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 § 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 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 11 follows.
STEP 1
Regarding claim 8, the claim recites a series of steps or acts, including dynamically detecting a blood pressure waveform of an arterial blood pressure signal generated by the invasive arterial blood pressure monitoring apparatus. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of dynamically detecting, responsive to the first flush sequence, presence of damping in the
detected blood pressure waveform by applying a machine learning algorithm to conduct an analysis of the detected blood pressure waveform which appears as a square wave stimulus. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. The detection of damping does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the detected damping, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of initiating a first flush sequence of one or more flushes of a catheter of the invasive arterial blood pressure monitoring apparatus; and dynamically detecting a blood pressure waveform of an arterial blood pressure signal generated by the invasive arterial blood pressure monitoring apparatus. Initiating flushes and detecting resulting waveforms is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the detecting steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claims 1 and 14, the apparatus and system recited in the claim is are generic and comprise generic components configured to perform the abstract idea. The recited a blood pressure monitor is a generic sensor configured to perform pre-solutional data gathering activity, and the processor and memory is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to abstract ideas and the display of data. Claims 2, 3, 6,9, 10, 13, 16, 17, and 20 recite abstract ideas in the form of mathematical concepts/mental processes. Claims 5,7, 12, 14, 19, and 20 recite insignificant post solutional activity. The detecting steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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 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.
Claim 16 is 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. The specification fails to recite how the machine learning algorithm generates the square wave. It is recommended claim 16 be amended to match the wording of claims 2 and 9.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 states that the detected blood pressure waveform appears as a “square wave stimulus”. It is unclear if the waveform itself appears as a square wave or if the stimulus that causes the waveform is square. For purposes of examination, the stimulus that causes the waveform is being interpreted as being square. The same issue is present in claims 8 and 15.
Claim 7 recites the limitation " the one or more estimated blood pressure waveforms ". There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the one or more estimated true blood pressure waveforms”. The same issue is present in claims 14 and 20.
Claim Rejections - 35 USC § 103
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, 8. And 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimbert (US 20150126880 A1 - cited by applicant ) in view of Reinhart (Detection of arterial pressure waveform error using machine learning trained algorithms - cited by applicant).
In regards to claim 1 Grimbert teaches a system, comprising: an invasive arterial blood pressure monitoring apparatus;
and a blood pressure monitor operatively connected to the invasive arterial blood pressure monitoring apparatus, the blood pressure monitor including one or more processors and a non-transitory memory operatively coupled to the one or more processors comprising a set of instructions executable by the one or more processors to cause the one or more processors to:
initiate a first flush sequence of one or more flushes of a catheter of the invasive arterial
blood pressure monitoring apparatus ([0137] “This method therefore uses the fast flushes initiated by the clinical staff and forming part of the protocol, which provide a source of echelons from which the method can be implemented”);
dynamically detect a blood pressure waveform of an arterial blood pressure signal
generated by the invasive arterial blood pressure monitoring apparatus ([0136] response signal R is a bp waveform) and
dynamically detect, responsive to the first flush sequence, presence of damping in the
detected blood pressure waveform by applying an algorithm to conduct
an analysis of the detected blood pressure waveform which appears as a square wave
stimulus ([0136] “find the dynamic parameters of the system, specifically the damping factor z”, [0040] Fast flush inherently has square wave).
Grimbert fails to teach a machine learning algorithm that detect the presence of damping in a blood pressure waveform. Rinehart teaches a machine learning algorithm that detects damping in a blood pressure waveform (Each of the three transducer error states (high, low, damped) were independently trained and assessed for in separate iterations of the training processes versus the Normal condition (i.e. normal vs. high, normal vs. low, normal vs. damped) with the intent of creating a separate detection algorithm for each condition (as opposed to a single multi-class detection algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Grimbert to use the labeling machine learning model of Rinehart to classify if the waveform contains damping and labeling it accordingly. Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of classifying the waveform to determine if intervention is needed due to the presence of damping.
In regards to claim 8 Grimbert teaches a computer implemented method of dynamically monitoring a blood pressure measurement, comprising:
initiating a first flush sequence of one or more flushes of a catheter of the invasive arterial blood pressure monitoring apparatus ([0137] “This method therefore uses the fast flushes initiated by the clinical staff and forming part of the protocol, which provide a source of echelons from which the method can be implemented”);
dynamically detecting a blood pressure waveform of an arterial blood pressure signal
generated by the invasive arterial blood pressure monitoring apparatus ([0136] response signal R is a bp waveform) and
dynamically detecting, responsive to the first flush sequence, presence of damping in the
detected blood pressure waveform by applying an algorithm to conduct
an analysis of the detected blood pressure waveform which appears as a square wave
stimulus ([0136] “find the dynamic parameters of the system, specifically the damping factor z”, [0040] Fast flush inherently has square wave).
Grimbert fails to teach a machine learning algorithm that detect the presence of damping in a blood pressure waveform. Rinehart teaches a machine learning algorithm that detects damping in a blood pressure waveform (Each of the three transducer error states (high, low, damped) were independently trained and assessed for in separate iterations of the training processes versus the Normal condition (i.e. normal vs. high, normal vs. low, normal vs. damped) with the intent of creating a separate detection algorithm for each condition (as opposed to a single multi-class detection algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Grimbert to use the labeling machine learning model of Rinehart to classify if the waveform contains damping and labeling it accordingly. Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of classifying the waveform to determine if intervention is needed due to the presence of damping.
In regards to claim 15 Grimbert teaches an apparatus comprising:
a blood pressure monitor including one or more processors and a non-transitory memory operatively coupled to the one or more processors comprising a set of instructions executable by the one or more processor to cause the one or more processors to ([0063] processors inherently have memory and are part of the arterial blood pressure sensing device):
initiate a first flush sequence of one or more flushes of a catheter of the invasive arterial
blood pressure monitoring apparatus ([0137] “This method therefore uses the fast flushes initiated by the clinical staff and forming part of the protocol, which provide a source of echelons from which the method can be implemented”);
dynamically detect a blood pressure waveform of an arterial blood pressure signal
generated by the invasive arterial blood pressure monitoring apparatus ([0136] response signal R is a bp waveform) and
dynamically detect, responsive to the first flush sequence, presence of damping in the
detected blood pressure waveform by applying an algorithm to conduct
an analysis of the detected blood pressure waveform which appears as a square wave
stimulus ([0136] “find the dynamic parameters of the system, specifically the damping factor z”, [0040] Fast flush inherently has square wave).
Grimbert fails to teach a machine learning algorithm that detect the presence of damping in a blood pressure waveform. Rinehart teaches a machine learning algorithm that detects damping in a blood pressure waveform (Each of the three transducer error states (high, low, damped) were independently trained and assessed for in separate iterations of the training processes versus the Normal condition (i.e. normal vs. high, normal vs. low, normal vs. damped) with the intent of creating a separate detection algorithm for each condition (as opposed to a single multi-class detection algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Grimbert to use the labeling machine learning model of Rinehart to classify if the waveform contains damping and labeling it accordingly. Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of classifying the waveform to determine if intervention is needed due to the presence of damping.
Claim(s) 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimbert (US 20150126880 A1 - cited by applicant ) in view of Reinhart (Detection of arterial pressure waveform error using machine learning trained algorithms - cited by applicant) as applied to claims 1, 8, and 15, in view of Reiner (US 20220133237 A1).
In regards to claim 2 modified Grimbert teaches the system of claim 1. Modified Grimbert fails to teach a system wherein the set of instructions cause the one or more processors to generate, in response to the application of the machine learning algorithm, a square wave to produce a second blood pressure waveform. Reiner teaches repeating data collection when an abnormality is detected in order to confirm the presence of the abnormality ([0119] “the program performs a subsequent action to determine the importance and validity of the data abnormality. This may include repeating the data collection (to validate the initial data measurement), correlating the recent data measure with comparable historical data measurements”. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the processor of modified Grimbert to perform a second fast flush to confirm if the damping is still present like the error confirming method of Reiner . Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of confirming there is damping present within the system and the previous measurement wasn’t an error.
In regards to claim 3 modified Grimbert teaches the system of claim 2, wherein the set of instructions cause the one or more processors to compare the detected blood pressure waveform to the second blood pressure waveform (Reiner [0119] new measurement data to previous data to confirm presence of abnormality) .
In regards to claim 5 modified Grimbert teaches the system of claim 3, wherein the wherein the set of instructions cause the one or more processors to generate, in response to a comparison which confirms the presence of damping in the detected blood pressure waveform a an alert signal in the presence of a light changing color ([0092] “Another function comprises detection of measuring artefacts and various incidents which might occur on the hydraulic connection and on the catheter, and such as to question the pertinence of information given by the monitor. Another function comprises providing clinical staff with information on the current state of the hydraulic connection and on any need for corrective action on its part (typically, fast flush or replacement of the tubing and/or of the catheter). This information is preferably supplied by means of a simplified man-machine interface, for example consisting of an indicator light which can take on a different colour as a function of each of situations (a), (b) and (c).”). Modified Grimbert fails to teach an audio warning signal, a video warning signal, or a haptic warning signal. The examiner takes official notice that the use of audio/video/haptic warning signals is well known and conventional in the art at the time of filing. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Grimbert to issue an audio, video, or haptic warning instead of a light warning. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In regards to claim 6 modified Grimbert teaches the system of claim 3, wherein the set of instructions cause the one or more processors to calculate, in response to a comparison which confirms the presence of damping in the detected blood pressure waveform, one or more estimated true blood pressure waveforms without damping based on the detected blood pressure waveform (Grimbert [0151] A first method comprises application, via software, of correction of the distorted incident raw signal followed by comparative study of both signals (raw and corrected). FIG. 10 shows a portion of signal acquired immediately after a fast flush, revealing the raw signal B and the corrected signal C, which allowed determining by percussion analysis the characteristics of the hydraulic connection connecting the catheter to the transducer and reconstructing the signal exempt of deformations.).
In regards to claim 7 modified Grimbert teaches the system of claim 6, wherein the set of instructions cause the one or more processors to display, on a display interface of the blood pressure monitor, of the detected blood pressure waveform in a foreground of the display interface and a range of the one or more estimated blood pressure waveforms in a background of the display interface (Grimbert Fig. 10 shows raw measured signal in front and of a corrected measured signal in the back as a dashed line, [0126-0127] Said system can also display the arterial pressure plottings, record them to ensure the traceability of measurements and/or perform quality follow-up. In particular, it can produce detailed display of different curves and parameters in graphic and/or digital form, for example: curves of raw and corrected arterial pressure, to observe the lines showing over- or under-estimations).
In regards to claim 9 modified Grimbert teaches the computer-implemented method of claim 8. Modified Grimbert fails to teach a method wherein dynamically detecting-damping comprises applying the machine learning algorithm to generate a square wave that produces a second blood pressure waveform. Reiner teaches repeating data collection when an abnormality is detected in order to confirm the presence of the abnormality ([0119] “the program performs a subsequent action to determine the importance and validity of the data abnormality. This may include repeating the data collection (to validate the initial data measurement), correlating the recent data measure with comparable historical data measurements”. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Grimbert to include performing a second fast flush to confirm if the damping is still present like the error confirming method of Reiner . Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of confirming there is damping present within the system and the previous measurement wasn’t an error.
In regards to claim 10 modified Grimbert teaches the computer-implemented method of claim 9, wherein the analysis comprises comparing the detected blood pressure waveform to the second blood pressure waveform (Reiner [0119] new measurement data to previous data to confirm presence of abnormality).
In regards to claim 12 modified Grimbert teaches the computer-implemented method of claim 10, further comprising generating, in response to a comparison which confirms presence of damping in the detected blood pressure waveform, an alert signal in the presence of a light changing color ([0092] “Another function comprises detection of measuring artefacts and various incidents which might occur on the hydraulic connection and on the catheter, and such as to question the pertinence of information given by the monitor. Another function comprises providing clinical staff with information on the current state of the hydraulic connection and on any need for corrective action on its part (typically, fast flush or replacement of the tubing and/or of the catheter). This information is preferably supplied by means of a simplified man-machine interface, for example consisting of an indicator light which can take on a different colour as a function of each of situations (a), (b) and (c).”). Modified Grimbert fails to teach an audio warning signal, a video warning signal, or a haptic warning signal. The examiner takes official notice that the use of audio/video/haptic warning signals is well known and conventional in the art at the time of filing. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Grimbert to issue an audio, video, or haptic warning instead of a light warning. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In regards to claim 13 modified Grimbert teaches the computer-implemented method of claim 10, further comprising calculating, in response to a comparison which confirms presence of damping in the detected blood pressure waveform, one or more estimated blood pressure waveforms without damping based on the detected blood pressure waveform (Grimbert [0151] A first method comprises application, via software, of correction of the distorted incident raw signal followed by comparative study of both signals (raw and corrected). FIG. 10 shows a portion of signal acquired immediately after a fast flush, revealing the raw signal B and the corrected signal C, which allowed determining by percussion analysis the characteristics of the hydraulic connection connecting the catheter to the transducer and reconstructing the signal exempt of deformations.).
In regards to claim 14 modified Grimbert teaches the computer-implemented method of claim 13, further comprising causing, on a display interface, a display of the detected blood pressure waveform in a foreground of a display interface and a range of the one or more estimated blood pressure waveforms in a background of the display interface (Grimbert Fig. 10 shows raw measured signal in front and of a corrected measured signal in the back as a dashed line, [0126-0127] Said system can also display the arterial pressure plottings, record them to ensure the traceability of measurements and/or perform quality follow-up. In particular, it can produce detailed display of different curves and parameters in graphic and/or digital form, for example: curves of raw and corrected arterial pressure, to observe the lines showing over- or under-estimations).
In regards to claim 16 modified Grimbert teaches the apparatus of claim 15. Modified Grimbert fails, wherein the machine learning algorithm generates a square wave to produce a second blood pressure waveform. Reiner teaches repeating data collection when an abnormality is detected in order to confirm the presence of the abnormality ([0119] “the program performs a subsequent action to determine the importance and validity of the data abnormality. This may include repeating the data collection (to validate the initial data measurement), correlating the recent data measure with comparable historical data measurements”. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the processor of modified Grimbert to perform a second fast flush to confirm if the damping is still present like the error confirming method of Reiner . Doing so would merely be combining prior art elements according to known methods in order to yield predictable result of confirming there is damping present within the system and the previous measurement wasn’t an error.
In regards to claim 17 modified Grimbert teaches the apparatus of claim 15, wherein the set of instructions cause the one or more processors to compare the detected blood pressure waveform to the second blood pressure waveform (Reiner [0119] new measurement data to previous data to confirm presence of abnormality) .
In regards to claim 19 modified Grimbert teaches the apparatus of claim 15, wherein the set of instructions cause the one or more processors to generate, in response to a comparison which confirms the presence of damping in the detected blood pressure waveform, one or more of an audio warning signal, a video warning signal, or a haptic warning signal (Grimbert [0151] A first method comprises application, via software, of correction of the distorted incident raw signal followed by comparative study of both signals (raw and corrected). FIG. 10 shows a portion of signal acquired immediately after a fast flush, revealing the raw signal B and the corrected signal C, which allowed determining by percussion analysis the characteristics of the hydraulic connection connecting the catheter to the transducer and reconstructing the signal exempt of deformations.).
In regards to claim 20 modified Grimbert teaches the apparatus of claim 19 wherein the set of instructions cause the one or more processors to: calculate, in response to a comparison which confirms the presence of damping in the detected blood pressure waveform, one or more estimated blood pressure waveforms without damping based on the detected blood pressure waveform (Grimbert [0151] A first method comprises application, via software, of correction of the distorted incident raw signal followed by comparative study of both signals (raw and corrected). FIG. 10 shows a portion of signal acquired immediately after a fast flush, revealing the raw signal B and the corrected signal C, which allowed determining by percussion analysis the characteristics of the hydraulic connection connecting the catheter to the transducer and reconstructing the signal exempt of deformations.), and cause, on a display interface-a display of the detected blood pressure waveform in a foreground of a display interface and a range of the estimated blood pressure waveform in a background of the display interface (Grimbert Fig. 10 shows raw measured signal in front and of a corrected measured signal in the back as a dashed line, [0126-0127] Said system can also display the arterial pressure plottings, record them to ensure the traceability of measurements and/or perform quality follow-up. In particular, it can produce detailed display of different curves and parameters in graphic and/or digital form, for example: curves of raw and corrected arterial pressure, to observe the lines showing over- or under-estimations).
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimbert (US 20150126880 A1 - cited by applicant ) in view of Reinhart (Detection of arterial pressure waveform error using machine learning trained algorithms - cited by applicant) in view of Reiner (US 20220133237 A1) as applied to claim 3, 10, and 15 in view of Webler (US 20150112210 A1).
In regards to claim 4 modified Grimbert teaches the system of claim 3, wherein in response to a comparison which confirms the presence of damping in the detected blood pressure waveform, a second flush sequence of one or more flushes of the catheter of the invasive blood pressure monitoring apparatus (Grimbert [0021] Now, the clinical staff more easily detects a damping factor or attenuation than resonance on the signal displayed on the monitor. In the first case, it usually performs one or more fast flushes of the tubing until the defect disappears). Modified Grimbert fails to teach automatic flushing. Webler teaches automatic flushing of an invasive blood pressure monitor ([0042] “This operating function can also be used to automatically flush the system”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Grimbert to automatically flush the catheter like the device of Webler until the damping returns to normal. Doing so would merely be a simple substitution of one flushing method for another in order to automate the process.
In regards to claim 11 modified Grimbert teaches the computer-implemented method of claim 10, further comprising initiating, in response to a comparison which confirms presence of damping in the detected blood pressure waveform, a second flush sequence of one or more flushes of the arterial catheter of the invasive arterial blood pressure monitoring apparatus (Grimbert [0021] Now, the clinical staff more easily detects a damping factor or attenuation than resonance on the signal displayed on the monitor. In the first case, it usually performs one or more fast flushes of the tubing until the defect disappears). Modified Grimbert fails to teach automatic flushing. Webler teaches automatic flushing of an invasive blood pressure monitor ([0042] “This operating function can also be used to automatically flush the system”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Grimbert to automatically flush the catheter like the device of Webler until the damping returns to normal. Doing so would merely be a simple substitution of one flushing method for another in order to automate the process.
In regards to claim 18 modified Grimbert teaches the apparatus of claim 15, wherein the set of instructions cause the one or more processors' to initiate, in response to a comparison which confirms presence of damping in the detected blood pressure waveform, a second flush sequence of one or more flushes of the arterial catheter of the invasive arterial blood pressure monitoring apparatus (Grimbert [0021] Now, the clinical staff more easily detects a damping factor or attenuation than resonance on the signal displayed on the monitor. In the first case, it usually performs one or more fast flushes of the tubing until the defect disappears). Modified Grimbert fails to teach automatic flushing. Webler teaches automatic flushing of an invasive blood pressure monitor ([0042] “This operating function can also be used to automatically flush the system”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Grimbert to automatically flush the catheter like the device of Webler until the damping returns to normal. Doing so would merely be a simple substitution of one flushing method for another in order to automate the process.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791
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