DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 should not be negated by the manner in which the invention was made.
Claim(s) 1-3,5-19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US 2020/0134825) in view of Hyun et al (US 2023/0062672)
As to claim 1, Li et al teaches an echocardiography guide method implemented by a processor, comprising:
receiving an echocardiographic image of an individual (The ultrasonic diagnostic apparatus includes a unit for transmitting an ultrasonic wave to a subject and receiving an ultrasonic wave that is a reflected wave from the subject to generate an ultrasonic image. A scheme of generating image data in the imaging unit differs depending on the modality, paragraph [0036]) segmenting a cross section of the echocardiographic image into a plurality of cardiac monolayer regions( the automatic measurement unit 210 performs segmentation in units of pixels on a measurement target having a shape instability or shape complexity such as an abdomen or a heart (S1507). A segmentation scheme in units of pixels may correspond to a rule-based scheme such as a template or a decision tree scheme or may correspond to a scheme of convolution of a feature amount of image and inverse analysis thereof using machine learning, paragraph [0154]). While Li teaches the limitation above, Li fails to teach “calculating a matching rate for the cross section from which the plurality of cardiac monolayer regions is segmented based on a standard cross section received in advance; determining whether to provide a guide based on the matching rate for the cross section; and providing the guide for acquiring a cross section having the preset matching rate or higher in response to whether to provide the guide. “ Hyun et al teaches the
The learning model for identifying the reference image matching with the certain image may have been trained based on correlations between reference images corresponding to a plurality of sections for a plurality of objects and position relations between the objects and a probe when the reference images are obtained, through an artificial neural network. Also, the learning model may have been trained based on correlations between reference images and scan marks in which probe marks are positioned on body marks of objects. Herein, the scan marks may be marks in which the probe marks are positioned on the body marks of the objects based on the position relations between the objects and the probe when the reference images are obtained (paragraph [0070]).Additionally, Hyun et al teaches FIG. 6A is a view for describing a process for guiding a user to obtain an ultrasonic image corresponding to a reference section omitted among a plurality of reference sections, in an ultrasonic diagnostic apparatus. It would have been obvious to one skilled in the art before filing the claimed invention to use the learning model to identify the matching rate in order to enhance the tracking performance and easily identify a position relation between the object and the probe. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, Li et al teaches the echocardiography guide method of claim 1, wherein the segmenting of the cross section of the echocardiographic image into the cardiac monolayer regions further includes segmenting each of the plurality of cardiac monolayer regions by using a first prediction model trained to segment the cross section into each of the plurality of cardiac monolayer regions (It is desirable that the learning data includes information for specifying a type of the structure (for example, a name of the structure (spine, umbilical vein, stomach, etc.) and position information of a region where the structure is located on the image (for example, coordinates of the region) corresponding to each of the target plane for learning or the region-of-interest plane for learning including the structure, paragraph [0042]).
As to claim 3, Li et al teaches the echocardiography guide method of claim 1, wherein the calculating of the matching rate for the cross section based on the standard cross section received in advance includes calculating the matching rate by using a matching rate calculation model configured to calculate the matching rate by outputting measurement values from the segmented cardiac monolayer regions and comparing the measurement value with a measurement value determined in the cross section based on the standard cross section received in advance( the total score computation unit computes a geometric score from a geometric positional relationship between regions of the structures detected by the structure extraction unit, and computes a total score using the score indicating the reliability and the geometric score, and the plane extraction unit selects the plane, the total score of which is high, as a plane of the target plan, paragraph [0145], claim 3 and claim 7).
As to claim 5, Li et al teaches the echocardiography guide method of claim 2, wherein the first prediction model which is trained to segment the cross section into each of the plurality of cardiac monolayer regions includes a first feature extraction unit configured to input the received echocardiographic image and segment a plurality of regions based on the anatomical structure within the echocardiographic image (image 710 of FIG. 7, the ultrasonic diagnostic apparatus 100 may obtain a first ultrasonic image 720, and display a first scan mark 730-1 to which a first position relation between an object and the probe when the first ultrasonic image 720 is obtained is reflected. As shown in FIG. 7, the ultrasonic diagnostic apparatus 100 may display the first ultrasonic image 720 and a plurality of scan marks 730-1, 730-2, 730-3, and 730-4 on one screen. Also, the ultrasonic diagnostic apparatus 100 may display the first ultrasonic image 720 and the plurality of scan marks 730-1, 730-2, 730-3, and 730-4 on different screens, paragraph [0113]), and a second feature extraction unit configured to input the received echocardiographic image and predict positions of a plurality of points within the echocardiographic image (The ultrasonic diagnostic apparatus 100 may obtain a second scan mark 1022 mapped with the second reference image. Herein, the second scan mark 1022 may be a mark to which information of a second position relation between the object and the probe when the second reference image is obtained is reflected. The ultrasonic diagnostic apparatus 100 may display the second scan mark 1022 on the second ultrasonic image 1021, paragraph [0137]).
As to claim 6, Li et al teaches the echocardiography guide method of claim 2, wherein the first prediction model is trained to segment the cross section into each of the plurality of cardiac monolayer regions, and is further configured to evaluate the region segmentation result by inputting an entropy, and mask information including an anatomical mask for each region (inferenced chamber mask anatomy) and an area for each region (inferenced chamber mask ratio) with respect to the segmented regions (he convolution layer of the feature amount obtained in the structure extraction step may be reused without change to relearn only the part of the inverse analysis by transfer learning, etc. The automatic measurement unit 210 acquires a mask of the measurement region based on the segmentation result in units of pixels (S1508) and performs approximate shape fitting (S1510). Based on a fitting result, a measurement result is computed (S1511), paragraph [0154][0157]).
As to claim 7, Li et al teaches the echocardiography guide method of claim 3, wherein the matching rate calculation model is configured to calculate a measurement value corresponding to an area and a size of each of the segmented cardiac monolayer regions by inputting a plurality of points in the echocardiographic image determined by the first prediction model (The input image size, the number of levels, and a corresponding region size in each layer are designed based on distribution analysis of relative sizes of a structure to be detected, a target plane, and a region-of-interest plane. The reduced model is relearned by learning data including the target plane for learning and the region-of-interest plane for learning. When the learning model is a model in which the input image size is reduced, the structure extraction unit 230 may reduce the plane obtained from the image data to the input image size and input the image to the learning model as an input image. In other words, as the learning model, an optimized reduced model is used based on distribution analysis of relative sizes of a predetermined structure, a target plane, and a region-of-interest plane, paragraph [0046]), and further configured to calculate the matching rate by comparing the measurement value determined from the cross section based on the standard cross section received in advance(The analysis unit 261 determines whether the target plane has been sufficiently narrowed based on the spatial distribution of the structures in the matching data (S1424). When plane acquisition has not ended, a search region for cutting out a subsequent plane group from the volume 1300 is estimated (S1425) and output to the plane selection unit 231, paragraph [0147]).
As to claim 8, Li et al teaches the echocardiography guide method of claim 7, wherein the matching rate calculation model is further configured to calculate the matching rate by evaluating measurement values measured in the segmented cardiac monolayer regions by inputting data from an entropy meter, a capacity meter, and a connectivity meter, and comparing the measurement value determined in the cross section based on the standard cross section received in advance(a position display bar 1721 indicating a relative position 1721 of the target plane may be displayed in the spatial plane display 1720. Further, a spatial distribution 1732 of the extracted structure and a relative position 1733 of the extracted plane may be displayed in comparison with an approximate model 1731 of the fetus by spatial analysis of the structure, paragraph [0165]).
The limitation of claims 9-19 has been addressed above.
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/ Primary Examiner, Art Unit 2664
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