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 § 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.
Claim(s) 20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim(s) 20 is /are directed to a “machine readable medium". However, according to paragraph [0054] of the specification, the broadest reasonable interpretation of the "machine readable medium" covers a transitory propagating signal which is non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007).
The examiner suggests amending the claim(s) to recite a “non-transitory computer-readable medium” storing a computer program or equivalent. Any amendment to the claims should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Finn (US 2018/0002039).
As to claim 1, Finn discloses a method for analysing images, comprising, by processor circuitry (para. 0048):
receiving data comprising three-dimensional, 3D, data (para. 0049) and two-dimensional, 2D, data, the 2D data comprising a subject image of a subject element of a component (para. 0049), and the 3D data comprising data representing a geometry of a reference element of the component (para. 0027, 0035, 0049; e.g., 3D CAD model);
determining a mapping between the subject image and the 3D data, wherein locations in the subject image are mapped to locations in 3D space (para. 0049; “Step 518 may further comprise aligning (or registering) the 2D image data from step 510 to the 3D reference model from step 514. The alignment may be performed by an optimization method, i.e., minimizing an objective function over a dataset, wherein the parameters for the optimization method may include a position and orientation of the component and an objective function may be a difference between the 2D image data of the component and an orthographic projection of the 3D reference model according to the position and orientation of the component”); and
determining a measurement of a property of the subject element using the mapping (para. 0050; “Step 520 may further comprise determining a feature dissimilarity between the aligned 2D image data (from step 518) and 3D reference model (from step 514) by computing features, such as surface and shape characteristics, of the component 20 by methods to identify and extract features. For example, processor 16 may determine differences or dissimilarities between the aligned 2D image data and 3D reference model. Step 520 may further comprise identifying features and determining differences or dissimilarities between the identified features using a statistical algorithm such as a HoG, Zernike moments or other algorithms”).
As to claim 2, Finn discloses the method of claim 1, wherein the 2D data comprises a reference image of an element; and wherein determining the mapping comprises:
determining a 2D to 3D mapping between the reference image and the 3D data, wherein locations in the reference image are mapped to locations in 3D space (para. 0049; Step 518 may further comprise aligning (or registering) the 2D image data from step 510 to the 3D reference model from step 514. The alignment may be performed by an optimization method, i.e., minimizing an objective function over a dataset, wherein the parameters for the optimization method may include a position and orientation of the component and an objective function may be a difference between the 2D image data of the component and an orthographic projection of the 3D reference model according to the position and orientation of the component”);
determining a 2D to 2D mapping between the subject image and the reference image of the 2D data (para. 0049; “a position and orientation of the component and an objective function may be a difference between the 2D image data of the component and an orthographic projection of the 3D reference model according to the position and orientation of the component”); and
combining the 2D to 2D mapping and the 2D to 3D mapping to determine the mapping between the subject image and the 3D data (para. 0050).
As to claim 3, Finn discloses the method of claim 2, wherein the subject element is a first element of the component, the reference element is a second element of the component and the reference image is an image of the reference element (para. 0021, 0022).
As to claim 4, Finn discloses the method of claim 2, wherein the subject element is a first element of the component, the reference image is an image of a second element of the component and the 3D data comprises model data representing the geometry of a reference element (para. 0027, 0035).
As to claim 5, Finn discloses the method of claim 2, wherein the 2D data comprises a plurality of image frames imaging a plurality of elements of the component, the method comprising: selecting the subject image from the plurality of image frames, wherein selecting the subject image comprises identifying the image frame in which the orientation of the subject element matches the orientation of the element in the reference image (para. 0025).
As to claim 6, Finn discloses the method of claim 5, wherein identifying the image frame in which the orientation of the subject element matches the orientation of the element in the reference image comprises identifying matching features in the subject image and the reference image (para. 0025, 0049).
As to claim 7, Finn discloses the method of claim 2, wherein the 2D to 2D mapping is determined prior to determining the 2D to 3D mapping (para. 0049).
As to claim 8, Finn discloses the method of claim 1, wherein: the subject element is a first element of the component (para. 0021, 0022); and the 3D data comprises 3D data which is: (i) acquired by measuring a second element of the component (para. 0021, 0022); or (ii) model data representing the geometry of a reference element (para. 0021, 0022, 0027).
As to claim 9, Finn discloses the method of claim 1, further comprising identifying coordinates in the subject image which are indicative of the property and wherein the mapping is a mapping of the coordinates (para. 0025, 0031).
As to claim 10, Finn discloses the method of claim 1, in which the subject element is an instance of a repeated element of the component (para. 0022).
As to claim 11, Finn discloses the method of claim 1, comprising using the measurement of the property of the subject element to determine a quality metric of the component (para. 0052).
As to claim 12, Finn discloses the method of claim 1, comprising selecting the subject image from a plurality of images of the subject element based on a conformity between an orientation of the subject element in the images and an intended orientation of the subject element (para. 0022).
As to claim 13, Finn discloses the method of claim 12, wherein selecting the subject image comprises carrying out image registration using feature mapping (para. 0025).
As to claim 14, Finn discloses the method of claim 1, wherein the locations in the subject image comprise pixels and/or wherein the locations in 3D space comprise voxels (para. 0022, 0024, e.g., 2D and 3D imaging of a component 20).
As to claims 15-20, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Slavens et al. disclose a method for evaluating a turbine component that includes inducing a thermal response of the component at an initial time, capturing a two-dimensional infrared image of the thermal response of the component with a thermal imaging device, wherein the two-dimensional infrared image comprises a plurality of infrared image pixels, generating a two-dimension to three-dimension mapping template to correlate two-dimensional infrared image data with three-dimensional locations on the component, mapping at least a subset of the plurality of infrared image pixels of the two-dimensional infrared image to three-dimensional coordinates using the mapping template, and generating a three-dimensional infrared image and infrared data of the component from the mapped infrared image pixels to three-dimensional coordinates, wherein the three-dimensional infrared image and infrared data is used to qualify the component for use.
Troy et al. disclose a system for inspecting a test article that incorporates a diagnostic imaging system for a test article. A command controller receives two dimensional (2D) images from the diagnostic imaging system. A three dimensional (3D) computer aided design (CAD) model visualization system and an alignment system for determining local 3D coordinates are connected to the command controller. Computer software modules incorporated in the command controller are employed, in aligning, the 2D images and 3D CAD model responsive to the local 3D coordinates. The 2D images and 3D CAD model are displayed with reciprocal registration. The alignment system is then directed to selected coordinates in the 2D images or 3D CAD model.
Bendall discloses systems and methods for interactive measurement based on three-dimensional representations of objects
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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