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 .
Prior arts cited in this office action:
Ishii (CN 111803101 A, hereinafter “Ishii”)
Taylor (US 20170053092, hereinafter “Taylor)
Claim Rejections - 35 USC § 103
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.
Claims 1—2, 4-13, 14-33 are rejected under 35 U.S.C. 103 as being unpatentable over Ishii (CN 111803101 A, hereinafter “Ishii”) in view of Taylor (US 20170053092, hereinafter “Taylor).
Regarding claims 1, 13, 26 and 27:
Ishii teaches a method implemented on a computing device having a processor and a computer-readable storage device (Ishii [0031], [0090], wherein Ishii teaches the medical image processing apparatus 200 is implemented by one or more processors. For example, the medical image processing device 200 may be a computer contained in a cloud computing system, and may also be a computer (independent computer) independent of other devices to act independently, the method can be implemented using machine learning models such as CNN), the method comprising:
obtaining image data of a subject, the subject including at least one first blood
vessel and at least one second blood vessel, wherein the at least one first blood vessel
and at least one second blood vessel constitute a blood flow path (Ishii [0008]-[0009], [0030], [0067], [0074], fig. 5; where Ishii teaches the generating function 243 based on two reconstructed image data R1, R2 each contained in the position relationship of the end part of the blood vessel, the analysis area contained in the blood vessel area (blood vessel area) is combined to generate three-dimensional blood vessel shape model 256. FIG. 6 is a diagram for explaining the combination of two blood vessels. The generation function 243 estimates the three-dimensional position of the respective front-end portions of the blood vessels B1 and B2 (the inlet of the flow path in the image) SP1, SP2 based on the change of the position of the contrast agent injected into the blood vessel (the flow of the contrast agent));
generating a first vascular model and a second vascular model based on the image
data of the subject, wherein the first vascular model and the second vascular model correspond to the at least one first blood vessel and the at least one second blood vessel, respectively (Ishii [0008]-[0009], [0030], [0074], fig. 5; where Ishii teaches a fist vascular model IFA and a second vascular model IFB that correspond to the at least one first blood vessel and the at least one second blood vessel, respectively );
coupling the first vascular model with the second vascular model to form a coupled vascular model respectively (Ishii [0008]-[0009], [0030], [0074], [0077], [0086], fig. 5; where Ishii teaches a fist coupling the first vascular model with the second vascular model); and
Ishii fails to explicitly teaches determining at least one of a value of the at least one first blood vessel or a value of a second hemodynamic parameter of the at least one second blood vessel based on the coupled vascular model.
However, Ishii teaches in addition, the parsing function 244 may also be based on the vascular shape model 256 to calculate the FSI (FSI Interaction) analysis of the velocity distribution of the blood flow in the blood vessel. In addition, the parsing function 244 may also calculate the inner diameter stenosis based on the inner diameter of the blood vessel (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5). Taylor teaches the accuracy of three-dimensional computational fluid dynamics technologies may be combined with the computational simplicity and performance capabilities of lumped parameter and one-dimensional models of blood flow. A three-dimensional geometric and physiologic model may be decomposed automatically into a reduced-order one-dimensional or lumped parameter model. The three-dimensional model may be used to compute the linear or nonlinear hemodynamic effects of blood flow through normal segments, stenoses, and/or branches, and to set the parameters of empirical models. The one-dimensional or lumped parameter models may more efficiently and rapidly solve for blood flow and pressure in a patient-specific model, and display the results of the lumped parameter or one-dimensional solutions. (Taylor [0264]).
Therefore, taking the teachings of Ishii and Taylor as a whole, one of ordinary skill in the art would understand that Ishii by calculating the FSI (FSI Interaction) analysis of the velocity distribution of the blood flow in the blood vessel in essence teaches determining the Hemodynamic parameter as claim by the applicant.
Regarding claim 2:
Ishii in view of Taylor teaches wherein the image data of the subject corresponds to at least two time phases of the subject (Ishii [0071]-[0073]; Taylor [0310]).
Regarding claim 4:
wherein the first vascular model includes at least one first bifurcation end, and the second vascular model includes at least one second bifurcation end, the coupling the first vascular model with the second vascular model:
determining a correspondence relationship between the at least one first bifurcation
end and the at least one second bifurcation end;
determining one or more bifurcation end pairs based on the correspondence
relationship, each of the one or more bifurcation end pairs including a first bifurcation
end and a corresponding second bifurcation end; and
connecting the first bifurcation end and the corresponding second bifurcation end of
each of the one or more bifurcation end pairs (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5).
Ishii in view of Yoneyama fails to explicitly teach connecting the first bifurcation end and the corresponding second bifurcation end of each of the one or more bifurcation end pairs via an intermediate model.
However, Ishii teaches generating function 243 based on two reconstructed image data R1, R2 each contained in the position relationship of the end part of the blood vessel, the analysis area contained in the blood vessel area (blood vessel area) is combined to generate three-dimensional blood vessel shape model 256. FIG. 6 is a diagram for explaining the combination of two blood vessels. The generation function 243 estimates the three-dimensional position of the respective front end portions of the blood vessels B1 and B2 (the inlet of the flow path in the image) SP1, SP2 based on the change of the position of the contrast agent injected into the blood vessel (the flow of the contrast agent). then, generating function 243 calculates the front end part SP1 of the three-dimensional coordinate (x1, y1, z1) and the front end SP2 of the three-dimensional coordinate (x2, y2, z2) between the distance D1, the distance D1 is the 1 predetermined distance Dth1 or less, It is determined that the front end portion SP1 and the front end portion SP2 have been combined. Ishii further teaches that that system may comprise a plurality of shooting system (at least more than 1). For example, a plurality of shooting system (such as the 1 shooting system SA, the 2 shooting system SB) at a plurality of time respectively from a plurality of directions (such as the 1 shooting axis AX1, 2 shooting axis AX2) shooting medical image of the blood vessel of the subject. More axis is contemplated as more shooting systems are contemplated.
Therefore, using an intermediate model to couple the model of R1 and R2 would have been obvious to one of ordinary skill in the art based on the cited prior arts. For example, when more shooting device are used more axes can also be used to generate model such as R1, R2, R3 etc. such that the third R3 can be images of the B3 section and coupling between R1 and R3(B3), R2 and R3(B) can be performed the same way coupling of R1 and R2 is done. In this way more areas and more blood vessels can be viewed and analyzed.
Regarding claim 5:
Ishii in view of Taylor Wherein determining at least one of a value of a first hemodynamic parameter of the at least one first blood vessel or a value of a second hemodynamic parameter of the at least one second blood vessel based on the coupled vascular model includes:
determining vascular features based on the coupled vascular model; and
determining the at least one of the value of the first hemodynamic parameter of the at least one first blood vessel or the value of the second hemodynamic parameter of the at least one second blood vessel based on the vascular features and a trained machine learning model (Ishii [0090], Taylor [0113], [0164], [0176], [0179]).
Regarding claim 6:
Ishii in view of Taylor wherein the determining at least one of a value of a first hemodynamic parameter of the at least one first blood vessel or a value of a second hemodynamic parameter of the at least one second blood vessel based on the coupled vascular model includes:
setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model;
determining a flow field distribution of the coupled vascular model based on the at
least one of the first boundary condition of the first vascular model or the second
boundary condition of the second vascular model; and
determining the at least one of the value of the first hemodynamic parameter of the at least one first blood vessel or the value of the second hemodynamic parameter of the at least one second blood vessel based on the flow field distribution of the coupled vascular model (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5; Taylor [0113], [0164], [0176], [0179]).
Regarding claim 7:
Ishii in view of Taylor wherein the first boundary condition includes at least one of a first entrance flow velocity, a first entrance blood mass flow rate, or a first entrance reference pressure at an entrance of the at least one first blood vessel, or a first exit flow velocity, a first exit blood mass flow rate, or a first exit reference pressure at an exit of the at least one first blood vessel (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5; Taylor [0113], [0164], [0176], [0179]).
Regarding claim 8:
Ishii in view of Taylor wherein the determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition of the first vascular model or the second boundary condition of the second vascular model includes:
generating a meshed coupled vascular model by gridding the coupled vascular
model; and
determining the flow field distribution of the coupled vascular model based on the
meshed coupled vascular model and the first boundary condition (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5; Taylor [0113], [0164], [0176], [0179]).
Regarding claim 9:
Ishii in view of Taylor wherein the meshed coupled vascular model includes a meshed first vascular model and a meshed second vascular model, the determining the flow field distribution of the coupled vascular model based on the meshed coupled vascular model and the first boundary condition including:
determining a first local flow field distribution of the meshed first vascular model based on the first boundary condition;
determining the second boundary condition of the second vascular model based on
the first local flow field distribution, wherein the second boundary condition includes at least one of a second entrance flow velocity, a second entrance blood mass flow rate, or a second entrance reference pressure at an entrance of the at least one second blood vessel, or second exit flow velocity, a second exit blood mass flow rate or a second exit reference pressure at an exit of the at least one second blood vessel; and
determining a second local flow field distribution of the meshed second vascular model based on the second boundary condition (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5; Taylor [0054], [0080] [0113], [0164], [0176], [0179]).
Regarding claim 10:
Ishii in view of Taylor wherein the determining at least one of a value of a first hemodynamic parameter of the at least one first blood vessel or a value of a second hemodynamic parameter of the at least one second blood vessel based on the flow field distribution of the coupled vascular model includes:
determining at least one of the value of the first hemodynamic parameter of the at least one first blood vessel based on the first local flow field distribution, or the value of the second hemodynamic parameter of the at least one second blood vessel based on the second local flow field distribution, the first hemodynamic parameter including at least one of a pressure, a wall stress, a wall shear stress (WSS), or a flow velocity at each of one or more positions of the at least a part of the at least one first blood vessel, and the second hemodynamic parameter including at least one of a pressure, a wall stress, a WSS, or a flow velocity, at each of one or more positions of the at least a part of the at least one second blood vessel (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4 and 5; Taylor [0104]-[0105], [0113], [0161]-[0164], [0176], [0179]).
Regarding claim 11:
Ishii in view of Taylor wherein the at least one first blood vessel includes a first main blood vessel and at least one first branch blood vessel, and the at least one second blood vessel includes a second main blood vessel and at least one second branch blood vessel, the method further including:
determining a value of a pressure gradient between the first main blood vessel and
the second main blood vessel (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], figs. 4, 5, and 21-23; Taylor [0104]-[0106], [0161]-[0164]).
Regarding claim 12:
Ishii in view of Taylor wherein the at least one first blood vessel includes a portal vein, and the at least one second blood vessel includes a hepatic vein, or the at least one first blood vessel includes an artery blood vessel and the at least one second blood vessel includes a vein blood vessel, or the at least one first blood vessel includes a vein blood vessel and the at least one second blood vessel includes a vein blood vessel (Taylor [0003]-[0004], [0104]-[0106], [0161]-[0164]).
Regarding claim 28:
Ishii in view of Taylor wherein the trained machine learning model is constructed based on a regression model that is trained based on a plurality of training samples, and each of the plurality of training samples includes one or more sample vascular features and a reference hemodynamic parameter, the one or more sample vascular features and the reference hemodynamic parameter corresponding to a same training sample being determined based on same sample image data (Ishii [0003]-[0004], [0090]).
Regarding claim 29:
Ishii in view of Taylor wherein the determining a hemodynamic parameter of the blood vessel based on the trained machine learning model and the image data includes:
generating the hemodynamic parameter of the at least one blood vessel by inputting the image data into the trained machine learning model (Ishii [0003]-[0004], [0090]).
Regarding claim 30:
Ishii in view of Taylor Wherein the at least one blood vessel includes a first blood vessel and a second blood vessel, and the determining a hemodynamic parameter of the blood vessel based on the trained machine learning model and the image data includes:
generating a first vascular model and a second vascular model based on the image data of the subject, wherein the first vascular model and the second vascular model correspond to the first blood vessel and the second blood vessel, respectively; and
generating the hemodynamic parameter of the at least one blood vessel based on
the first vascular model and the second vascular model (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], [0090], figs. 4, 5, and 21-23; Taylor [0104]-[0106], [0161]-[0164]).
Regarding claim 31:
Ishii in view of Taylor wherein the generating the hemodynamic parameter of the at least one blood vessel based on the first vascular model and the second vascular model includes:
coupling the first vascular model with the second vascular model to form a coupled
vascular model; and
generating the hemodynamic parameter of the at least one blood vessel by
inputting the coupled vascular model into the trained machine learning model (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], [0090], figs. 4, 5, and 21-23; Taylor [0104]-[0106], [0161]-[0164]).
Regarding claim 32:
Ishii in view of Taylor wherein the generating the hemodynamic parameter of the at least one blood vessel based on the first vascular model and the second vascular model includes:
generating the hemodynamic parameter of the at least one blood vessel by inputting the first vascular model with the second vascular model into the trained machine learning model (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], [0090], figs. 4, 5, and 21-23; Taylor [0104]-[0106], [0161]-[0164]).
Regarding claim 33:
Ishii in view of Taylor wherein the generating the hemodynamic parameter of the at least one blood vessel based on the first vascular model and the second vascular model includes:
extracting vascular features based on the first vascular model and the second vascular model; and
determining the hemodynamic parameter of the at least one blood vessel by inputting the vascular features into the trained machine learning model (Ishii [0008]-[0009], [0030], [0064], [0067],[0074], [0077], [0086], [0090], figs. 4, 5, and 21-23; Taylor [0104]-[0106], [0161]-[0164]).
Conclusion
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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 August 19, 2026
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