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 .
Response to Amendment
The amendments filed 03 December 2025 have been entered. Claims 23 – 33 and 46 are pending. Applicant’s amendments to the claims have overcome each and every objection to the abstract and claims previously applied in the office action dated 03 September 2025. Applicant’s amendments have overcome each and every rejection under 35 U.S.C. 112 previously applied in the office action dated 03 September 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 26 – 27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 27 recites the term “wherein the trained deep neural network comprises”. It is unclear if this is intended to be the same at the “trained deep neural network” of Claim 23, or the “trained recurrent neural network (RNN) deep neural network of Claim 26. Furthermore, it is unclear if the “trained recurrent neural network (RNN) deep neural network of Claim 26 is intended to be the same as the broad “trained deep neural network”, or a second trained deep neural network in addition to that of Claim 23. For the purposes of examination, the term “using a trained recurrent neural network (RNN) deep neural network” of Claim 26 is deemed to claim “using the trained deep neural network, comprising a trained recurrent neural network (RNN) deep neural network”. Then, the term “wherein the trained deep neural network comprises” in Claim 27 is deemed to claim “wherein the trained deep neural network further comprises”.
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 23 - 33 and 46 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.
Regarding Claim 23, the claim recites an apparatus, which is one of the statutory categories of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of Claims 23 - 33 and 46 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 23 – 33 and 46 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 23 – 33 and 46 recites an abstract idea.
Specifically, Independent Claim 23 recites (underlined are observations, judgements, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 23
A blood pressure monitor, comprising:
a set of sensors including at least an accelerometer and a pulse wave sensor; and
a processor configured to track arm orientation using a trained deep neural network based on signals from the accelerometer and configured to, based on a tracked arm orientation input and signals from the pulse wave sensor, calculate a transmural pressure.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claim 23, these limitations include:
based on a tracked arm orientation input and signals from the pulse wave sensor, evaluate a transmural pressure based on tracked arm orientation input and signals from the pulse wave sensor.
Similarly, Dependent Claims 24 – 33 and 46 include the following abstract limitations, in addition the aforementioned limitations in Independent Claim 23 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
calculate a transmural pressure by computing a transmural pressure error based on the tracked arm orientation and compensating for the transmural pressure error.
Evaluate a transmural pressure by computing a transmural pressure error based on the tracked arm orientation and compensating for the transmural pressure error.
all of which are grouped as mental processes or mathematical algorithms under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claim 23 (and its Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified in Claims 23 – 33 and 46), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
“set of sensors”
“accelerometer”
“pulse wave sensor”
“processor”
“signal acquisition element”
“barometer”
“gyroscope”
“magnetometer”
“time-of-flight sensor”
Additional elements recited include a “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor”.
in Independent Claim 23 (and its Dependent Claims). These components are recited at a high level of generality, i.e., as a processor performing a generic function of processing data (the tracking based on signals and calculating). These generic hardware component limitations for “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Independent Claim 23 includes pre-solution activity limitations, such as:
a set of sensors including at least an accelerometer and a pulse wave sensor; and
a processor configured to track arm orientation using a trained deep neural network based on signals from the accelerometer
In addition the aforementioned extra-solution activity limitations in Independent Claim 23, additional extra-solution activity limitations recited in Dependent Claims 24 – 33 and 46 include:
a signal acquisition element including a set of sensors that generate data responsive to transmural and relative external pressure.
the set of sensors further includes at least one of a barometer, gyroscope and a magnetometer.
the processor is configured to track arm orientation using a trained recurrent neural network (RNN) deep neural network.
the trained deep neural network comprises at least one bi-directional Long Short-Term Memory (LSTM) layer.
the processor is configured to track arm orientation by feeding output signals from the set of sensors into a Kalman Filter.
the set of sensors includes a time-of-flight sensor.
wherein a person specific calibration is performed to configure the processor to track the arm orientation.
the processor is configured to track arm orientation based on signals from the accelerometer, wherein the accelerometer is located at a user's wrist.
the processor is configured to track arm orientation based on signals from the accelerometer, wherein the accelerometer is located at a user's upper arm.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor” as recited Independent Claim 23 (and its Dependent Claims) are generically recited computer and hardware elements 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 ideas identified above in Independent Claim 23 (and its 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 and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer processor 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 Claim 23 (and its dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claim 23 (and its dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 23 – 33 and 46 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: “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor” as recited in Independent Claim 23 (and its dependent claims).
The additional elements of the “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor” in Independent Claim 23 (and its dependent claims), as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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, the “set of sensors” is described at [0233] as “various
noninvasive sensors and devices including:” ECG electrodes, PPGS sensors, IMU devices with a combination accelerometer/gyroscope, magnetometer, and motion coprocessor. The “sensors” are shown as a generic box element “Sensors” in Fig. 22.
Per Applicant’s specification, the “accelerometer” is described generically on [0192] as a “3-axis accelerometer that measures the linear acceleration of the wrist” and [0233] as part of a “combination accelerometer/gyroscope (STMicroelectronics LSM6DSM)”. The “accelerometer” is shown as generic box element “accelerometer” in the “baro-IMU sensors” box in Fig 7 and as generic box element “accelerometer” in the “sensors” box in Fig 22.
Per Applicant’s specification, the “pulse wave sensor” is described generically at [0007] as including “at least one plethysmograph sensor and at least one of (a) a second plethysmograph sensor, wherein the two plethysmograph sensors can be used to measure pulse transit time or pulse wave velocity and (b) a sensor that detects heartbeat that can be used to estimate pulse transit time or pulse wave velocity.” The “pulse wave sensor” are shown as a pulse wave detection system in Figure 2.
Per Applicant’s specification, the “processor” is described at [0233] as a “motion coprocessor (EM Microelectronics EM7180)” and [0284] as “the processor can include, but not be limited to, a
personal computer or workstation or other such computing system that includes a processor,
microprocessor, microcontroller device…” The processor is presented as generic box element “motion coprocessor” in the “sensors” box in Figure 22.
Per Applicant’s specification, the “signal acquisition element” is described generically at [0150] – [0152] as including a variety of sensors including, “a collection of sensors used to collect information related to external pressure”, an “accelerometer, gyroscope, magnetometer, and barometer”, and “a force sensitive sensor”, and “a muscle activation sense”, and “a sensor for monitoring the diameter of the artery”.
Per Applicant’s specification, the “barometer” is described at [0151] as part of the “acquisition system” and [0215] “To compensate for this error accumulation, sensor fusion techniques have been developed that include barometers to measure local air pressure.” The “barometer” is shown as generic box element “Barometric altimeter” in the “baro-IMU sensors” box in Fig 7.
Per Applicant’s specification, the “gyroscope” is described generically at [0192] as “3-axis gyroscope, which can be used to measure the angular velocity along the sensor axes.” The “gyroscope” is shown as generic box element “Gyro” in the “baro-IMU sensors” box in Fig 7.
Per Applicant’s specification, the “magnetometer” is described generically at [0192] as “3-axis magnetometer, which can be used to measure the local magnetic field, which can be used to calculate the yaw orientation angle.” The “magnetometer” is shown as generic box element “Magnetometer” in the “sensors” box in Fig 22.
Per Applicant’s specification, the “time-of-flight sensor” is described generically at [0192] as “A time-of-flight sensor (e.g., radar, lidar, etc.), which can be used to measure the distance to a reference site with known position, such as the ground or the torso.” It is shown as generic box element “time-of-flight” in the “Sensors” 2302 box in Fig 23.
Accordingly, in light of Applicant’s specification, the claimed terms “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor” are reasonably construed as a generic computing and hardware devices. 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 “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, “signal acquisition element”, “barometer”, “gyroscope”, “magnetometer”, and “time-of-flight sensor”. 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 Independent Claim 23 (and its dependent claims) 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 apparatus of Claims 23 – 33 and 46 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 23 – 33 and 46 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 for Step 2A Prong 2 in Independent Claim 23 (and its 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 23 – 33 and 46 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 23 – 33 and 46 amounts to significantly more than the abstract idea itself. Accordingly, Claims 23 – 33 and 46 are not patent eligible and rejected under 35 U.S.C. 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 23 – 27, 30 – 32, and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos et. al., (US 2017/0209053 A1), hereinafter Pantelopoulos, in view of Kaifosh et al. (United States Patent Application Publication US 2018/0020978 A1), hereinafter Kaifosh.
Regarding Claim 23, Pantelopoulos discloses A blood pressure monitor ([Abstract]), comprising:
a set of sensors (Fig 8B, “PPG sensor”, “ECG Sensor”, “Inertial/Motion Sensor”, “Location sensor (e.g. GPS)”, “Altimeter”, “Temperature Sensor”) including at least an accelerometer (Fig 8B, “Inertial/Motion Sensor”; [0020] “an accelerometer”) and a pulse wave sensor (Fig 8B, “PPG”; [0016] “obtaining proximal pulse wave data and distal pulse wave data from the one of more sensors”); and
a processor (Fig 8B, “Processor”) configured to track arm orientation ([0162] “functionality may be provided by a processor”) based on signals from the accelerometer ([0167] “The inertial sensor can also be used…the position or orientation of the user's body part to which the device is attached ( e.g., the orientation or position of the user's wrist or arm)…”) and configured to, based on the tracked arm orientation, calculate a transmural pressure based on signals from the pulse wave sensor (Fig 6, [0147] “transmural blood pressure”, [0148] “…transmural pressure…build a model in block 616”; [0144] “And the estimate of hydrostatic pressure is obtained from the set of inertial data and the set of altitude data.”; [0145] “Process 600 also involves obtaining proximal pulse wave data and distal pulse wave data by using the one or more sensors when the person is holding the limb at the first position.”; [0119] “includes a PPG sensor for distal wave data and the wrist device includes an ECG sensor for proximal wave data.”)
Pantelopoulos does not disclose using a trained deep neural network.
Kaifosh teaches an LSTM recurrent neural network for determining movement of human body segments, as trained by body-worn inertial sensors, to be used for mapping to represent hand movement in a VR environment. Specifically for Claim 26, Kaifosh teaches track arm orientation [0106] using a trained deep neural network ([0052] “In some embodiments, the statistical model may be a long short-term memory (LSTM) recurrent neural network that is trained using the biophysical data sensed from the one or more IMU devices worn by the user”; [0053] “a neural network model may be trained using mechanical motion data…movement statistics and constraints due to the articulation of the user’s arm…”; [0105] “deep neural networks… may be used”; [0106] “output layer…may provide set of output values corresponding to a respective set of possible musculo-skeletal position characteristics (e.g.,… orientation of one or more segments.)”)
Kaifosh presents a motivation to combine at [0002] with “In some computer applications that generate musculo-skeletal representations of the human body, it is desirable for the application to know the spatial positioning, orientation and movement of a user's body to provide a realistic representation of body movement.” And [0003] “predicting information about the positioning and movements of portions of a user’s body”. A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize that using a neural network to predict the arm location would be useful for determining a more accurate arm orientation from inertial sensors usable as an input to transmural blood pressure calculations in a blood pressure monitor, as with the blood pressure monitor and inertial sensors disclosed in Pantelopoulos. This predictive movement data could be used in any application in which data regarding orientation of the arm is required, such as for calculating transmural blood pressure as influenced by arm position (as taught by Pantelopoulos).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the arm position determination using a body-worn accelerometer (inertial sensors) for transmural blood pressure calculations as disclosed by Pantelopoulos with the deep neural network system for predicting arm position using training from body-worn accelerometer (inertial sensor) information as taught by Kaifosh, creating a single blood pressure monitor device with capability to obtain a “realistic representation of body movement” for accurate input into its transmural blood pressure calculations.
Regarding Claim 24, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 24, Pantelopoulos discloses further comprising a signal acquisition element (Fig 8B; [0173] “Physiological Sensors Table: Accelerometers”; “Gyroscopic sensors”)(Examiner notes that Applicant’s specification describes a “signal acquisition element” in [0006] as “a set of sensors that generate data responsive to transmural and relative external pressure. The sensors including at least two of a barometer, gyroscope, and an accelerometer”) including a set of signal acquisition sensors that generate data responsive to transmural and relative external pressure (Fig 8B; [0173] “Physiological Sensors Table: Accelerometers”; “Gyroscopic sensors” and Physiological data acquired: “Ballistocardiography (pulse waveforms, pulse arrival time…”; [0147]; [0144]; [0276] “transmits external pressure variations to the altimeter…”)(Examiner notes that Applicant’s specification describes a “signal acquisition element” in [0006] as “a set of sensors that generate data responsive to transmural and relative external pressure. The sensors including at least two of a barometer, gyroscope, and an accelerometer”).
Regarding Claim 25, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 25, Pantelopoulos discloses wherein the set of sensors further includes at least one of a barometer, a gyroscope ([0020] “…inertial sensor is selected from the group…gyroscope”) and a magnetometer ([0020] “…magnetometer”).
Regarding Claim 26, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 26, Pantelopoulos discloses wherein the processor (Fig 8B, “Processor”) is configured to track arm orientation ([0162]; [0167] “The inertial sensor can also be used…the position or orientation of the user's body part to which the device is attached ( e.g., the orientation or position of the user's wrist or arm)…”)
Pantelopoulos does not disclose using a recurrent neural network (RNN) trained deep neural network.
Kaifosh teaches an LSTM recurrent neural network for determining movement of human body segments, as trained by body-worn inertial sensors, to be used for mapping to represent hand movement in a VR environment. Specifically for Claim 26, Kaifosh teaches track arm orientation [0106] using a trained deep neural network ([0052] “In some embodiments, the statistical model may be a long short-term memory (LSTM) recurrent neural network that is trained using the biophysical data sensed from the one or more IMU devices worn by the user”; [0053] “a neural network model may be trained using mechanical motion data…movement statistics and constraints due to the articulation of the user’s arm…”; [0105]
“deep neural networks… may be used”; [0106] “output layer…may provide set of output values corresponding to a respective set of possible musculo-skeletal position characteristics (e.g.,… orientation of one or more segments.)”)
The motivation for Claim 26 to combine Pantelopoulos with Kaifosh is the same as that described in more detail in Claim 23. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the arm position determination using a body-worn accelerometer (inertial sensors) for transmural blood pressure calculations as disclosed by Pantelopoulos with the deep neural network system for predicting arm position using training from body-worn accelerometer (inertial sensor) information as taught by Kaifosh, creating a single blood pressure monitor device with capability to obtain a “realistic representation of body movement” for accurate input into its transmural blood pressure calculations.
Regarding Claim 27, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 26. Looking to the 112(b) interpretation for the dependency, Pantelopoulos in view of Kaifosh discloses The monitor of Claim 26. For the remainder of Claim 27, Pantelopoulos does not disclose wherein the trained deep neural network comprises at least one bi-directional Long Short-Term Memory (LSTM) layer.
Kaifosh teaches wherein the trained deep neural network comprises at least one bi-directional Long Short-Term Memory (LSTM) layer ([0052] “In some embodiments, the statistical model may be a long short-term memory (LSTM) recurrent neural network that is trained using the biophysical data sensed from the one or more IMU devices worn by the user”; [0053] “a neural network model may be trained using mechanical motion data…movement statistics and constraints due to the articulation of the user’s arm…”; [0105] “deep neural networks… may be used”; [0106] “output layer…may provide set of output values corresponding to a respective set of possible musculo-skeletal position characteristics (e.g., orientation of one or more segments,)”).
The motivation for Claim 27 to combine Pantelopoulos in view of Kaifosh is the same as that described in Claim 23 and 26. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the arm position determination using an accelerometer for transmural blood pressure calculations as disclosed by Pantelopoulos with the LSTM neural network system for predicting arm position using training from body-worn accelerometer information as taught by Kaifosh, creating a single blood pressure monitor device with capability to obtain a “realistic representation of body movement” for accurate input into its transmural blood pressure calculations.
Regarding Claim 30, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 30, Pantelopoulos discloses wherein a person specific calibration ([0002] “…that can be calibrated”; Fig 6 “Calibrating by Holding an Arm at Different Positions”, “606: Obtain a set of altitude data when the person is holding the limb at the position”) is performed to configure the processor to track the arm orientation (Fig 6, “Calibrating by Holding an Arm at Different Positions”)(Examiner notes that the signals obtained when the arm is held “at different positions” correspond to that arm orientation, including its “altitude”).
Regarding Claim 31, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 31, Pantelopoulos discloses wherein the processor is configured (Fig 8B, “Processor”; [0162]) to calculate a transmural pressure by computing a transmural pressure error ([0147] “perturbation pressure including hydrostatics pressure…build a model in block 616”; Fig 6)(Examiner notes that the error in the transmural pressure calculation is associated with neglecting “perturbation pressure” effects, therefore the “perturbation pressure” term is computing (and compensating for) that error.) based on the tracked arm orientation and compensating for the transmural pressure error ([0147] “relation of components of transmural blood pressure:
P
t
m
=
P
i
-
P
e
+
P
p
“,“
P
p
is perturbation pressure including hydrostatic pressure”)(Examiner notes again that “perturbation pressure” is compensating for transmural pressure error.)
Regarding Claim 32, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23, wherein the processor is configured to track arm orientation based on signals from the accelerometer (See citation above). For the remainder of Claim 32, Pantelopoulos discloses, wherein the accelerometer is located at a user's wrist ([0025] “the wrist-worn device further includes:…an accelerometer”)
Regarding Claim 46, Pantelopoulos in view of Kaifosh discloses as described above The monitor of Claim 23. For the remainder of Claim 46, Pantelopoulos does not disclose wherein the trained deep neural network comprises a convolutional neural network (CNN) or gated recurrent units (GRU).
Kaifosh teaches wherein the trained deep neural network comprises a convolutional neural network (CNN) or gated recurrent units (GRU) ([0105] “the statistical model may be a neural network…convolutional neural networks…may be used”)
The motivation for Claim 46 to combine Pantelopoulos and Kaifosh is similar as that described in more detail in Claim 23. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the arm position determination using a body-worn accelerometer (inertial sensors) for transmural blood pressure calculations as disclosed by Pantelopoulos with the convolutional neural network system for predicting arm position using training from body-worn accelerometer (inertial sensor) information as taught by Kaifosh, creating a single blood pressure monitor device with capability to obtain a “realistic representation of body movement” for accurate input into its transmural blood pressure calculations.
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos in view of Kaifosh, further in view of Luinge et. al. “Measuring orientation of human body segments using miniature gyroscopes and accelerometers”, hereinafter Luinge.
Regarding Claim 28, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 28, Pantelopoulos discloses wherein the processor (Fig 8B, “Processor”) is configured to track arm orientation ([0162]; [0167] “The inertial sensor can also be used…the position or orientation of the user's body part to which the device is attached ( e.g., the orientation or position of the user's wrist or arm)…”)
Pantelopoulos does not specifically disclose track arm orientation by feeding output signals from the set of sensors into a Kalman Filter. However, Pantelopoulos does disclose using a Kalman filter for filtering/cleaning PPG sensor data at [0185] – [0186].
Luinge teaches a system for measuring the kinematics of body segments using a body-worn inertial measurement unit (with an accelerometer and gyroscope), in which the signals are fed into a Kalman filter for more accurate results. Specifically for Claim 28, Luinge teaches track arm orientation by feeding output signals from the set of sensors ([Page 274] Fig 1 “Structure of Kalman filter estimation” shows flow of signal information through Kalman filter) into a Kalman Filter (Page 274] Fig 1; [Page 274, 3rd Full Paragraph] “design and evaluate a Kalman filter that fuses triaxial accelerometer and triaxial gyroscope signals for ambulatory recording of human body segment orientation.”; [Page 277, “3 Experimental methods” Section, including left Column—bottom and Right column--Paragraph 2] “inertial measurement unit (IMU)”, “The forearm IMU was placed on the dorsal side of the wrist…”; Fig 7 “RMS value of inclination error for three types of movement, obtained using Kalman filter and using accelerometer as inclinometer.”)
Luinge provides a motivation to combine in [Abstract] with “Using the Kalman filter described, an accurate and robust system for ambulatory motion recording can be realised.” A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize that a Kalman filter would be useful for processing the signals from body-worn inertial sensors, such as an accelerometer and gyroscope inertial sensors, in order to obtain more accurate orientation results for body segment measurements. This movement data could be used in any application in which data regarding orientation of the arm is required, such as for calculating transmural blood pressure as influenced by arm position (as taught by Pantelopoulos). A person having ordinary skill in the art would also recognize that a Kalman filter is a common, known method used to filter sensor data, and it is already mentioned for use in Pantelopoulos’ disclosure. In Pantelopoulos, Kalman filtering is used to clean the PPG sensor data in [0185] – [0186].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the blood pressure monitor and inertial sensors disclosed in Pantelopoulos with Luinge’s Kalman filtering of inertial sensor data for cleaning/filtering arm position data from body-worn inertial sensors, creating a single blood pressure monitor with more accurate arm positioning data from body-worn inertial sensors for transmural blood pressure applications.
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos in view of Kaifosh, further in view of Singhose et. al., (United States Patent Application Publication US 2020/0178851 A1), hereinafter Singhose.
Regarding Claim 29, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23. For the remainder of Claim 29, Pantelopoulos does not specifically disclose wherein the set of sensors includes a time-of-flight sensor.
Singhose teaches a system for tracking body movement using a time-of-flight sensor to detect human joint angles and movement, including the arm orientation. Specifically for Claim 29, Singhose teaches wherein the set of sensors includes a time-of-flight sensor ([0012] “markerless systems such as the Microsoft Kinect sensor estimate a human pose and joint position based on a depth map acquired with infrared or time-of-flight sensors…”; Figure 3)
Singhose provides a motivation to combine at [0008] with “low-cost sensors, such as the Microsoft Kinect sensor, can be non-invasive and used in a wider range of environments,” and “The Kinect has been widely used in the video-gaming industry and can be used to track up to 25 joints of a human skeleton”. A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize using a time-of-flight sensor such as the Kinect would be useful for obtaining reliable joint and limb movement data in a cost-effective, non-invasive way. This movement data could be used in any application in which data regarding orientation of the arm is required, such as for calculating transmural blood pressure as influenced by arm position (as taught by Pantelopoulos).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the blood pressure monitor and inertial sensors disclosed in Pantelopoulos with the time-of-flight arm orientation sensor taught by Singhose, creating a single blood pressure monitor with more accurate arm positioning data from multiple sensors for transmural blood pressure applications.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos in view of Kaifosh, further in view of Asada et. al., (United States Patent Application Publication US 2007/0055163 A1), hereinafter Asada.
Regarding Claim 33, Pantelopoulos in view of Kaifosh discloses as described above, The monitor of Claim 23, wherein the processor is configured to track arm orientation based on signals from the accelerometer (See citation above). For the remainder of Claim 33, Pantelopoulos does not specifically disclose wherein the accelerometer is located at a user's upper arm. However, Pantelopoulos does broadly disclose that sensors may be located at the upper arm, as shown in Figure 3A “upper arm 304”, [0108] “ultrasound sensor configured to capture the proximal pulse wave data at…the upper arm 304”, and [0111] “proximal pulse wave data may be obtained from…the upper arm 304”.
Asada teaches methods and apparatus for measuring arterial blood pressure at an extremity, including using an accelerometer attached to the subject’s arm via an upper arm band. Specifically for Claim 33, Asada teaches track arm orientation based on signals from the accelerometer ([0103] “additional accelerometers or multi-axis accelerometers can be used instead of single axis accelerometers to sense arm motion in three dimensions”), wherein the accelerometer is located at a user's upper arm (Fig 5, [0097] “A first accelerometer ACC1 502a is attached to the upper arm 504, with the axis 506a of the accelerometer aligned with the longitudinal direction of the upper arm.”)
Asada provides a motivation to combine at [0095] with “it is advantageous to measure the height, or vertical displacement, of the sensor relative to the heart”…and [0103] “Before computing the height h… additional accelerometers or multi-axis accelerometers can be used instead of single axis accelerometers to sense arm motion in three dimensions.” A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize that including accelerometers at additional locations, including at the upper arm, could enhance the ability to calculate the height and arm position-associated terms for transmural blood pressure calculation.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the blood pressure monitor and body-attached inertial sensors disclosed in Pantelopoulos with the upper arm-attached accelerometer taught by Asada, creating a single blood pressure monitor that can calculate accurate arm positioning data relative to the upper arm for transmural blood pressure applications.
Response to Arguments
Applicant's arguments filed 03 DECEMBER 2025 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 Rejections
Applicant argues at [Page 6, “Claims Rejections – 35 U.S.C. 101” Section, Paragraph 1 - 2] that 35 U.S.C. 101 rejections do not apply to blood pressure monitors because “regardless of USPTO guidelines”, statutory language contemplates such devices are patent-eligible subject matter. As described in the 35 U.S.C. 101 analysis above, claim 23 recites an apparatus, which is one of the statutory categories of invention (Step 1). The claim was then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1). However, based on the remaining prongs of 35 U.S.C. 101 analysis above, the claim includes the abstract idea to broadly “calculate a transmural pressure”. The argument is not persuasive.
Applicant argues at [Page 6, “Claims Rejections – 35 U.S.C. 101” Section, Paragraph 3] that a human mind cannot perform “sophisticated calculatory ability” required to calculate transmural pressure based on signals from a pulse wave sensor. Extra-solution activity is performed by the accelerometer, pulse wave sensor, and trained deep neural network as a tool to gather data in the well-understood, routine, and conventional way that they are normally used (measuring pulse wave data, measuring acceleration values, and receiving data and outputting a result). As recited in the claims, there is nothing particular in the broad recitation of “calculate a transmural pressure” that indicates sophisticated steps beyond the capability of a human that is using a textbook-type equation and the extra-solution activity inputs to yield a transmural pressure. The argument is not persuasive.
Applicant argues at [Page 6, “Claims Rejections – 35 U.S.C. 101” Section, Paragraph 4] that the claims do not recite an exception because they only “involve an exception” but do not recite one. Looking to the claims, there are abstract ideas recited in the independent claim at calculate a transmural pressure and calculate a transmural pressure by computing a transmural pressure error based on the tracked arm orientation and compensating for the transmural pressure error. The evaluation of the 35 U.S.C. 101 is based on what is positively recited in the claims. The argument is not persuasive.
Applicant argues at [Page 7, 1st Full Paragraph] that the claim as a whole is an improved blood pressure device that can automatically determine transmural blood pressure from arm orientation of a wearer as a practical application of the exception. The claims recite limitations that encompass an abstract idea of manipulating variables obtained from electronic components used in a usual way, and that variable manipulation can be accomplished with the aid of time, equations, and paper. There is nothing particular recited in the claims that uses the output of the abstract ideas for a practical application, or to improve either the “set of sensors”, “accelerometer”, “pulse wave sensor”, “processor”, or “trained deep neural network”. The tracked arm orientation activity performed by the “accelerometer” and signals from the pulse wave sensor are extra-solution data-gathering activity for subsequent steps. The abstract ideas of calculate a transmural pressure…and calculate a transmural pressure by computing a transmural pressure error based on the tracked arm orientation and compensating for the transmural pressure error…are not then realized into a practical application of the information that is observed, judged, and/or evaluated. From MPEP 2106.05(a): It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). The argument is not persuasive.
Applicant argues at [Page 7, 2nd Full Paragraph] that prior to the applicant’s invention, there was no blood pressure device that can provide transmural pressure based on signals from a pulse wave sensor based on tracked arm orientation, so the claims recite more than instructions to apply the exception using generic hardware components. As recited, the claim is broad enough to encompass the concept of a person with ordinary skill in the art, for example a researcher or university student, using a set of sensors with an accelerometer and a pulse wave sensor as data gathering tools to obtain pulse wave and orientation data in a usual way. The accelerometer data is then passed into a generically “trained deep neural network” to process accelerometer signals in a usual way. The abstract idea of broadly calculating a transmural pressure can then be performed by said researcher using the extra-solution data-gathering input value of orientation and pulse wave sensor data as input to an equation to calculate a transmural pressure. Alternatively, the broadest reasonable interpretation of the “calculate a transmural pressure” limitation would also include a person with ordinary skill in the art making a broad relative transmural pressure judgment of “high” or “low” based on the input. The argument is not persuasive.
Applicant argues at [Page 7, 2nd Full Paragraph] that the device requires no specific conscious user interaction other than wearing it, and no third party is required for the blood pressure to be obtained, so it is a technological improvement in the field of blood pressure monitoring. As recited, the claim is broad enough to encompass the concept of a person with ordinary skill in the art, for example a researcher or university student, using a set of sensors with an accelerometer and a pulse wave sensor as data gathering tools to obtain pulse wave and orientation data in a usual way. The accelerometer data is then passed into a generically “trained deep neural network” to process accelerometer signals in a usual way. The abstract idea of broadly calculating a transmural pressure can then be performed by said researcher using the extra-solution data-gathering input values from the orientation and pulse wave data as input to an equation to calculate a transmural pressure. Alternatively, even a broad relative transmural pressure judgment of “high” or “low” based on the input. The current recitation of the claim does not positively require that no third party is present for the blood pressure to be obtained. The argument is not persuasive.
Applicant argues at [Page 7, 3rd Full Paragraph] that an appeal regarding a blood pressure device of Ex parte SHIMUTA, Appeal 2024-001746, App. 16/278,879 found claims 1 and 9 to be allowable, and that this case is analogous “other than the different physical components”. Looking to the claims of the cited case:
Claim 1 of 16/278,879 recites components of an “electrocardiographic electrode,”, “photoplethysmographic sensor”, “light emitter”, “light receiver”, “pulse wave transit time acquirer”, “time measurer”, “blood pressure estimator”, and particular limitations (emphasized in the claims in the decision Page 3) of “a time measurer that measures a time elapsed from start of acquisition of the pulse wave transit time by the pulse wave transit time acquirer” and “the blood pressure estimator estimates the blood pressure after the elapsed time measured by the time measurer has become a predetermined time or more.”
Claim 9 of 16/278,879 recites components of an “electrocardiographic electrode,”, “photoplethysmographic sensor”, “light emitter”, “light receiver”, “pulse wave transit time acquirer”, “determiner”, “blood pressure estimator”, and particular limitations (emphasized in the claims in the decision Pages 3 and 4) of “a determiner that determines whether the pulse wave transit time has become stable after start of acquisition of the pulse wave transit time by the pulse wave transit time acquirer”, and “the blood pressure estimator estimates the blood pressure after the determiner has determined that the pulse wave transit time has become stable”.
In the PTAB decision, it was determined that there was [Decision: Page 7, 1st Full Paragraph] “a structurally complete, non-abstract invention in which the recited structural components interact with each other and involve using additional applications”. The Shimuta claims recite a particular time-dependent sensor data manipulation/validation that improves the accuracy of how the pulse wave transit time measurement occurs at the combination of the PPG and ECG electrodes themselves, by determining the stability of the signal during the measurement. This is a level of detail and device improvement that is not reflected in the instant, broadly-recited claims. In the instant claims, merely including the abstract idea of performing a broad “calculation of transmural pressure” in the context of a blood pressure device does not improve the performance of the accelerometer, pulse wave sensor, processor, or generic trained deep neural network. As discussed above, the claims recite limitations that encompass an abstract idea of manipulating variables obtained from electronic components used in a usual way, and that variable manipulation can be accomplished with the aid of time, equations, and paper. The argument is not persuasive.
Regarding the 35 U.S.C. 102/103 Rejections
Applicant argues at [Page 8, “Claim Rejections – 35 U.S.C. 102” Section] that Pantelopoulos does not teach a device providing a transmural pressure based on signals from a pulse wave sensor from tracked arm orientation, asserting that Pantelopoulos does not disclose that the orientation of the wrist or arm of the user “is the basis for PWA” or is used in calculation of transmural pressure. There is nothing particular in the claims that positively recites that arm orientation is the only or the basis of the transmural pressure calculation. Regarding inclusion in the calculation, Pantelopoulos discloses at [0167] that “The inertial sensor can also be used, either alone or in combination with other sensors, the position or orientation of the user's body part to which the device is attached ( e.g., the orientation or position of the user's wrist or arm). In some implementations, such information may be used to calibrate the PPG and/or taken into account in PWA.” (pulse wave analysis); and [0151] “Process 700 then obtains a transmural blood pressure level…See block 708… In some implementations, the total length that the pulse wave travels is estimated from inertial data and/or altitude data.” As such, the pulse wave analysis is disclosed to be used as part of the transmural pressure calculation. The argument is not persuasive.
Applicant summarily argues at [Page 8, “Claim Rejections – 35 U.S.C. 102” Section, Paragraph 3] that the claims that depend from the unanticipated claims are also not anticipated. Based on the 35 U.S.C. 103 rejection and discussion above, Pantelopoulos in view of Kaifosh does disclose the limitations of the independent claims. The argument is not persuasive.
Applicant argues at [Page 8, “Claim Rejections – 35 U.S.C. 103” Section] – [Page 9, top] that Pantelopoulos does not teach a device providing a transmural pressure based on signals from the pulse wave sensor and based on tracked arm orientation, and Kaifosh does not remedy that lack of teaching. As discussed above, Pantelopoulos does disclose providing a transmural pressure based on information from the inertial and/or altitude arm orientation data, such that Pantelopoulos in view of Kaifosh discloses the limitations of the independent claims. The argument is not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELISSA J MONTGOMERY whose telephone number is (571)272-2305. The examiner can normally be reached Monday - Friday 7:30 - 5:00 ET.
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791