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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR 10-2024-0146315, filed on 10/24/2024.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the Republic of Korea on 04/30/2024. It is noted, however, that applicant has not filed a certified copy of the KR 10-2024-0057935 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/14/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
FIG. 10: Although this figure includes the label 930, this label does not appear in the specification.
FIG. 11: Although this figure includes the labels D31, L12, and L11, these labels do not appear in the specification.
FIG. 12: Although this figure includes the labels D32, L22, and L21, these labels do not appear in the specification.
FIGS. 13 and 14: Although these figures include the label D23, this label does not appear in the specification.
FIG. 15: Although this figure includes the label D4, this label does not appear in the specification.
FIG. 16: Although this figure includes the labels 10a and 10b, these labels do not appear in the specification.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
FIG. 16: Although the specification states “Referring to FIG. 16, the processor 120 may control the display […] the diameter of the left atrium cross section, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta as an annotation 14b on one side of the cross-section image […] For example, an iconographic indicator 14a displayed with a numerical value may include a first indicator […]” [Page 32, Line 25-Page 33, Line 11], this figure does not include the labels 14a and 14b. Rather FIG. 16 includes labels 10a and 10b.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
[Page 1, Lines 14-16]: As written it reads “Representative examples of such medical imaging apparatuses may include ultrasonic diagnostic apparatuses, CT apparatuses, and MRI apparatuses”. However, this is the first indication of the terms “CT” and “MRI” therefore the terms should be spelled out to provide clarity.
[Page 4, Lines 8-9]: As written it reads “FIG. 12 illustrates another example of training data for trainingthe machine learning model”. However to correct the typo “trainingthe” should be “training the”.
[Page 7, Lines 7-8]: As written it reads “A portable ultrasonic imaging apparatus may include, for example, a smart phone, laptop computer, PDA, tablet PC, etc.”. However, this is the first indication of the terms “PDA” and “PC” therefore, the terms should be spelled out to provide clarity.
[Page 9, Line 18-20]: As written it reads “The processor 120 may also control the ultrasonic transmission/reception module 110 to convert the summed signal for each sub-array to analog to digital”. However, to be grammatically correct, the examiner believes the underlined “to” should be “from”.
[Page 12, Lines 23-25]: As written it reads “The processor 118 controls the reception module 117 to generate ultrasonic data by converting reception signals received from the transducer 115 to analog to digital […]”. However, to be grammatically correct, the examiner believes the underlined “to” should be “from”.
[Page 15, Lines 9-13]: As written it reads “the probe 20 may communicate using any one of wireless LAN […] RF communication”. However, this is the first indication of the terms “LAN” and “RF”, therefore, the terms should be spelled out to provide clarity.
[Page 16, Line 28-Page 17, Line 1]: As written it reads “The display 140 of the ultrasonic diagnostic apparatus 40 may display UIs indicating the device information of the probe 20”. However, this is the first indication of the term “UIs”, therefore the term should be spelled out to provide clarity.
[Page 18, Lines 15-16]: As written it reads “In addition, at least one of the main display 121 and the sub display 122 may be implemented as a touch screen and provide GUIs […]”. However, this is the first indication of the term “GUIs”, therefore the term should be spelled out to provide clarity.
[Page 19, Line 15-16]: As written it reads “Referring to FIGS. 5 and 6, an ultrasonic diagnostic apparatus 40c may be implemented in a portable type”. However, the examiner notes that FIG. 6 does not include the label 40c, rather it includes the label 40d. Therefore, the examiner would recommend updating the specification to either remove “and 6” (i.e. and consequently change “FIGS.” To “FIG”), or add the label “40d” after 40c (underlined).
[Page 20, Lines 1-3]: As written it reads “The ultrasonic imaging apparatus 40c may correct the ultrasonic image displayed on the input/output interface 145 using AI”. However, this is the first indication of the term “AI”, therefore the term should be spelled out to provide clarity.
[Page 20, Line 22]: As written it reads “input/output interfaces such as speakers, LEDs […]”. However, this is the first indication of the term “LEDs”, therefore the term should be spelled out to provide clarity.
Appropriate correction is required.
Claim Objections
Claim 8 is objected to because of the following informalities:
Regarding claim 8, as written it reads “the obtaining the processed image from the cross-sectional image comprises inputting a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image as input data to an AI model stored in memory […]”. However, this is the first indication of the term “AI” in the claims, therefore the term should be spelled out to provide clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea, specifically a mental process, without significantly more.
Regarding claims 1 and 11, the examiner notes that the claim is directed to a 1) a control method of an ultrasonic diagnostic apparatus and 2) an ultrasonic diagnostic apparatus. Therefore, the claims fall within the statutory categories of invention.
With reference to Step 2A, Prong One, the claim recites “obtain(ing) a processed image comprising contouring images of the plurality of target structures from the cross-sectional image; obtain(ing) a center of gravity of each of the contouring images; and obtain(ing) length information of the target structures based on intersection points between at least one line passing through centers of gravity of the contouring images and boundaries of the contouring images” (Claims 1 and 11).
The limitations, under broadest reasonable interpretation, cover performance of the limitation in the mind and/or read on acquiring and viewing images, selecting a point within the images corresponding to the center of gravity of an anatomical structure, and performing a measurement (i.e. with calipers, for example) of length based on a line passing through the center of gravity of an anatomical structure to the boundary of the anatomical structure. In this case, these steps represent actions which can be practically performed in the human mind by a user viewing an image, selecting a point corresponding to the center of gravity of an anatomical structure and performing measurements (i.e. of length) thereon. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components (i.e. a processor), then it falls within the “mental processes” grouping of abstract ideas.
Following step 2A, Prong Two of the two-prong analysis, the claim recites the following additional elements: “obtaining an animal ultrasonic image; extracting a cross-sectional image comprising cross sections of a plurality of target structures from the animal ultrasonic image” (Claim 1) and “a probe configured to transmit an ultrasonic signal to an object and receive an echo signal reflected from the object; a display configured to display an ultrasonic image obtained based on echo information received by the probe, an input interface configured to receive user input” (Claim 11). These additional elements do not integrate the judicial exception into a practical application because the claim as written does not include elements to 1) improve the functioning of a computer (See MPEP 2106.05(a)); 2) effect a particular treatment or prophylaxis (See MPEP 2106.04(d)(2)); 3) use a particular machine (See MPEP 2106.05(b)); 4) use the judicial exceptions in a meaningful way beyond generally linking the use to a particular technological environment (See MPEP 2106.05(h)). Furthermore, these steps do not integrate the judicial exception into a practical application because they add insignificant extra-solution activity in the form of data gathering to perform the abstract idea (See MPEP 2106.05(g)).
Following step 2B, the additional element(s) (i.e. “obtaining an animal ultrasonic image; extracting a cross-sectional image comprising cross sections of a plurality of target structures from the animal ultrasonic image” (Claim 1) and “a probe configured to transmit an ultrasonic signal to an object and receive an echo signal reflected from the object; a display configured to display an ultrasonic image obtained based on echo information received by the probe, an input interface configured to receive user input” (Claim 11)) do not amount to significantly more than the judicial exception the these limitations represent data gathering steps which utilize conventional tools (i.e. probe, display, input interface)) to perform well understood, routine and conventional activity (i.e. obtaining ultrasound images, see Mcleod US 2022/0280133 A1: FIGS. 1 and 4 and paragraphs [0018]-[0019], [0025]-[0026] and [0045]) in the field, to perform the abstract idea.
Regarding claims 2-10 and 12-20, the claims add additional limitations that append the judgement of claims 1 and 11, respectively, and/or do not include additional elements that are sufficient to amount to significantly more than the judicial exception, nor a practical application of the judicial exception because they disclose:
additional information about the target structures/length information (i.e. “wherein the target structures comprise a left atrium and an aorta”, see claims 2 and 12; “wherein the obtaining the length information of the target structures comprises obtaining a diameter of the left atrium, a diameter of the aorta, or a diameter ratio of the left atrium and the aorta” (Claim 3); “wherein the length information of the target structures comprises a diameter of the left atrium, a diameter of the aorta, or a diameter ratio of the left atrium and the aorta” (Claim 13); “wherein the obtaining the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta comprises calculating the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images” (Claim 4); “wherein the processor is configured to calculate the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images” (Claim 14));
additional information about the how the processed image is obtained (i.e. “wherein the obtaining the processed image from the cross-sectional image comprises inputting a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image as input data to an Al model stored in memory and obtaining a processed image comprising the contouring images of the plurality of target structures as output data” (Claim 8); “further comprising memory provided to store an Al model, wherein the processor is configured to input a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image as input data to the Al model stored in the memory and obtain a processed image comprising the contouring images of the plurality of target structures as output data” (Claim 18); “further comprising training the Al model by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data” (Claim 9); “wherein the processor is configured to train the Al model by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data” (Claim 19); “further comprising training the Al model by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures” (Claim 10); “wherein the processor is configured to train the Al model by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures” (Claim 20));
and/or constitute insignificant extra-solution activity (i.e. “further comprising displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together with the cross-sectional image” (Claim 5); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together with the cross-sectional image” (Claim 15); “wherein the displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together with the cross-sectional image comprises displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta as an annotation on one side of the cross-sectional image” (Claim 6); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta as an annotation on one side of the cross-sectional image” (Claim 16); “wherein the displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together with the cross-sectional image comprises displaying an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and wherein the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 7); “wherein the processor is configured to control the display to display an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 17)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over McLeod US 2022/0280133 A1 “McLeod” and further in view of Reda et al. US 2019/0019287 A1 “Reda”.
Regarding claims 1 and 11, McLeod teaches “A control method of an ultrasonic diagnostic apparatus comprising:” (Claim 1) (“FIG. 4 is a flow chart 400 illustrating exemplary steps 402-416 that may be utilized for automatically detecting an ultrasound image view 310 and focus to provide feedback 320-336 on measurement suitability, in accordance with various embodiments. Referring to FIG. 4, there is shown a flow chart 400 comprising exemplary steps 402 through 416” [0045]. In this case, the flow chart 400 shown in FIG. 4 represents a control method of an ultrasonic diagnostic apparatus.);
“An ultrasonic diagnostic apparatus comprising: a probe configured to transmit an ultrasonic signal to an object and receive an echo signal reflected from the object; a display configured to display an ultrasonic image obtained based on echo information received by the probe; an input interface configured to receive user input; and a processor configured to:” (Claim 11) (“FIG. 1 is a block diagram of an exemplary ultrasound system 100 that is operable to automatically detect an ultrasound image view and focus to provide feedback on measurement suitability, in accordance with various embodiments. […] The ultrasound system 100 comprises a transmitter 102, an ultrasound probe 104, a transmit beamformer 110, a receiver 118, a receive beamformer 120, A/D converters 122, a RF processor 124, a RF/IQ buffer 126, a user input device 130, a signal processor 132, an image buffer 136, a display system 134, and an archive 138” [0018]; “The ultrasound probe 104 may comprise a two dimensional (2D) array of piezoelectric elements. The ultrasound probe 104 may comprise a group of transmit transducer elements 106 and a group of receive transducer elements 108, that normally constitute the same elements” [0019]; “The user input device 130 may include button(s), rotary encoder(s), a touchscreen, motion tracking, voice recognition, a mousing device, keyboard, camera and/or any other device capable of receiving a user directive” [0025]; “The signal processor 132 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to process ultrasound scan data (i.e., summed IQ signal) for generating ultrasound images for presentation on a display system 134” [0026]. Therefore, the ultrasound system 100 shown in FIG. 1 represents an ultrasonic diagnostic apparatus comprising: a probe (i.e. ultrasound probe 104) configured to transmit an ultrasonic signal to an object and receive an echo signal reflected from the object; a display (i.e. display system 134) configured to display an ultrasonic image obtained based on echo information received by the probe; an input interface (i.e. user input device 130) configured to receive user input; and a processor (i.e. signal processor 132) configured to perform specific steps.);
“obtaining an animal ultrasonic image” (Claim 1); “control the probe to obtain an animal ultrasonic image” (Claim 11) (“At step 402, an ultrasound system 100 acquires ultrasound image 310 of a target. For example, the ultrasound system 100 may acquire ultrasound image views 310, such as a four chamber (4CH) or parasternal long-axis (PLAX) view, with an ultrasound probe 104 positioned at a scan position over a heart” [0046]; “The transmit beamformer 110 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to control the transmitter 102 which, through a transmit sub-aperture beamformer 114, drives the group of transmit transducer elements 106 to emit ultrasonic transmit signals into a region of interest (e.g., human, animal, underground cavity, physical structure and the like)” [0020]. Therefore, the method involves obtaining an animal ultrasonic image. Furthermore, the processor is configured to control the probe to obtain an animal ultrasonic image.);
“extract(ing) a cross-sectional image comprising cross sections of a plurality of target structures from the animal ultrasonic image” (Claims 1 and 11) (“At step 404, a signal processor 132 of the ultrasound system 100 automatically identifies an ultrasound image view 310 and focus based on detected anatomical structures in the ultrasound image 310” [0047]; “Referring to FIG. 3, the display 300 may comprise a PLAX ultrasound image view 310 and measurement suitability feedback 320-336” [0038]. As shown in FIG. 3, the PLAX ultrasound image view 310 includes a plurality of target structures. Therefore, the signal processor 132 performs the step of extracting a cross-sectional image (i.e. ultrasound image 310) comprising cross sections of a plurality of target structures (i.e. anatomical structure grade 332: left ventricle, 334: right ventricle, 336: left ventricle outflow tract; see FIG. 3) from the animal ultrasonic image.).
McLeod does not teach “obtain(ing) a processed image comprising contouring images of the plurality of target structures from the cross-sectional image” (Claims 1 and 11); “obtain(ing) a center of gravity of each of the contouring images” (Claims 1 and 11); and “obtain(ing) length information of the target structures based on intersection points between at least one line passing through centers of gravity of the contouring images and boundaries of the contouring images” (Claims 1 and 11).
Reda is within the same field of endeavor as the claimed invention because it involves a framework for automated measurement (see [Abstract]).
Reda teaches “obtain(ing) a processed image comprising contouring images of the plurality of target structures from the cross-sectional image” (Claims 1 and 11) (“A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours” [0007]; “FIG. 5 shows exemplary images 502a-d with extracted cross-sectional contours 504a-d of the aorta. The aorta contours may be extracted by the deep learning module 108” [0041]; “FIG. 6 shows an exemplary image 602a of an aorta and its corresponding schematic diagram 602b. The contour 604a-b of the aorta may be extracted by the deep learning module 108 as described previously” [0043]. Therefore, since the trained segmentation learning structure may generate one or more contours of the structure of interest, the trained segmentation learning structure represents a processor which performs the step of obtaining a processed image (i.e. see FIGS. 5 and 6 with contours 504a-504d, 604a-b) comprising contouring images of the plurality of target structures from the cross-sectional image.);
“obtain(ing) a center of gravity of each of the contouring images” (Claims 1 and 11) (“The location of the center of the mass of the contour is denoted by m, while the location of each contour point is denoted by p, and the distance between the center and the contour point is denoted by r.sub.i.” [0043]. While FIG. 6 shows the center of mass (i.e. center of gravity) of the contour of the aorta, the examiner notes that it would be obvious to perform this determination of the center of mass (i.e. center of gravity) for any contour present within the processed image. Therefore, the processor performs the step of obtaining a center of gravity of each of the contouring images.); and
“obtain(ing) length information of the target structures based on intersection points between at least one line passing through centers of gravity of the contouring images and boundaries of the contouring images” (Claims 1 and 11) (“Returning to FIG. 2, at 210, measurement unit 109 extracts one or more measurements from the one or more extracted contours. The measurements may be extracted at any point or continuously along the centerline or length of the structure of interest, thereby advantageously reducing the risk of missing important measurements between points” [0042]; “The measurements may include the maximum diameter of the contour, the diameter of the contour perpendicular to the axis along the maximum diameter, the average diameter of the contour and/or the area defined by each contour. Other types of measurements may also be extracted […] The location of the center of the mass of the contour is denoted by m, while the location of each contour point is denoted by p, and the distance between the center and the contour point is denoted by r.sub.i.” [0043]. Therefore, the processor is configured to perform the step of obtaining length information of the target structures (i.e. aorta, for example) based on intersection points between at least one line (i.e. ri) passing through centers of gravity (i.e. center of mass, m) of the contouring images and boundaries of the contouring images (i.e. along which the contour points pi are located).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod such that the processor is configured to perform the steps of obtain(ing) a processed image comprising contouring images of the plurality of target structures from the cross-sectional image; obtain(ing) a center of gravity of each of the contouring images, and obtain(ing) length information of the target structures based on intersection points between at least one line passing through centers of gravity of the contouring images and boundaries of the contouring images as disclosed in Reda in order to allow accurate measurements to be obtained such that abnormalities in structures may be identified (i.e. Reda: [0050]: “Any other statistical measure may also be generated and displayed. This visualization facilitates the detection of any abnormal dilation of the aorta”). In this case, obtaining 1) image contours, 2) a center of gravity for these image contours and 3) length information, are three of a finite number of steps which can be used to identify features within an ultrasound image with a reasonable expectation of success. Furthermore, it would be obvious to obtain the center of gravity (i.e. as performed by Reda) and perform measurements for each of the anatomical structures within the images obtained by the system of McLeod in order to allow a physician to be appraised of the characteristics of the anatomical structures present within the obtained/processed ultrasound image. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod such that the processor is configured to perform the steps of obtain(ing) a processed image comprising contouring images of the plurality of target structures from the cross-sectional image; obtain(ing) a center of gravity of each of the contouring images, and obtain(ing) length information of the target structures based on intersection points between at least one line passing through centers of gravity of the contouring images and boundaries of the contouring images as disclosed in Reda would yield the predictable result of allowing accurate measurements to be obtained such that abnormalities in structures may be identified (see Reda: [0050]).
Regarding claims 2 and 12, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above, and McLeod further teaches “wherein the target structures comprise a left atrium and an aorta” (“For example, the image view detection processor 140 may identify a four chamber (4CH) or parasternal long-axis (PLAX) ultrasound image view based on a detected presence of anatomical structures in the particular view, such as by detecting a left ventricle, right ventricle, left atrium, right atrium, aorta, left ventricle outflow tract, and/or any suitable anatomical structures” [0029]. Therefore, the target structures comprise a left atrium and an aorta.).
Regarding claims 3 and 13, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 2 and 12 above, and McLeod further teaches “wherein the obtaining the length information of the target structures comprises obtaining a diameter of the left atrium, a diameter of the aorta, or a diameter ratio of the left atrium and the aorta” (Claim 3); “wherein the length information of the target structures comprises a diameter of the left atrium, a diameter of the aorta, or a diameter ratio of the left atrium and the aorta” (Claim 13) (“The signal processor 132 may include measurement identification processor 150 that comprises suitable logic, circuitry, interfaces and/or code that may be operable to automatically select one or more measurements associated with anatomical structures in the ultrasound image view identified by the image view detection processor 140. […] For example, potentially relevant measurements associated with a 4CH view of a heart may include a left ventricle (LV) area measurement, right ventricle (RV) area measurement, left atrium (LA) area measurement, right atrium (RA) area measurement, RV length measurement, RV mid diameter measurement, RV base diameter measurement, and/or any suitable 4CH view measurement. As another example, potentially relevant measurements associated with a PLAX view of a heart may include an interventricular septum (IVS) measurement, left ventricle internal dimension (LVID) measurement, left ventricle posterior wall (LVPW) measurement, right ventricle internal dimension (RVID) measurement, left ventricle outflow tract (LVOT) measurement, left atrium (LA) measurement, aorta (Ao) measurement, and/or any suitable PLAX view measurement” [0031].
While paragraph [0031] performs an RV mid diameter measurement and an RV base diameter measurement, it would be obvious to perform diameter measurements (i.e. mid diameter and base diameter, for example) on the left atrium in order to perform an assessment thereof. These diameter measurements of the left atrium represent “any suitable 4CH view measurement”. Thus, since the signal processor 132 including the measurement identification processor 150 automatically selects one or more measurements such as a lest atrium measurement and an aorta measurement, the processor is configured to perform the step of obtaining a diameter of the left atrium, a diameter of the aorta, or a diameter ratio of the left atrium and the aorta.).
Regarding claims 4 and 14, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 3 and 13 above, and Reda further teaches “wherein the obtaining the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta comprises calculating the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images” (Claim 4); “wherein the processor is configured to calculate the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images” (Claim 14) (“The measurements may include the maximum diameter of the contour, the diameter of the contour perpendicular to the axis along the maximum diameter, the average diameter of the contour and/or the area defined by each contour. Other types of measurements may also be extracted. FIG. 6 shows an exemplary image 602a of an aorta and its corresponding schematic diagram 602b. The contour 604a-b of the aorta may be extracted by the deep learning module 108 as described previously. The location of the center of the mass of the contour is denoted by m, while the location of each contour point is denoted by p, and the distance between the center and the contour point is denoted by r.sub.i. Measurement unit 109 may determine the maximum diameter (maxD) as follows:
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i
” [0043]. As shown in FIG. 6, the contour is of the aorta. Therefore, the step of obtaining the diameter of the left atrium, the diameter of the aorta or the diameter ratio of the lest atrium and the aorta (i.e. carried out by the processor) comprises calculating the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta (i.e. specifically the diameter of the aorta) based on the intersection points between one line (i.e. ri) passing through all the centers of gravity (i.e. center of mass) of the respective contouring images of the left atrium and the aorta (i.e. aorta in FIG. 6) and the boundaries of the respective contouring images (i.e. wherein the contour points pi are on the boundary of the contour of the aorta).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of Mcleod such that the processor is configured to perform the step of calculating the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images as disclosed in Reda in order to allow accurate measurements to be obtained such that abnormalities in structures may be identified (i.e. Reda: [0050]: “Any other statistical measure may also be generated and displayed. This visualization facilitates the detection of any abnormal dilation of the aorta”). Calculating the diameter of the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the aorta and the boundaries of the respective contouring images is one of a finite number of techniques which can be used to assess the aorta with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of Mcleod such that the processor is configured to perform the step of calculating the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta based on the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images as disclosed in Reda in order to allow accurate measurements to be obtained such that abnormalities in structures may be identified (i.e. Reda: [0050]).
Regarding claims 5 and 15, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 4 and 14 above and McLeod further teaches displaying measurements “together with the cross-sectional image” (Claims 5 and 15) (“FIG. 2 is a display 300 of an exemplary four chamber (4CH) ultrasound image view 310 of a heart having measurement suitability feedback 320-336, in accordance with various embodiments. […] For example, the measurement identification processor 150 and/or the measurement grading processor 160 may present 4CH measurements 320, such as an LV area measurement, an LA area measurement, an RV area measurement, an RA area measurement, an RV mid, base, length measurement, and/or any suitable measurement” [0035]. As shown in FIG. 2, the measurements (i.e. 4CH measurements (i.e. LV area, LA area, RV area, RA area, RV mid, base, length)) are displayed together (i.e. on the same screen) with the ultrasound image view 310.).
However, McLeod does not teach “further comprising displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together” (Claim 5); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta” (Claim 15).
Reda discloses “further comprising displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta together” (Claim 5); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta” (Claim 15) (“FIG. 6 shows an exemplary image 602a of an aorta and its corresponding schematic diagram 602b. The contour 604a-b of the aorta may be extracted by the deep learning module 108 as described previously […] Measurement unit 109 may determine the maximum diameter (maxD)” [0043] and “Returning to FIG. 2, at 212, measurement unit 109 presents the one or more measurements in a graphical user interface” [0046]. Therefore, the graphical user interface displays the diameter (i.e. max diameter) of the aorta.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod the display performs the step of presenting the cross-sectional image together with the diameter of the left atrium, the diameter of the aorta or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as disclosed in Reda in order to allow accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]: “Any other statistical measure may also be generated and displayed. This visualization facilitates the detection of any abnormal dilation of the aorta”). Displaying the diameter of the aorta together with the cross-sectional image is one of a finite number of techniques which can be used to facilitate detection of the characteristics of the aorta within the ultrasound image with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod the display performs the step of presenting the cross-sectional image together with the diameter of the left atrium, the diameter of the aorta or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as disclosed in Reda would yield the predictable result of allowing accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]).
Regarding claims 6 and 16, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 5 and 15 above, and McLeod further teaches displaying measurements “together with the cross-sectional image […] as an annotation on one side of the cross-sectional image” (Claims 6 and 16) (See [0035] as discussed with respect to claims 5 and 15 above. As shown in FIG. 2, the measurements (i.e. 4CH measurements (i.e. LV area, LA area, RV area, RA area, RV mid, base, length)) are displayed together (i.e. on the same screen) with the ultrasound image view 310.), wherein the measurements are in an annotation on one side of the cross-sectional image (i.e. bottom right corner of the screen). Therefore, the processor performs the step of displaying measurements “together with the cross-sectional image […] as an annotation on one side of the cross-sectional image” (Claims 6 and 16).
However, McLeod does not teach “[…] displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta […]” (Claim 6); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta […]” (Claim 16).
Reda further teaches “[…] displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta […]” (Claim 6); “wherein the processor is configured to control the display to display the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta […]” (Claim 16) (See [0043] and [0046] as discussed with respect to claims 5 and 15 above. Therefore, the processor performs the step of displaying the diameter of the aorta (i.e. max diameter) on the graphical user interface.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod the display performs the step of presenting the cross-sectional image together with the diameter of the left atrium, the diameter of the aorta or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as disclosed in Reda, wherein the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as an annotation on one side of the cross-sectional area as disclosed in McLeod in order to allow accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]: “Any other statistical measure may also be generated and displayed. This visualization facilitates the detection of any abnormal dilation of the aorta”). Displaying the diameter of the aorta as an annotation on one side of the cross-sectional image (i.e. together with the cross-sectional image) is one of a finite number of techniques which can be used to facilitate detection of the characteristics of the aorta within the ultrasound image with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod the display performs the step of presenting the cross-sectional image together with the diameter of the left atrium, the diameter of the aorta or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as disclosed in Reda, wherein the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta (i.e. specifically, the diameter of the aorta) as an annotation on one side of the cross-sectional area as disclosed in McLeod would yield the predictable result of allowing accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]).
Regarding claims 7 and 17, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 5 and 15 above, and McLeod further teaches displaying measurement “together with the cross-sectional image” (See [0035] as discussed with respect to claims 5 and 15 above.).
However, McLeod does not teach “[…] displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta image comprises displaying an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and wherein the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 7); “wherein the processor is configured to control the display to display an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 17).
Reda teaches “[…] displaying the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta image comprises displaying an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and wherein the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 7); “wherein the processor is configured to control the display to display an iconographic indicator and a numerical value of the diameter of the left atrium, the diameter of the aorta, or the diameter ratio of the left atrium and the aorta, and the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images” (Claim 17) (See [0043] and [0046] as discussed with respect to claims 5 and 15 above and “Images 804a, 804b and 804c show the coronal, sagittal and axial views of the aorta respectively. Line 806 represents the largest diameter of the aorta cross-sectional contour, while line 808 represents the diameter in the orthogonal direction to the largest diameter of the aorta” [0052].
In this case, since the measurement unit 109 may determine the maximum diameter (see [0043]) and this measurement is presented in a graphical user interface (i.e. as a numerical value), the processor performs the step of displaying the diameter of the aorta and a numerical value of the diameter of the aorta.
As shown in FIG. 8, image 804c includes line 806 which terminates on the cross-sectional contour of the aorta (i.e. at two points). Therefore, this line 806 represents an iconographic indicator comprises a first indicator indicating the intersection points (i.e. portions of the line 806 attached to the aorta cross-sectional contour, see image 804c in FIG. 8) between one line passing through all the centers of gravity (i.e. center of mass, see [0043], FIG. 6) of the respective contouring images (i.e. of the aorta) of the left atrium and the aorta and the boundaries of the respective contouring images (i.e. cross-sectional contour), and a second indicator indicating a connecting line (i.e. 806) connecting the intersection points (i.e. portions of the line 806 attached to the aorta cross-sectional contour, see image 804c in FIG. 8) in the respective contouring images.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod such that the cross-sectional image includes an iconographic indicator (i.e. line 806), wherein the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images as disclosed in Reda in order to allow accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]: “Any other statistical measure may also be generated and displayed. This visualization facilitates the detection of any abnormal dilation of the aorta”). Displaying an iconographic indicator on the cross-sectional image is one of a finite number of techniques which can be used to effectively distinguish the diameter of the aorta with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod such that the cross-sectional image includes an iconographic indicator (i.e. line 806), wherein the iconographic indicator comprises a first indicator indicating the intersection points between one line passing through all the centers of gravity of the respective contouring images of the left atrium and the aorta and the boundaries of the respective contouring images, and a second indicator indicating a connecting line connecting the intersection points in the respective contouring images as disclosed in Reda would yield the predictable result of allow accurate measurements to be obtained/viewed such that abnormalities in structures may be identified (i.e. Reda: [0050]).
Regarding claims 8 and 18, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above, and Reda further teaches “wherein the obtaining the processed image from the cross-sectional image comprises inputting a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image as input data to an Al model stored in memory and obtaining a processed image comprising the contouring images of the plurality of target structures as output data” (Claim 8); “further comprising memory provided to store an Al model, wherein the processor is configured to input a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image as input data to the Al model stored in the memory and obtain a processed image comprising the contouring images of the plurality of target structures as output data” (Claim 18) (“FIG. 4 illustrates an exemplary architecture of a convolutional neural network (CNN) 400. […] A set of training images 402 is provided to the CNN 400. The training images 402 may include two-dimensional oblique images extracted from volumetric image data of the aorta from different patients” [0040]; “CNN 400 includes a contracting path (left side) and an expansive path (right side). The contracting path includes repeated application of three 5×5 convolutions (Conv) 404a-c, each followed by a 2×2 max pooling operation (MP). The expansive path includes repeated application of three 5×5 convolutions (Conv) 406a-c, each preceded by a 2×2 upsampling (UP) of the feature map. […] At the final convolution layer 412, a 5×5 convolution is used to map the feature map to the desired number of output images 414. The output images 414 include the contours of the aorta extracted from the training images 402. FIG. 5 shows exemplary images 502a-d with extracted cross-sectional contours 504a-d of the aorta. The aorta contours may be extracted by the deep learning module 108” [0041].
Therefore, method involves obtaining the processed image (i.e. showing contours of the aorta) from the cross-sectional image comprises inputting a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image (i.e. images obtained by the system of McLeod) as input data to an Al model stored in memory (i.e. CNN 400) and obtaining a processed image comprising the contouring images of the plurality of target structures as output data (i.e. output images 414 which include the contours of the aorta). Furthermore, the ultrasonic diagnostic apparatus further comprising a memory provided to store an Al model (i.e. CNN 400), wherein the processor is configured to input a cross-sectional image comprising cross sections of the plurality of target structures obtained from the animal ultrasonic image (i.e. images obtained by the system of McLeod as input data to the Al model stored in the memory and obtain a processed image comprising the contouring images of the plurality of target structures as output data (i.e. output images 414 which include the contours of the aorta).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod such that the cross-sectional images are input to the AI model (i.e. CNN 400) of Reda in order to effectively distinguish contours of the anatomical structures contained therein. Utilizing an AI model to distinguish the contours of an aorta (i.e. or other anatomical structures) is one of a finite number of techniques which can be used to identify anatomical features within an image with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod such that the cross-sectional images are input to the AI model (i.e. CNN 400) of Reda would yield the predictable result of effectively distinguishing contours of the anatomical structures contained therein.
Regarding claims 9 and 19, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 8 and 18 above, and Reda further teaches “further comprising training the Al model by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data” (Claim 9); “wherein the processor is configured to train the Al model by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data” (Claim 19) (See [0040] as discussed with respect to claims 8 and 18 above. Since the training images 402 may include two-dimensional oblique images extracted from volumetric image data from different patients and include square image patches extracted from volumetric images along the lengths of the aorta, these training images 402 represent images indicating the boundaries of respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data. Therefore, the method further comprises training the AI model (i.e. CNN 400) by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data (i.e. images obtained by the system of McLeod for example) and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data (i.e. training images 402 of Reda). Furthermore, the processor is configured to train the AI model by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod such that the processor performs the step of training the AI model (i.e. CNN 400 of Reda) by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data as disclosed in Reda in order to effectively distinguish contours of the anatomical structures contained therein. Training and utilizing an AI model to distinguish the contours of an aorta (i.e. or other anatomical structures) is one of a finite number of techniques which can be used to identify anatomical features within an image with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod such that the processor performs the step of training the AI model (i.e. CNN 400 of Reda) by using the cross-sectional image comprising the cross sections of the plurality of target structures as input data and an image indicating the boundaries of the respective target structures capable of being checked in the cross-sectional image comprising the cross sections of the plurality of target structures as training data as disclosed in Reda would yield the predictable result of effectively distinguishing contours of the anatomical structures contained therein.
Regarding claims 10 and 20, McLeod in view of Reda discloses all features of the claimed invention as discussed with respect to claims 9 and 19 above, and Reda further teaches “further comprising training the Al model by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures” (Claim 10); “wherein the processor is configured to train the Al model by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures” (Claim 20) (See [0040] as discussed with respect to claim 8 above. In this case, since the training images 402 may include two-dimensional oblique images extracted from volumetric image data from different patients and include square image patches extracted from volumetric images along the lengths of the aorta, these training images 402 represent images which indicate the boundaries of the respective target structure (i.e. the aorta, for example). Therefore, the processor is configured to perform the step of training the AI model by adding an image indicating the boundaries of the respective target structures (i.e. training images 402), in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and ultrasonic diagnostic apparatus of McLeod such that the processor performs the step of training the AI model (i.e. CNN 400 of Reda) by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures as disclosed in Reda in order to effectively distinguish contours of the anatomical structures contained therein. Training and utilizing an AI model to distinguish the contours of an aorta (i.e. or other anatomical structures) is one of a finite number of techniques which can be used to identify anatomical features within an image with a reasonable expectation of success. Thus, modifying the method and ultrasonic diagnostic apparatus of McLeod such that the processor performs the step of training the AI model (i.e. CNN 400 of Reda) by adding an image indicating the boundaries of the respective target structures, in which characteristic factors of the target structures are reflected, as training data to the cross-sectional image comprising the cross sections of the plurality of target structures as disclosed in Reda would yield the predictable result of effectively distinguishing contours of the anatomical structures contained therein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gogna et al. US 2023/0267618 A1 “Gogna” is pertinent to the applicant’s disclosure because it discloses “The first view plane image 910 includes, as an overlay, a first contour 912 showing the border of the anatomical ROI (herein the levator hiatus) as determined from the output of the segmentation model and the contour refinement model, as well as two measurement lines” [0071]. These two measurement lines terminate in circles on the first contour 912 and represents an iconographic indicator.
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