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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10-16, 18-19 and 28-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With regards to claim 10, in the second to last line, the limitation “refining the initial blood pressure prediction based on the arterial diameter” is recited. However, the specification does not provide in sufficient detail a description of how the initial blood pressure prediction is refined based on the arterial diameter. Paragraph [0080] of Applicant’s PG-Pub 2025/0082207 discloses that “the class mean predictor 446 may be used to determine a mean value for a tensive classification range. The mean value may be used as a rough prediction. The rough prediction may then be refined based on certain feature values measured from the PAPG signal. For example, an arterial diameter measurement might be used to refine the prediction.”. However, the specification does not provide, in sufficient detail, as to how the step of refining is performed. Specifically, the specification fails to provide a description as to how the arterial diameter measurement can be used to refine the prediction (i.e. no steps in sufficient detail set forth in the specification as to how the initial blood pressure prediction is refined). The claims therefore fail to comply with the written description requirement. See MPEP 2161.01, Section I.. Claim 28 is similarly rejected (see last two lines of claim 28).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16, 18-22 and 25-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 20 are directed to an apparatus, claim 10 is directed to a method and claim 28 is directed to a product (i.e. computer-readable medium).
With regards to claims 1, 10, 20 and 28, the claim(s) recite(s) determining a wave characteristic from the acoustic wave and estimating a tensive state classification based on the determined wave characteristic [and the mean value], wherein the tensive state classification is one of a plurality of tensive state classifications that each include different ranges of diastolic and systolic values, determining an arterial diameter, determining a mean value, determining an initial blood pressure prediction based on the mean value and refining the initial blood pressure prediction based on the arterial diameter. The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a control system”/means for determining a wave characteristic [interpreted as a processor] configured to perform the above steps, nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the “control system”/”means for..” language, “determining a wave characteristic…”, “determining a mean value..” and “estimating a tensive state classification based on the determined wave characteristic…” in the context of the claims encompasses a person viewing the acoustic wave on a display to mentally determine a wave characteristic, such as an amplitude or PTT of the acoustic wave, and mentally deciding, such as based on a look-up table/database, a tensive state classification based on the determined wave characteristic. Additionally, a person can be alerted visually or audibly about the aggregated data (or the estimated tensive state classification which is based on the aggregated data) and make a mental decision as to how to modify a transportation route (i.e. view different routes on a display and select an easier route) or modify a time to perform an activity (i.e. mentally decide when an activity is to be performed) based on the aggregated data. Furthermore, determining an arterial diameter, determining a mean value for a tensive state classification range, determining an initial blood pressure prediction based on the mean value and refining the initial blood pressure prediction based on the arterial diameter are directed to limitations that may be performed in the mind, such as by a user viewing a vessel on the screen and mentally determining/measuring the diameter of the vessel (i.e. arterial diameter), view the values for a tensive state classification range to mentally determine the mean value, mentally set the mean value to correspond to an initial blood pressure prediction and, via using a look-up table and or mental adjustment, mentally refine the initial blood pressure prediction based on the arterial diameter. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular,
the claims recite additional elements: 1) a light source system and a receiver system configured to detect an acoustic wave and 2) a processor (i.e. control system/means for determining a wave characteristic, configured to access aggregated data from a database). The processor is recited at a high-level of generality (i.e. a generic processor performing a generic computer function of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Further, the light source system and receiver system configured to detect an acoustic wave are recited at a high level of generality (i.e. as a means for gathering data for the other steps) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount
to significantly more than the judicial exception. The additional element of using a processor to perform the functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional element the light source system and detecting an acoustic wave amounts to no more than insignificant extra-solution activity. Mere insignificant extra-solution activity cannot provide an inventive concept. The claims are not patent eligible.
With regards to claims 2-6, 8-9, 11-15, 18-19, 21-22, 25, 27 and 29-30, the limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a control system”/means for determining a wave characteristic [interpreted as a processor] configured to perform the above steps, nothing in the claims preclude the steps from practically being performed in the mind. The limitations can be performed by mentally performing a decision/determination to perform the above limitations (i.e. for example, referring to claim 4, estimating a blood pressure reading using a look-up table/database). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the above claims do not appear to be patent eligible as they recite an abstract idea, and similarly as set forth above, do not integrate the judicial exception into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception.
With regards to claims 7, 16 and 26, the claims do not appear to patent eligible as the claims appear to be directed to insignificant extra-solution activity as they appear to be further limiting the steps of detecting data (i.e. data-gathering means) and thus directed to extrasolution activity.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-9, 20-22 and 25-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos et al. (US Pub No. 2017/0209053) in view of Kitchens et al. (US Pub No. 2022/0175258), Chen et al. (US Pub No. 2021/0282657) and Galm et al. (US Pub No. 2019/0223773), as evidenced by Zhang et al. (US Pub No. 2012/0290024)
With regards to claims 1, 20 and 26, Pantelopoulos et al. disclose a method, a computer-readable medium and an apparatus, comprising:
a receiver system (i.e. ultrasound sensor, 1310, 1302, 1303; “means for detecting an acoustic wave..”) configured to detect an acoustic wave of a blood vessel (paragraphs [0023], [0033]-[0035], [0108], referring to the sensors including an ultrasound sensor; paragraphs [0083], [0087], [0090], [0103], referring to pulse transit time (PTT) being estimated using ultrasound; Figures 13A,B); and
a control system including one or more general purpose single- or multi-chip processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or combinations thereof, (i.e. paragraph [0162], referring to the processor controlled to provide the functionality as described; “means for determining a wave characteristic”),
the control system being configured
to determine a wave characteristic (i.e PTT) from the acoustic wave (paragraphs [0082]-[0083], referring to measuring PTT using ultrasound, wherein pulse wav velocity can be determined using PTT and the negative correlation between PTT and blood pressure can be used to estimate blood pressure)
to access a model, the model including defining a relationship between associated tensive state classifications and wave characteristics (paragraph [0101], referring to the PTT to BP relationship or correlation, wherein by modelling the relationship between PTT and BP, one can use the model to predict BP from PTT; paragraph [0138], referring to the model relating blood pressure to PTT including a neural network model); and
to estimate a tensive state classification (i.e. “high” blood pressure wherein arterial walls are tense and hard vs. “low” blood pressure wherein arterial walls have less tension) based at least in part on the determined wave characteristic and the accessed model, wherein the tensive state classification (i.e. high vs. low blood pressure) is one of a plurality of tensive state classifications that each include different ranges of different ranges of diastolic and systolic values (paragraphs [0082]-[0083], referring to measuring PTT using ultrasound, wherein pulse wav velocity can be determined using PTT and the negative correlation between PTT and blood pressure can be used to estimate blood pressure, wherein “When the blood pressure is high, the arterial walls are tense and hard and the pulse wave travels faster. When the blood pressure is low, the arterial walls have less tension and the pulse wave travels slower”, and thus low/high blood pressure states represent different tensive states (i.e. tense arterial wall state vs. less tense arterial wall state); further, low/high blood pressures are known to be defined by different ranges of diastolic and systolic values, as evidenced by Zhang et al., paragraph [0007], which sets forth that high blood pressure is defined as a systolic blood pressure higher than 145 mmHG or a diastolic blood pressure higher than about 90 mmHg and low blood pressure is defined as systolic blood pressure lower than about 85 mmHg or diastolic blood pressure that is lower than about 55 mmHg).
However, though Pantelopoulos et al. disclose that the acoustic wave is detected and used to determine PTT as the wave characteristic, Pantelopoulos et al. do not specifically disclose that their apparatus further comprises a light source system including a light-emitting component and the acoustic wave corresponds to a photoacoustic response of the blood vessel to light emitted by the light source system.
Further, though Pantelopoulos et al. do disclose that accessing the model includes accessing a neural network model (paragraph [0138]), Pantelopoulos et al. do not specifically disclose that the accessing comprises accessing aggregated data from a database, the aggregated data including data from a plurality of users and at least one of their associated tensive state classification and wave characteristics.
Furthermore, Pantelopoulos et al. do not specifically disclose that the control system is further configured to suggest, to a user of the apparatus, a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data.
Additionally, with regards to claim 20, Pantelopoulos et al. do not specifically disclose that the means for determining the wave characteristic is further configured to select a predictive modelling operation from a plurality of predictive modelling operations based on at least one of the tensive state classification and the acoustic wave and to implement the predictive modelling operation via artificial intelligence to estimate blood pressure based at least in part on the determined wave characteristic.
Kitchens et al. disclose providing one or more photoacoustic plethysmography (PAPG)-based blood pressure estimation methods that are based on pulse transit time (PTT), wherein blood vessels are heated by incident light from a light source and are emitting acoustic waves which include ultrasonic waves (Abstract; paragraph [0054], [0063]-[0064]; Figure 1D)). Photoacoustic emissions from the illuminated tissues are detected by an ultrasonic receiver, which is a piezoelectric receiver, and may be used to detect volumetric changes in the blood that correspond to heart rate waveforms (paragraph [0064]; Figure 1D).
Before the effective filing date of the claimed invention, it would have been obvious to substitute the technique for detecting an acoustic wave to determine the PTT of Pantelopoulos et al. with a technique for detecting an acoustic wave to determine the PTT by using an apparatus that comprises a light source system including a light-emitting component and the acoustic wave corresponds to a photoacoustic response of the blood vessel to light emitted by the light source system, as taught by Kitchens et al., as the substitution of one known technique for detecting the acoustic wave to determine the PTT for another yields predictable results (i.e. effective/successful determination of PTT) to one of ordinary skill in the art. One of ordinary skill in the art would have been able to carry out such a substitution and the results are reasonably predictable.
However, though Pantelopoulos et al. do disclose that accessing the model includes accessing a neural network model (paragraph [0138]), the above combined references do not specifically disclose that the accessing comprises accessing aggregated data from a database, the aggregated data including data from a plurality of users and at least one of their associated tensive state classification and wave characteristics.
However, the above combined references do not specifically disclose that the control system is further configured to suggest, to a user of the apparatus, a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data.
Additionally, with regards to claim 20, the above combined references do not specifically disclose that the means for determining the wave characteristic is further configured to select a predictive modelling operation from a plurality of predictive modelling operations based on at least one of the tensive state classification and the acoustic wave and to implement the predictive modelling operation via artificial intelligence to estimate blood pressure based at least in part on the determined wave characteristic.
Chen et al. disclose a blood pressure measurement device including a processor, wherein a PPG signal is obtained using a PPG sensor and the processor calculates a first blood pressure value by the processor (30) according to at least the first biosignal (i.e. a wave characteristic) and a first blood pressure model (Abstract; paragraphs [0022]-[0023]). Building the first blood pressure model according to said first physiological data and said first blood pressure data may be performed using a deep learning algorithm, wherein the deep learning algorithm is a conventional neural network (i.e. artificial intelligence), wherein the blood pressure model can be calibrated according to the specific user, making it more adapted to the physiological status of the specified user and a customized blood pressure model is built (paragraphs [0023]-[0024], paragraph [0036]). The calibration process comprises using a set of weights as a parameter set and using a loss function (Formula 1), wherein the process aims to train a useable first blood pressure model by minimizing the loss function, etc. (paragraphs [0038]-[0046], note that the calibration process, via optimizing the loss function of the first specified blood pressure model, corresponds to selecting a predictive modelling operation from a plurality of predictive modelling operation [which is ultimately based in part on the aggregated data which is used to determine the first blood pressure model [see below]). Pulse Transit Time (PTT) is calculated from general users in advance, wherein said first blood pressure model, which can be a neural network model, is the result of a training based on both the PTT signals (i.e. wave characteristics) and said first blood pressure data from the general users (i.e. an associated tensive state classification) (paragraph [0026], note that aggregated data including data from a plurality of users (i.e. “general users”, wherein such accessing of data from users would inherently be acquired from a database/storage) and at least one of their associated tensive state classification (i.e. blood pressure) and wave characteristics (i.e. PTT) is accessed; note that the above customized blood pressure model, which is derived from the first blood pressure model, is thus ultimately based, at least in part, on the aggregated data).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the accessing [of the network model] of the above combined references comprise accessing aggregated data from a database, the aggregated data including data from a plurality of users and at least one of their associated tensive state classification and wave characteristics, as taught by Chen et al., as the above combined references require accessing a network model to estimate the tensive state classification (i.e. blood pressure) and Chen et al. teaches a known technique for using a network model to estimate the tensive state classification (i.e. blood pressure), wherein aggregated data from a database is accessed, the aggregated data including data from a plurality of users and at least one of their associated tensive state classification and wave characteristics. That is, using the known technique for using a network model to estimate the tensive state classification, as desired by the above combined references, by accessing aggregated data from a data base, wherein the aggregated data includes data from a plurality of users and at least one of their associated tensive state classification and wave characteristics, as taught by Chen et al., would have been obvious to one of ordinary skill in the art.
Additionally, with regards to claim 20, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the control system of the above combined references be further configured to select the predictive modelling operation from a plurality of predictive modelling operations based, at least in part, on the aggregated data, and to implement the [selected] predictive modelling operation via artificial intelligence to estimate blood pressure based at least in part on the determined wave characteristic, as taught by Chen et al., in order to provide a blood pressure model that is more adapted to the physiological status of the specified user and build a customized blood pressure model (paragraph [0036]).
However, the above combined references do not specifically disclose that the control system is further configured to suggest, to a user of the apparatus, a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data.
Galm et al. disclose a mobile electronic device is operable to detect and display a mental state of a user such as drowsiness or stress, wherein determination of, or changes in the determined blood pressure (BP), heartrate (HR) and/or heart rate variability (HRV) may be analyzed by the processor of the mobile electronic device to determine a stress level, drowsiness level or other mental state for the user of the mobile electronic device (Abstract; paragraph [0054]). The processor may determine if the drowsiness level is over a certain threshold, and if the user is over the threshold, the user may be presented information determined to help reduce the user’s current level (a “remedial action”) (paragraphs [0075]-[0076]; Figure 3). The remedial action may include, but is not limited to, haptic and/or audible notifications; verbiage, icon(s), color(s), and/or animation(s) presented on display 104 of the mobile electronic device 100 (paragraph [0076]). Information that may be presented to reduce a current stress level may include stress-coping mechanisms (e.g., breathing exercises) and/or relaxation activities (e.g., mild physical exercise) (paragraph [0076], note that a modification of a time (i.e. notification serves as an alert that it is time to perform the activity) to perform an activity (i.e. breathing exercises, mild physical exercise); Figure 3). Another application is in or with a navigation device, wherein the navigation device may be a component of the vehicle, a stand-alone component located in the vehicle, a smart phone of the user, a smartwatch worn by the user, or other device that assists the user in navigating (paragraph [0089]). The navigation device may select a certain route, or change to a certain route, in response to the drowsiness level of the user, wherein the navigation device may recommend routes to the user (paragraph [0089], note that the navigation device may then suggest to a user a modification of a transportation rout based ultimately on blood pressure data (which is used to determine the drowsiness level)).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the control system of the above combined references be further configured to suggest, to a user of the apparatus, a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data [which, in the above combined references, ultimately is used to determine blood pressure], as taught by Galm et al., in order to provide remedial action when drowsiness or a stress level of a user exceeds a threshold, thereby reducing the user’s current drowsiness/stress level (and thus blood pressure reduces as well) (paragraph [0076]).
With regards to claims 2, 3, 21 and 22, Pantelopoulos et al. disclose that the control system is further configured to determine a blood pressure reading (i.e. real-time health data [claims 3, 11, 21 and 29] as the blood pressure reading is provided in “real-time” at the time of processing) based on the determined wave characteristic (paragraphs [0137]-[0140], [0175], referring to obtaining an estimate of blood pressure based on a model created from calibration data points corresponding to calibration blood pressure measurements (i.e. tensive state classifications corresponding to high/low blood pressure measurements) and at least one pulse transit time (i.e. determined wave characteristic); paragraph [0367], referring to data from pressure sensor being used to detect strokes (i.e. real-time health data [claim 3]); Figures 4-6).
With regards to claim 4, Pantelopoulos et al. disclose that the control system is further configured to perform modelling operations on the acoustic wave to estimate a blood pressure reading (paragraph [0138], referring to fitting the model to the calibration data points, wherein the model relates blood pressure to PTT [which are ultimately obtained from the acoustic wave] and wherein the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model).
With regards to claim 5, Pantelopoulos et al. disclose that the control system is further configured to select a predictive modeling operation from a plurality of predictive modeling operations based on at least one of the tensive state classification and the acoustic wave (paragraph [0138], referring to the process deciding whether if enough calibration data points have been collected, wherein if there is enough calibration data points collected, the process proceeds to fit the model to the calibration data points, wherein in some implementations, the non-linear model describes blood pressure as a nonlinear function of PTT as shown in Eq. 7, while in other implementations, the linear model describes blood pressure as a decreasing linear function of PTT or fitting the model involves obtaining estimates of model parameters to minimize the distance between the calibration data points and the model prediction, etc.., wherein the multiple calibration data points are used to define the model that describes blood pressure as a function of PTT, and thus the predictive modelling operations, which are based on the calibration data points are based on at least the tensive state classification (i.e. blood pressure) and the acoustic wave (i.e. PTT is derived from the acoustic wave); alternatively, the calibration process is repeated (see step 418, Fig. 4) which would result in the selection of a different predictive modeling operations reflected from the acquiring of different calibration data points; Figures 4-6).
With regards to claims 6 and 25, Pantelopoulos et al. disclose that the control system is further configured to mathematically weight the wave characteristic and to select the predictive modelling operation based, at least in part, in a weighted wave characteristic (paragraph [0138], referring to the use of linear model or non-linear model, which would require some weighting of the parameters, such as PTT; Figures 4-6). Chen et al. further discloses this limitation (paragraphs [0038]-[0046]).
With regards to claim 7, as discussed above, the above combined references meet the limitations of claims 1 and 20. Further, Pantelopoulos et al. disclose that the control system is further configured select a predictive modeling operation (i.e. model) (paragraphs [0011], [0138], referring to the model, that relates blood pressure to PTT and is used to determine blood pressure from PTT, includes a neural network model). However, the above combined references do not specifically disclose that the control system is further configured to select the predictive modelling operation from a plurality of predictive modelling operations based, at least in part, on the aggregated data, and to implement the [selected] predictive modelling operation via artificial intelligence (i.e. via using a neural network model) to estimate blood pressure based at least in part on the determined wave characteristic. Chen et al. disclose a blood pressure measurement device including a processor, wherein a PPG signal is obtained using a PPG sensor and the processor calculates a first blood pressure value by the processor (30) according to at least the first biosignal (i.e. a wave characteristic) and a first blood pressure model (Abstract; paragraphs [0022]-[0023]). Building the first blood pressure model according to said first physiological data and said first blood pressure data may be performed using a deep learning algorithm, wherein the deep learning algorithm is a conventional neural network (i.e. artificial intelligence), wherein the blood pressure model can be calibrated according to the specific user, making it more adapted to the physiological status of the specified user and a customized blood pressure model is built (paragraphs [0023]-[0024], paragraph [0036]). The calibration process comprises using a set of weights as a parameter set and using a loss function (Formula 1), wherein the process aims to train a useable first blood pressure model by minimizing the loss function, etc. (paragraphs [0038]-[0046], note that the calibration process, via optimizing the loss function of the first specified blood pressure model, corresponds to selecting a predictive modelling operation from a plurality of predictive modelling operation [which is ultimately based in part on the aggregated data which is used to determine the first blood pressure model [see below]). Pulse Transit Time (PTT) is calculated from general users in advance, wherein said first blood pressure model, which can be a neural network model, is the result of a training based on both the PTT signals (i.e. wave characteristics) and said first blood pressure data from the general users (i.e. an associated tensive state classification) (paragraph [0026], note that aggregated data including data from a plurality of users (i.e. “general users”, wherein such accessing of data from users would inherently be acquired from a database/storage) and at least one of their associated tensive state classification (i.e. blood pressure) and wave characteristics (i.e. PTT) is accessed; note that the above customized blood pressure model, which is derived from the first blood pressure model, is thus ultimately based, at least in part, on the aggregated data). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the control system of the above combined references be further configured to select the predictive modelling operation from a plurality of predictive modelling operations based, at least in part, on the aggregated data, and to implement the [selected] predictive modelling operation via artificial intelligence to estimate blood pressure based at least in part on the determined wave characteristic as taught by Chen et al., in order to provide a blood pressure model that is more adapted to the physiological status of the specified user and build a customized blood pressure model (paragraph [0036]).
With regards to claims 8, 9 and 27 as discussed above, the above combined references meet the limitations of claim 1. However, they do not specifically disclose that the control system is further configured to determine a mean value for a tensive state classification range and determine the tensive state classification based on the mean value or that the control system if further configured to narrow a tensive state classification range using the mean value. Kitchens et al. disclose making a blood pressure estimation based on both a HRW analysis (i.e. such as based on PTT) and a hemodynamic analysis, wherein a blood pressure estimate (BP Estimate A) is based on HRW analysis and another blood pressure estimate (BP Estimate B) is based on a hemodynamic analysis (paragraphs [0146]-[0148], note that the multiple blood pressure estimates represent a tensive state classification range; Figure 14). A third blood pressure estimate (BP Estimate C) is obtained by taking a weighted average of BP Estimate A and BP Estimate B, wherein the weighting of the weighted average may be based on the relative accuracy of the blood pressure estimate based on the HRW analysis, as compared to the accuracy of the blood pressure estimate based on the hemodynamic analysis (paragraph [0149], note that a tensive state classification is represented by BP Estimate C which is based on a mean/average value for the tensive state classification range, wherein the tensive state classification range is narrowed using the mean/average value; Figure 14), thus providing a more accurate blood pressure estimation. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the control system of the above combined references be further configured to determine a mean value for a tensive state classification range and determine the tensive state classification based on the mean value or that the control system if further configured to narrow a tensive state classification range, as taught by Kitchens et al., in order to provide a more accurate blood pressure estimation (paragraph [0149]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 20 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. Galm has been introduced to teach suggesting, to a user of the apparatus, a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data.
With regards to the 35 USC 101 rejection, Applicant argues that the physically-grounded pipeline, comprising of pulse light emitted into tissue by the light source system, the receiver system detecting an acoustic wave, the control system extracting a wave characteristic, and generating a suggested modification to a transportation route or a time to perform an activity based on the aggregated data, is not performable in the human mind. Further, with regards to claims 10 and 28, Applicant argues that the claims recite a physically-grounded method/computer-readable medium implementation of the same pipeline, further requiring the physical determination of an arterial diameter from the acoustic wave, the determination of an initial blood pressure prediction based on the mean value for a tensive state classification range and the refinement of that initial blood pressure prediction based on the determined arterial diameter, wherein these operations are not performable in the human mind and are not mathematical concepts or methods of organizing human activity.
Examiner respectfully disagrees and notes that the steps of determining a wave characteristic from the acoustic wave, estimating a tensive state classification based at least in part on the determined wave characteristic and on the aggregated data, wherein the tensive state classification is one of a plurality of tensive state classifications that each include different ranges of diastolic and systolic values and suggesting a modification of a transportation route or a modification of a time to perform an activity based on the aggregated data are the particular limitations that are considered to be directed to limitations that covers performance of the limitations in the mind [but for the recitation of generic computer components]. The other limitations (i.e. pulsed light is emitted into tissue, receiver system detects an acoustic wave, etc.) are not considered to be directed to a mental process. Examiner respectfully disagrees that determining a wave characteristic, estimating a tensive state classification based at least in part on the determined wave characteristic and suggesting a modification, as claimed, are limitations that may be performed in the mind. Specifically, a person viewing the acoustic wave on a display can mentally determine a wave characteristic, such as an amplitude or PTT of the acoustic wave, and mentally decide, such as based on a look-up table/database, a tensive state classification based on the determined wave characteristic. Additionally, a person can be alerted visually or audibly about the aggregated data (or the estimated tensive state classification which is based on the aggregated data) and make a mental decision as to how to modify a transportation route (i.e. view different routes on a display and select an easier route) or modify a time to perform an activity (i.e. mentally decide when an activity is to be performed) based on the aggregated data. With regards to claims 10 and 28, the limitations of determining an arterial diameter, determining a mean value for a tensive state classification range, determining an initial blood pressure prediction based on the mean value and refining the initial blood pressure prediction based on the arterial diameter are directed to limitations that may be performed in the mind. Specifically, a user may view a vessel on the screen and mentally determine/measure the diameter of the vessel (i.e. arterial diameter), view the values for a tensive state classification range to mentally determine the mean value, mentally set the mean value to correspond to an initial blood pressure prediction and, via using a look-up table and or mental adjustment, mentally refine the initial blood pressure prediction based on the arterial diameter. Examiner notes that the limitations are recited at a high level of generality and, as set forth in MPEP 2106.04(a)(2), III, limitations directed to collecting information, analyzing it, and displaying certain results of the collection and analysis are directed to a mental process where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind.
Applicant further argues that the additional elements integrate any such abstract idea into a practical application. Specifically, Applicant argues that the light source system and receiver system are not generic data-gathering components as the specification sets forth that the receiver system and light source system are specialized components configured to induce and detect the photoacoustic effect in blood vessel tissue.
However, though the specification may set forth more specific structure, the light source system and receiver system are recited in the claims at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g) and MPEP 2106.05(b). As such, the additional elements do not integrate the abstract idea into a particular application.
Applicant further notes that the output of the suggesting to modify a transportation route or a modification of a time as claimed corresponds to an output that is a concrete, real-world result, wherein these are the types of particular treatments or application that integrate any alleged abstract idea into a practical application.
However, as set forth in the above 35 USC 101 rejection, the claimed suggestion is considered to be directed to a mental process and thus is directed to the abstract idea and therefore the limitation is not directed to additional elements. As such, Applicant’s above argument is moot.
Applicant further argues that the claims recite significantly more as the combination of the specialized photoacoustic hardware (i.e. light source system and receiver system), the aggregated multi-user tensive state database, the route and activity modification output, etc., constitute an ordered combination that is not well understood, routine, or conventional in the field, wherein no evidentiary basis has been provided that the specific ordered combination was well-understood, routine or conventional at the time of filing. Examiner notes that the aggregated multi-user tensive state database, the route and activity modification output, etc. are directed to the abstract idea. The light source system and the receiver system (i.e. which are known to be part of a photoacoustic system) are considered to be well-understood, routine or conventional/traditional in the field of biometric/biomedical sensing technologies, as is well known in the art and further is admitted by Applicant in paragraph [0002] of the filed application, which sets forth “A variety of different sensing technologies and algorithms are being implemented in devices for various biometric and biomedical applications, including health and wellness monitoring. This push is partly a result of the limitations in the usability of traditional measuring devices for continuous, noninvasive, and ambulatory monitoring. Some such devices are, or include photoacoustic devices.”.
As such, the 35 USC 101 rejection is hereby maintained.
With regards to the 35 USC 112(a) rejection, Applicant points to paragraphs [0062] and [0085], as well as Figure 9, for providing support for using the arterial diameter to refine the initial blood pressure prediction being a physically grounded operation well within the skill of the art.
Examiner respectfully disagrees that these paragraphs/Figure provides support in sufficient detail for the step of refining the initial blood pressure prediction based on the arterial diameter. Specifically, paragraph [0062] simply sets forth “For example, an arterial diameter measurement might be used to refine the prediction”, but does not set forth any steps or description as to how the arterial diameter measurement would be used to refine the prediction. Paragraph [0085] and Figure 9 do set forth that the PWV depends on the arterial diameter, etc., but includes no description of the steps describing how the arterial diameter measurement would be used to “refine” the blood pressure prediction. As set forth in MPEP 2161.01, I., “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”. As the specification lacks a description of how the arterial diameter is used for “refining the initial blood pressure prediction”, in sufficient detail, claims 10 and 28 remain rejected under 35 USC 112(a), lack of written description support.
It is noted that claims 10 and 28 are not currently rejected under prior art as the prior art does not teach or suggest refining the initial blood pressure prediction based on the arterial diameter, wherein the initial blood pressure prediction is determined based on the mean value determined for a tensive state classification range, in combination with the other claimed elements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798
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