DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 9-10 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Qi et al. (US20120136242A1, 2012-05-31; hereinafter “Qi”).
Regarding claim 1, Qi teaches an apparatus (“A positioning system” [clm 1]; “An endovascular navigation and positioning system” [abst]; [0105-0136, 0145-0163], [fig. 1-2, 6, 9-10; see fig. 1 reproduced below]), comprising:
an intravascular guidewire configured to be positioned within a blood vessel of a patient, wherein the intravascular guidewire comprises a distal portion with an intravascular blood flow sensor configured to generate intravascular blood flow data (“a transducer for mounting on a distal end of an endovascular instrument;” [clm 1]; “Exemplary features useful in the positioning schemes described herein include: […], a blood flow direction, a blood flow velocity, e.g., the highest, the lowest, the mean or the average velocity, a blood flow signature pattern, a blood flow characteristic at a particular frequency” [0040]; “The exemplary device is configured to obtain two different physiological signals from the body, in particular, a Doppler signal (an in vivo, non-image-based ultrasound signal) and an ECG signal.” [0114]; “the inventive guidance system described […] locate, guide and position catheters and/or guide wires equipped with sensors described herein within the vessels of the venous system” [0344]; The system includes a transducer on a distal end of an endovascular instrument (e.g., guide wire) which acquires a doppler signal used to derive blood flow information [0032-0071, 0105-0136, 0145-0163], [fig. 1-2, 6, 9-10; see fig. 1, 2 reproduced below]);
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Exemplary endovascular system with distal transducer for collecting Doppler blood flow data (Qi [fig. 1, 2])
an analog-to-digital converter operable to perform an analog-to-digital conversion on the intravascular blood flow data to produce digital intravascular blood flow data (“a pre-processor receiving the acoustic signal as an input, the pre-processor containing computer-readable instructions for manipulating the signal input to extract one or more acoustic features from the signal input;” [clm 1]; “The pre-processing may include, but is not limited to, conversion of a Doppler or an ECG signal from the time domain to the frequency domain, frequency to time domain, amplification, filtering, analog-to-digital conversion” [0106]; “the pre-processor includes conventional processing capabilities to receive and process ultrasound as with conventional ultrasound signals. The conventional processing capabilities may include conventional components needed to receive, process, and store the corresponding sensor data such as analog-to-digital (A/D) conversion. […] the received signals are transferred from the interface 201 through the switch 210 to a Doppler gain 217, analog filter 219, and Doppler analog-to-digital converter (ADC) 220 where the signal is amplified, filtered, and digitized.” [0152]; The input Doppler signals are converted as is typical in the digitalization of analog signals [0032-0071, 0105-0136, 0145-0163], [fig. 1-2, 6, 9-10]),
one or more processors (“a processor configured to receive the one or more extracted features,” [clm 1]; “collected signals are then pre-processed to produce one or more parameter inputs for use in a processor/controller” [0110]; [0105-0136, 0145-0163], [fig. 1-2, 6, 9-10]) configured to:
process the digital intravascular blood flow data to produce processed intravascular blood flow data (“The system 100 includes a pre-processor 139 and processor 140 configured to receive and process a signal from the non-imaging ultrasound transducer” [0148]; “the processor 140 is adapted and configured […] to receive and process physiological signals including, but not limited to, a venous blood flow direction, a venous blood flow velocity, a venous blood flow signature pattern, a pressure signature pattern, A-mode information, and a preferential non-random direction of flow,” [0157]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity) can be associated with certain location in the vasculature and in the blood stream” [0221]; The processor provides output related to position/guidance information of the device within the vasculature based on filtered digital physiological signals (i.e., processed blood flow data) which may emphasize certain frequencies [0105-0136, 0145-0163], [fig. 1-2, 6, 9-10]); and
output the processed intravascular blood flow data to a display (“an output device for displaying an indication of the output generated by the processor.” [clm 1]; “The system 100 also includes an output device 130 configured to display a result of information processed by the processor 140.” [0119]; “The exemplary output device also displays a variety of other information to the user such as tracing of the received Doppler signals and a meter representing the relative contributions of antegrade and retrograde flow.” [0122]; The display is configured to display information related to results determined by processor [0105-0136, 0145-0163], [fig. 1-2, 6, 9-10]),
wherein, to process the digital intravascular blood flow data, the one or more processors is configured to perform a Fast Fourier Transform (FFT) on the digital intravascular blood flow data to transform the digital intravascular blood flow data into a frequency domain (“calculate frequency spectrum (e.g. Fast Fourier Transform, FFT) of each data segment.” [0168]; “The exemplary low pass filter removes noise associated with wall movement (low frequency). […] The data is further subjected to other operations such as Hamming, fitting (e.g. to a Gaussian curve), and Fast Fourier Transformation (FFT) to focus on a specific parameter or feature of interest.” [0192]; [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11]).
Regarding claim 2, Qi teaches the apparatus of claim 1,
Qi further teaching wherein the intravascular blood flow sensor comprises an ultrasound transducer, and wherein the intravascular blood flow data comprises Doppler ultrasound echo signals (“a transducer for mounting on a distal end of an endovascular instrument;” [clm 1]; “Exemplary features useful in the positioning schemes described herein include: […], a blood flow direction, a blood flow velocity, e.g., the highest, the lowest, the mean or the average velocity, a blood flow signature pattern, a blood flow characteristic at a particular frequency” [0040]; “The exemplary device is configured to obtain two different physiological signals from the body, in particular, a Doppler signal (an in vivo, non-image-based ultrasound signal) and an ECG signal.” [0114]; [0032-0071, 0105-0136, 0145-0163], [fig. 1-2, 6, 9-10], [see claim 1 rejection]).
Regarding claim 3, Qi teaches the apparatus of claim 1,
Qi further teaching wherein, to process the digital intravascular blood flow data, the one or more processors is configured to perform clutter filtering on the digital intravascular blood flow data (“An input signal is obtained from the body […] The input signals are typically amplified, filtered or converted as is typical in the digitalization of analog signals and other signal processing techniques.” [0110]; “each of the retrograde and antegrade signals are subjected to a low pass filter, high pass filter, and spectrum analysis to extract flow information in the retrograde and antegrade directions. The exemplary low pass filter removes noise associated with wall movement (low frequency). The high pass filter and spectrum analysis separate the power spectrum data” [0192]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity)” [0221]; Selective filtering (i.e., clutter filtering) removes unwanted signals from blood vessel walls to emphasize the desired blood flow signals [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11]).
Regarding claim 4, Qi teaches the apparatus of claim 3,
Qi further teaching wherein the clutter filtering is configured to remove a contribution from at least one of stationary tissue or slow-moving tissue (“each of the retrograde and antegrade signals are subjected to a low pass filter, high pass filter, and spectrum analysis to extract flow information in the retrograde and antegrade directions. The exemplary low pass filter removes noise associated with wall movement (low frequency). The high pass filter and spectrum analysis separate the power spectrum data” [0192]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity)” [0221]; [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11], [see claim 3 rejection]).
Regarding claim 5, Qi teaches the apparatus of claim 3,
Qi further teaching wherein the one or more processors is configured to perform the clutter filtering in the frequency domain after the FFT is performed (“The software implementing the pre-processing techniques described can be applied in different ways. In various embodiments, the software controls are applied to the frequency domain after performing a Fast Fourier Transform (FFT) or in the time domain (no FFT).” [0209]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity)” [0221]; The software controls implementing preprocessing techniques – the selective filtering/clutter filtering – may be applied to the frequency domain after performing FFT [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11], [see claim 3 rejection]).
Regarding claim 6, Qi teaches the apparatus of claim 5,
Qi further teaching wherein, to perform the clutter filtering, the one or more processors is configured to blank a low frequency bin (“each of the retrograde and antegrade signals are subjected to a low pass filter, high pass filter, and spectrum analysis to extract flow information in the retrograde and antegrade directions. The exemplary low pass filter removes noise associated with wall movement (low frequency).” [0192]; The selective filtering may apply a low pass filter which removes noise (i.e., blanks) low frequencies to eliminate the noise associated with blood vessel walls [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11]).
Regarding claim 7, Qi teaches the apparatus of claim 3,
Qi further teaching wherein the one or more processors is configured to perform the clutter filtering in a time domain before the FFT is performed (“The pre-processing may include, but is not limited to, conversion of a Doppler or an ECG signal from the time domain to the frequency domain, frequency to time domain, amplification, filtering, analog-to-digital conversion,” [0106]; “the software controls are applied to the frequency domain after performing a Fast Fourier Transform (FFT) or in the time domain (no FFT).” [0209]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity)” [0221]; The software controls implementing processing techniques – the selective filtering/clutter filtering – may be applied in the time domain wherein FFT has not been performed yet [0105-0136, 0145-0175], [fig. 1-2, 6, 9-11; see fig. 11 reproduced below]).
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Spectrum analysis including FFT is applied after filtering sampled Doppler data (Qi [fig. 11])
Regarding claim 9, Qi teaches the apparatus of claim 7,
Qi further teaching wherein the clutter filtering comprises a high-pass filter, wherein the high-pass filter comprises at least one of infinite impulse response (IIR) architecture or finite impulse response (FIR) (“at step 1110, filter antegrade and retrograde flow using a band pass filter.” [0166]; “the device extracts Doppler directional data (e.g. antegrade and retrograde or left and right channel). […] each of the retrograde and antegrade signals are subjected to a low pass filter, high pass filter, and spectrum analysis to extract flow information in the retrograde and antegrade directions. The exemplary low pass filter removes noise associated with wall movement (low frequency). The high pass filter and spectrum analysis separate the power spectrum data” [0192]; “the pre-processor and/or processor include filters. Selective filtering of certain frequencies may be used to remove undesired artifacts and frequency components, e.g., high frequencies indicative of a high degree of turbulence. Selective filtering also may be used to emphasize certain frequencies […] the lowest and the highest relevant frequency of the spectrum (i.e. the lowest and the highest relevant detected blood velocity)” [0221]; Method step 1110 applied by endovascular navigation and positioning system filters antegrade and retrograde flow using IIR bandpass filter to remove low frequency wall movement [0105-0136, 0145-0163], [fig. 1-2, 6, 9-11; see fig. 11 reproduced below], [see claim 3 rejection]).
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IIR bandpass filter applied at step 1110 (Qi [fig. 11])
Regarding claim 10, Qi teaches the apparatus of claim 1,
Qi further teaching wherein the intravascular blood flow data comprises velocity data (“Exemplary features useful in the positioning schemes described herein include: […], a blood flow direction, a blood flow velocity, e.g., the highest, the lowest, the mean or the average velocity, a blood flow signature pattern, a blood flow characteristic at a particular frequency” [0040]; “The exemplary device is configured to obtain two different physiological signals from the body, in particular, a Doppler signal (an in vivo, non-image-based ultrasound signal) and an ECG signal.” [0114]; [0032-0071, 0105-0136, 0145-0163], [fig. 1-2, 6, 9-10], [see claim 1 rejection])).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being obvious over Qi as applied to claim 7 above, in view of Mourad et al. (WO2004107963A2, 2004-12-16; hereinafter “Mourad”).
Regarding claim 8, Qi teaches the apparatus of claim 7,
Qi further teaching wherein the clutter filtering comprises average accumulator and subtraction (“The frequency cutoffs are based on correlations with blood flow velocities. Low frequencies are associated with blood flow in the peripheral venous system, while high frequencies are associated with blood flow closer to the heart such as in the central venous system. In addition, where frequency bandwidths are used, a value or parameter can be averaged over the frequency range encompassed by the frequency bandwidth.” [0200]; “This feature is generated by calculating a moving average to the spectrum of antegrade and retrograde data previously extracted by the pre-processor (step 1131). The averaged value information is aligned with a respective heart beat” [0202]; “one or more parameters may be related to or aligned with Doppler signal information at a specific moment in the heart cycle. A parameter or feature that is aligned includes an average value, a summation, a truth value (e.g. has it passed a threshold value), a maximum value or a minimum value, and the like.” [0250]; [fig. 1-2, 6, 9-11]);
but Qi may fail to explicitly teach filtering comprises a boxcar average.
However, in the same field of endeavor, Mourad teaches an apparatus (“A system for determining the ICP of a subject” [clm 25]; [fig. 11A-11B]);
Mourad further teaching wherein the filtering comprises a boxcar average accumulator and subtraction (“ICP prediction may be implemented using linear filters, including those with both infinite impulse response (IIR) and finite impulse response (FIR) properties.” [p.24, ln.6-7]; “Range-Doppler processing provides a useful decomposition of the spatial and temporal (i.e. Doppler) scattering properties of the target of interest. Sensor time series data are divided into frames, often overlapped, multiplied by the transmitted waveform replica and then transfomied into the frequency domain via the Fast Fourier Transform (FFT) algorithm. These operations implement, very efficiently, a bank of matched filters, each matched to a narrow range of Doppler shifts.” [p.57, ln.7-12]; “the predicted ICP values were averaged and compared with averages of the invasively measured ICP. When we averaged our time traces for Patient #4 with a one- minute running box-car filter, time traces of invasively measured ICP (upper trace at left) compared well with time traced of predicted ICP (lower trace at left),” [p.72, ln.10-13]; [fig. 11A-11B, 13]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to combine the apparatus taught by Qi with the boxcar average taught by Mourad. Conventional systems typically cannot be used in the venous vasculature because the blood flow is so turbulent and complicated as to be wholly indecipherable by a user (Qi [0100]). The combined system may collect huge amounts of data long considered as an impediment (e.g. noise) and advantageously use the data to improve performance and/or accomplish tasks previously considered impossible (Qi [0101]). Furthermore, range-Doppler processing is an efficient implementation of matched filtering that has been used in the radar and sonar signal processing community for many years. It is a robust technique, in part because it makes very few assumptions about the statistical nature of the environment and targets that it encounters (Mourad [p.56-57, ln.34-7]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bjaerum et al. (Clutter filter design for ultrasound color flow imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2002 Feb;49(2):204-16) reviews and analyzes three classes of filters: finite impulse response (FIR), infinite impulse response (IIR), and regression filters. The quality of the filters was assessed based on the frequency response, as well as on the bias and variance of a mean blood velocity estimator using an autocorrelation technique [abst].
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JAMES FRANKLIN MCDONALD III
Examiner
Art Unit 3797
/SHAHDEEP MOHAMMED/ Primary Examiner, Art Unit 3797