DETAILED ACTION
This action is pursuant to claims filed on 12/29/2025. Claims 1-6, 8-10, 12-16, and 18-20 are pending. An action on the merits of claims 1-6, 8-10, 12-16, and 18-20 is as follows.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/28/2026 has been entered.
Claim Rejections - 35 USC § 112
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 1-6, 8-10, 12-16, and 18-20 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.
Regarding claim 1, the claim recites the limitation “obtain a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models” in lines 7-8. It is unclear if this limitation requires a plurality of blood pressure variations for each blood pressure estimation model, or if it only requires one blood pressure variation for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one blood pressure variation per blood pressure estimation model. Claims 2-6 and 8-10 are also rejected due to their dependence on claim 1.
Further regarding claim 1, the claim recites the limitation “obtain a plurality of combining coefficients respectively for the plurality of blood pressure estimation models” in lines 11-12. It is unclear if this limitation requires a plurality of combining coefficients for each blood pressure estimation model, or if it only requires one combining coefficient for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one combining coefficient per blood pressure estimation model. Claims 2-6 and 8-10 are also rejected due to their dependence on claim 1.
Regarding claim 3, the claim recites the limitation “a respective blood pressure variation” in line 3 and line 4. It is unclear if this limitation is meant to refer to the respective blood pressure variation in claim 2, lines 3-4, or a different blood pressure variation. Additionally, it is unclear if these limitations refer to the same blood pressure variation, or if they refer to different ones. If it is meant to refer to the respective blood pressure variation from claim 2, it needs to refer back to it. If it is meant to refer to a different respective blood pressure variation, it needs to be distinguished from the respective blood pressure variation from claim 2. For purposes of examination, it is being interpreted as referring to the respective blood pressure variation from claim 2.
Regarding claim 12, the claim recites the limitation “obtaining a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models” in lines 5-6. It is unclear if this limitation requires a plurality of blood pressure variations for each blood pressure estimation model, or if it only requires one blood pressure variation for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one blood pressure variation per blood pressure estimation model. Claims 13-16 and 18-19 are also rejected due to their dependence on claim 12.
Further regarding claim 12, the claim recites the limitation “obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models” in lines 8-9. It is unclear if this limitation requires a plurality of combining coefficients for each blood pressure estimation model, or if it only requires one combining coefficient for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one combining coefficient per blood pressure estimation model. Claims 13-16 and 18-19 are also rejected due to their dependence on claim 12.
Regarding claim 20, the claim recites the limitation “obtain a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models” in lines 7-8. It is unclear if this limitation requires a plurality of blood pressure variations for each blood pressure estimation model, or if it only requires one blood pressure variation for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one blood pressure variation per blood pressure estimation model.
Further regarding claim 20, the claim recites the limitation “obtain a plurality of combining coefficients respectively for the plurality of blood pressure estimation models” in lines 11-12. It is unclear if this limitation requires a plurality of combining coefficients for each blood pressure estimation model, or if it only requires one combining coefficient for each blood pressure estimation model. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as only requiring one combining coefficient per blood pressure estimation model.
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 1, 6, 8, 12, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bolger (US 20220414181) in view of Yoon (US 20210000429), Moussavi (US 20210401364), and Albadawi (US 20180132731).
Regarding independent claim 1, Bolger teaches an apparatus for estimating blood pressure ([0021]: “FIG. 10 shows a wearable device suitable for executing embodiments of the present disclosure”; [0024]: “One aspect of the present disclosure applies the above-described process to the translation of biological stochastic signals for the purpose of cuff-less blood pressure prediction”; [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure”. The blood pressure prediction is analogous to the blood pressure estimation.), the apparatus comprising:
a photoplethysmogram (PPG) sensor configured to measure a PPG signal from an object ([0062]: “the source signal can be obtained from an input signal which can be a continuous signal or waveform obtained from a sensor such as a PPG sensor”); and
a processor ([0078]: “A computing system 900 can be configured to perform any of the operations disclosed herein. Computing system includes one or more computing device(s) 902. The one or more computing device(s) 902 of the computing system 900 comprise one or more processors”) configured to:
input the PPG signal into each of a plurality of blood pressure estimation models ([0036]: “the first component 112 and the second component 114 of the plurality of components 112, 114 can be generated by first decomposing, or deconstructing, the source signal 102 into a first deconstructed source component 128 and a second deconstructed source component 130”; [0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122, are used to predict a set estimated target components, such as the plurality of estimated components 116, 118, from a set of transformed source components, such as components 112, 114.”; [0030]: “The source signal 102 can be obtained from an input signal (not shown). The input signal can be a continuous signal or waveform obtained from a sensor such as a PPG sensor or an ECG sensor”. The source signal is from the PPG signal, which is then transformed into the components 112 and 114, which is then input into the prediction models, which are the blood pressure estimation models.);
obtain a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models based on the PPG signal input into each of the plurality of blood pressure estimation models ([0070]: “mapping 604 comprises predicting, using a plurality of prediction models, a plurality of predicted arterial blood pressure (ABP) components from the plurality of PPG components”. the predicted arterial blood pressure components are the blood pressure variations.), wherein the plurality of blood pressure estimation models comprise a first blood pressure estimation model and a second blood pressure estimation model ([0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122”. The first trained machine learning model is the first blood pressure estimation model and the second trained machine learning model is the second blood pressure estimation model.).
However, Bolger does not teach obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations.
Yoon discloses an apparatus and method for calibrating bio-information estimation models. Specifically, Yoon teaches obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations ([0097]: “a correlation coefficient of the blood pressure estimation model may be obtained by analyzing an individual correlation distribution of one or more individual features having a high correlation.”). Bolger and Yoon are analogous art as they are related to the same field of endeavor for estimating blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the coefficients from Yoon into the device from Bolger as it allows the device to have a value that can be used for comparison between the different models, which allows the device to determine which model is has the highest correlation, which can determine how the models are performing.
However, the Bolger/Yoon combination does not teach selecting at least one blood pressure estimation model among the first blood pressure estimation model and the second blood pressure estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one blood pressure estimation model.
Moussavi discloses systems and methods for evaluating physiological parameters using machine learning models. Specifically, Moussavi teaches selecting at least one estimation model among the first estimation model and the second estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one estimation model ([0077]: “Only the models with a correlation coefficient exceeding a certain threshold, e.g. 70% of the maximum absolute average correlation (i.e. the maximum one of the absolute values of the average correlations respectively calculated for the different models) are used for further analysis”. The further analysis can be the estimation of blood pressure from Bolger). Bolger, Yoon, and Moussavi are analogous art as they are all related to solving a similar problem of evaluating and using machine learning models to estimate physiological parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the selection of the models from Moussavi into the Bolger/Yoon combination as it allows the combination to only use models that are performing adequately and providing highly correlated results, which can ensure the combination is providing detailed, accurate results in the estimation.
The Bolger/Yoon/Moussavi combination teaches training the machine learning models with a dataset that is suitable for the desired functionality (Bolger, [0051]: “The trained machine learning models can be trained using any suitable dataset specific to the underlying task and training approach specific to the underlying machine learning algorithm used”), however the combination does not teach the specific steps of training the models.
Albadawi discloses methods and devices for blood pressure monitoring. Specifically, Albadawi discloses wherein the processor is further configured to: divide a plurality of training data into a first data group comprising first training data having blood pressure variations with an absolute value below a blood pressure variation value threshold and a second data group comprising second training data having blood pressure variations with an absolute value above the blood pressure variation value threshold; train the first blood pressure estimation model based on the first data group; and train the second blood pressure estimation model based on the second data group ([0052]: “method 600 may determine a first regression representation using a first subset from the set of measurements, a second regression representation using a second subset from the set of measurements”; [0033]: “the computing device 140 may sample a first subset of measurements from the training dataset 132, a portion of which may each have a blood pressure value greater than or equal to a first threshold value (e.g., >=135 mmHg) and a second portion of which may each have a blood pressure value less than a second threshold value (e.g., <130 mmHg)”. The thresholds can be the same, and therefore the first regression representation (analogous to the first blood pressure estimation model) can be trained with the training dataset below the threshold, and the second regression representation (analogous to the second blood pressure estimation model) can be trained with the training dataset above the threshold.). Bolger, Yoon, and Albadawi are analogous art as they are all related to the same field of endeavor of determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the training process from Albadawi into the Bolger/Yoon/Moussavi combination as the combination is silent on the training process of the machine learning models, and Albadawi discloses a suitable training process in an analogous device.
Regarding claim 6, the Bolger/Yoon/Moussavi/Albadawi combination teaches the apparatus of claim 1, wherein the processor is further configured to: obtain a final blood pressure variation based on the blood pressure variations of the selected at least one of the plurality of blood pressure estimation models, and estimate the blood pressure based on the final blood pressure variation (Bolger, [0027]: “The target signal 104 is then generated from the plurality of estimated components 116, 118.”; [0058]: “The reconstruction 134 of the plurality of transformed target components 136, 138 combines the plurality of transformed target components 136, 138 to generate the target signal 104”; [0023’: “The target components are individually predicted and then combined to reconstruct a predicted target signal”. The target signal is the final blood pressure variation which is used to determine the estimated blood pressure, which is determined from the combined estimated components, which are the plurality of blood pressure variations.).
Regarding claim 8, the Bolger/Yoon/Moussavi/Albadawi combination teaches the apparatus of claim 6, wherein the processor is further configured to obtain, as the final blood pressure variation, a statistical value including a mean value or a median value of the blood pressure variations of the selected at least one of the plurality of blood pressure estimation models (Bolger, [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure. Examples of machine learning algorithms suitable for blood pressure prediction include linear regression, support vector regression, Bayesian regression, and regression based deep neural networks. Within a regression session, these techniques can predict systolic blood pressure (SBP), diastolic blood pressure (DBP), and/or mean arterial pressure (MAP)”. The mean arterial pressure is the mean value of the blood pressure variations.).
Regarding independent claim 12, Bolger teaches a method of estimating blood pressure ([0058]: “Any suitable method for combining the plurality of transformed target components 136, 138 to generate the target signal 104 can be used."; [0024]: “One aspect of the present disclosure applies the above-described process to the translation of biological stochastic signals for the purpose of cuff-less blood pressure prediction”; [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure”. The blood pressure prediction is analogous to the blood pressure estimation.), the method comprising:
measuring a photoplethysmogram (PPG) signal from an object ([0062]: “the source signal can be obtained from an input signal which can be a continuous signal or waveform obtained from a sensor such as a PPG sensor”);
inputting the PPG signal into each of a plurality of blood pressure estimation models ([0036]: “the first component 112 and the second component 114 of the plurality of components 112, 114 can be generated by first decomposing, or deconstructing, the source signal 102 into a first deconstructed source component 128 and a second deconstructed source component 130”; [0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122, are used to predict a set estimated target components, such as the plurality of estimated components 116, 118, from a set of transformed source components, such as components 112, 114.”; [0030]: “The source signal 102 can be obtained from an input signal (not shown). The input signal can be a continuous signal or waveform obtained from a sensor such as a PPG sensor or an ECG sensor”. The source signal is from the PPG signal, which is then transformed into the components 112 and 114, which is then input into the prediction models, which are the blood pressure estimation models.);
obtaining a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models based on the PPG signal input into each of the plurality of blood pressure estimation models ([0070]: “mapping 604 comprises predicting, using a plurality of prediction models, a plurality of predicted arterial blood pressure (ABP) components from the plurality of PPG components”. the predicted arterial blood pressure components are the blood pressure variations.); , wherein the plurality of blood pressure estimation models comprise a first blood pressure estimation model and a second blood pressure estimation model ([0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122”. The first trained machine learning model is the first blood pressure estimation model and the second trained machine learning model is the second blood pressure estimation model.).
However, Bolger does not teach obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations.
Yoon discloses an apparatus and method for calibrating bio-information estimation models. Specifically, Yoon teaches obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations ([0097]: “a correlation coefficient of the blood pressure estimation model may be obtained by analyzing an individual correlation distribution of one or more individual features having a high correlation.”). Bolger and Yoon are analogous art as they are related to the same field of endeavor for estimating blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the coefficients from Yoon into the method from Bolger as it allows the method to have a value that can be used for comparison between the different models, which allows the method to determine which model is has the highest correlation, which can determine how the models are performing.
However, the Bolger/Yoon combination does not teach selecting at least one blood pressure estimation model among the first blood pressure estimation model and the second blood pressure estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one blood pressure estimation model.
Moussavi discloses systems and methods for evaluating physiological parameters using machine learning models. Specifically, Moussavi teaches selecting at least one estimation model among the first estimation model and the second estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one estimation model ([0077]: “Only the models with a correlation coefficient exceeding a certain threshold, e.g. 70% of the maximum absolute average correlation (i.e. the maximum one of the absolute values of the average correlations respectively calculated for the different models) are used for further analysis”. The further analysis can be the estimation of blood pressure from Bolger). Bolger, Yoon, and Moussavi are analogous art as they are all related to solving a similar problem of evaluating and using machine learning models to estimate physiological parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the selection of the models from Moussavi into the Bolger/Yoon combination as it allows the combination to only use models that are performing adequately and providing highly correlated results, which can ensure the combination is providing detailed, accurate results in the estimation.
The Bolger/Yoon/Moussavi combination teaches training the machine learning models with a dataset that is suitable for the desired functionality (Bolger, [0051]: “The trained machine learning models can be trained using any suitable dataset specific to the underlying task and training approach specific to the underlying machine learning algorithm used”), however the combination does not teach the specific steps of training the models.
Albadawi discloses methods and devices for blood pressure monitoring. Specifically, Albadawi discloses wherein the method further comprises: dividing a plurality of training data into a first data group comprising first training data having blood pressure variations with an absolute value below a blood pressure variation value threshold and a second data group comprising second training data having blood pressure variations with an absolute value above the blood pressure variation value threshold; training the first blood pressure estimation model based on the first data group; and training the second blood pressure estimation model based on the second data group ([0052]: “method 600 may determine a first regression representation using a first subset from the set of measurements, a second regression representation using a second subset from the set of measurements”; [0033]: “the computing device 140 may sample a first subset of measurements from the training dataset 132, a portion of which may each have a blood pressure value greater than or equal to a first threshold value (e.g., >=135 mmHg) and a second portion of which may each have a blood pressure value less than a second threshold value (e.g., <130 mmHg)”. The thresholds can be the same, and therefore the first regression representation (analogous to the first blood pressure estimation model) can be trained with the training dataset below the threshold, and the second regression representation (analogous to the second blood pressure estimation model) can be trained with the training dataset above the threshold.). Bolger, Yoon, and Albadawi are analogous art as they are all related to the same field of endeavor of determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the training process from Albadawi into the Bolger/Yoon/Moussavi combination as the combination is silent on the training process of the machine learning models, and Albadawi discloses a suitable training process in an analogous device.
Regarding claim 16, the Bolger/Yoon/Moussavi/Albadawi combination teaches the method of claim 12, wherein the estimating the blood pressure comprises: obtaining a final blood pressure variation based on the blood pressure variations of the selected at least one of the plurality of blood pressure estimation models, and estimating the blood pressure based on the final blood pressure variation (Bolger, [0027]: “The target signal 104 is then generated from the plurality of estimated components 116, 118.”; [0058]: “The reconstruction 134 of the plurality of transformed target components 136, 138 combines the plurality of transformed target components 136, 138 to generate the target signal 104”; [0023’: “The target components are individually predicted and then combined to reconstruct a predicted target signal”. The target signal is the final blood pressure variation which is used to determine the estimated blood pressure, which is determined from the combined estimated components, which are the plurality of blood pressure variations.).
Regarding claim 18, the Bolger/Yoon/Moussavi/Albadawi combination teaches the method of claim 16, wherein the estimating the blood pressure comprises obtaining, as the final blood pressure variation, a statistical value including a mean value or a median value of the blood pressure variations of the selected at least one of the plurality of blood pressure estimation models (Bolger, [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure. Examples of machine learning algorithms suitable for blood pressure prediction include linear regression, support vector regression, Bayesian regression, and regression based deep neural networks. Within a regression session, these techniques can predict systolic blood pressure (SBP), diastolic blood pressure (DBP), and/or mean arterial pressure (MAP)”. The mean arterial pressure is the mean value of the blood pressure variations.).
Regarding independent claim 20, Bolger teaches an electronic device ([0021]: “FIG. 10 shows a wearable device suitable for executing embodiments of the present disclosure”) comprising:
a main body (the computing environment 1002 and screen 1004 in Fig. 10 makes up the main body);
a photoplethysmogram (PPG) sensor configured to measure a PPG signal from an object ([0062]: “the source signal can be obtained from an input signal which can be a continuous signal or waveform obtained from a sensor such as a PPG sensor”); and
a processor disposed in the main body ([0078]: “A computing system 900 can be configured to perform any of the operations disclosed herein. Computing system includes one or more computing device(s) 902. The one or more computing device(s) 902 of the computing system 900 comprise one or more processors”), the processor being configured to:
input the PPG signal into each of a plurality of blood pressure estimation models ([0036]: “the first component 112 and the second component 114 of the plurality of components 112, 114 can be generated by first decomposing, or deconstructing, the source signal 102 into a first deconstructed source component 128 and a second deconstructed source component 130”; [0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122, are used to predict a set estimated target components, such as the plurality of estimated components 116, 118, from a set of transformed source components, such as components 112, 114.”; [0030]: “The source signal 102 can be obtained from an input signal (not shown). The input signal can be a continuous signal or waveform obtained from a sensor such as a PPG sensor or an ECG sensor”. The source signal is from the PPG signal, which is then transformed into the components 112 and 114, which is then input into the prediction models, which are the blood pressure estimation models.);
obtain a plurality of blood pressure variations respectively for the plurality of blood pressure estimation models based on the PPG signal input into each of the plurality of blood pressure estimation models ([0070]: “mapping 604 comprises predicting, using a plurality of prediction models, a plurality of predicted arterial blood pressure (ABP) components from the plurality of PPG components”. the predicted arterial blood pressure components are the blood pressure variations.), wherein the plurality of blood pressure estimation models comprise a first blood pressure estimation model and a second blood pressure estimation model ([0047]: “Prediction models, e.g. first trained machine learning model 120 and second trained machine learning model 122”. The first trained machine learning model is the first blood pressure estimation model and the second trained machine learning model is the second blood pressure estimation model.).
However, Bolger does not teach obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations.
Yoon discloses an apparatus and method for calibrating bio-information estimation models. Specifically, Yoon teaches obtaining a plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on the obtained plurality of blood pressure variations ([0097]: “a correlation coefficient of the blood pressure estimation model may be obtained by analyzing an individual correlation distribution of one or more individual features having a high correlation.”). Bolger and Yoon are analogous art as they are related to the same field of endeavor for estimating blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the coefficients from Yoon into the device from Bolger as it allows the device to have a value that can be used for comparison between the different models, which allows the device to determine which model is has the highest correlation, which can determine how the models are performing.
However, the Bolger/Yoon combination does not teach selecting at least one blood pressure estimation model among the first blood pressure estimation model and the second blood pressure estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one blood pressure estimation model.
Moussavi discloses systems and methods for evaluating physiological parameters using machine learning models. Specifically, Moussavi teaches selecting at least one estimation model among the first estimation model and the second estimation model based on the plurality of combining coefficients having a combining coefficient that is greater than or equal to a predetermined threshold value, and estimating the blood pressure by using the selected at least one estimation model ([0077]: “Only the models with a correlation coefficient exceeding a certain threshold, e.g. 70% of the maximum absolute average correlation (i.e. the maximum one of the absolute values of the average correlations respectively calculated for the different models) are used for further analysis”. The further analysis can be the estimation of blood pressure from Bolger). Bolger, Yoon, and Moussavi are analogous art as they are all related to solving a similar problem of evaluating and using machine learning models to estimate physiological parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the selection of the models from Moussavi into the Bolger/Yoon combination as it allows the combination to only use models that are performing adequately and providing highly correlated results, which can ensure the combination is providing detailed, accurate results in the estimation.
The Bolger/Yoon/Moussavi combination teaches training the machine learning models with a dataset that is suitable for the desired functionality (Bolger, [0051]: “The trained machine learning models can be trained using any suitable dataset specific to the underlying task and training approach specific to the underlying machine learning algorithm used”), however the combination does not teach the specific steps of training the models.
Albadawi discloses methods and devices for blood pressure monitoring. Specifically, Albadawi discloses wherein the processor is further configured to: divide a plurality of training data into a first data group comprising first training data having blood pressure variations with an absolute value below a blood pressure variation value threshold and a second data group comprising second training data having blood pressure variations with an absolute value above the blood pressure variation value threshold; train the first blood pressure estimation model based on the first data group; and train the second blood pressure estimation model based on the second data group ([0052]: “method 600 may determine a first regression representation using a first subset from the set of measurements, a second regression representation using a second subset from the set of measurements”; [0033]: “the computing device 140 may sample a first subset of measurements from the training dataset 132, a portion of which may each have a blood pressure value greater than or equal to a first threshold value (e.g., >=135 mmHg) and a second portion of which may each have a blood pressure value less than a second threshold value (e.g., <130 mmHg)”. The thresholds can be the same, and therefore the first regression representation (analogous to the first blood pressure estimation model) can be trained with the training dataset below the threshold, and the second regression representation (analogous to the second blood pressure estimation model) can be trained with the training dataset above the threshold.). Bolger, Yoon, and Albadawi are analogous art as they are all related to the same field of endeavor of determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the training process from Albadawi into the Bolger/Yoon/Moussavi combination as the combination is silent on the training process of the machine learning models, and Albadawi discloses a suitable training process in an analogous device.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the Bolger/Yoon/Moussavi/Albadawi combination as applied to claims 1 and 12 above, and further in view of Watson (US 9259160).
Regarding claim 2, the Bolger/Yoon/Moussavi/Albadawi combination teaches the apparatus of claim 1.
However, the Bolger/Yoon/Moussavi/Albadawi combination is silent on how the combining coefficients are determined.
Watson discloses systems and methods for determining when to update a blood pressure measurement. Specifically, Watson teaches wherein the processor is further configured to: obtain a plurality of differences between a reference value and a respective blood pressure variation of the plurality of blood pressure variations, and obtain the plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on a respective obtained difference of the plurality of differences (Claim 1: “computing, by the processor, a first difference based at least in part on the first monitored metric value and the first reference metric value; (e) computing, by the processor, a second difference based at least in part on the second monitored metric value and the second reference metric value; (f) computing, by the processor, a composite metric based on the first difference and the second difference; (g) updating, by the processor, the current BP measurement using the NIBP device when the composite metric exceeds a threshold; (h) determining, by the processor, at least one calibration coefficient of a photoplethysmography based non-invasive blood pressure (PNIBP) calculation formula when the composite metric exceeds the threshold”). Bolger, Yoon, Albadawi, and Watson are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the calculations used to determine the coefficients from Watson into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the calculations used to determine the coefficient, and Watson discloses suitable calculations in an analogous device.
Regarding claim 13, the Bolger/Yoon/Moussavi/Albadawi combination teaches the method of claim 12.
However, the Bolger/Yoon/Moussavi/Albadawi combination is silent on how the combining coefficients are determined.
Watson teaches wherein obtaining the plurality of combining coefficients comprises: obtaining a plurality of differences between a reference value and a respective blood pressure variation of the plurality of blood pressure variations, and obtaining the plurality of combining coefficients respectively for the plurality of blood pressure estimation models based on a respective obtained difference of the plurality of differences (Claim 1: “computing, by the processor, a first difference based at least in part on the first monitored metric value and the first reference metric value; (e) computing, by the processor, a second difference based at least in part on the second monitored metric value and the second reference metric value; (f) computing, by the processor, a composite metric based on the first difference and the second difference; (g) updating, by the processor, the current BP measurement using the NIBP device when the composite metric exceeds a threshold; (h) determining, by the processor, at least one calibration coefficient of a photoplethysmography based non-invasive blood pressure (PNIBP) calculation formula when the composite metric exceeds the threshold”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the calculations used to determine the coefficients from Watson into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the calculations used to determine the coefficient, and Watson discloses suitable calculations in an analogous device.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over the Bolger/Yoon/Moussavi/Albadawi/Watson combination as applied to claim 2 above, and further in view of Wei (US 20220095938).
Regarding claim 3, the Bolger/Yoon/Moussavi/Albadawi/Watson combination teaches the apparatus of claim 2.
However, the Bolger/Yoon/Moussavi/Albadawi/Watson combination does not teach wherein at least one of the plurality of differences comprises at least one of an absolute value of a value, obtained by subtracting the reference value from an absolute value of a respective blood pressure variation, or a Euclidean distance between the absolute value of a respective blood pressure variation and the reference value.
Wei discloses a method for determining a blood pressure value of a patient. Specifically, Wei teaches wherein at least one of the plurality of differences comprises at least one of an absolute value of a value ([0028]: “The blood pressure value to be determined may be an absolute value of the blood pressure”). Bolger, Yoon, Albadawi, Watson, and Wei are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the absolute value from Wei into the Bolger/Yoon/Moussavi/Albadawi/Watson combination as the combination is silent on the specific calculations used to determine the difference, and Wei discloses a suitable calculation in an analogous device.
The Bolger/Yoon/Moussavi/Albadawi/Watson/Wei combination teaches the plurality of differences obtained by subtracting the reference value from an absolute value of a respective blood pressure variation, or a Euclidean distance between the absolute value of a respective blood pressure variation and the reference value (Wei, [0028]: “The blood pressure value to be determined may be an absolute value of the blood pressure”; Watson, Claim 1: “computing, by the processor, a first difference based at least in part on the first monitored metric value and the first reference metric value; (e) computing, by the processor, a second difference based at least in part on the second monitored metric value and the second reference metric value; (f) computing, by the processor, a composite metric based on the first difference and the second difference; (g) updating, by the processor, the current BP measurement using the NIBP device when the composite metric exceeds a threshold; (h) determining, by the processor, at least one calibration coefficient of a photoplethysmography based non-invasive blood pressure (PNIBP) calculation formula when the composite metric exceeds the threshold”).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the Bolger/Yoon/Moussavi/Albadawi/Watson combination as applied to claims 2 and 13 above, and further in view of Park (US 20200113453).
Regarding claim 4, the Bolger/Yoon/Moussavi/Albadawi/Watson combination teaches the apparatus of claim 2.
Watson discloses determining the coefficients based on a difference (Claim 1: “computing, by the processor, a first difference based at least in part on the first monitored metric value and the first reference metric value; (e) computing, by the processor, a second difference based at least in part on the second monitored metric value and the second reference metric value; (f) computing, by the processor, a composite metric based on the first difference and the second difference; (g) updating, by the processor, the current BP measurement using the NIBP device when the composite metric exceeds a threshold; (h) determining, by the processor, at least one calibration coefficient of a photoplethysmography based non-invasive blood pressure (PNIBP) calculation formula when the composite metric exceeds the threshold”), however, the Bolger/Yoon/Moussavi/Albadawi/Watson combination does not teach wherein the processor is further configured to: obtain, as at least one of the plurality of combining coefficients, a value obtained by dividing a respective difference of the plurality of differences by a sum of the plurality of differences.
Park discloses an apparatus and method for estimating blood pressure. Specifically, Park teaches wherein the difference is a value obtained by dividing a respective difference of the plurality of differences by a sum of the plurality of differences ([0069]: “the amplitude of the differential signal and/or the amplitude of the bio-signal, corresponding to each of the times, may be set as a weighted value to be applied to each time, so that a higher weighted value is applied to time values corresponding to a higher amplitude of a differential signal and/or a higher amplitude of the bio-signal, and an internally dividing point may be obtained by integrating the time values, to which weighted values are applied, and by dividing the added value by the sum of the weighed values”). Bolger, Yoon, Albadawi, Watson, and Park are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the calculations from Park into the Bolger/Yoon/Moussavi/Albadawi/Watson combination as the combination is silent on the specific calculations used to determine the difference, and Park discloses a suitable calculation in an analogous device.
Regarding claim 14, the Bolger/Yoon/Moussavi/Albadawi/Watson combination teaches the apparatus of claim 13.
Watson discloses determining the coefficients based on a difference (Claim 1: “computing, by the processor, a first difference based at least in part on the first monitored metric value and the first reference metric value; (e) computing, by the processor, a second difference based at least in part on the second monitored metric value and the second reference metric value; (f) computing, by the processor, a composite metric based on the first difference and the second difference; (g) updating, by the processor, the current BP measurement using the NIBP device when the composite metric exceeds a threshold; (h) determining, by the processor, at least one calibration coefficient of a photoplethysmography based non-invasive blood pressure (PNIBP) calculation formula when the composite metric exceeds the threshold”), however, the Bolger/Yoon/Moussavi/Albadawi/Watson combination does not teach wherein the obtaining the plurality of combining coefficients comprises: obtaining, as at least one of the plurality of combining coefficients, a value obtained by dividing a respective difference of the plurality of differences by a sum of the plurality of differences.
Park teaches wherein the difference is a value obtained by dividing a respective difference of the plurality of differences by a sum of the plurality of differences ([0069]: “the amplitude of the differential signal and/or the amplitude of the bio-signal, corresponding to each of the times, may be set as a weighted value to be applied to each time, so that a higher weighted value is applied to time values corresponding to a higher amplitude of a differential signal and/or a higher amplitude of the bio-signal, and an internally dividing point may be obtained by integrating the time values, to which weighted values are applied, and by dividing the added value by the sum of the weighed values”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the calculations from Park into the Bolger/Yoon/Moussavi/Albadawi/Watson combination as the combination is silent on the specific calculations used to determine the difference, and Park discloses a suitable calculation in an analogous device.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the Bolger/Yoon/Moussavi/Albadawi combination as applied to claims 1 and 12 above, and further in view of Kwon (KR 20200021207). Citations to KR 20200021207 will refer to the English Machine Translation that accompanies this Office Action.
Regarding claim 5, the Bolger/Yoon/Moussavi/Albadawi combination teaches the apparatus of claim 1, wherein the processor is further configured to: obtain a final blood pressure variation by applying the plurality of combining coefficients to a respective blood pressure variation of the plurality of blood pressure variations (Moussavi, [0077]: “Only the models with a correlation coefficient exceeding a certain threshold, e.g. 70% of the maximum absolute average correlation (i.e. the maximum one of the absolute values of the average correlations respectively calculated for the different models) are used for further analysis”. The combining coefficients are applied to the blood pressure variations to determine which values are acceptable for analysis.) and by combining the plurality of blood pressure variations (Bolger, [0027]: “The target signal 104 is then generated from the plurality of estimated components 116, 118.”; [0058]: “The reconstruction 134 of the plurality of transformed target components 136, 138 combines the plurality of transformed target components 136, 138 to generate the target signal 104”; [0023’: “The target components are individually predicted and then combined to reconstruct a predicted target signal”. The target signal is the final blood pressure variation, which is determined from the combined estimated components, which are the plurality of blood pressure variations.).
However, the Bolger/Yoon/Moussavi/Albadawi combination does not teach the specific type of combining performed.
Kwon discloses an apparatus and method for estimating blood pressure. Specifically, Kwon teaches linearly combining the plurality of blood pressure variations and estimating the blood pressure by adding a reference blood pressure to the final blood pressure variation ([0106]: “blood pressure can be estimated by linearly combining each amount of change or each rate of change. As another example, blood pressure can be estimated by assigning weights to each amount of change or each rate of change, performing a linear combination, and applying a scaling factor to the result of the linear combination. Each weight and scaling factor may be defined differently depending on the type of blood pressure to be calculated and/or the characteristics of the user.”; [0087]: “For example, to estimate average blood pressure, the reference average blood pressure of the corresponding user can be used as a scaling factor. Similarly, reference diastolic blood pressure and reference systolic blood pressure can be used as scaling factors to estimate diastolic and systolic blood pressure.”. The scaling factor includes the reference blood pressure, which is added to the linearly combined final blood pressure estimation.). Bolger, Yoon, Albadawi, and Kwon are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of linearly combining the blood pressure variations and the estimation steps from Kwon into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the type of combining used and the steps used to estimate the blood pressure, and Kwon discloses suitable steps in an analogous device.
Regarding claim 15, the Bolger/Yoon/Moussavi/Albadawi combination teaches the method of claim 12, wherein estimating the blood pressure comprises: obtaining a final blood pressure variation by applying the plurality of combining coefficients to a respective blood pressure variation of the plurality of blood pressure variations (Moussavi, [0077]: “Only the models with a correlation coefficient exceeding a certain threshold, e.g. 70% of the maximum absolute average correlation (i.e. the maximum one of the absolute values of the average correlations respectively calculated for the different models) are used for further analysis”. The combining coefficients are applied to the blood pressure variations to determine which values are acceptable for analysis.) and by combining the plurality of blood pressure variations (Bolger, [0027]: “The target signal 104 is then generated from the plurality of estimated components 116, 118.”; [0058]: “The reconstruction 134 of the plurality of transformed target components 136, 138 combines the plurality of transformed target components 136, 138 to generate the target signal 104”; [0023’: “The target components are individually predicted and then combined to reconstruct a predicted target signal”. The target signal is the final blood pressure variation, which is determined from the combined estimated components, which are the plurality of blood pressure variations.).
However, the Bolger/Yoon/Moussavi/Albadawi combination does not teach the specific type of combining performed.
Kwon discloses an apparatus and method for estimating blood pressure. Specifically, Kwon teaches linearly combining the plurality of blood pressure variations and estimating the blood pressure by adding a reference blood pressure to the final blood pressure variation ([0106]: “blood pressure can be estimated by linearly combining each amount of change or each rate of change. As another example, blood pressure can be estimated by assigning weights to each amount of change or each rate of change, performing a linear combination, and applying a scaling factor to the result of the linear combination. Each weight and scaling factor may be defined differently depending on the type of blood pressure to be calculated and/or the characteristics of the user.”; [0087]: “For example, to estimate average blood pressure, the reference average blood pressure of the corresponding user can be used as a scaling factor. Similarly, reference diastolic blood pressure and reference systolic blood pressure can be used as scaling factors to estimate diastolic and systolic blood pressure.”. The scaling factor includes the reference blood pressure, which is added to the linearly combined final blood pressure estimation.). Bolger, Yoon, Albadawi, and Kwon are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of linearly combining the blood pressure variations and the estimation steps from Kwon into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the type of combining used and the steps used to estimate the blood pressure, and Kwon discloses suitable steps in an analogous device.
Claims 9-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the Bolger/Yoon/Moussavi/Albadawi combination as applied to claims 1 and 12 above, and further in view of Hu (US 20220406464).
Regarding claim 9, the Bolger/Yoon/Moussavi/Albadawi combination teaches the apparatus of claim 1, wherein the processor is further configured to: calculate a statistical value including a mean or standard deviation of the obtained plurality of blood pressure variations (Bolger, [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure. Examples of machine learning algorithms suitable for blood pressure prediction include linear regression, support vector regression, Bayesian regression, and regression based deep neural networks. Within a regression session, these techniques can predict systolic blood pressure (SBP), diastolic blood pressure (DBP), and/or mean arterial pressure (MAP)”. The mean arterial pressure is the mean value of the blood pressure variations.).
However, the Bolger/Yoon/Moussavi/Albadawi combination does not teach obtaining the plurality of combining coefficients based on the calculated statistical value.
Hu discloses a prediction method and system of low blood pressure. Specifically, Hu teaches obtaining the plurality of combining coefficients based on the calculated statistical value ([0053]: “the relation coefficient also includes the calculation result of the systolic blood pressure and the calculation result of the mean arterial pressure”). Bolger, Yoon, Albadawi, and Hu are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include using the statistical value in the combining coefficients from Hu into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the specific values used to compute the coefficients, and Hu discloses a suitable calculation in an analogous device.
Regarding claim 10, the Bolger/Yoon/Moussavi/Albadawi/Hu combination teaches the apparatus of claim 9, wherein the processor is further configured to: based on the statistical value being greater than a first predetermined value, determine a high combining coefficient of the plurality of combining coefficients for a blood pressure estimation model among the plurality of blood pressure estimation models having a blood pressure variation above a second predetermined value, and based on the statistical value being less than the first predetermined value, determine a high combining coefficient of the plurality of combining coefficients for a blood pressure estimation model among the plurality of blood pressure estimation models having a blood pressure variation below the second predetermined value (Yoon, [0097]: “a correlation coefficient of the blood pressure estimation model may be obtained by analyzing an individual correlation distribution of one or more individual features having a high correlation.”. The coefficient having a high correlation can classify as a high combining coefficient. Additionally, since the high combining coefficient is determined regardless of whether the blood pressure variation is above or below the predetermined value, then it is determined for all the values, which is taught in this limitation.).
Regarding claim 19, the Bolger/Yoon/Moussavi/Albadawi combination teaches the method of claim 12, wherein obtaining the plurality of combining coefficients comprises: calculating a statistical value including a mean or standard deviation of the obtained plurality of blood pressure variations (Bolger, [0005]: “Blood pressure estimation is typically approached as a regression problem, whereby features are extracted from a combination of biological signals, which typically include PPG and electrocardiogram (ECG). Features can also include signals obtained from activity or environment sensors such as accelerometers, pressure sensors, blood oxygen sensors, and the like. These features are then input to a machine learning algorithm in order to predict blood pressure. Examples of machine learning algorithms suitable for blood pressure prediction include linear regression, support vector regression, Bayesian regression, and regression based deep neural networks. Within a regression session, these techniques can predict systolic blood pressure (SBP), diastolic blood pressure (DBP), and/or mean arterial pressure (MAP)”. The mean arterial pressure is the mean value of the blood pressure variations.).
However, the Bolger/Yoon/Moussavi/Albadawi combination does not teach obtaining the plurality of combining coefficients based on the calculated statistical value.
Hu discloses a prediction method and system of low blood pressure. Specifically, Hu teaches obtaining the plurality of combining coefficients based on the calculated statistical value ([0053]: “the relation coefficient also includes the calculation result of the systolic blood pressure and the calculation result of the mean arterial pressure”). Bolger, Yoon, Albadawi, and Hu are analogous art as they are all related to the same field of endeavor for determining blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include using the statistical value in the combining coefficients from Hu into the Bolger/Yoon/Moussavi/Albadawi combination as the combination is silent on the specific values used to compute the coefficients, and Hu discloses a suitable calculation in an analogous device.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended the claims to overcome the claim objections and 112(b) rejections, however the amendments have introduced new 112(b) rejections.
Applicant’s arguments with respect to the 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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