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
Applicant’s arguments, see pg. 13-14, filed 14 Apr 2026, with respect to the drawing objections have been fully considered and are persuasive. The drawing objections of 14 Jan 2026 have been withdrawn in view of amended Fig. 4.
Applicant’s arguments, see pg. 14, filed 14 Apr 2026, with respect to the specification objections have been fully considered and are persuasive. The specification objections of 14 Jan 2026 have been withdrawn in view of the amended specification.
Applicant’s arguments, see pg. 14-15, filed 14 Apr 2026, with respect to the claim objections have been fully considered and are persuasive. The claim objections of 14 Jan 2026 have been withdrawn in view of the amended claims.
Applicant’s arguments, see pg. 15-17, filed 14 Apr 2026, with respect to the 35 U.S.C. 112(a) rejections have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejections of 14 Jan 2026 have been withdrawn in view of the amended and cancelled claims.
Applicant’s arguments, see pg. 17-18, filed 14 Apr 2026, with respect to the 35 U.S.C. 112(b) rejections have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejections of 14 Jan 2026 have been withdrawn in view of the amended and cancelled claims.
Applicant’s arguments, see pg. 18-31, filed 14 Apr 2026, with respect to the 35 U.S.C. 102 and 103 rejections have been considered but are not persuasive in part.
Regarding 35 U.S.C. 102 rejections to claims 1, 14-18, and 20, Applicant’s arguments, see pg. 18-22, are moot because the new ground of rejection does not rely on the prior rejection of record for any teaching or matter specifically challenged in the argument. See the 35 U.S.C. 103 rejections to these claims presented below.
Regarding the 35 U.S.C. 103 rejections to claims 2-7, 10-13, and 19, Applicant’s arguments, see pg. 22-26 and 28-31, are moot because the new ground of rejection does not rely on the prior rejection of record. See the 35 U.S.C. 103 rejections to these claims presented below.
Regarding claim 3, Applicant argues, see pg. 23-24, that “Page 327 of Zoghbi recites “Only highest velocity signal as a measurement of vena contracta area (VCA).” However, Zoghbi does not disclose or render obvious the claimed feature: “determining regurgitation degree based on a maximum value, average value, or weighted value of the regurgitation information in the regurgitation information data graph” in claim 3. … claim 3 operates on the frame-number-indexed regurgitation information data graph (as recited in claim 1) and automatically performs mathematical post-processing …” However, the Examiner respectfully disagrees. As Applicant acknowledges, claim 3 merely recites “determining regurgitation degree based on a maximum value, average value, or weighted value of the regurgitation information in the regurgitation information data graph”, wherein the regurgitation information data graph comprises the regurgitation information (see claim 1: “generating … a regurgitation information data graph based on the regurgitation information …”), and does not explicitly specify the relationship between any of “a maximum value, average value, or weighted value of the regurgitation information” and the frame number of the plurality of color Doppler images. While Bonnefous discloses a regurgitation information data graph comprising the regurgitation information and the frame number of the plurality of Doppler images (see Fig. 22 of Bonnefous), Zoghbi, as noted in the Non-Final Office Action of 14 Jan 2026 as well as presented in the 35 U.S.C. 103 rejection to claim 3 below, discloses determining a highest velocity signal as a measurement of vena contracta area, or a maximum value of the regurgitation information (pg. 327 of Zoghbi: b. Vena contracta (width and area)). Therefore, the combination of Bonnefous and Zoghbi discloses “determining regurgitation degree based on a maximum value, average value, or weighted value of the regurgitation information in the regurgitation information data graph”. See the 35 U.S.C. 103 rejection to claim 3 presented below.
Regarding the 35 U.S.C. 103 rejections to claims 2 and 8-9, specifically to Bonnefous in view of Yang, Applicant’s arguments, see pg. 22-26 and 28-31, have been fully considered, but are not persuasive.
Applicant argues, see pg. 26-27, that “Yang does not remedy the deficiencies of Bonnefous set forth above with respect to amended claim 1”. However, the Examiner respectfully disagrees. Yang indeed discloses determining a regurgitation information by identifying pixels whose RGB values are within a red threshold range in the color Doppler data (Figure 3: Mitral regurgitation (MR) Jet Area within a red threshold on color Doppler data; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area) and calculating a length, a width, and an area of a regurgitation region based on a count of the identified pixels in the regurgitation region (Figure 3: MR Jet Area based on MR length and width; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area), which are newly recited in the independent claims. See the 35 U.S.C. 103 rejections presented below.
Regarding claim 9 specifically, Applicant argues, see pg. 27-28, that “Yang teaches a static, manual, single frame classification rule for a clinician to apply at the bedside. Claim 9, by contrast, is not an isolated classification rule. Claim 9 is expressly built upon the frame-number-indexed regurgitation information data graph of claim 1 ... the claimed system in claim 9 automatically processes area data corresponding to multiple frame numbers selected from the data graph.” However, the Examiner respectfully disagrees. Claim 9 does not recite any frame number nor the regurgitation information data graph. Therefore, a broadest reasonable interpretation of claim 9 includes automating Yang’s manual process of classifying the regurgitation degree based on the ratio of the area of the regurgitation region to the area of the target position. See the 35 U.S.C. 103 rejection to claim 9 presented below.
Applicant’s arguments, see pg. 31, filed 14 Apr 2026, with respect to the new claims 21-23 being patentable have been considered but are not persuasive. See the 35 U.S.C. 103 rejections to the new claims 21-23 presented below.
Status of Claims
Claims 1-10, 13, and 15-23 are currently under examination. Claims 11-12 and 14 have been cancelled and claims 21-23 have been newly added since the Non-Final Office Action of 14 Jan 2026.
Drawings
The drawings were received on 14 Apr 2026. These drawings are acceptable.
Claim Objections
Claims 1, 18, and 20 are objected to because of the following informalities:
“determining, by the at least one processor, regurgitation information” should read “determining, by the at least one processor, a regurgitation information” (claims 1 and 18); and
“determine, by the at least one processor of the computer, regurgitation information” should read “determining, by the at least one processor of the computer, a regurgitation information” (claims 1 and 18).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
New claim 23 recites the limitation “wherein the determining, by the at least one processor, the regurgitation degree of the target position by combining the plurality of results comprises: determining the regurgitation degree of the target position based on the area of the regurgitation region, the width of the regurgitation region, and a speed of the regurgitation”. A review of the original specification, however, does not disclose in sufficient detail “determining the regurgitation degree of the target position based on the area of the regurgitation region, the width of the regurgitation region, and a speed of the regurgitation”. In particular, [0083] of the original specification merely discloses in a single sentence “For example, if the regurgitation degree of the target position obtained based on the length of the regurgitation region and the width of the regurgitation region is the mild regurgitation, and the regurgitation degree of the target position obtained based on the regurgitation speed of the regurgitation region is the moderate regurgitation, combining the results of the regurgitation degrees of the above three target portions, the regurgitation degree of the target portion may be determined as the mild regurgitation.” Such single exemplary sentence does not support the breadth of the new claim, that the new claim introduces new matter not supported by the original specification.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-10, 13, and 15-23 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 1 recites the new limitation “a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region”. It is unclear whether “a length” and “a width” in the limitation are of “a regurgitation region” or otherwise. The separate articles of “a” for length and width make the limitation unclear. Claims 2-10, 13, 15-17, and 21-23 inherit the deficiency by the nature of their dependency on claim 1. For purposes of the examination, the new limitation is being given a broadest reasonable interpretation as “a regurgitation region’s length, width, and area are calculated based on a count of the identified pixels in the regurgitation region”, thus both the length and the width are also of the regurgitation region.
Claim 18 recites the new limitation “a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region”. It is unclear whether “a length” and “a width” in the limitation are of “a regurgitation region” or otherwise. The separate articles of “a” for length and width make the limitation unclear. Claim 19 inherits the deficiency by the nature of their dependency on claim 18. For purposes of the examination, the new limitation is being given a broadest reasonable interpretation as “a regurgitation region’s length, width, and area are calculated based on a count of the identified pixels in the regurgitation region”, thus both the length and the width are also of the regurgitation region.
Claim 20 recites the amended limitation “A non-transitory computer readable storage medium, storing computer instructions, when the computer reads the computer instructions, the computer instructions direct …” The antecedent basis for “the computer” in the limitation is unclear. For the purposes of the examination, the limitation is being given a broadest reasonable interpretation as “A non-transitory computer readable storage medium, storing computer instructions, when a computer reads the computer instructions, the computer instructions direct …”
Claim 20 recites the limitation “determine, by the at least one processor of the computer, regurgitation information of a plurality of color Doppler images …” The antecedent basis for “the at least one processor” is unclear. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “determine, by at least one processor of the computer, regurgitation information of a plurality of color Doppler images …”
Claim 20 recites the new limitation “a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region”. It is unclear whether “a length” and “a width” in the limitation are of “a regurgitation region” or otherwise. The separate articles of “a” for length and width make the limitation unclear. For purposes of the examination, the new limitation is being given a broadest reasonable interpretation as “a regurgitation region’s length, width, and area are calculated based on a count of the identified pixels in the regurgitation region”, thus both the length and the width are also of the regurgitation region.
New claim 23 recites the limitation “wherein the determining, by the at least one processor, the regurgitation degree of the target position by combining the plurality of results comprises: determining the regurgitation degree of the target position based on the area of the regurgitation region, the width of the regurgitation region, and a speed of the regurgitation”. Determining a regurgitation degree based on a speed of regurgitation is unclear (see the 35 U.S.C. 112(a) enablement rejections in the Non-Final Office Action of 14 Jan 2025 directed to determining a regurgitation degree based on a speed of regurgitation). For the purposes of the examination, the limitation is being given a broadest reasonable interpretation as “wherein the determining, by the at least one processor, the regurgitation degree of the target position by combining the plurality of results comprises: determining the regurgitation degree of the target position based on the area of the regurgitation region and the width of the regurgitation region”.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 8-10, 15-18, 20-21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnefous et al. (US PG Pub No. 2024/0366182, priority date of 17 Aug 2021) – hereinafter referred to as Bonnefous – in view of Yang et al. (Yang et al. Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography. Ann Transl Med 2022 Jan; 10(1):3. doi: 10.21037/atm-21-3449. A copy previously provided in the Non-Final Office Action of 14 Jan 2026.) – hereinafter referred to as Yang.
Regarding claim 1, Bonnefous discloses a system for monitoring regurgitation (at least Fig. 1 and [0049]-[0050]: calculate regurgitant flow associated with orifice in a patient's valve) comprising:
at least one storage device (Fig. 2: memory 264) including a set of instructions ([0068]: memory 264 storing instructions 266); and
at least one processor (Fig. 2: processor 260) in communication with at least one storage device ([0065]: processor circuit 210 includes processor 260 and memory 264 that are in direct or indirect communication with each other),
wherein when executing the set of instructions, the at least one processor is directed to cause the system to perform operations ([0065]: memory 264 storing instructions 266 that when executed by the processor 260 cause the processor 260 to perform the operations) including:
determining, by the at least one processor, regurgitation information of a plurality of color Doppler images based on color Doppler data of a target position (Fig. 3: steps 345-355; [0140]-[0143]: compute flow volume and orifice area based on the adjusted 3D model from acquired 3D color Doppler data; [0072]: 3D color Doppler data of a mitral valve acquired over time); and
generating, by the at least one processor, a regurgitation information data graph based on the regurgitation information of the plurality of color Doppler images (Fig. 3: step 360 and [0144]: display results of generating, comparing, and adjusted virtual model of data for each ultrasound image frame in the systolic phase to the user; Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase),
the regurgitation information data graph reflecting a correspondence between at least one frame number of at least one of the plurality of color Doppler images and the regurgitation information of the at least one color Doppler image (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames and vertical axes 2290, 2294 refer to volume flow and orifice area, respectively).
Bonnefous does not disclose:
wherein the regurgitation information is determined by identifying pixels whose RGB values are within a red threshold range in the color Doppler data, and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region.
In the same field of ultrasound imaging of a regurgitation, Yang, however, teaches:
determining a regurgitation information by identifying pixels whose RGB values are within a red threshold range in the color Doppler data (Figure 3: Mitral regurgitation (MR) Jet Area within a red threshold on color Doppler data; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area), and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region (Figure 3: MR Jet Area based on MR length and width; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area).
It is well known in the art that in calculating an area, the area’s length and width must be known. Thus, Yang’s calculated area of regurgitation region inherently includes calculating the regurgitation region’s length and width by segmenting the color Doppler images (Figure 3 and pg. 4: Automatic self-supervised feature extraction: MR jet area is calculated from color Doppler segmentation images).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Yang’s method of determining a regurgitation information by pixel identification and calculating an area of a regurgitation region. One of ordinary skill in the art would have combined the elements as claimed by known methods (i.e., identifying red pixels of regurgitation within the color Doppler image data and calculating an area of the regurgitation, as disclosed by Yang), and the combination would have yielded a reasonable expectation of success since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. The motivation for the combination would have been to increase sensitivity of detecting a regurgitation in a color Doppler image (pg. 7 of Yang: Diagnostic accuracy of physicians without and with support of AI segmentation model).
Regarding claim 2, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous discloses, as noted above in claim 1:
the regurgitation information data graph including an orifice area (Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase).
Bonnefous does not disclose:
determining, by the at least one processor, a regurgitation degree of the target position based on the regurgitation information data graph.
In the same field of ultrasound imaging of a regurgitation, Yang, however, teaches:
determining a regurgitation degree of the target position based on the orifice area (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: confirm the presence, severity, and etiology of mitral regurgitation (MR) based on at least effective regurgitation orifice area (EROA)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Yang’s method of determining a regurgitation degree. Yang discloses a technique of manually determining a regurgitation degree of a target position based on a calculated orifice area, and Yang’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a calculated orifice area from ultrasound imaging would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Yang’s technique of manually determining a regurgitation degree of a target position based on a calculated orifice area) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation by calculating an orifice area) that was ready for improvement, and the results (in this case, a processor configured to determine a regurgitation degree of the target position based on a length of the regurgitation region of the target position and a length of the target position) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be for evaluating “the presence, severity, and etiology of MR (mitral regurgitation)”, as taught by Yang (pg. 4: D. Establishing ground truth reference standard for MR classification in the test dataset), automatically.
Regarding claim 8, Bonnefous in view of Yang discloses all limitations of claim 2, as discussed above, and Bonnefous does not disclose:
determining, by the at least one processor, the area of the regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images; and
determining, by the at least one processor, the regurgitation degree of the target position based on the area of the regurgitation region of the target position and an area of the target position.
In the same field of ultrasound imaging of a regurgitation, Yang, however, teaches:
determining an area of a regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images (Figure 3; pg. 4-5: Establishing ground truth reference standard for MR classification in the test dataset: measurement of central Doppler jet area relative to left atrium (LA) area); and
determining the regurgitation degree of the target position based on the area of the regurgitation region of the target position and an area of the target position (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: severity of MR (mitral regurgitation) determined based on central Doppler jet area relative to LA area).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Yang’s method of determining an area of a regurgitation region of the target position and a regurgitation degree based on the area of the regurgitation region of the target position and an area of the target position. Yang discloses a technique of manually determining an area of a regurgitation region of the target position and a regurgitation degree based on the area of the regurgitation region of the target position and an area of the target position, and Yang’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on an area of the regurgitation region of the target position and an area of the target position would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Yang’s technique of manually determining an area of a regurgitation region of the target position and a regurgitation degree based on the area of the regurgitation region of the target position and an area of the target position) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation in a heart using ultrasound imaging) that was ready for improvement, and the results (in this case, a processor configured to determine a regurgitation degree of the target position based on an area of the regurgitation region of the target position and an area of the target position) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be to evaluate “the presence, severity, and etiology of MR (mitral regurgitation)”, as taught by Yang (pg. 4: D. Establishing ground truth reference standard for MR classification in the test dataset), automatically.
Regarding claim 9, Bonnefous in view of Yang discloses all limitations of claim 8, as discussed above, and Yang further teaches (also see claim 8 above):
in response to determining that the area of the regurgitation region is less than 3cm^2 or a ratio of the area of the regurgitation region to the area of the target position is less than 20%, determining, by the at least one processor, that the regurgitation degree of the target position is a mild regurgitation (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: severity of MR (mitral regurgitation) determined based on central Doppler jet area - mild MR defined as <20% LA (left atrium));
in response to determining that the area of the regurgitation region is in the range of 3cm^2 - 4.5cm^2 or the ratio of the area of the regurgitation region to the area of the target position being in the range of 20% - 50%, determining, by the at least one processor, that the regurgitation degree of the target position is a moderate regurgitation (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: severity of MR (mitral regurgitation) determined based on central Doppler jet area - moderate MR defined as central MR jet area of 20%-50% of LA area); and
in response to determining that the area of the regurgitation region is greater than 4.5 cm^2 or the ratio of the area of the regurgitation region to the area of the target position is greater than 50%, determining, by the at least one processor, that the regurgitation degree of the target position is a severe regurgitation (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: severity of MR (mitral regurgitation) determined based on central Doppler jet area - severe MR defined as central MR jet area > 50%).
Regarding claim 10, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Yang further discloses (also see claim 1 above):
determining a plurality of results of the regurgitation degree of the target position based on a plurality of types of regurgitation information of the plurality of color Doppler images (Figure 3: Six indexes (MR jet length/LA length, MR jet length, LA width, LA area, MR jet area, MR jet area/LA), as types of regurgitation information, are evaluated; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images; Figure 4: Box plot figure for indexes vs. degrees of regurgitation, including dots representing results of the regurgitation degree); and
determining the regurgitation degree of the target position by combining the plurality of results (Figure 4: Box plot figure for indexes vs. degrees of regurgitation, including median index value for a degree of regurgitation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Yang’s method of determining a plurality of results of a regurgitation degree and combining the plurality of results to determine the regurgitation degree. Yang discloses a technique of manually determining a plurality of results of a regurgitation degree and combining the plurality of results to determine the regurgitation degree, and Yang’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a plurality of results of the regurgitation degree would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Yang’s technique of manually determining a plurality of results of a regurgitation degree and combining the plurality of results to determine the regurgitation degree) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation in a heart using ultrasound imaging) that was ready for improvement, and the results (in this case, a processor configured to determine a plurality of results of a regurgitation degree and combining the plurality of results to determine the regurgitation degree) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be to automatically evaluate “the presence, severity, and etiology of MR”, as taught by Yang (pg. 4: D. Establishing ground truth reference standard for MR classification in the test dataset), that are reliable.
Regarding claim 15, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous further discloses:
wherein the regurgitation information comprises at least one of: the length of the regurgitation region, the width of the regurgitation region, the area of the regurgitation region (Fig. 3: step 345-355 and [0140]-[0141]: compute orifice area for any of the acquired ultrasound image), a direction of blood flow in the regurgitation region, or a speed of blood flow in the regurgitation region (Fig. 20-21: vectors 2040; [0159]-[0160]: color Doppler data 2050 shown shows movement of blood from the ventricle 2020 to atrium 2010, and relative velocity (speed and direction) of blood at various locations within the ventral shown by the length of the vectors 2040).
Regarding claim 16, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous further discloses:
wherein the regurgitation information data graph is obtained based on one or more color Doppler images selected from the plurality of color Doppler images (Fig. 22 and [0166]: points 2212, 2222 correspond to individual ultrasound image frames and user may add or remove any of points 2212 from the plot 2200),
wherein in each of the one or more color Doppler images, the regurgitation information is greater than a threshold (Fig. 22: curves 2210, 2220 are greater than 0, or positive volume flow or orifice area).
Regarding claim 17, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous further discloses:
wherein the target position includes at least one of: a tricuspid valve, a mitral valve (Fig. 4 and [0073]: mitral valve 430), or an aortic valve.
Regarding claim 21, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous discloses:
wherein the regurgitation information data graph including the regurgitation information (Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase).
Bonnefous does not disclose:
inputting the regurgitation information data graph into a trained machine learning model, and obtaining a regurgitation degree of the target position outputted by the trained machine learning model.
In the same field of ultrasound imaging of a regurgitation, Yang, however, suggests:
inputting the regurgitation information into a trained machine learning model (pg. 10: Discussion: indices of MR severity used as diagnostic markers or candidate variables), and obtaining a regurgitation degree of the target position outputted by the trained machine learning model (pg. 10: Discussion: an automatic and reliable tool to diagnosis the presence and assess severity of MR using the indices of MR severity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Yang’s suggestion of using a trained machine learning model to obtain a regurgitation degree of a target position based on the regurgitation information. Yang discloses that there is a recognized need in the art to automate the determinization of a regurgitation degree, since “it is difficult for clinicians to reliably identify the frame containing the maximum jet area and to accurately quantify and assign MR severity from these indexes, particularly for many borderline lesions” (see pg. 10: Discussion of Yang). Further, Yang explicitly suggests utilizing a trained machine learning model to obtain a regurgitation degree of a target position based on the regurgitation information (also see pg. 10: Discussion of Yang: “An automatic and reliable tool to diagnosis the presence and asses severity of MR would offer many advantages to improve diagnostic accuracy and workflow efficiency.”). Therefore, one of ordinary skill in the art could have pursued the known potential solution of utilizing a trained machine learning model to obtain a regurgitation degree of a target position based on the regurgitation information, as suggested by Yang, with a reasonable expectation of success. See MPEP 2143.I.E. The motivation for the modification would be “An automatic and reliable tool to diagnosis the presence and asses severity of MR would offer many advantages to improve diagnostic accuracy and workflow efficiency”, as taught by Yang (pg. 10: Discussion of Yang).
Regarding claim 23, Bonnefous in view of Yang discloses all limitations of claim 10, as discussed above, and Yang further discloses (also see claim 10 above):
determining the regurgitation degree of the target position based on the area of the regurgitation region and the width of the regurgitation region (pg. 4: Establishing ground truth reference standard for MR classification in the test dataset: confirm the presence, severity, and etiology of MR based on at least the average of the value of MR jet area).
As noted above in claim 1, it is well known in the art that in calculating an area, the area’s length and width must be known. Thus, Yang’s calculated area of regurgitation region inherently includes calculating the regurgitation region’s length and width by segmenting the color Doppler images (Figure 3 and pg. 4: Automatic self-supervised feature extraction: MR jet area is calculated from color Doppler segmentation images).
Regarding claim 18, Bonnefous discloses a method (at least Fig. 2-3) for monitoring a regurgitation implemented on a computing device having at least one processor (Fig. 2: processor 260) and at least one computer-readable storage medium (Fig. 2: memory 264; [0049]-[0050]: calculate regurgitant flow associated with orifice in a patient's valve), the method comprising:
determining, by the at least one processor, regurgitation information of a plurality of color Doppler images based on color Doppler data of a target position (Fig. 3: steps 345-355; [0140]-[0143]: compute flow volume and orifice area based on the adjusted 3D model from acquired 3D color Doppler data; [0072]: 3D color Doppler data of a mitral valve acquired over time); and
generating, by the at least one processor, a regurgitation information data graph based on the regurgitation information of the plurality of color Doppler images (Fig. 3: step 360 and [0144]: display results of generating, comparing, and adjusted virtual model of data for each ultrasound image frame in the systolic phase to the user; Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase),
the regurgitation information data graph reflecting a correspondence between at least one frame number of at least one of the plurality of color Doppler images and the regurgitation information of the at least one color Doppler image (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames and vertical axes 2290, 2294 refer to volume flow and orifice area, respectively).
Bonnefous does not disclose:
wherein the regurgitation information is determined by identifying pixels whose RGB values are within a red threshold range in the color Doppler data, and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region.
In the same field of ultrasound imaging of a regurgitation, Yang, however, teaches:
determining a regurgitation information by identifying pixels whose RGB values are within a red threshold range in the color Doppler data (Figure 3: Mitral regurgitation (MR) Jet Area within a red threshold on color Doppler data; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area), and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region (Figure 3: MR Jet Area based on MR length and width; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area).
It is well known in the art that in calculating an area, the area’s length and width must be known. Thus, Yang’s calculated area of regurgitation region inherently includes calculating the regurgitation region’s length and width by segmenting the color Doppler images (Figure 3 and pg. 4: Automatic self-supervised feature extraction: MR jet area is calculated from color Doppler segmentation images).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s method to include Yang’s method of determining a regurgitation information by pixel identification and calculating an area of a regurgitation region. One of ordinary skill in the art would have combined the elements as claimed by known methods (i.e., identifying red pixels of regurgitation within the color Doppler image data and calculating an area of the regurgitation, as disclosed by Yang), and the combination would have yielded a reasonable expectation of success since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. The motivation for the combination would have been to increase sensitivity of detecting a regurgitation in a color Doppler image (pg. 7 of Yang: Diagnostic accuracy of physicians without and with support of AI segmentation model).
Regarding claim 20, Bonnefous discloses a non-transitory computer readable storage medium (Fig. 2: memory) storing computer instructions ([0068]: memory 264 storing instructions 266), when the computer reads the computer instructions, the computer instructions direct the computer to ([0065]: memory 264 storing instructions 266 that when executed by the processor 260 cause the processor 260 to perform the operations):
determine, by the at least one processor of the computer, regurgitation information of a plurality of color Doppler images based on color Doppler data of a target position (Fig. 3: steps 345-355; [0140]-[0143]: compute flow volume and orifice area based on the adjusted 3D model from acquired 3D color Doppler data; [0072]: 3D color Doppler data of a mitral valve acquired over time); and
generate, by the at least one processor of the computer, a regurgitation information data graph based on the regurgitation information of the plurality of color Doppler images (Fig. 3: step 360 and [0144]: display results of generating, comparing, and adjusted virtual model of data for each ultrasound image frame in the systolic phase to the user; Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase),
the regurgitation information data graph reflecting a correspondence between at least one frame number of at least one of the plurality of color Doppler images and the regurgitation information of the at least one color Doppler image (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames and vertical axes 2290, 2294 refer to volume flow and orifice area, respectively).
Bonnefous does not disclose:
wherein the regurgitation information is determined by identifying pixels whose RGB values are within a red threshold range in the color Doppler data, and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region.
In the same field of ultrasound imaging of a regurgitation, Yang, however, teaches:
determining a regurgitation information by identifying pixels whose RGB values are within a red threshold range in the color Doppler data (Figure 3: Mitral regurgitation (MR) Jet Area within a red threshold on color Doppler data; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area), and
a length, a width, and an area of a regurgitation region are calculated based on a count of the identified pixels in the regurgitation region (Figure 3: MR Jet Area based on MR length and width; pg. 4: Automatic self-supervised feature extraction: measurements were derived from the color Doppler segmentation images, including MR jet area).
It is well known in the art that in calculating an area, the area’s length and width must be known. Thus, Yang’s calculated area of regurgitation region inherently includes calculating the regurgitation region’s length and width by segmenting the color Doppler images (Figure 3 and pg. 4: Automatic self-supervised feature extraction: MR jet area is calculated from color Doppler segmentation images).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s product to include Yang’s method of determining a regurgitation information by pixel identification and calculating an area of a regurgitation region. One of ordinary skill in the art would have combined the elements as claimed by known methods (i.e., identifying red pixels of regurgitation within the color Doppler image data and calculating an area of the regurgitation, as disclosed by Yang), and the combination would have yielded a reasonable expectation of success since both Bonnefous and Yang are directed to monitoring a regurgitation in a heart using ultrasound imaging. The motivation for the combination would have been to increase sensitivity of detecting a regurgitation in a color Doppler image (pg. 7 of Yang: Diagnostic accuracy of physicians without and with support of AI segmentation model).
Claims 2-3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnefous in view of Yang, as applied to claim 1 above, and further in view of Zoghbi et al. (Zoghbi et al. Recommendations for Noninvasive Evaluation of Native Valvular Regurgitation. Journal of the American Society of Echocardiography. 2017 Apr; 30(4):303-371. doi: 10.1016/j.echo.2017.01.007. A copy previously provided in the Non-Final Office Action of 14 Jan 2026.) – hereinafter referred to as Zoghbi.
Regarding claim 2, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous discloses, as noted above in claim 1:
the regurgitation information data graph including an orifice area (Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase).
Bonnefous does not disclose:
determining, by the at least one processor, a regurgitation degree of the target position based on the regurgitation information data graph.
In the same field of ultrasound imaging of a regurgitation, Zoghbi, however, teaches:
determining a regurgitation degree of the target position based on the orifice area (pg. 327: b. Vena contracta (width and area): Vena contracta (VC) as a measure of regurgitant orifice, and direct measurement of VC area (VCA) > 0.4 cm^2 denoted as severe mitral regurgitation (MR)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Zoghbi’s method of determining a regurgitation degree. Zoghbi discloses a technique of manually determining a regurgitation degree of a target position based on a calculated orifice area, and Zoghbi’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Zoghbi are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a calculated orifice area from ultrasound imaging would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Zoghbi’s technique of manually determining a regurgitation degree of a target position based on a calculated orifice area) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation by calculating an orifice area) that was ready for improvement, and the results (in this case, a processor configured to determine a regurgitation degree of the target position based on the calculated orifice area included in the regurgitation information data graph) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be for “evaluating the severity of MR (mitral regurgitation) with Doppler echocardiography”, as taught by Zoghbi (pg. 324: D. Doppler Methods of Evaluating MR Severity), automatically.
Regarding claim 3, Bonnefous in view of Yang and Zoghbi discloses all limitations of claim 2, as discussed above, and Zoghbi further teaches (also see claim 2 above):
wherein determining the regurgitation degree of the target position based on the orifice area comprises: determining the regurgitation degree of the target position based on a maximum value, average value, or weighted value of the regurgitation information in the orifice area (pg. 327: b. Vena contracta (width and area): Only highest velocity signal as a measurement of vena contracta area (VCA)).
Regarding claim 6, Bonnefous in view of Yang and Zoghbi discloses all limitations of claim 2, as discussed above, and Bonnefous does not disclose:
determining, by the at least one processor, the width of the regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images; and
determining, by the at least one processor, the regurgitation degree of the target position based on a ratio of the width of the regurgitation region of the target position to an inner diameter of the target position.
In the same field of ultrasound imaging of a regurgitation, Zoghbi, however, teaches:
determining a width of a regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images (pg. 336: a. Color flow Doppler: ratio of width of atrial regurgitation (AR) jet (regurgitation) to left ventricular outflow tract (LVOT) diameter in centrally directed jets (regurgitation) obtained in pastrasternal long-axis view, just apical to the aortic valve); and
determining the regurgitation degree of the target position based on a ratio of the width of the regurgitation region of the target position to an inner diameter of the target position (pg. 336: a. Color flow Doppler: ratio of width of AR jet to LVOT diameter in centrally directed jets obtained to assess the severity of regurgitation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Zoghbi’s method of determining a width of a regurgitation region of the target position and a regurgitation degree based on a ratio of the width of the regurgitation region of the target position to an inner diameter of the target position. Zoghbi discloses a technique of manually determining a width of a regurgitation region of the target position and a regurgitation degree of a target position based on a calculated ratio of the width of the regurgitation region of the target position to an inner diameter of the target position, and Zoghbi’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Zoghbi are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a calculated ratio of the width of the regurgitation region of the target position to an inner diameter of the target position would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Zoghbi’s technique of manually determining a width of a regurgitation region of the target position and a regurgitation degree of a target position based on a calculated ratio of the width of the regurgitation region of the target position to an inner diameter of the target position) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation in a heart using ultrasound imaging) that was ready for improvement, and the results (in this case, a processor configured to determine a regurgitation degree of the target position based on a ratio of the width of the regurgitation region of the target position to an inner diameter of the target position) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be “to assess the severity of regurgitation (at aortic valve)”, as taught by Zoghbi (pg. 326: a. Color flow Doppler), automatically.
Regarding claim 7, Bonnefous in view of Yang and Zoghbi discloses all limitations of claim 6, as discussed above, and Zoghbi further teaches (also see claim 6):
in response to determining that the ratio of the width of the regurgitation region of the target position to the inner diameter of the target position is in a range of 20%-40%, determining that the regurgitation degree of the target position is a mild regurgitation;
in response to determining that the ratio of the width of the regurgitation region of the target position to the inner diameter of the target position is in a range of 40%-60%, determining that the regurgitation degree of the target position is a moderate regurgitation; or
in response to determining that the ratio of the width of the regurgitation region of the target position to the inner diameter of the target position is greater than 60%, determining that the regurgitation degree of the target position is a severe regurgitation (pg. 336: 2. Doppler Methods. a. Color flow Doppler: Jet width in LVOT: width of AR jet compared with the LVOT diameter to assess the severity of regurgitation - a ratio of <25% indicating mild, 25%-64% indicating moderate, and >= 65% indicating severe).
Claims 2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnefous in view of Yang, as applied to claim 1 above, and further in view of Messing et al. (Messing et al. Mild tricuspid regurgitation: a benign fetal finding at various stages of pregnancy. Ultrasound Obstet Gynecol. 2005 Oct; 26:606-610. doi: 10.1002/uog.1999. A copy previously provided in the Non-Final Office Action of 14 Jan 2026.) – hereinafter referred to as Messing.
Regarding claim 2, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous discloses, as noted above in claim 1:
the regurgitation information data graph including a blood flow (Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase).
Bonnefous does not disclose:
determining, by the at least one processor, a regurgitation degree of the target position based on the regurgitation information data graph.
In the same field of ultrasound imaging of a regurgitation, Messing, however, teaches:
determining a regurgitation degree of the target position based on the blood flow (pg. 607-608: METHODS: tricuspid regurgitation (TR) was identified by color Doppler scanning, and pulse-wave Doppler was used to measure blood flow across the valve; TR was classified into severity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Messing’s method of determining a regurgitation degree. Messing discloses a technique of manually determining a regurgitation degree of a target position based on a blood flow, and Messing’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Messing are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a calculated orifice area from ultrasound imaging would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Messing’s technique of manually determining a regurgitation degree of a target position based on a blood flow) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation by a blood flow at an orifice) that was ready for improvement, and the results (in this case, a processor configured to determine a regurgitation degree of the target position based on the blood flow included in the regurgitation information data graph) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be to categorize the tricuspid regurgitation (pg. 607-608 of Messing) automatically.
Regarding claim 4, Bonnefous in view of Yang and Messing discloses all limitations of claim 2, as discussed above, and Bonnefous does not disclose:
determining, by the at least one processor, the length of the regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images; and
determining, by the at least one processor, the regurgitation degree of the target position based on a ratio of the length of the regurgitation region of the target position to a length of the target position along a regurgitation direction.
In the same field of ultrasound imaging of a regurgitation, Messing, however, teaches:
determining a length of a regurgitation region of the target position based on the regurgitation information of the plurality of color Doppler images (pg. 608: Methods. The length of the jet into the RA was further classified into four degrees of severity: mild (length of jet < 1/3 of the distance to the opposite atrial wall); mild-to-moderate (length of jet between 1/3 and 2/3 of the distance to the opposite atrial wall); and severe (jet reaching the opposite atrial wall)); and
determining the regurgitation degree of the target position based on a ratio of the length of the regurgitation region of the target position to a length of the target position along a regurgitation direction (pg. 608: Methods. The length of the jet into the RA was further classified into four degrees of severity: mild (length of jet < 1/3 of the distance to the opposite atrial wall); mild-to-moderate (length of jet between 1/3 and 2/3 of the distance to the opposite atrial wall); and severe (jet reaching the opposite atrial wall)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Messing’s method of determining a length of a regurgitation region of the target position and a regurgitation degree based on the length of the regurgitation region of the target position and a length of the target position. Messing discloses a technique of manually determining a length of a regurgitation region of the target position and a regurgitation degree based on the length of the regurgitation region of the target position and a length of the target position, and Messing’s technique is applicable to Bonnefous’s system for monitoring a regurgitation, since both Bonnefous and Messing are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a length of the regurgitation region of the target position and a length of the target position would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Messing’s technique of manually determining a length of a regurgitation region of the target position and a regurgitation degree based on the length of the regurgitation region of the target position and a length of the target position) to a known device (in this case, Bonnefous’s system for monitoring a regurgitation by a blood flow at an orifice) that was ready for improvement, and the results (in this case, a processor configured to determine a length of a regurgitation region of the target position and a regurgitation degree based on the length of the regurgitation region of the target position and a length of the target position) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be to classify a degree of the tricuspid regurgitation (pg. 607-608 of Messing) automatically.
Regarding claim 5, Bonnefous in view of Yang and Messing discloses all limitations of claim 6, as discussed above, and Messing further teaches (also see claim 4 above):
in response to determining that a ratio of the length of the regurgitation region of the target position to the length of the target position along the regurgitation direction is less than 1/3, determining, by the at least one processor, that the regurgitation degree of the target position is a mild regurgitation (pg. 608: Methods. The length of the jet into the RA was further classified into four degrees of severity: mild (length of jet < 1/3 of the distance to the opposite atrial wall)));
in response to determining that the ratio value of the length of the regurgitation region of the target position to the length of the target position along the regurgitation direction is in a range of 1/3-2/3, determining, by the at least one processor, that the regurgitation degree of the target position is a moderate regurgitation (pg. 608: Methods. The length of the jet into the RA was further classified into four degrees of severity: mild-to-moderate (length of jet between 1/3 and 2/3 of the distance to the opposite atrial wall)); and
in response to determining that the ratio value of the length of the regurgitation region of the target position to the length of the target position along the regurgitation direction is greater than 2/3, determining, by the at least one processor, that the regurgitation degree of the target position is a severe regurgitation (pg. 608: Methods. The length of the jet into the RA was further classified into four degrees of severity: severe (jet reaching the opposite atrial wall)).
Claims 13 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnefous in view of Yang, as applied to claim 1 above, and further in view of Viggen et al. (US PG Pub No. 2018/0344292) – hereinafter referred to as Viggen.
Regarding claim 13, Bonnefous in view of Yang discloses all limitations of claim 1, as discussed above, and Bonnefous further discloses:
the at least one processor responsive to an interaction between a user and the regurgitation information data (Fig. 22 and [0166]: user can add or remove points 2212, 2222 along the curve of plot 2200; [0165]: each curve based on measured volume flow and orifice area of each ultrasound image).
Bonnefous does not disclose:
displaying, by the at least one processor, a color Doppler image of the target position according to the interaction,
wherein a regurgitation information of the color Doppler image is displayed on the color Doppler image.
In the same field of ultrasound imaging of a regurgitation, Viggen, however, teaches:
displaying, by at least one processor, a color Doppler image of the target position according to the interaction between a user and the regurgitation information data graph (Fig. 4-6 and [0040]-[0043]: user selects via a user interface input device a cycle out of all of the traced cycles of the Doppler spectrum 204 and Doppler spectrum 204 and measurement parameter display 214 are updated based on the user selection; [0031]: 2D Doppler image 208 representing where blood flow velocities shown in Doppler spectrum 204 are measured);
displaying a regurgitation information of the color Doppler image on the color Doppler image (Fig. 4-6: measurement parameter display 214 and 2D Doppler image 208).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s system to include Viggen’s method of displaying a color Doppler image including a regurgitation information according to a user input. One of ordinary skill in the art would have combined the elements as claimed by known methods (i.e., displaying by a processor a color Doppler image including a regurgitation information according to a user input, as disclosed by Viggen), and the combination would have yielded a reasonable expectation of success since both Bonnefous and Viggen are directed to analyzing ultrasound images of a regurgitation. The motivation for the combination would have been to allow a user input in “automatically calculating and displaying the individual cycle measurement parameters” in diagnosing a patient ([0041] of Viggen).
Regarding claim 22, Bonnefous in view of Yang and Viggen discloses all limitations of claim 13, as discussed above, and Bonnefous further discloses:
displaying ([0063]: display 132) the plurality of color Doppler images (Fig. 20-21: color Doppler images 2000, 2100), the at least one frame number of at least one of the plurality of color Doppler images (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames), the regurgitation information (Fig. 22 and [0162]-[0166]: vertical axes 2290, 2294 of plot 2200 refer to volume flow and orifice area, respectively), and a blood flow curve reflecting a relationship between a blood flow and the at least one frame number of at least one of the plurality of color Doppler images (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames and regurgitant volume flow curve 2210 in plot 2200),
wherein the blood flow curve shows a magnitude of the blood flow in a plurality of Doppler frames before a current frame in real time (Fig. 22 and [0162]-[0166]: horizontal axis 2292 of plot 2200 refers to image frames and regurgitant volume flow curve 2210 throughout a systolic phase of a measured cardiac cycle).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Bonnefous in view of Yang, as applied to claim 18 above, and further in view of Zoghbi.
Regarding claim 19, Bonnefous in view of Yang discloses all limitations of claim 18, as discussed above, and Bonnefous discloses, as noted above in claim 18:
wherein the regurgitation information data graph including an orifice area (Fig. 22 and [0161]: plot 2200 provides the user of the system 100 with a graphical representation of both the regurgitant volume flow and orifice area of an orifice of a mitral valve throughout a systolic phase).
Bonnefous does not disclose:
determining, by the at least one processor, a regurgitation degree of the target position based on the regurgitation information data graph.
In the same field of ultrasound imaging of a regurgitation, Zoghbi, however, teaches:
determining a regurgitation degree of the target position based on the orifice area (pg. 327: b. Vena contracta (width and area): Vena contracta (VC) as a measure of regurgitant orifice, and direct measurement of VC area (VCA) > 0.4 cm^2 denoted as severe mitral regurgitation (MR)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bonnefous’s method to include Zoghbi’s method of determining a regurgitation degree. Zoghbi discloses a technique of manually determining a regurgitation degree of a target position based on a calculated orifice area, and Zoghbi’s technique is applicable to Bonnefous’s method for monitoring a regurgitation, since both Bonnefous and Zoghbi are directed to monitoring a regurgitation in a heart using ultrasound imaging. One of ordinary skill in the art would recognize that automating determination of a regurgitation degree by a processor based on a calculated orifice area from ultrasound imaging would be advantageous for automating a manual process. See MPEP 2144.04.III. and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Therefore, one of ordinary skill in the art would apply a known technique (in this case, Zoghbi’s technique of manually determining a regurgitation degree of a target position based on a calculated orifice area) to a known method (in this case, Bonnefous’s method for monitoring a regurgitation by calculating an orifice area) that was ready for improvement, and the results (in this case, determining, by a processor, a regurgitation degree of the target position based on the calculated orifice area included in the regurgitation information data graph) would have been predictable to one of ordinary skill in the art. See MPEP 2143.I.D. The motivation for the modification would be for “evaluating the severity of MR (mitral regurgitation) with Doppler echocardiography”, as taught by Zoghbi (pg. 324: D. Doppler Methods of Evaluating MR Severity), automatically.
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 Younhee Choi whose telephone number is (571)272-7013. The examiner can normally be reached M-F 9AM-5PM EST.
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/Y.C./Examiner, Art Unit 3797
/ANHTUAN T NGUYEN/Supervisory Patent Examiner, Art Unit 3795
7/16/26
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