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
This action is in response to Applicant’s remarks, filed on 4/27/2026. The amendments to claim(s) 1 have been entered. No claims have been cancelled, and no new claims have been entered. Accordingly, claim(s) 1-10 remain pending for examination.
Response to Arguments
Applicant’s arguments, see p. 4-7, with respect to the rejection of claim(s) 1-10 have been fully considered.
Regarding the rejection(s) under 35 U.S.C. § 102 and 35 U.S.C. §103, Examiner respectfully disagrees with the remarks and does not find Applicant’s arguments persuasive. New grounds of rejection are made in view of the following: new amendments provided by Applicant and attached remarks; updated search and review of pertinent, eligible prior art; and/or different interpretation of the previously applied references. Regarding the rejection of claim(s) 1-10 under 35 U.S.C. § 102 and 35 U.S.C. § 103, Applicant provides the following:
For example, Qi fails to teach each and every claimed feature. In pertinent part, as amended, independent claim 1 recites:
one or more processors configured to: process the digital intravascular blood flow data to produce processed intravascular blood flow data; and
output the processed intravascular blood flow data comprising a spectral output to a display,
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, thereby producing the spectral output from the FFT.
(Emphasis added).
As amended, claim 1 now recites that the processed intravascular blood flow
data output comprises a "spectral output ... from the FFT." [See, e.g., Specification, 25, 43-44]
The Office Action contends that Qi teaches "output[ting] the processed
intravascular blood flow data to a display" where "to process the digital intravascular-4-
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." [Office Action, p. 5-6, Qi,119, 122, 145-163, 192].
Qi does not teach the amended limitation. Qi discloses a system to "provide
specific results related to the guidance, positioning and confirmation of location." [See,
e.g., Qi, 155, 156, 158]. In particular, Qi discloses:
[0156] The exemplary device is enabled to transmit and receive signals to collect information for use in the navigation and placement process of the invention. In various embodiments, the device transmits a non-imaging ultrasound signal into the vasculature using a non-imaging ultrasound transducer on the endovascular device. The device receives a reflected ultrasound signal with the non- imaging ultrasound transducer.
[0157] In one aspect, the processor 140 is adapted and configured using software, firmware or other programming capabilities 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, as well as the capability to pre-process these signals to provide
parameter features as inputs to the processor....
[0158] The exemplary processor provides an output related to the probable position of or guidance information relating to the device within the vasculature. The exemplary processor contains, for example, rules, functions, or relationships used to determine the probable location and/or movement of the device within the body. Various embodiments of the guidance system described herein apply mathematical analysis to the pre-processed inputs
including, for example and without limitation, mathematical functions, moving windows, weights, adaptive weights, partitioning of a power spectrum, ratios of pre-processed inputs, iterations, updating of values, fuzzy logic, neural networks, membership functions, features, inference rules,
output, and feedback, alone or in any combination.
[0159] In general, the processing of the signal information to provide a result to the output device generally includes the following operations. The first step is to extract information related to one or more desired parameters from the signal
inputs (e.g. retrograde/antegrade power)....
[0160] Next, the processor receives the extracted information as inputs. ... Next, the processor evaluates membership values for the possible output membership functions. ... Next, the processor calculates a score based on the membership functions and derives a definite result related to the tip
location....
[0161] Finally, the processor output is translated into an indicator displayed on an output device. The indicator may be a unique light color, symbol, or other straightforward
indication to a user.
(Emphasis added).
The steps disclosed in paragraphs 165-170 in reference to FIG. 11 and the subsequent steps in paragraphs 171-175 appear to follow a similar process as the reproduced disclosure from Qi above.
Qi further discloses that "rather than displaying acquired signal information, the output device displays straightforward indicators to the clinician based on the underlying processing of the signals" so that the "indicators ... indicate the tip location in an easy-to-understand manner." (Emphasis added). [Qi, 119].
Evidently, Qi does not disclose displaying FFT-generated spectral blood-flow data. On the contrary, Qi teaches that the underlying signal information is processed into simplified indicators for guidance and location. At most, Qi shows that blood-flow- related signals are used to generate downstream display content and guidance indicators.
Even if Qi is "configured to display a result of information processed by the processor" and "displays a variety of other information to the user," Qi persistently emphasizes that the displayed information is simplified and straightforward, andparticularly for navigation and positioning of a device in a vasculature. [See, e.g., Qi 102, 119, 122].
That is materially different from amended claim 1, which requires "output[ting] processed intravascular blood flow data comprising [the FFT] spectral output to a display" such that it may be used for "a diagnostic assessment" of a vessel, not navigation and positioning of a device as in Qi. [Specification, 9, 50]. Therefore, Qi fails to teach every element of amended claim 1.
Examiner respectfully disagrees with Applicant’s remarks regarding the teachings of Qi; however, a new rejection to independent claim 1 under 35 U.S.C. § 103 over Qi in view of Kanz is made to address the amended language. In particular, Qi teaches the amended claim 1, except for explicitly teaching the feature of outputting the processed intravascular blood flow data comprising a spectral output to a display. Regarding Qi, Applicant should note that the spectral output may be inferred based on blood velocity profile (Qi [0276]). Examiner respectfully notes that Qi does indeed teach the feature of performing a Fast Fourier Transform (FFT) on digital intravascular blood flow data to transform the digital intravascular blood flow data into a frequency domain, thereby producing the spectral output from the FFT (Qi [fig. 11], [see claim 1 rejection]), as discussed in the previous office action. Qi discloses the following:
“Next, at step 1120, calculate frequency spectrum (e.g. Fast Fourier Transform, FFT) of each data segment.”
[0168] (emphasis added)
“The pre-processing generally includes separating desired parameters from the sampled data. In one example, the device extracts Doppler directional data (e.g. antegrade and retrograde or left and right channel). The data may be extracted at different memory locations if it is presented as a continuous incoming data stream from the sampler. In the illustrated example, 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 as shown in FIG. 11. 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] (emphasis added)
As provided above, Qi indeed teaches the use of FFT to calculate the frequency spectrum of intravascular blood flow data. Examiner respectfully agrees with Applicant that Qi may fail to explicitly teach display of spectral output. To address this feature the teachings of Kanz are provided. Kanz teaches a multipurpose host system for invasive cardiovascular diagnostic measurement acquisition and display (Kanz [clm 1]). In particular, Kanz discloses “the host system 100 relies on transducers and external diagnostic instrumentation to: (1) process the raw sensor information rendered by transducers/sensors inserted within a patient and (2) provide the information to the host 100 in particular digital or analog formats.” (Kanz [0037]), and also discloses “In addition to displaying a large number of variables, the system can display a number of waveforms in the waveform portion 245 of the interface. Example waveforms include ECG, Aortic pressure, velocity spectra, IPV and trend.” (Kanz [0057], [fig. 13]).
Examiner respectfully notes that Applicant’s arguments only address independent claim(s) 1, and no remarks regarding the subject matter of the dependent claim(s) have been presented. Accordingly, the rejections to dependent claims 2-10 are modified to address Applicant’s amendments and the new rejection to independent claim(s) 1 and are sustained. The rejections of claim(s) 1-10 under 35 U.S.C. § 103 are maintained.
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) 1-7 and 9-10 is/are rejected under pre-AIA 35 U.S.C. 103 as being obvious over Qi et al. (US20120136242A1, 2012-05-31; hereinafter “Qi”) in view of Kanz et al. (US20080269572A1, 2008-10-30; hereinafter “Kanz”) as provided by Applicant.
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, 9-11]),
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, thereby producing the spectral output from the FFT (“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]; “performing a Fast Fourier Transform (FFT) or in the time domain (no FFT). […] spectral power can be computed in the frequency domain from the shape of the power spectrum for each of the considered signals (e.g. directional Doppler). In an exemplary embodiment, the spectral power of the directional Doppler spectra is used to differentiate between retrograde and antegrade blood flow.” [0209]; “one feature used for correlating the Doppler frequency (velocity) distributions to the anatomical locations relates to the spectral power or the area under a specific Doppler frequency curve (the integral computed of the frequency spectrum) in conjunction with the uniformity of differences in frequencies over the entire frequency range.” [0276]; Spectral power is computed by performing FFT [0105-0136, 0145-0163, 0209], [fig. 1-2, 6, 9-11]);
but Qi may fail to explicitly teach outputting the processed intravascular blood flow data comprising a spectral output to a display. However, Applicant should note that the spectral output may be inferred based on blood velocity profile (Qi [0276]).
However, in the same field of endeavor, Kanz teaches an apparatus (“A multipurpose host system for invasive cardiovascular diagnostic measurement acquisition and display,” [clm 1]; [0031-0039], [fig. 1, 4-5, 10-15]), comprising:
process digital intravascular blood flow data to produce processed intravascular blood flow data; and output the processed intravascular blood flow data comprising a spectral output to a display (“a multipurpose host system 100 is, by way of example, a personal computer architecture-based system for assessing real-time invasive cardiovascular parameters from within a blood vessel (e.g., blood pressure and flow measurements).” [0032]; “input sensor types driving the output displays include pressure, flow, and temperature sensors mounted upon a flexible elongate member including combinations thereof placed, for example, upon a single guide wire or catheter.” [0034]; “the host system 100 relies on transducers and external diagnostic instrumentation to: (1) process the raw sensor information rendered by transducers/sensors inserted within a patient and (2) provide the information to the host 100 in particular digital or analog formats.” [0037]; “Additionally, imaging data, such as Intravascular Ultrasound, […] may be obtained, analyzed, and/or displayed upon the multipurpose application interface supported by the host system” [0066]; [0031-0039], [fig. 1, 4-5, 10-15; see fig. 13 reproduced below]),
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, thereby producing the spectral output from the FFT (“The PCI card 112 includes, by way of example, a digital signal processor (DSP) that samples data provided by the communicatively coupled input sensors and processes the sampled data to render digital data […] Exemplary processes performed by the DSP include: A/D and D/A conversions, FFTs, level shifting, normalizing, and scaling.” [0033]; “In addition to displaying a large number of variables, the system can display a number of waveforms in the waveform portion 245 of the interface. Example waveforms include ECG, Aortic pressure, velocity spectra, IPV and trend.” [0057]; Combo graph can display various flow and pressure data including velocity spectra based on FFT processing of digital Doppler signals [0031-0039, 0050-0064], [fig. 1, 4-5, 10-15; see fig. 13 reproduced below]).
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Flow data including velocity spectra rendered by intravascular sensors (Kanz [fig. 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 by outputting spectral output to a display as taught by Kanz. 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]). Each diagnostic display mode is driven by a designated set of parameter generation modules associated with particular input signals received by the host system from a communicatively coupled sensor. The combination mode allows parameters associated with pressure and flow modes to be displayed simultaneously on a single graphical display (Kanz [0035]).
Regarding claim 2, Qi and Kanz teach 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 and Kanz teach 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 and Kanz teach 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 and Kanz teach 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 and Kanz teach 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 and Kanz teach 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 and Kanz teach 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 and Kanz teach 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(s) 8 is/are rejected under 35 U.S.C. 103 as being obvious over Qi and Kanz as applied to claim 7 above, further in view of Mourad et al. (WO2004107963A2, 2004-12-16; hereinafter “Mourad”).
Regarding claim 8, Qi and Kanz teach 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 and Kanz 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James F. McDonald III whose telephone number is (571)272-7296. The examiner can normally be reached M-F; 8AM-6PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Koharski can be reached at 5712727230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JAMES FRANKLIN MCDONALD III
Examiner
Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
Prosecution Insights