DETAILED ACTIONS
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 office action is in response to the amendments/arguments submitted by the Applicant(s) on 04/08/2026.
Status of the Claims
Claims 1-16 are pending.
Claims 17-20 are Withdrawn.
Response to Arguments
Rejections Under 35 U.S.C. 102
Applicant's arguments, see remarks page 7-13, filed 04/08/2026.
with respect to the rejection(s) of Claims under 35 U.S.C. 102(a)(1) has been considered, and are moot because a new ground of rejections is set forth below.
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.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. “Design and Implementation of a Smartphone-Based Portable Ultrasound Pulsed-Wave Doppler Device for Blood Flow Measurement”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Fr 182 frequency Control, vol. 59, no. 1, January 2012, hereinafter Huang”2012), and in view of Huang et al. (hereinafter, Huang”2011) “Implementation of a Smart-Phone Based Portable Doppler flowmeter”, 2011 IEEE International Ultrasonics Symposium, Orlando, FL, USA, 2011, pp. 1056-1059.Uploaded by the examiner, and in further view of CAO XIABO (CN 108366160 B, hereinafter Cao).
Regrading Claim 1, Huang”2012 teaches,
A method of calibrating a blood flow monitoring device (Fig. 1 shows the block diagram of the portable smartphone-based PW Doppler ultrasound device”). comprising:
positioning a remote, and a blood flow monitor within physical proximity of one another device (Huang”2012, Figure 8, the prototype of smartphone-based portable flowmeter device. page 183, left column, “Fig. 1 shows the block diagram of the portable smartphone-based PW Doppler ultrasound device. The system
included two independent modules: a low-cost analog circuit for Doppler shift signal processing, as shown in the dash-dotted-line box (front-end), and a smartphone for Doppler spectrogram analysis, which is shown in the
dashed-line box (back-end).”, PW Doppler flowmeter can be stacked with a smartphone for portable application. See Figure 8).
the remote device having a microphone and a speaker the blood flow monitor having a speaker and configured to emit Doppler sounds (Huang”2012, Figure 1, and 5 Smart phone microphone jack), the remote device is a smart phone, and microphone and speaker are inherent element of a smart phone);
placing the remote device in a calibration mode such that the microphone of the remote device is active and can receive Doppler sounds emitted by the speaker of the blood flow monitor emitting Doppler sounds through the speaker of the blood flow monitor; receiving the Doppler sounds as received Doppler sounds using the microphone of the remote device (Huang”2012, Figure 1, Figure 8, Page 182, right column, middle paragraph, “10-MHz surface transducer was attached on the skin which was designed for transmitting and receiving ultrasound signals for real-time measurement of blood flow velocity. The audio Doppler shift signals were obtained using analog circuits”. Page 184, left col. “The directional output signals were then fed to the smartphone for Doppler spectrogram analysis”. See abstract “the Doppler signals were fed directly to an Android-based smartphone for Doppler spectrogram analysis and display in real-time”);
; identifying any differences between the received Doppler sounds and the received remote device sounds (Huang”2012, Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen. Page 184, left col. “The directional output signals were then fed to the smartphone for Doppler spectrogram analysis. The pilot frequency can be removed numerically by normalizing the baseline of Doppler spectrogram to 0 Hz”. Figure 3, Page 184, right col.” Fig. 3 shows a flowchart of the Doppler spectrogram analysis that was performed on a smartphone (Desire, HTC Co., Taoyuan, Taiwan). The software implementation was carried out on the Android 2.2”. The Android package file was installed on the smartphone for Doppler spectrogram analysis”).
Huang”2012 teaches in Fig. 1 the block diagram of the portable smartphone-based PW Doppler ultrasound device, however, Huang”2012 figure 1 does not show the skin and vessel in contact with the surface transducer of the monitoring system.
However, secondary reference Huang”2011 figure 1 shows the detail picture of the surface transducer of the monitor in contact with skin, and vessel. See (figure 1, Huang”2011 below).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the modified Huang”2012’s figure 1 of block diagram of a smartphone-based PW Doppler ultrasound device to incorporate Huang”2011’s block diagram of smartphone based portable PW doppler flowmeter system with the benefit of visually displaying the blood flow meter system surface transducer in contact with blood vessel. (Huang”2011, Figure 1, Page 1057, Left col.).
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Figure 1, secondary prior art Huang”2011.
Huang”2012 and Huang”2011 teaches placing the smart-phone close to the PW Doppler flowmeter, and receiving doppler raw signal and storing the signal. Using a software or app the smart phone calibrates the signals to generate Doppler spectrum to determine blood flow. The smart phone is installed with necessary hardware and software to process the doppler signal. Figure 3 shows the steps of signal processing by the smart phone and automatically processing the signal to adapt to the smart phone. If it is not inherent, then it is obvious in the art that software interface installed in the smart phone is capable of configuring, tuning, using, and monitoring ultrasound frequency response generation hardware logic.
Combined Huang”2012 and Huang”2011 are silent on emitting the received Doppler sounds through the speaker of the remote device as remote device sounds; receiving the remote device sounds using the microphone of the remote device
However, Cao teaches emitting the received Doppler sounds through the speaker of the remote device as remote device sounds; receiving the remote device sounds using the microphone of the remote device. (Cao, page 3, middle paragraph, “mobile terminal control loudspeaker broadcast the test sound source that sets up in advance, and audio frequency automation test equipment receives the audio signal of loudspeaker broadcast through standard microphone simultaneously, then analysis software compares received audio signal and built-in test sound source again to obtain the distortion factor and the true loudness value of loudspeaker work under current environment, and then can obtain the correlation properties of loudspeaker” (NOTE: “the test sound” could be any sound signals, it could be doppler sound received from the sound source such as doppler monitor as taught by Huang”2012), calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds (Page 5, middle paragraph, testing the whole machine, namely playing the audio through the mobile phone, then collecting the audio signal by the audio automatic testing equipment, comparing and analyzing the audio signal with the played original audio signal, and then calibrating the comparison and analysis result with the golden machine calibration standard to obtain a testing result).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the modified Huang”2012’s method of calibrating the remote device to incorporate Cao’s mobile terminal sound quality calibration step with the benefit of improved calibrated sound quality displayed by the audio device and more consistent with the characteristics of sound source and clearer (Cao, Page 13, middle paragraph).
Regrading Claim 2, combination of Huang”2012, Huang”2011 and Cao teaches the
claim 1,
combined Huang”2012 and Huang”2011are silent further teaches wherein calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds “received remote device sound” accomplished using. sound equalization characterization techniques.
However, Cao teaches wherein calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds “received remote device sound” accomplished using. sound equalization characterization techniques. (Cao, page 3, middle paragraph, “mobile terminal control loudspeaker broadcast the test sound source that sets up in advance, and audio frequency automation test equipment receives the audio signal of loudspeaker broadcast through standard microphone simultaneously, then analysis software compares received audio signal and built-in test sound source again to can obtain the distortion factor and the true loudness value of loudspeaker work under current environment, and then can obtain the correlation
properties of loudspeaker”. NOTE: “the test sound” could be any sound signals, it could be doppler sound received from the sound source such as doppler monitor as taught by Huang”2012)).
Regrading Claim 3, combination of Huang”2012, Huang”2011 and Cao teaches the claim 1,
Huang”2012 further teaches wherein the Doppler sounds are pre-recorded characterized sounds (Huang”2012, Figure 1, Figure 8, Page 182, right column, middle paragraph, “10-MHz surface transducer was attached on the skin which was designed for transmitting and receiving ultrasound signals for real-time measurement of blood flow velocity. The audio Doppler shift signals were obtained using analog circuits”).
Regrading Claim 4, combination of Huang”2012, Huang”2011 and Cao teaches the claim 1,
Huang”2012 further teaches wherein the Doppler sounds relate to patient data. (Huang, Figure 1, Figure 8, Page 182, right column, middle paragraph, “10-MHz surface transducer was attached on the skin which was designed for transmitting and receiving ultrasound signals for real-time measurement of blood flow velocity. The audio Doppler shift signals were obtained using analog circuits”. Figure 8, the portable Doppler device for human blood flow monitor).
Regrading Claim 5, combination of Huang”2012, Huang”2011 and Cao teaches the claim 4,
Huang”2012 further teaches wherein the Doppler sounds relate to blood flow through a vein. (Huang”2012, Abstract, “Blood flow measurement using Doppler ultrasound has become a useful tool for diagnosing cardiovascular diseases and as a physiological monitor”).
Regrading Claim 6, combination of Huang”2012, Huang”2011 and Cao teaches the claim 4,
Huang”2012 further teaches, wherein the Doppler sounds relate to blood flow through an artery (Huang”2012, Abstract, “Blood flow measurement using Doppler ultrasound has become a useful tool for diagnosing cardiovascular diseases and as a physiological monitor”).
Regrading Claim 7, combination of Huang”2012, Huang”2011 and Cao teaches the claim 1,
Huang”2012 further teaches wherein receiving the Doppler sounds as received Doppler sounds using the microphone of the remote device is accomplished such that the received Doppler sounds are saved locally to the remote device. (Huang”2012, Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen”).
Regrading Claim 8, combination of Huang”2012, Huang”2011 and Cao teaches the claim 1,
Huang”2012 further teaches wherein receiving the remote device sounds using the microphone of the remote device is accomplished such that the received remote device sounds are saved locally to the remote device (Huang, Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen”).
Regrading Claim 9, Huang”2012 teaches,
A method of calibrating a blood flow monitoring device (Fig. 1 shows the block diagram of the portable smartphone-based PW Doppler ultrasound device”). comprising:
placing a tissue flap at a point of treatment, the tissue flap including a section of tissue that includes a blood vessel attaching a sensor to the tissue flap to monitor blood flow through the blood vessel (Huang”2012, Figure 8, “the prototype of smartphone-based portable flowmeter device. Transducer placed on vessel. Page 182, right column, middle paragraph”) the sensor attached to the tissue flap such that it contacts the blood vessel having a speaker and configured to emit Doppler sounds activating the blood flow monitor such that the blood flow monitor is monitoring blood flow through the blood vessel (Huang”2012, Figure 8, Page 182, right column, middle paragraph, “10-MHz surface transducer was attached on the skin which was designed for transmitting and receiving ultrasound signals for real-time measurement of blood flow velocity. The audio Doppler shift signals were obtained using analog circuits”. Page 184, left col. “The directional output signals were then fed to the smartphone for Doppler spectrogram analysis”.);
Secondary reference (Huang 2012 figure 1 shows the sensor in contact with the skin and blood vessel including block diagram for back end as the smart phone see the figure above (claim 1).
positioning a remote device and the blood flow monitor within physical proximity of one another (Huang”2012 Figure 1, and Figure 8, the prototype of smartphone-based portable flowmeter device. page 183, PW Doppler flowmeter can be stacked with a smartphone for portable application. See Figure 8)., the remote device having a microphone and a speaker (Huang”2012, Figure 8, the remote device is a smart phone, and microphone and speaker are inherent element of a smart phone);
placing the remote device in a calibration mode such that the microphone of the remote device is active and can receive Doppler sounds relating to the blood flow through the blood vessel emitted by the speaker of the blood flow monitor; (Huang”2012, Figure 1, Figure 8, Page 182, right column, middle paragraph, “10-MHz surface transducer was attached on the skin which was designed for transmitting and receiving ultrasound signals for real-time measurement of blood flow velocity. The audio Doppler shift signals were obtained using analog circuits”. Page 184, left col. “The directional output signals were then fed to the smartphone for Doppler spectrogram analysis”. See abstract “the Doppler signals were fed directly to an Android-based smartphone for Doppler spectrogram analysis and display in real-time”);
identifying any differences between the received Doppler sounds and the received remote device sounds (Huang”2012, Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen. Page 184, left col. “The directional output signals were then fed to the smartphone for Doppler spectrogram analysis. The pilot frequency can be removed numerically by normalizing the baseline of Doppler spectrogram to 0 Hz”.Figure 3, Page 184, right col.” Fig. 3 shows a flowchart of the Doppler spectrogram analysis that was performed on a smartphone (Desire, HTC Co., Taoyuan, Taiwan). The software implementation was carried out on the Android 2.2”. The Android package file was installed on the smartphone for Doppler spectrogram analysis”).
Huang”2012 teaches in Fig. 1 the block diagram of the portable smartphone-based PW Doppler ultrasound device, however, Huang”2012 figure 1 does not show the skin and vessel in contact with the surface transducer of the monitoring system.
However, secondary reference Huang”2011 figure 1 shows the detail picture of the surface transducer of the monitor in contact with skin, and vessel. See (figure 1, Huang”2011 above in claim 1).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the modified Huang”2012’s figure 1 of block diagram of a smartphone-based PW Doppler ultrasound device to incorporate Huang”2011’s block diagram of smartphone based portable PW doppler flowmeter system with the benefit of visually displaying the blood flow meter system surface transducer in contact with blood vessel. (Huang”2011, Figure 1, Page 1057, Left col.).
Huang”2012 and Huang”2011 teaches placing the smart-phone close to the PW Doppler flowmeter, and receiving doppler raw signal and storing the signal. Using a software or app the smart phone calibrates the signals to generate Doppler spectrum to determine blood flow. The smart phone is installed with necessary hardware and software to process the doppler signal. Figure 3 shows the steps of signal processing by the smart phone and automatically processing the signal to adapt to the smart phone. If it is not inherent, then it is obvious in the art that software interface installed in the smart phone is capable of configuring, tuning, using, and monitoring ultrasound frequency response generation hardware logic.
Combined Huang”2012 and Huang”2011 are silent on emitting the received Doppler sounds through the speaker of the remote device as remote device sounds; receiving the remote device sounds using the microphone of the remote device
However, Cao teaches emitting the received Doppler sounds through the speaker of the remote device as remote device sounds; receiving the remote device sounds using the microphone of the remote device. (Cao, page 3, middle paragraph, “mobile terminal control loudspeaker broadcast the test sound source that sets up in advance, and audio frequency automation test equipment receives the audio signal of loudspeaker broadcast through standard microphone simultaneously, then analysis software compares received audio signal and built-in test sound source again to can obtain the distortion factor and the true loudness value of loudspeaker work under current environment, and then can obtain the correlation properties of loudspeaker” NOTE:” the test sound” could be any sound signals, it could be doppler sound received from the sound source such as doppler monitor as taught by Huang”2012). calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds (Page 5, middle paragraph,” testing the whole machine, namely playing the audio through the mobile phone, then collecting the audio signal by the audio automatic testing equipment, comparing and analyzing the audio signal with the played original audio signal, and then calibrating the comparison and analysis result with the golden machine calibration standard to obtain a testing result”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the modified Huang”2012’s method of calibrating the remote device to incorporate Cao’s mobile terminal sound quality calibration step with the benefit of improved calibrated sound quality displayed by the audio device and more consistent with the characteristics of sound source and clearer (Cao, Page 13, middle paragraph).
Regrading Claim 10, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Combination of Huang”2012 and Huang”2011 are silent further teaches wherein calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds “received remote device sound” accomplished using. sound equalization characterization techniques.
However, Cao teaches wherein calibrating the remote device based on the identified differences between the received Doppler sounds and the received remote device sounds “received remote device sound” accomplished using. sound equalization characterization techniques. (Cao, page 3, middle paragraph, “mobile terminal control loudspeaker broadcast the test sound source that sets up in advance, and audio frequency automation test equipment receives the audio signal of loudspeaker broadcast through standard microphone simultaneously, then analysis software compares received audio signal and built-in test sound source again to can obtain the distortion factor and the true loudness value of loudspeaker work under current environment, and then can obtain the correlation
properties of loudspeaker”. NOTE: “the test sound” could be any sound signals, it could be doppler sound received from the sound source such as doppler monitor as taught by Huang”2012).
Regrading Claim 11, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Huang”2012 further teaches wherein the Doppler sounds relate to blood flow through a vein. (Huang”2012, Abstract, “Blood flow measurement using Doppler ultrasound has become a useful tool for diagnosing cardiovascular diseases and as a physiological monitor”).
Regrading Claim 12, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Huang”2012 further teaches, wherein the Doppler sounds relate to blood flow through an artery (Huang”2012, Abstract, “Blood flow measurement using Doppler ultrasound has become a useful tool for diagnosing cardiovascular diseases and as a physiological monitor”).
Regrading Claim 13, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Huang”2012 further teaches wherein receiving the Doppler sounds as received Doppler sounds using the microphone of the remote device is accomplished such that the received Doppler sounds are saved locally to the remote device. (Huang”2012 Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen”).
Regrading Claim 14, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Huang”2012 further teaches wherein receiving the remote device sounds using the microphone of the remote device is accomplished such that the received remote device sounds are saved locally to the remote device (Huang”2012 , Page 182, right col.” Subsequently, the Doppler signals were digitized by the built-in ADC of the smartphone via a microphone jack. Doppler spectrum processing was implemented through a program written on the Android platform (Google Inc., Mountain View, CA) for a stand-alone smartphone. Both Doppler signals and Doppler spectrograms can be stored on a built-in memory card, as well as displayed in real time on the touch screen”).
Regrading Claim 15, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 9,
Huang”2012 further teaches wherein the remote device has a visual display field; and wherein the blood flow monitor has a visual display field. (Huang”2012, Figure 1, smart-phone display, remote device is a smart-phone))
Regrading Claim 16, combination of Huang”2012, Huang”2011 and Cao teaches the method of claim 15,
Huang”2012 further teaches further comprising displaying data relating to the blood flow through the blood vessel on the visual display field of the blood flow monitor and further comprising displaying data relating to the blood flow through the blood vessel on the visual display field of the remote device. (Huang”2012, Figure 1, Figure 6-7, abstract, “After hardware processing, the Doppler signals were fed directly to a smartphone for Doppler spectrogram analysis and display in real time”. Conclusion, “The Doppler spectrogram was computed on a smartphone using an Android application program. In this system, both the signal and spectrogram
can be stored and displayed in real time.”).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
VAN et al. (US 2021/0085280 A1) recites “A system for monitoring blood distribution in a subject, the system comprising a processor(38) responsive to Doppler ultrasound data representing arterial blood flow in at least two different locations of the subject, such as the neck and the arm, to obtain velocity (C, B1, B2, B3) or volumetric flow rate at each location, to monitor changes in a predetermined function of the blood flows, and to provide an output indicative of the monitored changes which may result from blood volume centralization. This can indicate the onset of hypovolemia or hypervolemia” (abstract)
Cahan et al. (US 2017/0224279 A1) discloses “A system and method for monitoring a health status of a subject. The system comprises: a medical device implantable in the subject and having a passage or compartment through which blood flows through; a sensor device embedded at or near a surface of said passage within said medical device for generating signals suitable for measuring a Doppler shift effect occurring within said passage; and a control device coupled to said sensor device for measuring a liquid blood flow rate within said passage based on sensor generated signals outputs. The embedded sensor device comprises a first piezo-electric element configured to generate an acoustic excitation signal and a second piezo-electric element configured to receive said acoustic excitation signal. The second piezo-electric element emits a signal responsive to said acoustic excitation signal. Control device in real time compares a generated output signal with the input excitation signal to determine a Doppler frequency shift measurement” (Abstract).
James C. Yuen, “Enabling Remote Monitoring Using Free Apps and
Smart Devices for a Free-Flap Adjunct Monitor”, Plastic Reconstruction Surgery, Glob Open. 2017 Sep 21;5(9)
Abstract: Remote monitoring capability does not currently exist for Periflux (Perimed AB, Järfälla, Sweden) laser Doppler and other perfusion monitors. Two simple adaptations using free apps (applications) and smart devices can enable transmission of the perfusion readout to the surgeon's smartphone. A literature review was conducted to identify reports relating to remote free flap monitoring. In addition, 2 wireless methodologies are devised: One method uses a free app that converts a smart device into a camera, stationed next to the perfusion monitor, to stream live video of the laser Doppler readout to the surgeon's smartphone; a second method uses a free app installed on a bedside laptop computer, which is connected to the laser Doppler flowmeter via a data cord. A live feed of the computer's desktop as a teleconference host is transmitted to the surgeon's smart device over the Internet. These 2 methodologies were employed on 9 and 8 free flaps, respectively, as a pilot study. All free flaps were monitored remotely for 4–6 days with near 100% reliability. The Internet connectivity became disrupted only on several occasions, requiring simple Wi-Fi and software reset. Minor mechanical issues were encountered with the video streaming method. Literature review identified very few articles describing remote monitoring of free flaps. The 2 methodologies reported here provided reliable continuous transmission of quantitative data of flap perfusion to smart devices via Internet connection, which can revolutionize the microsurgeon's practice if his/her adjunctive perfusion monitor with display does not yet have Wi-Fi capability.
(Plast Reconstr Surg Glob Open 2017; Published online 21 September 2017.)
Jana et al. ‘Implementation of smartphone-based blood flow diagnoses from Doppler spectrogram”, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems.
Abstract—"This paper introduces a new scheme to identify the blood flow condition based on a processing of the Ultrasound Doppler spectrograms on an Android Smartphone. A set of blood flow spectrograms are processed to denoise and some features are extracted. These features are used in supervised classifiers (support vector machine and k-nearest neighbors) to detect the blood flow abnormalities. Due to better performance of support vector machine, an Android application is implemented based on this classifier for diagnosing arterial diseases”.
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/DILARA SULTANA/Examiner, Art Unit 2858 July 31, 2026
/SON T LE/Primary Examiner, Art Unit 2858
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