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 Arguments
Regarding claim interpretation
Examiner notes that the claim interpretation set forth previously is maintained as no arguments/amendments are made necessitating withdrawal of the claim interpretation. Examiner notes that the uncertainty value remains to be interpreted in light of applicant’s specification as noted below.
Regarding 35 U.S.C. 112
Examiner notes that the previously set forth 112(b) rejections are withdrawn in view of the amendments to the claims.
Regarding prior art
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 § 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.
Claims 1, 3-6, 8, and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nair et al. (US 20140177935 A1), hereinafter Nair. Examiner notes that Nair is cited in applicant’s IDS received 03/37/2025.
Regarding claim 1,
Nair discloses an imaging system (at least fig. 1A (100) and corresponding disclosure in at least [0020],at least fig. 2 (200) and corresponding disclosure in at least [0036], and at least fig. 3 (300) and corresponding disclosure in at least [0055]), comprising:
An intravascular ultrasound (IVUS) catheter (at least fig. 1A (108) and corresponding disclosure in at least [0024]) configured to be operated by a user during an imaging procedure of a blood vessel of a subject ([0034] which discloses the IVUS sensing catheter 150 is advanced beyond the area of the vascular structure to be imaged and pulled back as the transducers are operating, thereby exposing and imaging a longitudinal portion of the vessel. Furthermore, it is noted that an IVUS catheter is necessarily configured to be operated by a user during an imaging procedure of a blood vessel);
and a processor (at least fig. 1A (101) and corresponding disclosure in at least [0021]. See also at least fig. 3 (300) and corresponding disclosure in at least [0055] and [0057] which discloses In some embodiments, portions of the user interface component 300 are incorporated into components of the processing system 100 described with reference to FIGS. 1A, 1B, and 1C and FIG. 2) configured for communication with the IVUS catheter (see at least fig. 1A), wherein the processor is configured to:
control, during the imaging procedure and with a first setting for an imaging parameter, the IVUS catheter to obtain image data representative of an imaging view of the blood vessel ([0036] which discloses processing framework 200 executing on some embodiments of the imaging system 101. That control the operation of the imaging system 101, including the acquisition, processing, and display of medical sensing data associated with one or more modalities),
determine a structure of the blood vessel based on the image data ([0063] which discloses the list builder module 304 receives medical sensing data corresponding to a modality of the system 100 and utilizes the medical data to determine the mode options to present. The medical data may be the current sensing data and [0064] which discloses In a further example, the list builder module 304 receives IVUS imaging data, utilized a border-detection process on the imaging data to determine the size of the surrounding vasculature and presents imaging modes having focal distances sized accordingly. In a further example, the list builder module uses a border-detection process to determine a segment of vasculature corresponding to the IVUS data and presents imaging modes configured to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module 304 presents imaging modes configured to produce an optimal image based on the type of plaque );
determine a second setting for the imaging parameter based on the structure ([0069] Based in part on the user's mode selection (from the presented imaging modes which are based on the structure), the operating parameter module 306 of the adaptive interface module 302 determines a set of operating parameters for the system 100. See also [0075] which discloses By fine-tuning operating parameters based on other available environmental data, the parameter module 306 can further optimize and enhance a structure of interest without requiring any further user input. In some embodiments, the module 306 responds to changes in the environment in real time without prompting. Thus, the parameter module 306 may perform dynamic adaptive enhancement of operating parameters in response to changing conditions. see the parameter module 306 receives IVUS sensing data indicating an area of suspected calcification. Based in part on the IVUS data, the parameter module 306 determines an operating parameter that improves the grayscale contrast the corresponding area to improve characterization of the arterial tissue. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data containing a hot spot caused by a strong ultrasonic reflector such as a coronary stent. In response to a user selection, the parameter module 306 determines an operating parameter that enables a fluid flow analysis to detect stent malapposition. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly)
update the first setting for the imaging parameter to the second setting for the imaging parameter ([0069] Based in part on the user's mode selection (from the presented imaging modes which are based on the structure), the operating parameter module 306 of the adaptive interface module 302 determines a set of operating parameters for the system 100. [0077] which discloses the parameter module 306 receives IVUS sensing data indicating an area of suspected calcification. Based in part on the IVUS data, the parameter module 306 determines an operating parameter that improves the grayscale contrast the corresponding area to improve characterization of the arterial tissue. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data containing a hot spot caused by a strong ultrasonic reflector such as a coronary stent. In response to a user selection, the parameter module 306 determines an operating parameter that enables a fluid flow analysis to detect stent malapposition. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly.);
and control, during the imaging procedure and using the second setting for the imaging parameter, the IVUS catheter to obtain updated image data representative of the imaging view of the blood vessel ([0087] In block 516, a set of medical sensing data such as IVUS ultrasound echo data is received. The set of medical sensing data is processed according to the operating parameters in block 518. [0055] which discloses the user selects an imaging mode from the presented list, and the interface component 300 optimizes a behavior of the sensing device and/or a processing component of the medical system 100 accordingly. The user interface component 300 may also perform dynamic adaptive enhancement of operating parameters in response to changing conditions without further user attention.)
Regarding claim 3,
Nair further discloses wherein the processor is configured to determine the second setting automatically so as to increase image quality when the first setting is updated to the second setting ([0055] which discloses the user selects an imaging mode from the presented list, and the interface component 300 optimizes a behavior of the sensing device and/or a processing component of the medical system 100 accordingly and [0064] which discloses presents imaging modes configured to produce an optimal image based on the type of plaque. See also [0075] which discloses By fine-tuning operating parameters based on other available environmental data, the parameter module 306 can further optimize and enhance a structure of interest without requiring any further user input and [0077] which discloses list builder module then sets an operating parameter to enhance identification and analysis of the type of plaque).
Regarding claim 4,
Nair further discloses wherein the structure comprises an object property associated with an uncertainty value ([0077] another exemplary embodiment, the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly. In a further example, the list builder module uses the border-detection process to identify a segment of vasculature corresponding to the IVUS data and configures an operating parameter according to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module then sets an operating parameter to enhance identification and analysis of the type of plaque. Examiner notes that all of the structures necessarily comprise an object property (e.g. border, segment, size, degree of calcification, etc.) associated with an uncertainty value in its broadest reasonable interpretation, where the claim does not specify the uncertainty value nor calculation/determination thereof. In other words, any of the object properties of the structures would necessarily be associated to some uncertainty value (e.g. degree of error, quality, etc.)), and wherein the processor is configured to determine the second setting based on the uncertainty value or on a gradient of the uncertainty value (Examiner notes that the determination of the second setting is based on the structure and therefore any uncertainty or gradient of uncertainty associated therewith especially since the second setting is determined to optimize/enhance the structure as disclosed in at least [0055], [0064], [0075], and [0077]).
Regarding claim 5,
Nair further discloses wherein the processor is configured to determine the second setting so as to decrease the uncertainty value of the object property ([0055] which discloses the user selects an imaging mode from the presented list, and the interface component 300 optimizes a behavior of the sensing device and/or a processing component of the medical system 100 accordingly and [0064] which discloses presents imaging modes configured to produce an optimal image based on the type of plaque. See also [0075] which discloses By fine-tuning operating parameters based on other available environmental data, the parameter module 306 can further optimize and enhance a structure of interest without requiring any further user input and [0077] which discloses list builder module then sets an operating parameter to enhance identification and analysis of the type of plaque. Examiner notes that by enhancing the identification and analysis of the plaque, a structure of interest, optimizing parameters etc, that the determined second setting is necessarily to decrease the uncertainty value of the object property (i.e. the increase quality). Additionally/alternatively, “so as to decrease the uncertainty value of the object property” is directed towards intended use, where determining the second setting of the prior art must merely be capable of being used to decrease the uncertainty value. Because second setting is to optimize/enhance the structure of interest/image it is necessarily capable of being used to decrease any uncertainty values of the object property accordingly).
Regarding claim 6,
Nair further teaches wherein the processor is configured to determine the object property based on a current imaging parameter ([0077] which discloses the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly. In a further example, the list builder module uses the border-detection process to identify a segment of vasculature corresponding to the IVUS data and configures an operating parameter according to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module then sets an operating parameter to enhance identification and analysis of the type of plaque).
Regarding claim 8,
Nair further discloses wherein, to determine the structure, the processor is configured to generate a label associated with the first setting for the imaging parameter ([0083]-[0084] which discloses a set of imaging mode options is assembled for presenting to a user and [0064] which discloses the list builder module 304 receives IVUS imaging data, utilized a border-detection process on the imaging data to determine the size of the surrounding vasculature and presents imaging modes having focal distances sized accordingly. In a further example, the list builder module uses a border-detection process to determine a segment of vasculature corresponding to the IVUS data and presents imaging modes configured to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module 304 presents imaging modes configured to produce an optimal image based on the type of plaque. In yet a further example, the list builder module 304 analyzes the received sensing data and flags particular imaging modes as selectable only after a warning is displayed and/or additional confirmation is received. Such assembling/building of a list is considered the same as generating a label. Additionally/alternatively, it is noted that determining of a segment, size, plaque structure, etc. would necessarily generate data (thus a label) associated therewith for further use by the system to provide corresponding imaging modes/parameters accordingly), wherein the processor is configured to determine the second setting based on the label ([0069] and [0077]).
Regarding claim 13,
Nair further discloses wherein the processor is configured to determine the second setting based on the structure and not directly based on the image data ([0069] Based in part on the user's mode selection (from the presented imaging modes which are based on the structure), the operating parameter module 306 of the adaptive interface module 302 determines a set of operating parameters for the system 100. See also [0075] which discloses By fine-tuning operating parameters based on other available environmental data, the parameter module 306 can further optimize and enhance a structure of interest without requiring any further user input. In some embodiments, the module 306 responds to changes in the environment in real time without prompting. Thus, the parameter module 306 may perform dynamic adaptive enhancement of operating parameters in response to changing conditions. see the parameter module 306 receives IVUS sensing data indicating an area of suspected calcification. Based in part on the IVUS data, the parameter module 306 determines an operating parameter that improves the grayscale contrast the corresponding area to improve characterization of the arterial tissue. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data containing a hot spot caused by a strong ultrasonic reflector such as a coronary stent. In response to a user selection, the parameter module 306 determines an operating parameter that enables a fluid flow analysis to detect stent malapposition. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly)
Regarding claim 14,
Nair further teaches wherein the structure comprises at least one of a vessel wall, a plaque deposit, a calcium deposit, or a lumen border ([0064] which discloses the list builder module 304 receives IVUS imaging data, utilized a border-detection process on the imaging data to determine the size of the surrounding vasculature and presents imaging modes having focal distances sized accordingly. In a further example, the list builder module uses a border-detection process to determine a segment of vasculature corresponding to the IVUS data and presents imaging modes configured to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification).
Regarding claim 15,
Nair further teaches wherein the object property comprises at least one of an identity, a type, a segmentation, or a delineation of the structure ([0064] which discloses the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module 304 presents imaging modes configured to produce an optimal image based on the type of plaque. [0077] which discloses the parameter module 306 receives IVUS sensing data indicating an area of suspected calcification. Based in part on the IVUS data, the parameter module 306 determines an operating parameter that improves the grayscale contrast the corresponding area to improve characterization of the arterial tissue. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data containing a hot spot caused by a strong ultrasonic reflector such as a coronary stent. In response to a user selection, the parameter module 306 determines an operating parameter that enables a fluid flow analysis to detect stent malapposition. In another exemplary embodiment, the parameter module 306 receives IVUS sensing data, determines the size of the surrounding vasculature from the imaging data, and sets an operating parameter corresponding to focal distance accordingly. In a further example, the list builder module uses the border-detection process to identify a segment of vasculature corresponding to the IVUS data and configures an operating parameter according to the particular vasculature segment. In a further example, the list builder module 304 identifies a plaque structure from IVUS imaging data, and applies an algorithm to the imaging data to estimate a degree of calcification. The list builder module then sets an operating parameter to enhance identification and analysis of the type of plaque.)
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 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Nair in view of Gupta et al. (US 20130190600 A1), hereinafter Gupta.
Regarding claim 2,
Nair teaches the elements of claim 1 as previously stated. Nair further teaches wherein the processor is configured to: output, to a display, an image to a user based on the updated image data ([0047] which discloses the workflow components are configured to control the acquisition of medical sensing data such as by starting and stopping data collection at calculated times, displaying acquired and processed patient data. See also [0081] which discloses the user interface 400 includes one or more display panes 402 for displaying current medical sensing data).
Nair fails to explicitly teach wherein the processor is configured to:
Iteratively perform the control operation, the determine structure operation, the determine second setting operation, the update operation, and the control operation, wherein each iteration uses the updated setting from a previous iteration;
end iteration when a threshold for uncertainty associated with the second setting for the imaging parameter is satisfied When that determination is made, and when iteration has ended, output, to the display, the image to the user based on the updated image data.
Nonetheless, Gupta, in a similar field of endeavor involving ultrasound imaging, teaches
A processor (at least fig. 1 (110 and 108) and corresponding disclosure in at least [0029]) configured to iteratively perform the following steps:
control, during an imaging procedure and with a first setting for an imaging parameter, an ultrasound probe to obtain image data representative of an imaging view of a feature of interest (at least fig. 3 (312) and corresponding disclosure in at least [0049]),
determine a structure of a feature of interest based on the image data (at least fig. 3 (316) and corresponding disclosure in at least [0050]);
determine a second setting for the imaging parameter based on the structure (at least fig. 3 (324) and corresponding disclosure in at least [0054], where it is noted that any adjustments of the instrument settings by the processor are necessarily determined and are based on the extracted features of interest in its broadest reasonable interpretation)
update the first setting for the imaging parameter to the second setting for the imaging parameter (at least fig. 3 (324) and corresponding disclosure in at least [0054]);
and control, during the imaging procedure and using the second setting for the imaging parameter, the ultrasound probe to obtain updated image data representative of the imaging view of the feature of interest (at least fig. 3 (312) when repeated after fine adjustments to the ultrasound instrument settings are made),
wherein each iteration uses the updated setting from a previous iteration (see at least fig. 3);
end iteration when a threshold for uncertainty associated with the second setting for the imaging parameter is satisfied (at least fig. 3 (320 “Yes”) and corresponding disclosure in at least [0054]); and
when iteration has ended, output, to a display, an image to a user based on the updated image data ([0034] which discloses the optimal image frame identified by the rating platform 112 may be visualized on the display 116).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Nair to include iteratively performing the control steps, determining steps, and updating step, as taught by Gupta in order to provide a system which determines and outputs and optimal image having the optimal scanning parameters automatically. Such a modification would provide for automatic updating of imaging parameters until it is verified that the current image frame has an acceptable quality based on clinical guidelines and visual acceptability of the system (Gupta [0042]). Such a modification would further enhance the quality of images provided by Nair by providing fine tuning of the imaging parameters to obtain images with sufficient quality accordingly.
Regarding claim 7,
Nair teaches the elements of claim 4 as previously stated. Nair fails to explicitly teach wherein the uncertainty value is provided by a user via a user interface (UI).
Nonetheless, Gupta, in a similar field of endeavor involving ultrasound imaging, teaches wherein a structure comprises an object property associated with an uncertainty value ([0051] which discloses [0051] Furthermore, at step 318, a score or quality metric representative of a quality of the current image frame may be generated. In accordance with aspects of the present technique, the quality metric may be generated based on the anatomical region of interest and [0041] which discloses To that end, the quality metric generator module 206 may be configured to retrieve a corresponding model from a model database 214 and compare the current image frame with the associated model to generate the quality metric. For example, if the anatomical region of interest includes the fetal head, the quality metric generator module 206 may be configured to retrieve a determined model of the fetal head from the model database 214 and compare a current image frame with the retrieved model to generate the quality metric), wherein the processor is configured to determine a second setting based on the uncertainty value (see at least fig. 3 (320 and 324) and corresponding disclosure in at least [0054]) , and wherein the uncertainty value is provided by a user via a user interface (UI) ([0065] which discloses based on the feedback provided by the indicators 604, 608, the clinician… may decide if it is desirable to acquire more image frame of the fetal head. Examiner notes that such a decision by a user of acceptable or not (i.e. an uncertainty value) would necessarily be done by the user via user interface 118 as disclosed in [0035]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Nair to include providing an uncertainty value by a user via a user interface as taught by Gupta in order to provide more user control over the acceptability of the imaging settings. Such a modification would allow for verification of whether the current image frame has an acceptable quality based on clinical guidelines and visual acceptability of the clinician (Gupda [0042]) and providing updated parameters when the clinician decides that the current image frame is not acceptable (Gupta [0054]). Such a modification would allow for fine-tuning of the imaging parameter in order to provide images which are found to be acceptable to a user.
Claims 9-10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Nair 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,
Nair teaches the elements of claim 8 as previously stated. Nair 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 Nair to include generating the label as an output of a pre-trained machine learning model as taught by Annangi 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).
Regarding claim 12,
Nair, teaches the elements of claim 1 as previously stated. Nair further teaches wherein the IVUS catheter is configured to emit an ultrasound signal and wherein the imaging parameter includes ultrasonic waveform parameters, emitter power, amplification, amplification, and emitter/receiver patterns, however, it is not made explicitly clear whether such parameters includes a frequency of the ultrasound signal.
Nonetheless, Annangi, in a similar field of endeavor involving ultrasound imaging, teaches wherein an imaging parameter which is updated from a first setting to a second setting based on a structure identified in an ultrasound image includes a frequency of an emitted ultrasound signal (See at least fig. 3 depicting the updating of the transmit frequency from a first setting to a second setting (i.e. optimal transmit frequency) based on key landmark detection 312).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the imaging parameter of Nair to include a frequency of the ultrasound signal as taught by Annangi in order to provide optimized frequency parameters accordingly. Such a modification would allow for optimal quality of the structure identified in the ultrasound image by determining the optimal transmit frequency which provides a highest quality image for subsequent imaging.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Nair as applied to claim 8 above, and further in view of Nair (US 20140180078 A1), hereinafter Nair ‘078
Regarding claim 11,
Nair teaches the elements of claim 1 as previously stated. Nair fails to explicitly teach wherein the IVUS catheter is a multi-frequency ultrasound imaging device.
Nonetheless, Nair ‘078 in a similar field of endeavor involving IVUS imaging, teaches an IVUS catheter (at least fig. 1A (102) and corresponding disclosure in at least [0032]) which is a multi-frequency ultrasound imaging device ([0003]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the IVUS catheter of Nair to be a multi-frequency ultrasound imaging device as taught by Nair ‘078, in order to provide for high resolution intravascular multi-frequency imaging (Nair ‘078 [0003]). Additionally/alternatively, such a modification amounts to merely a simple substitution of one known IVUS catheter type for another yielding predictable results with respect to intravascular ultrasound imaging, thereby rendering the claim obvious (MPEP 2143).
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 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
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