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 Interpretation
Regarding the limitation “uncertainty value” recited in the claims, it is interpreted that the uncertainty value may be any value such as a numerical value, a rating representing quality, a binary value, or a qualitative value/feedback in light of the specification in at least pg. 4 lines 9-10 which discloses the uncertainty is below a set threshold the frequency settings are maintained, pg. 10 line 34-pg. 11 line 2 which describes the feedback input from the user may be a rating on the image quality, and pg 16 lines 27-30 which describes the feedback of the human operator may be qualitative binary feedback such as “separation/segmentation or (overall) image quality of structure got better/worse/ or current frame got better/worse” to optimize the frequency.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-7 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 2 recites the limitation “iteratively perform the control operation, the determine operation, the update operation, and the control operation; end iteration when a threshold for an uncertainty associated with the second setting for the imaging parameter is satisfied”. The limitation is unclear as it appears to refer back to control operations with the first and second settings, thus it is unclear if the claim is attempting to merely repeat the control operations using the same first and second settings for the imaging parameters or if the operations are performed with different/updated settings for the imaging settings. For examination purposes, it has been interpreted that the operations are performed iteratively using new/different settings, however, clarification is required.
Claim 3 recites the limitation “the first processor is configured to adjust the imaging parameter automatically so as to increase image quality”. It is unclear if the adjustment of the imaging parameter is the same as the updating of the first setting for the imaging parameter or if this is a different “adjusting” of the imaging parameter. In other words, updating the first setting for the imaging parameter includes an interpretation of adjusting the imaging parameter, however, due to the difference in wording it is unclear if the claim is attempting to set forth a different adjusting of the imaging parameter or if the claims is attempting to further set forth that updating is done automatically so as to increase image quality. For examination purposes, it has been interpreted to mean either the same or different from the updating, however, clarification is required.
Claim 4 recites the limitation “adjust the imaging parameter automatically so as to increase image quality”. It is unclear if the adjustment of the imaging parameter is the same as the updating of the first setting for the imaging parameter or if this is a different “adjusting” of the imaging parameter. In other words, updating the first setting for the imaging parameter includes an interpretation of adjusting the imaging parameter, however, due to the difference in wording it is unclear if the claim is attempting to set forth a different adjusting of the imaging parameter or if the claims is attempting to further set forth that updating is done automatically so as to increase image quality. For examination purposes, it has been interpreted to mean either the same or different from the updating, however, clarification is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Byrd et al. (US 7654958 B2 and included in applicant’s IDS), hereinafter Byrd in view of Tierney et al. (US 20100305442 A1), hereinafter Tierney.
Regarding claim 1,
Byrd teaches an imaging system (see at least fig. 5) , comprising:
An ultrasound catheter (at least fig. 5 (10 and 20) and corresponding disclosure in at least Col. 3 lines 20-24) configured to be operated by a user during an imaging procedure of a feature of interest of a subject (See at least fig. 4);
and a processor (at least fig.5 (30) and corresponding disclosure in at least Col. 3 lines 20-27 which disclose the workstation having a controller which may be a computer, such as a personal computer… operating software that causes the controller to perform the control functions herein) configured for communication with the ultrasound catheter (10 and 20), wherein the processor is configured to:
control, during the imaging procedure and with a first setting for an imaging parameter, the ultrasound catheter to obtain image data representative of an imaging view of the feature of interest (at least fig. 4 (440) and corresponding disclosure in at least Col. 6 lines 56-67);
determine, based on the image data, a second setting for the imaging parameter (at least fig. 4 (450) and corresponding disclosure in at least Col. 7 lines 1-15)
update the first setting for the imaging parameter to the second setting for the imaging parameter (Col. 7 lines 1-16 which disclose the frequency may be adjusted (thus updated) up or down as determined by the imaging processing system as necessary);
and control, during the imaging procedure and using the second setting for the imaging parameter, the ultrasound catheter to obtain updated image data representative of the imaging view of the feature of interest (Col. 7 lines 26-33 which discloses In step 460, the image obtained at the new frequency F.sub.1 (examiner notes this requires controlling and using the second setting for the imaging parameter, the ultrasound catheter to obtain) is processed to determine a measure of the image quality (e.g., resolution) of the feature selected for imaging. Then, in step 470, the two measures of resolution for images taken at F.sub.0 and F.sub.1 are compared to determine if the image quality (e.g., resolution) is improved or degraded as a result of the change in imaging frequency)
Byrd fails to explicitly teach wherein the ultrasound catheter is an intravascular ultrasound catheter and wherein the feature of interest is a blood vessel.
Nonetheless, Tierny, in a similar field of endeavor involving ultrasound catheters, teaches an intravascular ultrasound catheter (at least fig. 1 (102) and corresponding disclosure in at least [0022]) configured to be operated by a user during an imaging procedure of a blood vessel of a subject ([0031] which discloses when the one or more transducers 312 are disposed in the catheter 102 and inserted into a blood vessel of a patient, the one more transducers 312 may be used to form an image of the walls of the blood vessel and tissue surrounding the blood vessel).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the ultrasound catheter of Byrd to be an intravascular ultrasound catheter as taught by Tierney in order to perform imaging of blood vessels accordingly. Such a modification would provide additional diagnostic capabilities to the system of Byrd. Additionally/alternatively, such a modification amounts to merely a simple substitution of one known ultrasound catheter for another yielding predictable results with respect to ultrasound imaging using a catheter thus rendering the claim obvious (MPEP 2143).
Regarding claim 2,
Byrd further teaches wherein the processor is configured to:
Iteratively perform the control operation, the determine operation, the update operation, and the control operation (Col. 7 lines 40-46 which discloses Steps 450 through 480 are repeated until the system determines there is no change or a degradation in image quality when frequency is decreased from F.sub.i-1 to F.sub.i.);
end iteration when a threshold for uncertainty associated with the second setting for the imaging parameter is satisfied When that determination is made, (Col. 7 lines 40-62 which discloses the medical imaging system sets the imaging frequency to the frequency that provided the best measure of image quality (e.g., F.sub.i-1) in step 490 and Steps 450 through 480 are repeated until the system determines there is no change or a degradation in image quality when frequency is decreased from F.sub.i-1 to F.sub.i.);
and when iteration has ended, output, to a display, an image to the user based on the updated image data (Col. 3 lines 47-50 disclosing a video display showing the real-time image received from the ultrasound imaging system and Col. 8 lines 11-14 which discloses displayed along with the current ultrasound image. Examiner notes that such displaying of a current ultrasound image/real-time ultrasound image means that the an image is displayed to the user based on the current/real time image data thus based on the updated image data).
Regarding claim 3,
Byrd further teaches wherein the processor is configured to adjust the imaging parameter automatically so as to increase image quality (Col. 7 lines 40-45 which discloses steps 450 through 480 are repeated until the system determines there is no change or a degradation in image quality when frequency is decreased from F.sub.i-1 to F.sub.i).
Regarding claim 4,
Byrd further teaches wherein the processor is further configured to: predict an object property associated with an uncertainty value (Col. 6 lines 56-67 which discloses step 430, the image processing capability determines a measure of the image quality of the feature selected for imaging, such as by calculating a measure of resolution by measuring the definition of a boundary and the image processing capability may determine the range over which echoes from a surface are received along a vector, which may be combined with statistical measures of the changes in intensity along the vector in the vicinity of the structure); and adjust the imaging parameter based on the uncertainty value (Col. 7 lines 40-45 which discloses steps 450 through 480 are repeated until the system determines there is no change or a degradation in image quality when frequency is decreased from F.sub.i-1 to F.sub.i)
Regarding claim 5,
Byrd further teaches wherein the processor is configured to adjust the imaging parameter so as to decrease the uncertainty value of the object property (Col. 7 lines 33-62 which discloses Steps 450 through 480 are repeated until the system determines there is no change or a degradation in image quality when frequency is decreased from F.sub.i-1 to F.sub.i. When that determination is made, the medical imaging system sets the imaging frequency to the frequency that provided the best measure of image quality (e.g., F.sub.i-1) in step 490. Examiner notes that providing the best measure of image quality thus decreases any uncertainty value of the object property).
Regarding claim 6,
Byrd further teaches wherein the processor is configured to predict the object property based on a current imaging parameter (Col. 6 lines 56-67 which disclose by calculating a measure of resolution by measuring the definition of a boundary. For example, the image processing capability may determine the range over which echoes from a surface are received along a vector, which may be combined with statistical measures of the changes in intensity along the vector in the vicinity of the structure and at least 440 which discloses process image of feature of interest to measure a resolution at base frequency (i.e. a current imaging parameter)).
Regarding claim 7,
Byrd further teaches wherein the uncertainty value is provided by a user via a user interface (UI) (Col. 6 lines 37-55 which discloses this may be followed by a confirmation step that queries the user whether the image has been sufficiently optimized. If the user responds that further optimization is required, then the process shown in FIG. 4 may repeat (even with a smaller delta frequency))
Regarding claim 8,
wherein, to determine the second setting, the processor is configured to generate a label associated with the first setting for the imaging parameter, wherein the processor is configured to determine the second setting based on the label (Col. 6 line 56 – Col. 7 line 62 disclosing the determination of a measure of the image quality (e.g. resolution) of the feature selected for imaging and comparing the resulting image quality measurements. Examiner notes that a measurement of quality is considered a label associated with the first setting in its broadest reasonable interpretation. Additionally/alternatively, the definition of the frequency (e.g. F0, F1…) is considered a label associated with the first setting for the imaging parameter).
Regarding claim 11,
Byrd, as modified, further teaches wherein the IVUS catheter is a multi-frequency ultrasound imaging device (Byrd Col. 4 lines 56-64 which discloses the system is adjustable from about 2 MHz to about 20 MHz in about 0.5 MHz intervals. In this manner, the medical imaging system may adjust the imaging frequency to an imaging frequency selected from the group consisting essentially of 2.0 MHz, 2.5 MHz, 3.0 MHz . . . 19.0 MHz, 19.5 MHz, 20.0 MHz. In an embodiment, the increment of imaging frequency is more or less than about 0.5 MHz intervals. In an embodiment, the increment of imaging frequency is about 0.1 MHz. and Tierney [0036] which discloses the IVUS imaging system 100 operates within a frequency range of 5 MHz to 100 MHz).
Regarding claim 12,
Byrd, as modified, further teaches wherein the IVUS catheter is configured to emit an ultrasound signal (Byrd Col. 4 lines 51-62 which discloses the catheter-based ultrasound probe includes an array of ultrasound transducers for generating ultrasound pulse(s), the array of ultrasound transducers, such that the system has an imaging frequency range of about 2 MHz to about 20 MHz and Tierney [0023] which discloses the processor 106 may be used to control at least one of the frequency, amplitude, repetition rate, or duration of the electrical pulses transmitted from the ultrasound transmitter 108), and wherein the imaging parameter includes a frequency of the ultrasound signal (Byrd Col. 7 lines 26-33 which discloses In step 460, the image obtained at the new frequency F.sub.1 is processed to determine a measure of the image quality (e.g., resolution) of the feature selected for imaging. Then, in step 470, the two measures of resolution for images taken at F.sub.0 and F.sub.1 are compared to determine if the image quality (e.g., resolution) is improved or degraded as a result of the change in imaging frequency and Col. 4 lines 51-62 which discloses the medical imaging system may adjust the imaging frequency to an imaging frequency selected from the group consisting essentially of 2.0 MHz, 2.5 MHz, 3.0 MHz . . . 19.0 MHz, 19.5 MHz, 20.0 MHz)
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Byrd and Tierney as applied to claim 8 above, and further in view of Annangi et al. (US 20210174496 A1 and included in applicant’s IDS), hereinafter Annangi.
Regarding claim 9,
Byrd, as modified, teaches the elements of claim 8 as previously stated. Byrd fails to explicitly teach wherein the processor is configured to generate the label as an output of a pre-trained machine learning model.
Annangi, in a similar field of endeavor involving ultrasound imaging, teaches wherein a processor is configured to generate a label as an output of a pre-trained machine learning model ([0031]-[0032] which discloses the one or more frequency models 211 may include one or more neural networks or other machine learning models trained to output a respective second image quality metric that represents an image quality factor that changes as a function of transmit frequency. The one or more frequency models 211 may include a first frequency model that assesses speckle size (referred to as a speckle model), a second frequency model that assess key landmarks (referred to as a landmark detection model), and a third frequency model that assess global image quality relative to a population-wide library of ultrasound images (referred to as a global image quality model). The speckle model may be trained to output a speckle image quality metric that reflects a level of smoothness of speckling in the input ultrasound image. As speckling smoothness increases as frequency increases, the speckle image quality metric may increase as frequency increases. The landmark detection model may be trained to output a landmark image quality metric that reflects the appearance/visibility of certain anatomical features (landmarks) in the input ultrasound image. For example, as transmit frequency increases, certain anatomical features, such as the mitral valves, may start to decrease in image quality/appearance. Thus, the landmark detection model may identify the key landmarks in the input ultrasound image and output the landmark image quality metric based on the image quality/visibility of the identified key landmarks. Because the key landmarks change as the scan plane/anatomical view change, the landmark detection model may include a plurality of different landmark detection models, each specific to a different scan plane or anatomical view and The global image quality model may be trained to assess the overall image quality of an input ultrasound image relative to a population-wide library of ultrasound images. For example, the global image quality model may be trained with a plurality of ultrasound images of a plurality of different patients, with each training ultrasound image annotated or labeled by an expert (e.g., cardiologist or other clinician) with an overall image quality score (e.g., on a scale of 1-5 with 1 being a lowest image quality and 5 being a highest image quality). The global image quality model, after training/validation, may then generate an output of a global image quality metric that reflects the overall image quality of an input ultrasound image relative to the training ultrasound images. By including an image quality metric that reflects image quality relative to a wider population, patient-specific image quality issues may be accounted for).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Byrd, as currently modified, to include generating the label as an output of a pre-trained machine learning model as taught by Annagni in order to provide trained and validated networks which would provide enhanced image quality assessment that reflects the overall image quality of an input ultrasound image relative to training ultrasound images and further to account for patient-specific image quality issues (Annangi [0032])
Regarding claim 10,
The system of claim 9, wherein the pre-trained machine learning model includes a neural network ([0031] which discloses the one or more frequency models may include one or more neural networks)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROOKE L KLEIN whose telephone number is (571)270-5204. The examiner can normally be reached Mon-Fri 7:30-4.
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/BROOKE LYN KLEIN/Primary Examiner, Art Unit 3797