPEP 2161.01 recites that claims may lack written description when the claims define the invention in functional languagespecifying a desired result but the specification does not sufficiently describe how the function isperformed or the result is achieved. For software, this can occur when the algorithm orsteps/procedure for performing the computer function are not explained at all or are notexplained in sufficient detail (simply restating the function recited in the claim is not necessarilysufficient). In other words, the algorithm or steps/procedure taken to perform the function mustbe described with sufficient detail so that one of ordinary skill in the art would understand howthe inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsectionIV.”. With respect to claims 55 and 56, these claims are rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function of identifying unique physiological markers and additionally fails to describe what such unique markers entail.
Claims 57-61 each recite that the cardiac monitoring instructions estimates a particular value and claim 58 requires the use of a regression model. The specification does not detail how the inputs (presumed to be the cardiac signals of claim 43) are processed, considered, or otherwise transformed to produce the recited output values. MPEP 2161.01 recites that claims may lack written description when the claims define the invention in functional languagespecifying a desired result but the specification does not sufficiently describe how the function isperformed or the result is achieved. For software, this can occur when the algorithm orsteps/procedure for performing the computer function are not explained at all or are notexplained in sufficient detail (simply restating the function recited in the claim is not necessarilysufficient). In other words, the algorithm or steps/procedure taken to perform the function mustbe described with sufficient detail so that one of ordinary skill in the art would understand howthe inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsectionIV.”. With respect to claims 57-61, these claims are rejected under §112, first paragraph, based on lack of written description because the specification fails to provide the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function or particular details on how such as regression algorithm is trained. In particular paragraphs 0015, 0032, 0044, 0067, and 0076-0078 provide only functional language that the recited parameters may be estimated. These paragraphs indicate that the parameters are produced by deploying the regression model but the regression model itself has not been particularly described. The steps taken by the regression model to produce these outputs has not been particularly described. The specification recites that the regression model is trained using invasive sensor values but does not describe particular details as to what this training entails and how it results in the final model capable of performing the recited functions.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 43-44, 46-49, 53, and 55-61 are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman US Patent Application Publication Number US 20210259560 A1 hereinafter Venka in view of Shadforth US Patent Application Publication Number US 20220095955 A1 hereinafter Shadforth
Regarding claim 43, Venka discloses a system (Abstract) comprising:
A circuit board (Paragraphs 0041, 0075, and 0077: the control and/or signal processing circuitry);
a processor (Paragraph 0078: a microprocessor);
non-volatile memory (Paragraph 0078: a memory),
an inertial measurement unit sensor (Paragraphs 0041 and 0047-0048: an accelerometer), and
a microphonic sensor (Paragraph 0041: an audio sensor which may include one or more microphone units);
wherein the circuit board includes the processor, the non-volatile memory, the inertial measurement unit sensor, and the microphonic sensor (Paragraphs 0075, 0077-0078, and 0081: the accelerometer, the audio sensor, the processor, and the memory are all in communication with one another in the monitoring device. The monitoring device may include other necessary electronic components and circuitry. These teachings are considered to render the limitation of these elements being on a circuit board as obvious since their particular positional arrangement and method of communication is subject to routine optimization and experimentation with no surprising technical effect);
wherein the processor is configured to execute cardiac monitoring instructions stored on the non-volatile memory (Abstract: a method of monitoring a state of a heart; Paragraphs 0034 and 0099-0100: determine the state or condition of the heart from the gathered data);
wherein the cardiac monitoring instructions include instructions for collecting cardiac data signals (Paragraphs 0099-0100: the recorded data including the audio data, ECG data, and accelerometer data can be considered to determine a state or condition of an organ or organ system such as the heart);
wherein the microphonic sensor is configured to capture sound wave cardiac data signals indicative of the cardiac health of the user (Paragraphs 0075 and 0087: the audio sensor may collect heart sounds).
Venka fails to further disclose the system wherein the inertial measurement unit sensor is configured to receive seismic cardiac data signals indicative of a cardiac health of a user.
Shadforth teaches methods and systems facilitate the acquisition of ballistocardiographic signals and the determination and use of ballistocardiograph signal related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject (Abstract). Thus, Shadforth falls within the same field of endeavor as Applicant’s Invention.
Shadforth teaches a system wherein the inertial measurement unit sensor is configured to receive seismic cardiac data signals indicative of a cardiac health of a user (Paragraphs 0013-0014, 0079, and 0082: the accelerometers may be used to capture SCG and/ BCG signals).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the accelerometer of Venka to capture SCG and BCG signals as taught by Shadforth because doing so allows Venka to capture additional contextual data about the user’s heart that may improve classification accuracy of the heart conditions being detected without requiring additional componentry to be added to Venka.
Regarding claim 44, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system further including a classification model (Paragraph 0111: the trained algorithm may be a classifier); wherein the classification model is configured to indicate one or more of hypertension, ischemic cardiomyopathy, aortic stenosis, aortic regurgitation, mitral stenosis, and mitral regurgitation based on the cardiac data signals (Paragraph 0131: pulmonary hypertension or pulmonary atrial hypertension; Paragraph 0140: ischemia; Paragraph 0134: aortic and mitral stenosis and regurgitation).
Regarding claim 46, Venka in view of Shadforth teaches the system of claim 44. Modified Venka further teaches the system wherein the classification model uses machine learning to analyze the cardiac health of the user (Paragraph 0111: the trained algorithm may be a classifier; Paragraph 0131: pulmonary hypertension or pulmonary atrial hypertension; Paragraph 0140: ischemia; Paragraph 0134: aortic and mitral stenosis and regurgitation).
Regarding claim 47, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the system is configured to transmit a message to a computing device of a healthcare professional based upon the cardiac data signals (Paragraphs 0136-0138: an output indicative of the state of the heart may be provided; Paragraphs 0079 and 0164-0167: the measured data and/or the processed data may be transmitted over a network or via the internet to provide output through a web-based interface for review by a clinician. Paragraph 0183: the data collected from the user may be published to other computing devices of user’s involved in the subject’s care).
Regarding claim 48, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the inertial measurement unit sensor and the microphonic sensor are both integrated into a handheld electronic device (Paragraph 0041: the microphone and accelerometer are internal to the housing of the monitoring device; Paragraph 0052: the monitoring device is handheld).
Regarding claim 49, Venka in view of Shadforth teaches the system of claim 48. Modified Venka further teaches the system wherein the handheld electronic device includes a smartphone (Paragraphs 0084 and 0100: the data may be transmitted to the computing device such as a smartphone).
Regarding claim 53, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the circuit board includes a transceiver configured to communicate with a network (Paragraph 0079: the monitoring device may transmit data over a network via a transceiver).
Regarding claim 55, (As best understood in light of the above presented 35 USC 112 rejections) Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the cardiac monitoring instructions are configured to identify a plurality of unique physiological markers of the user based on historical cardiac signals (Paragraphs 0036, 0101-0103, 0109-0110, 0118, and 0153: the classifier is trained based on historical data and identifies features of the cardiac sound data, or unique physiological markers of the user).
Regarding claim 56, (As best understood in light of the above presented 35 USC 112 rejections) Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the cardiac monitoring instructions are configured to identify a plurality of unique physiological markers of the user based on historical cardiac sound wave data signals captured by the microphonic sensor (Paragraphs 0036, 0101-0103, 0109-0110, 0118, and 0153: the classifier is trained based on historical data and identifies features of the cardiac sound data, or unique physiological markers of the user).
Regarding claim 57, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the cardiac monitoring instructions include estimating, by the processor, intracardiac pressure (Paragraph 0046: a pressure inside the heart).
Regarding claim 58, Venka in view of Shadforth teaches the system of claim 57. Modified Venka further teaches the system wherein the cardiac monitoring instructions include estimating, by the processor, intracardiac pressure by using a regression model (Paragraphs 0101 and 0111: the algorithm may be a regression algorithm).
Regarding claim 59, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the cardiac monitoring instructions include instructions to estimate an ejection fraction (Paragraph 0108: the algorithm may determine ejection fraction).
Regarding claims 60-61, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further suggests the system wherein the cardiac monitoring instructions include instructions to estimate a cardiac output (Paragraph 0054: the monitoring device may be used to obtain indications to change a medication based on cardiac output which appears to suggest that cardiac output is determined).
Venka fails to explicitly teach the system wherein the cardiac monitoring instructions include instructions to estimate a cardiac output or blood pressure.
Shadforth teaches a system utilizing synchronously obtained photoplethysmogram (PPG) data, electrocardiogram (ECG), and ballistocardiogram (BCG) or seismocardiogram (SCG) data to provide estimates of pressure-related metrics such as cardiac output, and blood pressure (Paragraphs 0183-0191)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of modified Venka to include a PPG measurement device and to use the additional contextual data from this input in combination with the already collected data of modified Venka to estimate additional cardiovascular parameters such as blood pressure and cardiac output because collecting PPG adds additional contextual information to the cardiac information collected by the system and may result in more accurate estimations of certain conditions and may further allow additional conditions and/or parameters to be estimated by the system.
Claim 45 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman US Patent Application Publication Number US 20210259560 A1 hereinafter Venka in view of Shadforth US Patent Application Publication Number US 20220095955 A1 hereinafter Shadforth as applied to claim 44 above and further in view of Inan US Patent Application Publication Number US 20230293082 A1 hereinafter Inan.
Regarding claim 45, Venka in view of Shadforth teaches the system of claim 44. Modified Venka further teaches the system wherein the classification model has been trained using training data (Paragraphs 0102 and 0118-0119: specific training sets for various conditions). But fails to further disclose the training using intracardiac pressure data measured from a catheter and/or an invasive sensor.
Inan teaches systems and methods for measuring hemodynamic parameters with wearable cardiovascular sensing (Abstract). Thus, Inan falls withing the same field of endeavor as Applicant’s invention.
Inan teaches that classifiers can be trained using gold standard data including hemodynamic data from a heart catheter (Paragraphs 0174, 0206, 0209, 0225, 0241, and 0248-0251: the data is obtained simultaneously from an intracardiac catheter sensor and SCG and ECG signals. The data is synchronized and the gold-standard data from the catheter is used to train the model which utilizes the ECG and SCG data). Inan teaches that such training allows the system to estimate intracardiac pressure from ECG and SCG data (Paragraphs 0202 and 0206).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of modified Venka to train the classification model using gold standard data acquired from an invasive sensor such as a heart catheter because Venka already contemplates using different training data for detecting different conditions (Venka: Paragraphs 0102 and 0118-0119) and using the synchronized gold-standard data as taught by Inan would allow the system to estimate intracardiac parameters such as pressure (Inan: paragraphs 0202 and 0206) as well as the cardiac conditions of Venka.
Claim 50 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman US Patent Application Publication Number US 20210259560 A1 hereinafter Venka in view of Shadforth US Patent Application Publication Number US 20220095955 A1 hereinafter Shadforth as applied to claim 43 above and further in view of Ransbury US Patent Application Publication Number US 20180317789 A1 hereinafter Ransbury.
Regarding claim 50 Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the inertial measurement unit sensor and the microphonic sensor are both integrated into the monitoring device (Paragraph 0041: the microphone and accelerometer are internal to the housing of the monitoring device)
Modified Venka fails to further disclose the system wherein the IMU and microphonic sensors are integrated into a wearable electronic device.
Ransbury teaches devices and methods for remote monitoring of heart activity. In some embodiments, a wearable heart monitoring devices for monitoring heart activity comprises one or more acoustic sensors, including at least one microphone configured to operate in a frequency range related to human hearing, and at least one accelerometer configured to operate in the range of low or sub-audible frequencies (Abstract).
Ransbury teaches a system wherein the IMU and microphonic sensors are integrated into a wearable electronic device (Paragraphs 0026-0029: The monitoring device may be implemented as a wearable system; Fig. 6).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the monitoring device of modified Venka to be a wearable device such as the one described by Ransbury because making the device wearable may help in keeping the device positioned appropriately for the desired duration of measurement which may result in better signal quality compared to if the device is held by the user.
Claims 51-52 are rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman US Patent Application Publication Number US 20210259560 A1 hereinafter Venka in view of Shadforth US Patent Application Publication Number US 20220095955 A1 hereinafter Shadforth further in view of Ransbury US Patent Application Publication Number US 20180317789 A1 hereinafter Ransbury as applied to claim 50 above and further in view of Tran US Patent Application Publication Number US 20070273504 A1 hereinafter Tran
Regarding claims 51-52, (As best understood in light of the above presented 35 USC 112(b) rejections above) Venka in view of Shadforth further in view of Ransbury teaches the system of claim 50. Modified Venka fails to further disclose the system wherein the wearable electronic device includes a smartwatch; and wherein the wearable device includes a smart ring.
Tran teaches a health care monitoring system for a person includes one or more wireless nodes forming a wireless mesh network; a wearable appliance having a sound transducer coupled to the wireless transceiver; and a bioelectric impedance (BI) sensor coupled to the wireless mesh network to communicate BI data over the wireless mesh network to detect a heart attack or a stroke attack (Abstract). Thus, Tran is reasonably pertinent to the problem at hand.
Tran teaches a mesh network of monitoring devices which may include devices such as smartwatches and smart rings. The sensors may be attached to a variety of different locations depending on the parameter being detected and may be implemented into various wearable housings such as smart-watches, smart-rings, smart-earrings, abdominal bands, and other form factors that allow the sensors to be attached at the desired locations. The collected data is transmitted to a base station for analysis. (Paragraphs 0045-0047, 0302, and 0306: the sensors may be housed in smart watches or rings and wirelessly communicate with a base station; Paragraphs 0227-0232: various sensors can be implemented in different locations to monitor cardiac parameters such as stroke volume and cardiac output. The sensors may form a mesh network)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of modified Venka to include a plurality of sensors in a mesh network disposed at different locations in suitable form factors as taught by Tran because such a mesh network of sensors allows modified Venka to collect a variety of different types of data from desirable locations and utilize such data to obtain a better understanding of the patient’s cardiac condition. Peripheral sensors at various positions on the body allow for the detection and/or calculation of parameters that may be otherwise difficult to determine for a wearable device disposed on the user’s chest alone such as stroke volume and cardiac output (Tran: paragraphs 0227 and 0236). Thus it would be obvious to implement such sensors in suitable form factors for their respective positions and the presence of the sensor and wireless communication capability to integrate into the mesh network is considered to teach the various devices being “smart” devices.
Claim 54 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatraman US Patent Application Publication Number US 20210259560 A1 hereinafter Venka in view of Shadforth US Patent Application Publication Number US 20220095955 A1 hereinafter Shadforth as applied to claim 43 above and further in view of Jumbe US Patent Application Publication Number US 20210345939 A1 hereinafter Jumbe.
Regarding claim 54, Venka in view of Shadforth teaches the system of claim 43. Modified Venka further teaches the system wherein the circuit board includes a diaphragm configured to enhance the sound wave cardiac data signals captured by the microphonic sensor.
Jumbe teaches a sensing system comprising a hand-held sensing device with a vibracoustic sensor module (VSM) (Abstract). Thus, Jumbe falls within the same field of endeavor as Applicant’s invention.
Jumbe teaches a system wherein the inertial measurement unit sensor is configured to receive seismic cardiac data signals indicative of a cardiac health of a user (Paragraphs 0151-0155 and 0265: the system can include combinations of microphonic and inertial type sensors to measure vibroacoustic signals. These signals may include seismocardiographic signals). The microphone sensor may include diaphragm condenser microphones (Paragraph 0126).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure modified Venka to utilize a diaphragm condenser microphone as the microphonic sensor because it is a simple substitution of one known element (the microphone of Venka) for another known element (the microphone of Jumbe) with no surprising technical effect (the microphone detects heart sounds).
Conclusion
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/MATTHEW ERIC OGLES/ Examiner, Art Unit 3791
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