DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/06/2026 has been entered.
Response to Amendment
Claims 1 and 5-6 have been amended. Claims 2-4 have been cancelled. Claims 1 and 5-6 remain pending.
Response to Arguments
35 U.S.C. 112(a) and 112(f)
Regarding “one or more processors and a memory” on Page 8 of Remarks, Applicant has removed the recited “one or more processors and a memory …”, and therefore the 112(a) rejection with the recited limitation is withdrawn.
Regarding “a data receiving module” or “a data receiving unit” on Pages 8-9 of Remarks, Applicant argues that “The data receiving unit 110 is disclosed in the Specification as receiving … and receiving …”, and “Amended claim 1 now recites the data receiving unit as configured to receive … and …”. The recited limitations are functional, but do not specify structural details on how the function of “data receiving” is performed. Therefore, the issued 112(f) and 112(a) for the limitation are not withdrawn.
Regarding “derived variable extraction unit” on Page 9 of Remarks, Applicant argues that “each derived variable being generated based on a time elapsed from … to …”, which “directly tracks the disclosure at paragraph [0031] …”. Both the claimed limitation and the disclosure at paragraph [0031] and paragraph [0046] indicate that the “derived variable” relates to or is based on the “time elapsed”, which is not sufficient for one person of ordinary skill in the art to either understand what such derived variable is or how to obtain such derived variable. Therefore, the issued 112(f) and 112(a) for the limitation are not withdrawn.
Regarding “photoplethysmography analysis unit” on Page 10 of Remarks, Applicant has made amendments by including more details such as an encoding region and a decoding region. However, the newly included limitations are merely an architecture of artificial neural network that is recited at a high level of generality, and are not sufficient for understanding how structurally the recited photoplethysmography analysis unit is implemented to perform its claimed function. Therefore, the issued 112(f) for the limitation is not withdrawn.
Regarding “non-invasive blood pressure analysis” on Page 11 of Remarks, its structure includes merely “a second learning model” that accepts two vectors of a same length, which is not sufficient. Therefore, the issued 112(f) for the limitation is not withdrawn.
Regarding “blood pressure state prediction unit” on Page 11 of Remarks, Applicant argues that the unit now “defines the specific algorithmic procedure (SBP/DBP determination followed by four-category classification), which constitutes sufficient structure”. Examiner respectfully disagrees. Neither the claim or the Specification (Para 0055) specifies how the systolic blood pressure and diastolic blood pressure are determined based on the generated blood pressure waveform, or how a patient’s status is classified into one of the 4 categories based on SBP and DBP. There is no sufficient structure to support the claimed limitation. Therefore, the issued 112(f) for the limitation is not withdrawn.
35 U.S.C. 101 rejections
On Pages 14-17 of Remarks, Applicant explains the amendments for Claims 1 and 5-6 to overcome the previously issued 101 rejections. The 101 rejections for Claims 1 and 5-6 are withdrawn.
35 U.S.C. 103 rejections
On Pages 18-19 of Remarks, Applicant argues that, regarding the claimed “derived variable extraction unit” in amended Claim 1, the combination of Wang and Addison fails to teach the limitation of “a derived variable extraction unit configured to generate …”, and specifically the disclosed “feature 604” of Addison is a single elapsed time value at a single time point. The argument is moot because of new ground of rejection. Specifically, the derived features of Addison that correspond to the claimed “derived variable” are derived for every “particular time” when blood pressure is estimated and calibrated.
On Pages 19-20 of Remarks, Applicant argues that, regarding the claimed “non-invasive blood pressure analysis unit” in amended Claim 1, neither Wang nor Addison disclose (i) generating a plurality of derived variables spanning a prediction section, or (ii) pairing those variables with a matching number of copies of the blood pressure measurement value as input to a second learning model. The argument is moot because of new ground of rejection. See details in section of Claim Rejections.
On Page 21 of Remarks, Applicant argues that, regarding the claimed “blood pressure waveform prediction unit” in amended Claim 1, reference Aguirre's attention mechanism (a single encoder-decoder pipeline) is different from the claimed “Cross-Attention layer” or the “two separate streams of information” of “the third learning model”. Examiner respectfully disagrees. Specification does not specify the detailed structure of the third learning model, or how the claimed “cross-attention layer” is used inside the third learning model. Further, the claimed “second feature value” is not specified in Specification on what it could be, and as a value is more possibly to be some calibration value, rather than a stream of information. As for the attention mechanism of Aguirre, the cited cross-attention structure is widely used in Transformer-type architecture, in which attention is computed based on information collected from both encoder and decoder.
On Page 22 of Remarks, Applicant argues that, regarding the claimed “blood pressure prediction unit” in amended Claim 1, none of Wang, Addison or Aguirre teaches determining SBP and DBP from the continuous waveform and classifying the blood pressure state into one of low blood pressure, normal, prehypertension and high blood pressure. The argument is moot because of new ground of rejection. See details in section of Claim Rejections.
On Pages 22-23 of Remarks, Applicant argues that none of Wang, Addison or Aguirre teaches the specific multi-model pipeline of amended Claim 1, and the asserted motivation to combine the references is insufficient. Examiner respectfully disagrees. First, while the application does propose a pipeline with multiple models, based on description of Specification, it is unclear why the multi-model pipeline is designed as such, or in other words, why not use end-to-end learning instead. For example, the first learning model is to determine a “primary arterial blood pressure waveform” from PPG measurement values. There is no specification on what the recited “primary” means or what information is extracted from the PPG values by the first learning model. The derived variable extraction unit is to determine derived variable based on elapsed time. There is no detail or example on what the recited “derived variable” could be, so that the purpose or function of such unit is unknown. The same issue is with the recited “second feature” determined by the second learning model. Without any understanding on what the second feature and the primary ABP waveform are, one would not understand the purpose or function of the third learning model. Hence, description in current specification provides rather insufficient information on the various models and units and their inputs/outputs, so that the design of the multi-model pipeline, e.g. number of models, seems arbitrary. Second, reference Wang contains at least 4 steps of processing or analysis (in blocks 13, 14, 15 and 16 of its Fig. 1), and at least 3 of the steps use machine-learning based methods.
On Pages 23-25 of Remarks, Applicant argues that, regarding amended Claims 5-6, reference Barak does not cure the deficiencies of the combination of Wang, Addison and Aguirre discussed above. See discussion above.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a data receiving unit configured to receive …” in Claim 1 and in Claim 5.
A review of the Specification indicates that no structural information of the “data receiving unit” is provided in the Specification (also refer to the section of 35 U.S.C 112(a)).
"a photoplethysmography analysis unit configured to …” in Claim 1 and in Claim 5.
A review of the Specification discloses that the corresponding structure for “a photoplethysmography analysis unit” is formed of “a deep learning-based algorithm such as Resnet algorithm, U-net algorithm, Attention algorithm, and Self-Attention algorithm” (Para 0042).
“a derived variable extraction unit configured to generate …” in Claim 1 and in Claim 5.
A review of the Specification discloses indicates that sufficient structural information of the “derived variable extraction unit” is not provided in the Specification (see Para 0046; also refer to the section of 35 U.S.C 112(a)).
“a non-invasive blood pressure analysis unit configured to input …” in Claim 1 and in Claim 5.
A review of the Specification discloses that the corresponding structure for “a non-invasive blood pressure analysis unit” is formed of “one or more of several known algorithms such as the LSTM algorithm” (Para 0050).
“a blood pressure state prediction unit configured to determine … and to classify … ” in Claim 1 and in Claim 5.
A review of the Specification indicates that sufficient structure is not provided to support the function of determining systolic and diastolic blood pressure values and the function of classifying a blood pressure state of a patient based on the systolic/diastolic blood pressure values (also refer to the section of 35 U.S.C 112(a)).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1 and 5-6 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 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 1, Lines 12-14, and Claim 5, Lines 3-7, recite “a data receiving unit configured to receive …”. The Specification does not disclose any detail on this limitation.
Claim 1, Lines 25-31, and Claim 5, Lines 16-21, recite “a derived variable extraction unit configured to generate …”. Specification, Para 0031 and Para 0046, describes how derived variables are generated, but it merely species that such derived variables relate to the time elapsed from the time point of the most recent non-invasive blood pressure measurement to the time point to be predicted, which is not adequate or clear enough for one person of ordinary skill in the art to perform the generating process.
Claim 1, Lines 42-47, and Claim 5, Lines 31-35, recite “a blood pressure state prediction unit configured to determine … and to classify …”, and Claim 6, Lines 31-35, recites “determining … and classifying”. Specification, Para 0055, discloses classifying the blood pressure state into any one of low, normal, prehypertension and high blood pressure, but does not specify how to classify it, e.g. what threshold values to be used, or what method (manual or automatic) to be used.
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 limitation “data receiving unit” in Claims 1 and 5 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Specification, Para 0029, 0038-0040 merely specify what information is received, but not how such information is received. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 6 is also rejected under 35 U.S.C. 112(b) because it inherits the indefiniteness of the claim it depends upon.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 20230293117 A1; hereafter Wang), in view of Addison et al (US 20210193311 A1; hereafter Addison) and Aguirre et al (Sensors 2021, 21, 2167; hereafter Aguirre).
With regard to Claim 1, Wang discloses a deep learning-based (Wang, Para 0087; “… after the deep learning processing of Approximation Network and Refinement Network”) continuous arterial blood pressure monitoring system (Wang, Para 0008; “The method or system is comfortable, continuous, and suitable for all-day use, and the accuracy of estimation of BPs and/or CBPs are sufficiently improved.”), comprising:
a photoplethysmography sensor configured to measure a photoplethysmography measurement value of a patient (Wang, Para 0035; “an upper-arm wearable apparatus including a PPG sensor and sensing modeling-used PPG waveform signals …”);
a non-invasive blood pressure measurement device including a cuff (Wang, Abstract; “A system for estimating BPs … comprises … a cuff-based BP measuring apparatus …”; Para 0065; “The cuff-based BP measuring apparatus 12 has been calibrated so as to provide … an accurate blood pressure.”) configured to obtain a most recently measured non-invasive blood pressure measurement value of the patient by applying a pressure to a blood vessel while the cuff is worn on a body part of the patient, the most recently measured non-invasive blood pressure measurement value including a systolic blood pressure value and a diastolic blood pressure value (Wang, Fig. 1 shows that “a cuff 121 included in a cuff-based BP measuring apparatus 12” (Para 0065) is worn on an upper arm of a subject, and generates “Real SBP/DBP” (i.e. systolic blood pressure and diastolic blood pressure). The disclosed “Real SBP/DBP” is measured directly from the subject and fed into the system, so corresponds to “most recently measured” values);
a data receiving unit (the PPG signal receiver and analyzer 13) configured to receive the photoplethysmography measurement value and the most recently measured non-invasive blood pressure measurement value (Wang, Fig. 1 shows that the PPG to BP subsystem (101) includes the PPG signal Receiver and analyzer (13) and also receives “Real SBP/DBP”);
a photoplethysmography analysis unit (PPG to PVR transformer 15) configured to normalize the photoplethysmography measurement value (Wang, Para 0081; “… the normalization value (Maxima and minima) for upper-arm PPG and PVR waveform signals were calculated from the upper-arm PPG database in this embodiment.”), to input a normalized photoplethysmography measurement value to a first learning model (Wang, Para 0067; “a PPG to PVR transformer 15 converts the PPG waveform signals to refined PVR waveforms using deep machine learning”), and to output a primary arterial blood pressure waveform (refined PVR waveforms), the first learning model including an encoding region and a decoding region, the encoding region including a plurality of convolutional layers configured to extract a plurality of features in a plurality of encoding steps, the first learning model being configured to store the plurality of features extracted in the plurality of encoding steps, and the decoding region being configured to output the primary arterial blood pressure waveform based on the stored plurality of features (Wang, Para 0084; “U-Net comprises a network constructed using only convolutional layers … The network structure is constructed using a symmetric pair of Encoder Network and Decoder Network.” The disclosed “U-Net” inherently comprises the claimed structures of the first learning model);
a derived variable extraction unit (the PPG signal receiver and analyzer 13) configured to generate a plurality of derived variables (Wang, Para 0084; “… the characteristic parameters including waveform parameters and time-related parameters extracted by the PPG signal receiver and analyzer 13. The waveform parameters include …”);
a non-invasive blood pressure analysis unit (PPG to BP estimator and calibrator 14) configured to input, to a second learning model (Wang, Para 0066; “PPG to BP estimator and calibrator 14 … it uses the age grouping method and further calibrates preliminary values by machine learning (ML) algorithms or other algorithms (e.g. regression algorithms, artificial neural networks (ANN), fuzzy logic, and support vector machine) …”), the plurality of derived variables and the most recently measured non-invasive blood pressure measurement value (“Real SBP/DBP” in Fig. 1), and to output a second feature value (calibrated-estimated SBP/DBP);
a blood pressure waveform prediction unit (CBP estimator) configured to input the primary arterial blood pressure waveform and the second feature value to a third learning model (Wang, Para 0088; “… the CBP estimator 16 further uses linear regression equations to estimate CBPs in clinic use, and substitutes the calibrated-estimated BPs and waveform parameters of the user’s refined PVR waveform into the linear regression equation to have estimated CBPs.” The disclosed “linear regression equations” is a learning model, with their regression coefficients learnt from training data), and to output a continuous arterial blood pressure waveform (Wang, Para 0006; “The translation of PPG signals into arterial blood pressure (ABP) is described in another non-patent literature entitled “PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks” (written by Nabil Ibtehaz, M. Sohel Rahman; Electrical Engineering and Systems Science: Signal Processing, published on May 5, 2020), the teachings of which are incorporated herein by reference in their entirety.”. The reference of Ibtehaz et al discloses “… a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals” (Abstract)); and
a blood pressure state prediction unit configured to determine a systolic blood pressure and a diastolic blood pressure from the continuous arterial blood pressure waveform (Wang, Para 0006; “PPG2ABP … written by Nabil Ibtehaz … the teachings of which are incorporated herein by reference in their entirety”. Ibtehaz et al discloses in Page 6 that SBP and DBP are calculated as maximum and minimum of continuous arterial blood pressure (ABP) waveform, respectively), and to classify a blood pressure state of the patient as one blood pressure state selected from low blood pressure, normal, prehypertension, and high blood pressure based on the determined systolic blood pressure and the determined diastolic blood pressure (Wang, Para 0006; “Jia-Wei Chen et al … the teachings of which are incorporated herein by reference in their entirety”. Chen et al discloses in Page 8 “… actual BP and PPG data and information from participants who were diagnosed with normotension (BP < 130/90 mm Hg), prehypertension, and hypertension …”, and in Page 14 “The abnormal BP levels were defined as hypertension (SBP/DBP ≥ 130/80 mmHg) and hypotension (SBP/DBP < 90/60 mmHg) …” ).
Wang does not clearly and explicitly disclose:
the plurality of derived variables being generated for a prediction section and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section, and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section,
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables, and
a learning model including a Cross-Attention layer and a forward neural network.
Addison in the same field of endeavor discloses:
the plurality of derived variables being generated for a prediction section (Addison, Para 0080; “the specified time period may be the entire time period that has elapsed since the most recent occurrence of the calibration point …”) and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section (Addison, Abstract; “receive … a PPG signal at a particular time … and determine … a blood pressure of the patient at the particular time”. According to the disclosure, for each particular time, a blood pressure is determined based on a PPG signal, and therefore, the period for the prediction (of blood pressure) is the same (the thus same length) as the period of acquiring PPG signal), and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section (Addison, Para 0095; “… CNIBP monitoring device 100 (e.g., processing circuitry 110) may use features 600, 602, 604, and 606 as inputs into the CNIBP model 124 to determine the blood pressure of a patient at time t.”. The disclosed combination of features 602 and 604 (see Fig. 6B), for the disclosed “time t”, corresponds to the claimed “each derived variable” for “corresponding time point”. Of the disclosed features, features 604 are “the value of the time elapsed since the most recent calibration point” (Para 0093)),
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables (Addison, Fig. 6B shows the features as input for determining blood pressure for a particular time t, which include features 602 and 604 (corresponding to the claimed “each derived variable” as discussed above) and features 606 (“BP at Calibration Point”). Para 0067 describes an example of determining blood pressure for time point t3 in the period of t0 to t1 in Fig. 4. When calibrating or determining blood pressure for all time points in the period, a same number (also same as the number of the time points) of the combination of features 602 and 604 and features 606 are derived and inputted into the trained network, which agrees with the claim).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, as suggested by Addison, in order for each time point of prediction to input a copy of blood pressure measured at calibration point and derive a variable based on elapsed time. One of ordinary skill in the art would have been motivated to make the modification of improving prediction accuracy for the estimated blood pressure (Addison, Para 0086; “… by using the calibration data received at the most recent calibration point as additional inputs for training CNIBP model 124, CNIBP model 124 may improve its accuracy in determining the blood pressure of patients.”).
Wang and Addison do not explicitly and clearly disclose a learning model including a Cross-Attention layer and a forward neural network.
Aguirre in the same field of endeavor discloses a learning model including a Cross-Attention layer (Aguirre, Abstract; “this methodology is capable of transforming PPG into an ABP pulse …”; Page 6, Para 2; “The proposed deep learning architecture is inspired by seq2seq encoder-decoder [36] models with attention mechanism.” Fig. 6 of Aguirre shows that the block “Attention” combines the outputs of both the encoder (from input Xl) and the decoder (from output of each time step concatenated with demographic information vector), so is a typical cross-attention operation) and a forward neural network (Aguirre, Fig. 6 show that the model also includes multiperceptron layers (MPLv and MPLc), which are feed-forward neural network layers). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang and Addison, as suggested by Aguirre, in order to comprise a cross-attention component and a feed-forward component in the network. One of ordinary skill in the art would have been motivated to make the modification for the benefit of achieving a cross-attention mechanism that is known to enable higher efficiency and accuracy as compared to regular neural networks.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Addison and Aguirre, in view of Barak et al (US 20200253564 A1; hereafter Barak).
With regard to Claim 5, Wang discloses a deep learning-based (Wang, Para 0087; “… after the deep learning processing of Approximation Network and Refinement Network”) continuous arterial blood pressure monitoring system (Wang, Para 0008; “The method or system is comfortable, continuous, and suitable for all-day use, and the accuracy of estimation of BPs and/or CBPs are sufficiently improved.”) comprising:
a data receiving unit (the PPG signal receiver and analyzer 13) configured to receive a photoplethysmography measurement value of a patient (Wang, Para 0035; “an upper-arm wearable apparatus including a PPG sensor and sensing modeling-used PPG waveform signals …”) and a most recently measured non-invasive blood pressure measurement value of the patient (Wang, Abstract; “A system for estimating BPs … comprises … a cuff-based BP measuring apparatus …”; Para 0065; “The cuff-based BP measuring apparatus 12 has been calibrated so as to provide … an accurate blood pressure.”), the most recently measured non-invasive blood pressure measurement value including a systolic blood pressure value and a diastolic blood pressure value (Wang, Fig. 1 shows that “a cuff 121 included in a cuff-based BP measuring apparatus 12” (Para 0065) is worn on an upper arm of a subject, and generates “Real SBP/DBP” (i.e. systolic blood pressure and diastolic blood pressure). The disclosed “Real SBP/DBP” is measured directly from the subject and fed into the system, so corresponds to “most recently measured” values);
a photoplethysmography analysis unit (PPG to PVR transformer 15) configured to normalize the photoplethysmography measurement value (Wang, Para 0081; “… the normalization value (Maxima and minima) for upper-arm PPG and PVR waveform signals were calculated from the upper-arm PPG database in this embodiment.”), to input a normalized photoplethysmography measurement value to a first learning model (Wang, Para 0067; “a PPG to PVR transformer 15 converts the PPG waveform signals to refined PVR waveforms using deep machine learning”), and to output a primary arterial blood pressure waveform (refined PVR waveforms), the first learning model including an encoding region and a decoding region, the encoding region including a plurality of convolutional layers configured to extract a plurality of features in a plurality of encoding steps, the first learning model being configured to store the plurality of features extracted in the plurality of encoding steps, and the decoding region being configured to output the primary arterial blood pressure waveform based on the stored plurality of features (Wang, Para 0084; “U-Net comprises a network constructed using only convolutional layers … The network structure is constructed using a symmetric pair of Encoder Network and Decoder Network.” The disclosed “U-Net” inherently comprises the claimed structures of the first learning model);
a derived variable extraction unit (the PPG signal receiver and analyzer 13) configured to generate a plurality of derived variables (Wang, Para 0084; “… the characteristic parameters including waveform parameters and time-related parameters extracted by the PPG signal receiver and analyzer 13. The waveform parameters include …”);
a non-invasive blood pressure analysis unit (PPG to BP estimator and calibrator 14) configured to input, to a second learning model (Wang, Para 0066; “PPG to BP estimator and calibrator 14 … it uses the age grouping method and further calibrates preliminary values by machine learning (ML) algorithms or other algorithms (e.g. regression algorithms, artificial neural networks (ANN), fuzzy logic, and support vector machine) …”), the plurality of derived variables and the most recently measured non-invasive blood pressure measurement value (“Real SBP/DBP” in Fig. 1), and to output a second feature value (calibrated-estimated SBP/DBP);
a blood pressure waveform prediction unit (CBP estimator) configured to input the primary arterial blood pressure waveform and the second feature value to a third learning model (Wang, Para 0088; “… the CBP estimator 16 further uses linear regression equations to estimate CBPs in clinic use, and substitutes the calibrated-estimated BPs and waveform parameters of the user’s refined PVR waveform into the linear regression equation to have estimated CBPs.” The disclosed “linear regression equations” is a learning model, with their regression coefficients learnt from training data), and to output a continuous arterial blood pressure waveform (Wang, Para 0006; “The translation of PPG signals into arterial blood pressure (ABP) is described in another non-patent literature entitled “PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks” (written by Nabil Ibtehaz, M. Sohel Rahman; Electrical Engineering and Systems Science: Signal Processing, published on May 5, 2020), the teachings of which are incorporated herein by reference in their entirety.”. The reference of Ibtehaz et al discloses “… a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals” (Abstract));
a blood pressure state prediction unit configured to determine a systolic blood pressure and a diastolic blood pressure from the continuous arterial blood pressure waveform (Wang, Para 0006; “PPG2ABP … written by Nabil Ibtehaz … the teachings of which are incorporated herein by reference in their entirety”. Ibtehaz et al discloses in Page 6 that SBP and DBP are calculated as maximum and minimum of continuous arterial blood pressure (ABP) waveform, respectively), and to classify a blood pressure state of the patient as one blood pressure state selected from low blood pressure, normal, prehypertension, and high blood pressure based on the determined systolic blood pressure and the determined diastolic blood pressure (Wang, Para 0006; “Jia-Wei Chen et al … the teachings of which are incorporated herein by reference in their entirety”. Chen et al discloses in Page 8 “… actual BP and PPG data and information from participants who were diagnosed with normotension (BP < 130/90 mm Hg), prehypertension, and hypertension …”, and in Page 14 “The abnormal BP levels were defined as hypertension (SBP/DBP ≥ 130/80 mmHg) and hypotension (SBP/DBP < 90/60 mmHg) …”); and
transmitting the determined results to at least one of a central monitoring device and an electronic medical record system (Wang, Para 0006; “Jia-Wei Chen et al … the teachings of which are incorporated herein by reference in their entirety”. Chen et al discloses in Page 5 “… the measured PPG-based BP and heart rate (HR), as well as oxygen saturation, could be transmitted wirelessly to a mobile phone for display and recording of multimodal physiological data in the mobile application (APP)”).
Wang does not clearly and explicitly disclose:
receiving data through a serial port or a LAN port of a commercial patient monitoring device,
the plurality of derived variables being generated for a prediction section and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section, and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section,
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables,
a learning model including a Cross-Attention layer and a forward neural network, and
a data transmitting unit configured to convert the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and to transmit the voltage to a blood pressure measurement module of the commercial patient monitoring device, or to transmit the continuous arterial blood pressure waveform in digital form.
Addison in the same field of endeavor discloses:
the plurality of derived variables being generated for a prediction section (Addison, Para 0080; “the specified time period may be the entire time period that has elapsed since the most recent occurrence of the calibration point …”) and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section (Addison, Abstract; “receive … a PPG signal at a particular time … and determine … a blood pressure of the patient at the particular time”. According to the disclosure, for each particular time, a blood pressure is determined based on a PPG signal, and therefore, the period for the prediction (of blood pressure) is the same (the thus same length) as the period of acquiring PPG signal), and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section (Addison, Para 0095; “… CNIBP monitoring device 100 (e.g., processing circuitry 110) may use features 600, 602, 604, and 606 as inputs into the CNIBP model 124 to determine the blood pressure of a patient at time t.”. The disclosed combination of features 602 and 604 (see Fig. 6B), for the disclosed “time t”, corresponds to the claimed “each derived variable” for “corresponding time point”. Of the disclosed features, features 604 are “the value of the time elapsed since the most recent calibration point” (Para 0093)),
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables (Addison, Fig. 6B shows the features as input for determining blood pressure for a particular time t, which include features 602 and 604 (corresponding to the claimed “each derived variable” as discussed above) and features 606 (“BP at Calibration Point”). Para 0067 describes an example of determining blood pressure for time point t3 in the period of t0 to t1 in Fig. 4. When calibrating or determining blood pressure for all time points in the period, a same number (also same as the number of the time points) of the combination of features 602 and 604 and features 606 are derived and inputted into the trained network, which agrees with the claim).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, as suggested by Addison, in order for each time point of prediction to input a copy of blood pressure measured at calibration point and derive a variable based on elapsed time. One of ordinary skill in the art would have been motivated to make the modification of improving prediction accuracy for the estimated blood pressure (Addison, Para 0086; “… by using the calibration data received at the most recent calibration point as additional inputs for training CNIBP model 124, CNIBP model 124 may improve its accuracy in determining the blood pressure of patients.”).
Wang and Addison do not explicitly and clearly disclose:
receiving data through a serial port or a LAN port of a commercial patient monitoring device,
a learning model including a Cross-Attention layer and a forward neural network, and
a data transmitting unit configured to convert the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and to transmit the voltage to a blood pressure measurement module of the commercial patient monitoring device, or to transmit the continuous arterial blood pressure waveform in digital form.
Aguirre in the same field of endeavor discloses a learning model including a Cross-Attention layer (Aguirre, Abstract; “this methodology is capable of transforming PPG into an ABP pulse …”; Page 6, Para 2; “The proposed deep learning architecture is inspired by seq2seq encoder-decoder [36] models with attention mechanism.” Fig. 6 of Aguirre shows that the block “Attention” combines the outputs of both the encoder (from input Xl) and the decoder (from output of each time step concatenated with demographic information vector), so is a typical cross-attention operation) and a forward neural network (Aguirre, Fig. 6 show that the model also includes multiperceptron layers (MPLv and MPLc), which are feed-forward neural network layers). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang and Addison, as suggested by Aguirre, in order to comprise a cross-attention component and a feed-forward component in the network. One of ordinary skill in the art would have been motivated to make the modification for the benefit of achieving a cross-attention mechanism that is known to enable higher efficiency and accuracy as compared to regular neural networks.
Wang, Addison and Aguirre do not explicitly and clearly disclose:
receiving data through a serial port or a LAN port of a commercial patient monitoring device, and
a data transmitting unit configured to convert the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and to transmit the voltage to a blood pressure measurement module of the commercial patient monitoring device, or to transmit the continuous arterial blood pressure waveform in digital form.
Barak in the same field of endeavor discloses:
receiving data through a serial port or a LAN port of a commercial patient monitoring device (Barak, Para 0023; “Typically, PCMs (Patient Care Monitors) obtain signals from PPG sensors …”; Para 0256; “Cable 107 connects tracking transducer 102 to PCM 103.” Para 0279; “Interface circuit 304 may format data for transmission serial RS232 connection, USB, Ethernet (LAN), or any other data format in which PCM 103 is capable of receiving BPV and SVV data.”), and
a data transmitting unit (the combination of interface circuit 604 and D/A converter 505 in Fig. 5) configured to convert the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and to transmit the voltage to a blood pressure measurement module of the commercial patient monitoring device (Barak; Para 0265; “Wires 204 provide arterial pressure values to PCM 103, for example to be displayed.” Fig. 5 shows that the values first go through block 505 (D/A converter) before being transmitted by wires 204), or to transmit the continuous arterial blood pressure waveform in digital form (Barak; Para 0265; “D/A converter 505 are optional”; when such converter is not used, the transmitted data is in digital form).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, Addison and Aguirre, as suggested by Barak, in order to transmit data among acquisition apparatus, processing device and patient monitor using standard wired methods. One of ordinary skill in the art would have been motivated to make the modification for the benefit of transferring data and results in a reliable way and enabling efficient monitoring of patient’s vital information.
With regard to Claim 6, Wang discloses a continuous arterial blood pressure monitoring method (Wang, Para 0008; “The method or system is comfortable, continuous, and suitable for all-day use, and the accuracy of estimation of BPs and/or CBPs are sufficiently improved.”), comprising:
receiving a photoplethysmography measurement value of a patient from a photoplethysmography sensor (Wang, Para 0035; “an upper-arm wearable apparatus including a PPG sensor and sensing modeling-used PPG waveform signals …”) and receiving a most recently measured non-invasive blood pressure measurement value of the patient from a non-invasive blood pressure measurement device including a cuff (Wang, Abstract; “A system for estimating BPs … comprises … a cuff-based BP measuring apparatus …”; Para 0065; “The cuff-based BP measuring apparatus 12 has been calibrated so as to provide … an accurate blood pressure.”), the most recently measured non-invasive blood pressure measurement value including a systolic blood pressure value and a diastolic blood pressure value (Wang, Fig. 1 shows that “a cuff 121 included in a cuff-based BP measuring apparatus 12” (Para 0065) is worn on an upper arm of a subject, and generates “Real SBP/DBP” (i.e. systolic blood pressure and diastolic blood pressure). The disclosed “Real SBP/DBP” is measured directly from the subject and fed into the system, so corresponds to “most recently measured” values);
normalizing the photoplethysmography measurement value (Wang, Para 0081; “… the normalization value (Maxima and minima) for upper-arm PPG and PVR waveform signals were calculated from the upper-arm PPG database in this embodiment.”) and inputting a normalized photoplethysmography measurement value to a first learning model (Wang, Para 0067; “a PPG to PVR transformer 15 converts the PPG waveform signals to refined PVR waveforms using deep machine learning”) including an encoding region and a decoding region, extracting a plurality of features through a plurality of convolutional layers in a plurality of encoding steps, storing the plurality of features extracted in the plurality of encoding steps (Wang, Para 0084; “U-Net comprises a network constructed using only convolutional layers … The network structure is constructed using a symmetric pair of Encoder Network and Decoder Network.” The disclosed “U-Net” inherently comprises the claimed structures of the first learning model), and outputting a primary arterial blood pressure waveform in a decoding step based on the stored plurality of features (refined PVR waveforms);
generating a plurality of derived variables (Wang, Para 0084; “… the characteristic parameters including waveform parameters and time-related parameters extracted by the PPG signal receiver and analyzer 13. The waveform parameters include …”);
inputting, to a second learning model (Wang, Para 0066; “PPG to BP estimator and calibrator 14 … it uses the age grouping method and further calibrates preliminary values by machine learning (ML) algorithms or other algorithms (e.g. regression algorithms, artificial neural networks (ANN), fuzzy logic, and support vector machine) …”), the plurality of derived variables and non-invasive blood pressure measurement values (“Real SBP/DBP” in Fig. 1), and extracting a second feature value (calibrated-estimated SBP/DBP);
inputting the primary arterial blood pressure waveform and the second feature value to a third learning model (Wang, Para 0088; “… the CBP estimator 16 further uses linear regression equations to estimate CBPs in clinic use, and substitutes the calibrated-estimated BPs and waveform parameters of the user’s refined PVR waveform into the linear regression equation to have estimated CBPs.” The disclosed “linear regression equations” is a learning model, with their regression coefficients learnt from training data), and outputting a continuous arterial blood pressure waveform (Wang, Para 0006; “The translation of PPG signals into arterial blood pressure (ABP) is described in another non-patent literature entitled “PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks” (written by Nabil Ibtehaz, M. Sohel Rahman; Electrical Engineering and Systems Science: Signal Processing, published on May 5, 2020), the teachings of which are incorporated herein by reference in their entirety.”. The reference of Ibtehaz et al discloses “… a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals” (Abstract));
determining a systolic blood pressure and a diastolic blood pressure from the continuous arterial blood pressure waveform (Wang, Para 0006; “PPG2ABP … written by Nabil Ibtehaz … the teachings of which are incorporated herein by reference in their entirety”. Ibtehaz et al discloses in Page 6 that SBP and DBP are calculated as maximum and minimum of continuous arterial blood pressure (ABP) waveform, respectively), and classifying a blood pressure state of the patient as one blood pressure state selected from low blood pressure, normal, prehypertension, and high blood pressure based on the determined systolic blood pressure and the determined diastolic blood pressure (Wang, Para 0006; “Jia-Wei Chen et al … the teachings of which are incorporated herein by reference in their entirety”. Chen et al discloses in Page 8 “… actual BP and PPG data and information from participants who were diagnosed with normotension (BP < 130/90 mm Hg), prehypertension, and hypertension …”, and in Page 14 “The abnormal BP levels were defined as hypertension (SBP/DBP ≥ 130/80 mmHg) and hypotension (SBP/DBP < 90/60 mmHg) …”);
transmitting the determined results to at least one of a central monitoring device and an electronic medical record system (Wang, Para 0006; “Jia-Wei Chen et al … the teachings of which are incorporated herein by reference in their entirety”. Chen et al discloses in Page 5 “… the measured PPG-based BP and heart rate (HR), as well as oxygen saturation, could be transmitted wirelessly to a mobile phone for display and recording of multimodal physiological data in the mobile application (APP)”).
Wang does not clearly and explicitly disclose:
the plurality of derived variables being generated for a prediction section and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section, and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section,
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables,
a learning model including a Cross-Attention layer and a forward neural network, and
converting the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and transmitting the voltage to a blood pressure measurement module of the commercial patient monitoring device, or transmitting the continuous arterial blood pressure waveform in digital form.
Addison in the same field of endeavor discloses:
the plurality of derived variables being generated for a prediction section (Addison, Para 0080; “the specified time period may be the entire time period that has elapsed since the most recent occurrence of the calibration point …”) and having a length corresponding to a length of the photoplethysmography measurement value for the prediction section (Addison, Abstract; “receive … a PPG signal at a particular time … and determine … a blood pressure of the patient at the particular time”. According to the disclosure, for each particular time, a blood pressure is determined based on a PPG signal, and therefore, the period for the prediction (of blood pressure) is the same (the thus same length) as the period of acquiring PPG signal), and each derived variable being generated based on a time elapsed from a measurement time point of the most recently measured non-invasive blood pressure measurement value to a corresponding time point within the prediction section (Addison, Para 0095; “… CNIBP monitoring device 100 (e.g., processing circuitry 110) may use features 600, 602, 604, and 606 as inputs into the CNIBP model 124 to determine the blood pressure of a patient at time t.”. The disclosed combination of features 602 and 604 (see Fig. 6B), for the disclosed “time t”, corresponds to the claimed “each derived variable” for “corresponding time point”. Of the disclosed features, features 604 are “the value of the time elapsed since the most recent calibration point” (Para 0093)),
a plurality of copies of blood pressure measurement value as input, and the number of the plurality of copies being equal to a number of the plurality of derived variables (Addison, Fig. 6B shows the features as input for determining blood pressure for a particular time t, which include features 602 and 604 (corresponding to the claimed “each derived variable” as discussed above) and features 606 (“BP at Calibration Point”). Para 0067 describes an example of determining blood pressure for time point t3 in the period of t0 to t1 in Fig. 4. When calibrating or determining blood pressure for all time points in the period, a same number (also same as the number of the time points) of the combination of features 602 and 604 and features 606 are derived and inputted into the trained network, which agrees with the claim).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, as suggested by Addison, in order for each time point of prediction to input a copy of blood pressure measured at calibration point and derive a variable based on elapsed time. One of ordinary skill in the art would have been motivated to make the modification of improving prediction accuracy for the estimated blood pressure (Addison, Para 0086; “… by using the calibration data received at the most recent calibration point as additional inputs for training CNIBP model 124, CNIBP model 124 may improve its accuracy in determining the blood pressure of patients.”).
Wang and Addison do not explicitly and clearly disclose:
a learning model including a Cross-Attention layer and a forward neural network, and
converting the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and transmitting the voltage to a blood pressure measurement module of the commercial patient monitoring device, or transmitting the continuous arterial blood pressure waveform in digital form.
Aguirre in the same field of endeavor discloses a learning model including a Cross-Attention layer (Aguirre, Abstract; “this methodology is capable of transforming PPG into an ABP pulse …”; Page 6, Para 2; “The proposed deep learning architecture is inspired by seq2seq encoder-decoder [36] models with attention mechanism.” Fig. 6 of Aguirre shows that the block “Attention” combines the outputs of both the encoder (from input Xl) and the decoder (from output of each time step concatenated with demographic information vector), so is a typical cross-attention operation) and a forward neural network (Aguirre, Fig. 6 show that the model also includes multiperceptron layers (MPLv and MPLc), which are feed-forward neural network layers). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang and Addison, as suggested by Aguirre, in order to comprise a cross-attention component and a feed-forward component in the network. One of ordinary skill in the art would have been motivated to make the modification for the benefit of achieving a cross-attention mechanism that is known to enable higher efficiency and accuracy as compared to regular neural networks.
Wang, Addison and Aguirre do not explicitly and clearly disclose converting the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and transmitting the voltage to a blood pressure measurement module of the commercial patient monitoring device, or transmitting the continuous arterial blood pressure waveform in digital form.
Barak in the same field of endeavor discloses:
converting the continuous arterial blood pressure waveform into a voltage using a digital-to-analog converter and transmitting the voltage to a blood pressure measurement module of the commercial patient monitoring device (Barak; Para 0265; “Wires 204 provide arterial pressure values to PCM 103, for example to be displayed.” Fig. 5 shows that the values first go through block 505 (D/A converter) before being transmitted by wires 204), or transmitting the continuous arterial blood pressure waveform in digital form (Barak; Para 0265; “D/A converter 505 are optional”; when such converter is not used, the transmitted data is in digital form).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, Addison and Aguirre, as suggested by Barak, in order to transmit data between processing device and patient monitor using standard wired methods. One of ordinary skill in the art would have been motivated to make the modification for the benefit of transferring results in a reliable way and enabling timely monitoring of patient’s vital information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEI ZHANG whose telephone number is (571)272-7172. The examiner can normally be reached Monday-Friday 8am-5pm E.T..
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/L.Z./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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