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 .
Election/Restrictions
Applicant’s election without traverse of Group I Claims 1-11 and 19-21 in the reply filed on August 22nd 2025 is acknowledged. Claims 12-18 are canceled. New Claims 22-27 have been added. Claims 1-11, and 19-27 are pending.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1 & 2 are objected to because of the following informalities:
Claims 1 & 2, ‘the blood pressuring monitoring’ should read ‘the blood pressure monitoring’.
Appropriate correction is required.
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.
Claim 4 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4, ‘the second training set’, there is insufficient antecedent basis for this limitation in this claim. Examiner interprets this limitation as ‘the individualized training set data’, as previously recited since there is no mention of any second training set anywhere else within the disclosure.
Claim Rejections - 35 USC § 101
Claims 19-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 19-27 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 19-27 recites at least one step or instruction for receiving data, generating data, making determinations from the data, manipulating data, and outputting data, which is grouped as a mental process under the 2019 PEG.
Accordingly, each of Claims 19-27 recites an abstract idea.
Specifically,
Claim 19: A method, comprising: generating, while a user wears a blood pressure monitoring device, training sensor data (Observation); training a first neural layer of an analysis model of the blood pressure monitoring device with a machine learning process using the training sensor data while holding fixed a second neural layer of the analysis model; and generating, with the analysis model after the machine learning process, estimated blood pressure values (Observation) for the user.
Claim 22: A method, comprising: generating first inertial data (Observation) with a first inertial measurement unit and a second inertial measurement unit of a patch worn by a user; updating an analysis model of a control unit of an electronic device based on the first inertial sensor data; generating, after updating the analysis model, second inertial data (Observation) with the first inertial measurement unit and the second inertial measurement unit of the patch worn by the user; receiving the second inertial sensor data (Observation) with the control unit; generating, with the analysis model, blood pressure data (Observation) of the user based on the second inertial sensor data; and outputting the blood pressure measurement data (Judgement/opinion) with the electronic device.
(The underlined portions above are abstract ideas interpreted as mental processes under the 2019 PEG, such as observations, judgements, opinions, determinations, interpretations of data, related to receiving, manipulating, analyzing, determining and outputting of data)
Further, dependent Claims 20-21, 23-27 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 19 and 22 (and their respective dependent Claims 19 and 22) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 19 and 22), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of a blood pressure monitoring device, analysis model, a first inertial measurement unit and a second inertial measurement unit, a patch are generically recited computer elements in independent Claims 19 and 22 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 19 and 22 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process) using rules (e.g., computer instructions) executed by a computer (e.g., a blood pressure monitoring device as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 19 and 22 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 19 and 22 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 19-27 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: a blood pressure monitoring device, analysis model, a first inertial measurement unit and a second inertial measurement unit, a patch.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, a blood pressure monitoring device (Pg. 5, lines 24-Pg. 6, line 3), analysis model (Pg. 8, line 23-Pg. 9, line 17), a first inertial measurement unit and a second inertial measurement unit (Pg. 7, line 7-26), a patch (Pg. 3, lines 17-19; Pg. 11, lines 6-13).
Accordingly, in light of Applicant’s specification, the claimed term a blood pressure monitor device is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the a blood pressure monitoring device. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 19-27 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the methods of Claims 19-27 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 19-27 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 19 and 22 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 19-27 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 19-27 amounts to significantly more than the abstract idea itself. Accordingly, Claims 19-27 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20220061676 A1 to Wilson et al. (hereinafter, Wilson).
Regarding Claim 19, Wilson discloses a method (Wilson: Abstract), comprising: generating, while a user wears a blood pressure monitoring device, training sensor data (Wilson: Para. [0083] ‘The digital twin module 350 generates 630 an additional training dataset of biological data and patient data for a particular patient.’; Para. [0040] ‘A patient using the metabolic health manager is outfitted with one or more wearable sensors configured to continuously record biosignals, herein referred to as wearable sensor data. … the wearable sensor may record … record blood pressure measurements’);
training a first neural layer of an analysis model of the blood pressure monitoring device with a machine learning process using the training sensor data while holding fixed a second neural layer of the analysis model (Wilson: Para. [0118]; Fig. 8C; Para. [0107] '(e.g., LSTM and GRU layers from the Tensorflow library) to process sequential features into a diastolic BP predictions and systolic BP predictions, respectively. LSTM and GRU cells are variants of recurrent neural networks that allow for the training of models capable of learning over long sequences of time variant data.'; Para. [0110] ‘(each of the systolic BP prediction submodel 840 and the diastolic BP prediction submodel 845 implement a neural network with Dense layers (e.g., a Dense neural network from the Tensorflow library) to process the static features described above.'); and
generating, with the analysis model after the machine learning process, estimated blood pressure values for the user (Wilson: Para. [0119] ‘the submodel of the long-term prediction model 830 combines the signals from the two input channels into an aggregate estimate of a patient's blood pressure.’).
Regarding Claim 20, Wilson discloses The method of claim 19. Wilson further discloses wherein the neural network is a long short- term neural network (Wilson Para. [0107] ‘each of the systolic BP prediction submodel 840 and the diastolic BP prediction submodel 845 implement a long-short term memory recurrent neural network (LSTM) with GRU cells (e.g., LSTM and GRU layers from the Tensorflow library) to process sequential features into a diastolic BP predictions and systolic BP predictions, respectively.’).
Claim(s) 22-27 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20220249002 A1 to Chapman et al. (hereinafter, Chapman).
Regarding Claim 22, Chapman discloses a method (Chapman: Abstract), comprising: generating first inertial data with a first inertial measurement unit and a second inertial measurement unit of a patch worn by a user (Chapman, Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …blood pressure sensor …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’; Para. [0053] ‘The patch 202 can include a substrate 206 configured to couple to the user's body (e.g., to the surface of the skin) via adhesives or other suitable temporary attachment techniques.’; Fig. 2A);
updating an analysis model of a control unit of an electronic device based on the first inertial sensor data (Chapman: Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as… motion sensor (e.g., accelerometer, gyroscope)…’; Para. [0064] ‘sensors 222… measurements for one or more health parameters, such as… blood pressure… implement one or more algorithms, such as algorithms for sensor calibration, signal conditioning, determining presence of and/or values for health parameters based on the sensor signals, predicting current and/or future values for health parameters based on the sensor signals, etc.’; Fig. 4, items 441-445; Para. [0133-0134] ‘models, excitation signals, perturbations, correction factors, analyzed responses, and/or system parameters can be updated, adjusted, and/or retrained over time using machine learning engines or techniques. For example, a biosensor device of the present technology can be configured to use an independent and/or unique drive signals for each electrode and/or each biosensor of the device, and the individual drive signals can be separately calibrated with machine learning using user-specific data.’);
generating, after updating the analysis model, second inertial data with the first inertial measurement unit and the second inertial measurement unit of the patch worn by the user (Chapman: Para. [0042] ‘the system 102 can analyze the obtained input data, including historical data, current real-time data, continuously supplied data, and/or any other data (e.g., using a statistical analysis, machine learning analysis, etc.), and generate output data.’(Note: since Chapman teaches continuously collecting and retraining in in real-time, then the functionality of continuous real-time monitoring would mean data is constantly generated even after the analysis model is updated, would occur); Para. [0058] ‘a second electrical signal from the sensor(s) 222 to generate a second health measurement (e.g., a physiological parameter). The processor 214 can be configured to receive and process any number of electrical signals (e.g., two, three, four, five, or more electrical signals) obtained by different sensing components of the device 200 to generate measurements of multiple health parameters (e.g., two, three, four, five or more different health parameters). Optionally, the processor 214 can use the health measurements to generate predictions, recommendations, notifications, etc.’; Para. [0111] ‘The method 440 (at block 446) can use several measurements taken over time to detect the occurrence of one or more events and/or changes in the system-dependent parameters and/or in the properties of the surrounding environment by, for example, (a) comparing measurements taken later in time to measurements to previous measurements and/or (b) comparing measurements to various other corresponding baselines or thresholds.’);
receiving the second inertial sensor data with the control unit (Chapman: Para. [0064] ‘The device 200 can be configured to obtain and process the signals generated by… the sensor(s) 222 in order to determine measurements for one or more health parameters, such as … blood pressure… the electronics assembly 212 is configured to implement one or more algorithms, such as algorithms for … determining presence of and/or values for health parameters based on the sensor signals, predicting current and/or future values for health parameters based on the sensor signals, etc.’);
generating, with the analysis model, blood pressure data of the user based on the second inertial sensor data (Chapman: Para. [0062] ‘ activity or motion sensor (e.g., accelerometer, gyroscope)’; Para. [0067] ‘The health measurements produced by the device 200 can be used to generate personalized healthcare guidance, such as one or more predictions, recommendations, suggestions, feedback, and/or diagnosis for a number of diseases, conditions, or health states. For example, blood pressure can be monitored and/or predicted based on… activity data’ (emphasis added.); Fig. 4); and
outputting the blood pressure measurement data with the electronic device (Chapman: Para. [0042]).
Regarding Claim 23, Chapman discloses The method of claim 22. Chapman further discloses wherein the first and second inertial measurement units each include a respective accelerometer (Chapman, Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …blood pressure sensor …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’).
Regarding Claim 24, Chapman discloses The method of claim 22. Chapman further discloses wherein the first and second inertial measurement units each include a respective accelerometer and a respective gyroscope (Chapman, Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …blood pressure sensor …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’).
Regarding Claim 25, Chapman discloses The method of claim 22. Chapman further discloses wherein the analysis model includes a neural network (Chapman: Para. [0044] ‘the system 102 is configured to analyze the input data and generate the output data using one or more machine learning models. The machine learning models can include… artificial neural networks (e.g., perceptron, multilayer perceptrons, back-propagation, stochastic gradient descent, Hopfield networks, radial basis function networks), deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks’).
Regarding Claim 26, Chapman discloses The method of claim 25. Chapman further discloses wherein the neural network is a long short-term neural network (Chapman: Para. [0044] ‘the system 102 is configured to analyze the input data and generate the output data using one or more machine learning models. The machine learning models can include… artificial neural networks (e.g., perceptron, multilayer perceptrons, back-propagation, stochastic gradient descent, Hopfield networks, radial basis function networks), deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks’).
Regarding Claim 27, Chapman discloses The method of claim 25. Chapman further discloses wherein the neural network includes a first neural layer and a second neural layer (Chapman: Para. [0044] ‘the system 102 is configured to analyze the input data and generate the output data using one or more machine learning models. The machine learning models can include… artificial neural networks (e.g.,… multilayer perceptrons … long short-term memory networks’).
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220061676 A1 to Wilson et al. (hereinafter, Wilson) in view of US 20210282720 A1 to Soeseno et al. (hereinafter, Soeseno).
Regarding Claim 1, Wilson discloses a method (Wilson: Abstract), comprising: training an analysis model with a first machine learning process to generate estimated blood pressure values (Wilson: Para. [0006] ‘For a particular patient, the baseline metabolic model is further trained based on patient-specific biosignals to generate a daily or regular estimation of the patient's blood pressure. Based on trends in a patient's heart rate measurements and exercise data recorded by a wearable sensor, the trained patient-specific virtual blood pressure monitor generates an estimation of a patient's blood pressure.’);
gathering individualized training set data from the blood pressure monitoring device while the blood pressuring monitoring device is coupled to a user (Wilson: Para. [0083] ‘The digital twin module 350 generates 630 an additional training dataset of biological data and patient data for a particular patient.’; Para. [0040] ‘A patient using the metabolic health manager is outfitted with one or more wearable sensors configured to continuously record biosignals, herein referred to as wearable sensor data. … the wearable sensor may record … record blood pressure measurements’); and
retraining, with a second machine learning process utilizing the individualized training set data, a portion of the analysis model (Wilson: Para. [0110] ‘a personalized Dense neural network is generated by retraining the baseline Dense neural network using a training dataset of exclusively the patient's own data.’)
Wilson does not explicitly disclose loading the analysis model into a control unit of a blood pressure monitoring device.
However, Soeseno teaches loading the analysis model into a control unit of a blood pressure monitoring device (Soeseno: Para. [0014])
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the method of predicting blood pressure of a patient to specify loading the model into the blood pressure measurement device as taught by Soeseno to enable adjusting the parameter set to establish a specific blood pressure model to the user (Soeseno: Para. [0014]).
Claim(s) 2-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220061676 A1 to Wilson et al. (hereinafter, Wilson) in view of US 20210282720 A1 to Soeseno et al. (hereinafter, Soeseno) in further view of US 20220249002 A1 to Chapman et al. (hereinafter, Chapman).
Regarding Claim 2, Wilson in view of Soeseno discloses the method of claim 1. Wilson does not explicitly disclose wherein the blood pressuring monitoring device includes a first inertial measurement unit and a second inertial measurement unit each configured to generate sensor data based on blood flow of the user.
However, Chapman teaches wherein the blood pressuring monitoring device includes a first inertial measurement unit and a second inertial measurement unit each configured to generate sensor data based on blood flow of the user (Chapman: Para. [0062] ‘activity or motion sensor (e.g., accelerometer, gyroscope)’ (emphasis added); Para. [0067] ‘The health measurements produced by the device 200 can be used to generate personalized healthcare guidance, such as one or more predictions, recommendations, suggestions, feedback, and/or diagnosis for a number of diseases, conditions, or health states. For example, blood pressure can be monitored and/or predicted based on… activity data’ (emphasis added.); Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’ ).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the blood pressure monitoring device of Wilson to include the inertial measurement units taught by Chapman as one known technique among many to monitor and predict blood pressure (Chapman: Para. [0067] ‘blood pressure can be monitored and/or predicted based on optical data (e.g., PPG data), electrical data (e.g., ECG data), heart rate data, user data, and/or activity data’).
Regarding Claim 3, Wilson in view of Soeseno in view of Chapman discloses The method of claim 2. Wilson does not explicitly disclose wherein the blood pressure monitoring device includes a flexible patch configured to be placed on a skin of the user, wherein the first and second inertial measurement units are deployed on the patch.
However, Chapman further teaches wherein the blood pressure monitoring device includes a flexible patch configured to be placed on a skin of the user (Chapman: Para. [0052] ‘The device 200 can be a wearable patch sensor configured to be applied to a user's body in order to obtain user health data in a non-invasive or minimally invasive manner. The device 200 can be used in any of the systems and methods described herein (e.g., as the biosensor 104a of FIG. 1).’; Para. [0053] ‘The patch 202 can include a substrate 206 configured to couple to the user's body (e.g., to the surface of the skin) via adhesives or other suitable temporary attachment techniques.’),
wherein the first and second inertial measurement units (Chapman: Para. [0062] ‘ activity or motion sensor (e.g., accelerometer, gyroscope)’; Para. [0067] ‘The health measurements produced by the device 200 can be used to generate personalized healthcare guidance, such as one or more predictions, recommendations, suggestions, feedback, and/or diagnosis for a number of diseases, conditions, or health states. For example, blood pressure can be monitored and/or predicted based on… activity data’ (emphasis added.))
are deployed on the patch (Chapman, Para. [0062] ‘some or all of the sensor(s) 222 can instead be located in the patch 202’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the blood pressure monitoring device of Wilson to specify including a flexible patch configured to be placed on skin of the user and deploying the inertial measurement units on the patch as taught by Chapman to ensure connection of the required sensors to the surface of skin (Chapman: Para. [0053] ‘The patch 202 can include a substrate 206 configured to couple to the user's body (e.g., to the surface of the skin) via adhesives or other suitable temporary attachment techniques.’; Para. [0062] ‘some or all of the sensor(s) 222 can instead be located in the patch 202… in order to generate measurements of the user's skin’)
Regarding Claim 4, Wilson in view of Soeseno in view of Chapman discloses The method of claim 3. Wilson further discloses wherein gathering the individualized training set data includes receiving blood pressure measurement data from a control blood pressure monitor placed on the user while the blood pressure monitoring device is placed on the user (Wilson: Para. [0089] ‘Over the course of an initialization period (e.g., a patient's first 35 days using the platform 130), lab test data, sensor data, and patient data are collected for the patient and correlated with the patient's true blood pressure. During the initialization period, a patient may wear a physical DBPM to monitor their true blood pressure’).
Regarding Claim 5, Wilson in view of Soeseno in view of Chapman discloses The method of claim 4. Wilson further discloses comprising using the blood pressure measurement data as labeled data in the individualized training set data (Wilson: Para. [0083] ‘Similar to the historical training dataset, the biological data and patient data of the training dataset are assigned a timestamp and a label to characterize how each biological input impact the particular patient's metabolic state. Using the training dataset of patient-specific data, the digital twin module 350 trains 640 a personalized metabolic model.’).
Regarding Claim 6, Wilson in view of Soeseno in view of Chapman discloses The method of claim 2. Wilson further discloses comprising generating, with the analysis model, estimated blood pressure values of the user based on the sensor data after the retraining of the analysis model (Wilson: Para. [0108] ‘FIG. 6, the systolic BP prediction submodel 840 and the diastolic BP prediction submodel each train a baseline LSTM using a training dataset of data from a population of patients. Next, each of the submodels 840 and 845 train a personalized LSTM by retraining the baseline LSTM model using a training dataset of exclusively the patient's own data. Accordingly, the weights of each personalized LSTM model are initialized using the trained weights from the baseline LSTM, such that the personalized LSTM represents a variation of the baseline LSTM that is fine-tuned based on the patient's own data.’).
Regarding Claim 7, Wilson in view of Soeseno in view of Chapman discloses The method of claim 2. Wilson further discloses wherein the analysis model includes a neural network including a first neural layer and a second neural layer, wherein the second machine learning process includes retraining the second neural layer while holding fixed the first neural layer (Wilson: Para. [0118]; Fig. 8C; Para. [0107] '(e.g., LSTM and GRU layers from the Tensorflow library) to process sequential features into a diastolic BP predictions and systolic BP predictions, respectively. LSTM and GRU cells are variants of recurrent neural networks that allow for the training of models capable of learning over long sequences of time variant data.'; Para. [0110] ‘(each of the systolic BP prediction submodel 840 and the diastolic BP prediction submodel 845 implement a neural network with Dense layers (e.g., a Dense neural network from the Tensorflow library) to process the static features described above.');
Regarding Claim 8, Wilson in view of Soeseno in view of Chapman discloses The method of claim 7. Wilson further discloses wherein the neural network includes a long short-term memory neural network (Wilson: Para. [0108]).
Regarding Claim 9, Wilson in view of Soeseno in view of Chapman discloses The method of claim 8. Wilson further discloses comprising, after retraining the analysis model (Wilson: Para. [0110] ‘a baseline Dense neural network is trained using a training dataset of data from a population of patients. Next, a personalized Dense neural network is generated by retraining the baseline Dense neural network using a training dataset of exclusively the patient's own data.’):
passing the sensor data to the analysis model (Wilson: Para. [0097] ‘Each of the submodels 840 and 845 are trained to analyze two sets of inputs: sequential features and static features. Sequential features represent passively recorded sensor data that continuously changes over time including per-minute heart rate and step counts, patient medication information, and time-based interaction features (e.g., elapsed time between consecutive inputs, elapsed time between inputs and labels, etc.).’); and
generating, for each of a plurality of windows of the sensor data, an estimated blood pressure value (Wilson: Para. [0109] ‘a subset of the sequential features (e.g., sequential features recorded during a three day interval), such that each of the submodels 840 and 845 iteratively process sequential features recorded over a time period to output a blood pressure prediction for the corresponding interval.’; Para. [0112] ‘aggregate prediction of a patient’s rolling systolic blood pressure average… diastolic blood pressure average’).
Wilson does not explicitly disclose generating sensor data with the first and second inertial measurement units.
However, Chapman teaches generating sensor data with the first and second inertial measurement units (Chapman, Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’(emphasis added); Para. [0067] ‘The health measurements produced by the device 200 can be used to generate personalized healthcare guidance, such as one or more predictions, recommendations, suggestions, feedback, and/or diagnosis for a number of diseases, conditions, or health states. For example, blood pressure can be monitored and/or predicted based on… activity data’ (emphasis added.)).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the generation of sensor data of Wilson to be conducted by a first and second inertial measurement unit as taught by Chapman as one known technique among many to monitor and predict blood pressure (Chapman: Para. [0067] ‘blood pressure can be monitored and/or predicted based on optical data (e.g., PPG data), electrical data (e.g., ECG data), heart rate data, user data, and/or activity data’).
Regarding Claim 10, Wilson in view of Soeseno in view of Chapman discloses The method of claim 9. Wilson further discloses wherein each window includes a plurality of sensor data samples (Wilson: Para. [0097]
‘ Each of the submodels 840 and 845 are trained to analyze two sets of inputs: sequential features and static features. Sequential features represent passively recorded sensor data that continuously changes over time including per-minute heart rate and step counts, patient medication information, and time-based interaction features (e.g., elapsed time between consecutive inputs, elapsed time between inputs and labels, etc.). ’).
Regarding Claim 11, Wilson in view of Soeseno in view of Chapman discloses The method of claim 9. Wilson further discloses wherein each estimated blood pressure value includes a systolic blood pressure value and a diastolic blood pressure value (Wilson: Para. [0112]; Fig. 8A).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220061676 A1 to Wilson et al. (hereinafter, Wilson) in view of US 20220249002 A1 to Chapman et al. (hereinafter, Chapman).
Regarding Claim 21, Wilson discloses The method of claim 19. Wilson does not explicitly disclose wherein generating the training sensor data includes generating the training sensor data with a first inertial measurement unit and a second inertial measurement unit of the blood pressure monitoring device.
However, Chapman teaches generating the training sensor data with first and second inertial measurement units (Chapman, Para. [0062] ‘The sensor(s) 222 can include any suitable combination of sensors for monitoring various health parameters, such as …activity or motion sensor (e.g., accelerometer, gyroscope), … etc. Each sensor can generate a respective set of signals, which can be received and processed by the processor 214 to generate health measurements and/or other user data. In some embodiments, the device 200 includes at least one, two, three, four, five, or more different sensors 222 for measuring physiological and/or other user parameters.’(emphasis added); Para. [0067] ‘The health measurements produced by the device 200 can be used to generate personalized healthcare guidance, such as one or more predictions, recommendations, suggestions, feedback, and/or diagnosis for a number of diseases, conditions, or health states. For example, blood pressure can be monitored and/or predicted based on… activity data’ (emphasis added.)).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the generation of sensor data of Wilson to be conducted by a first and second inertial measurement unit as taught by Chapman as one known technique among many to monitor and predict blood pressure (Chapman: Para. [0067] ‘blood pressure can be monitored and/or predicted based on optical data (e.g., PPG data), electrical data (e.g., ECG data), heart rate data, user data, and/or activity data’).
Conclusion
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/SHAWN CURTIS BROUGHTON/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791