DETAILED ACTION
Claims 9-20 are presented for examination.
Claims 1-8 are withdrawn per election.
This office action is in response to the election submitted on 12-MAR-2026.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “computed tomography” in claim 17 is used by the claim to mean “CAT,” while the accepted meaning is “CT.” The term “CAT” has an accepted meaning of “Computed Axial Tomography”. The term is indefinite because the specification does not clearly redefine the term. [0146] of the specification as published has the same language. For purposes of examination, the limitation is interpreted as “computed tomography (CT)”.
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 9-11 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over
LAPPAS et al., U.S. Patent Application Publication 2018/0095450 A1 (hereinafter ‘LAPPAS’) in view of
MARIAMPILLAI et al., U.S. Patent Application Publication 2016/0275703 A1 (hereinafter ‘MARIAMPILLAI’).
Regarding Claim 9: A method of compensating for shrinking and distortion of an additively manufactured part caused by a sintering process, comprising:
LAPPAS teaches receiving a design shape of the part; ([0157] LAPPAS “…The customer can provide a design or a model for the 3D object. The customer can provide the design in the form of a stereo lithography (STL) file. The customer can provide a design where the design can be a definition of the shape and dimensions of the 3D object in any other numerical or physical form (e.g., structure). In some cases, the customer can provide a 3D model, sketch, or image as a design of an object to be generated…”)
LAPPAS teaches applying a pre-process transformation to the design shape to create a pre-process shape of the part; ([0124] LAPPAS “…Methods, software, apparatus, and systems described herein can be used to quantify an alteration caused by the generating process, predict the alteration induced by the generating process, create one or more computer-based models that compensate for the alteration (e.g., deformation), generate 3D objects having improved dimensional accuracy, or any combination thereof…”)
LAPPAS teaches additively manufacturing an evaluation part according to the pre-process shape of the part; (Fig. 9 and [0166] LAPPAS “…A test object can then be formed (e.g., 1306) using instructions that consider ( e.g., are based on) the geometric model of the requested object. In some embodiments, the forming process comprises a 3D printing process. In some embodiments, the forming process comprises molding, casting, extruding, or machining. The forming process can comprise additive or subtractive processing…”)
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LAPPAS teaches subjecting the additively manufactured evaluation part to a sintering process; ([0175] LAPPAS “…In a 3D printing process, the deposited pre-transformed material may be fused, ( e.g., sintered or melted), bound or otherwise connected to form at least a portion of the desired 3D object. Fusing, binding or otherwise connecting the material is collectively referred to herein as "transforming" the material. Fusing the material may refer to melting, smelting, or sintering a pre-transformed material…”)
LAPPAS teaches conducting … scans of the evaluation part to determine a deviation profile; ([0258] LAPPAS “…In some embodiments, the geometric model of the requested object corresponds to an image ( e.g., scan) of an object ( e.g., a test object) and/or data obtained using any suitable rendering technique…” [0138] LAPPAS “…For example, manufacturing requirements may dictate that particular dimensions of the 3D object are within a specified threshold (e.g., tolerance). Such deviation may comprise deformation. FIG. 1 shows an example of a structural deviation in a general sense. FIG. 1 shows an example of a model of a 3D object comprising a bent structure 100, and its respective formed (e.g., printed) 3D object comprising a planar structure 110 that deviates from the bent structure 100…”)
LAPPAS teaches determining a volumetric deformation map from the deviation profile and adjusting the pre-process shape of the part according to the volumetric deformation map to produce a production shape of the part; and ([0168] LAPPAS “…At times, it is desirable to monitor deformation in primitive portions of a 3D object. A primitive portion may be a ( e.g., characteristic) portion of one or more 3D objects. The process of developing forming ( e.g., printing) instructions for a 3D object primitive portion may comprise: (i) generating a test model of the primitive portion, (ii) generating a test object, and (iii) comparison between the two. The creation of the forming (e.g., printing) instructions for a desired 3D object may further comprise (iv) altering the test model of the primitive portion ( e.g., geometric alteration), and returning to operation (i). The development of the forming (e.g., printing) instructions for a desired 3D object primitive portion may further comprise an iterative process until a satisfactory 3D object may be generated using the forming (e.g., printing) instructions. The iterative process may comprise geometrical calibration. The development of forming (e.g., printing) instruction may (e.g., further) comprise simulations…”)
LAPPAS teaches additively manufacturing the part according to the production shape of the part. ([0165] LAPPAS “…The test model of the satisfactory test 3D object may serve as a basis for modification (e.g., 906) of the model of the 3D object (e.g., 911) to form a modified model of the 3D object (e.g., 915), which in turn is used to form, e.g., print ( e.g., 907) the requested (e.g., desired) 3D object 916…”)
LAPPAS does not appear to explicitly disclose
and averaging a plurality of scans
However, MARIAMPILLAI teaches averaging a plurality of scans ([0124-125] MARIAMPILLAI “…FIG. 14 shows example slices 217 of partial surfaces generated using 2D ray casting and intersection criteria on CT spine slice. The three distinct viewpoints generate three distinct partial surfaces, which are then merged through a union (a logical OR operation) of the three partial surfaces slices at 221. An example slice of merged data 220 is shown in FIG. 15. The pixel data that is stored in the image shown in FIG. 15 is then turned into position data in the virtual coordinate system based on the information present in the DICOM header, such as the dimensions of the voxels in each image, the slice number of image, and the (x,y,z) position of a corner of the image volume stack…”)
LAPPAS and MARIAMPILLAI are analogous art because they are from the same field of endeavor, computer modeling and simulation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the averaging a plurality of scans as disclosed by LAPPAS by conducting scans of the evaluation part to determine a deviation profile as disclosed by MARIAMPILLAI.
One of ordinary skill in the art would have been motivated to make this modification in order to generate improved volumetric models as discussed in [0005] by MARIAMPILLAI “…The present disclosure relates to the generation of partial surface models from volumetric datasets for subsequent registration of such partial surface models to surface topology datasets. Specifically, given an object that is imaged using surface topology imaging and another volumetric modality, the volumetric dataset is processed in combination with an approach viewpoint to generate one or more partial surfaces of the object that will be visible to the surface topology imaging system. This procedure can eliminate internal structures from the surfaces generated from volumetric datasets, thus increases the similarity of the dataset between the two different modalities, enabling improved and quicker registration…”
Regarding Claim 10: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
MARIAMPILLAI teach the step of determining the volumetric deformation map includes smoothing an amount of surface noise in the deviation profile. ([0096] MARIAMPILLAI “…In cases where noise may be an issue, a more stringent criterion is beneficial, which requires M consecutive points to satisfy the intersection criteria 210 before it may be added to the final partial surface. Specifically, the ray is traversed and when M points are found sequentially to meet the surface intersection criteria 210, one of the points, which may be specified as part of the surface intersection criteria 210 (e.g. the first point the ray intersects with, or the point equidistant from the first and last point), is saved to partial surface 215…”)
Regarding Claim 11: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
LAPPAS teaches the step of determining the volumetric deformation map includes adjusting for at least one internal feature not observable via conducting the plurality of scans. ([0140] LAPPAS “…For example, various mechanisms leading to dimensional inaccuracy. The comparison between the test model (e.g., 100) and the test object (e.g., test 3D object, 110) may comprise comparing their respective markers (e.g., in terms of relative distances, FLS, volume, and/or shape). The result may aid in experimental calculation of (internal) stresses and/or strains of at least a portion of the 3D object. The experimental calculation(s) may allow for an understanding of the material behavior during the forming process ( e.g., the material from which the 3D object is built, or the desired material for the 3D object). In some embodiments, the comparison and/or strategic placement of the one or more markers may facilitate formation of functionally graded materials ( e.g., comprising various microstructures at different portions of the 3D object). FIG. 10 shows an example of a requested 3D object 1020, deformed 3D object 1000 respective to the requested 3D object 1020, and a 3D object 1012 that comprises additions 1010 (e.g., in the form of stalactites, which can extend beyond height H of the requested object 1020) with respect to the requested 3D object 1020…”)
Regarding Claim 15: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
LAPPAS teaches the pre-process transformation includes applying a negative offset of at least a portion of the part. ([0257] LAPPAS “…For example, a geometric model can be adjusted by applying a negative displacement to the geometric model. For instance, the negative displacement can correspond to the adjustment function (update function) g(x,) described above with reference to Equation 1…”)
Regarding Claim 16: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein the distortion map includes
LAPPAS teaches vectors to corresponding points on the pre-process shape. ([0275] LAPPAS “…The CAD drawing (geometric model) was corrected to adjust for the deviations using a distance matrices and displacement vector calculations. The corrected geometric model was used to generate print instructions. The print instructions were used to form the requested Inconel 3D object having a desired geometry…”)
Regarding Claim 17: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
MARIAMPILLAI teaches the plurality of scans includes at least one computed tomography (CAT) scan. (See rejection under 35 U.S.C. 112(b) above for claim interpretation. [0040] MARIAMPILLAI “Various embodiments of the present disclosure utilize the orientation of the surface topology imaging system relative to the object being imaged to generate one or more partial surfaces from one or more volumetric datasets (such as, for example, CT, MRI or ultrasound volumetric datasets), which can then be registered with a surface topology dataset obtained by the surface topology imaging system…”)
Regarding Claim 18: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
LAPPAS teaches the step of averaging the plurality of scans includes positioning the plurality of scans with respect to each other to minimize registration errors. ([0145] LAPPAS “…In some embodiments, empirical methods without the use of markers are used to estimate an amount of expected deformation. For example, a registration process involving applying a rigid-body transformation from coordinates of a point cloud to a CAD coordinate system can be used. In some cases, using markers (whether they are added or are pre-existing features) can provide improved results over registration processes. For example, in some cases, a forming process ( or other suitable transformation process) can result in a large degree of deformation when compared to an original geometric model having a requested geometry. Using markers at different regions of the object can reduce errors related to registration…”)
Regarding Claim 19: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
LAPPAS teaches the step of averaging the plurality of scans includes aligning a plurality of meshes of the scans by ([0162] LAPPAS “…In some embodiments, the positions/locations of the markers are chosen based on the geometry and expected alteration ( e.g., deformation) of the object that result from its formation. For example, in some embodiments, marker locations are chosen based on portions of a surface ( or volume) of the geometric model with tessellations (mesh) densities that are greater than a predetermined density. In some embodiments, the orientation of the markers with respect to a surface ( or volume) of the geometric model is controlled. For example, in some embodiments, a marker is oriented (e.g., substantially) normal with respect to surface location of the geometric model. In some embodiment, the geometric model with the model markers, is further processed by altering the geometric model to a tessellated version (i.e., having tessellations (e.g., surface mesh)). FIGS. 19A-19C show perspective views of an example geometric model of the requested object 1900 (e.g., computer aided design (CAD) drawing) and associated model markers. FIG. 19A shows geometric model 1900 having a requested geometry. FIG. 19B shows the geometric model after model markers 1902 (e.g., hemispherical recesses) are added to surfaces of the geometric model. FIG. 19C shows the geometric model with markers converted to tessellated versions 1904 (a surface mesh). An object can be formed ( e.g., printed) using instructions ( e.g., printing instructions) that consider (e.g., based on) the geometric model. Any suitable system and associated forming process( es) can be used to form the object, such as described herein. The instructions (e.g., printing instructions) can include specifics related to the forming process, e.g., including instructions for the forming of multiple layers during the forming process, as described herein…”)
MARIAMPILLAI teaches computing the principal axis of each scan and rotating the respective meshes to align the principle axis. ([0126] MARIAMPILLAI “…While the above embodiment only uses rotations about the axial direction of the spine, other rotations about other arbitrary axis may also be used depending on the application and/or positioning of the surface topology system…”)
Regarding Claim 20: LAPPAS and MARIAMPILLAI teach The method of claim 9
LAPPAS teaches wherein after the step of aligning the plurality of meshes of the scans, ([0162] LAPPAS “…In some embodiments, the positions/locations of the markers are chosen based on the geometry and expected alteration ( e.g., deformation) of the object that result from its formation. For example, in some embodiments, marker locations are chosen based on portions of a surface ( or volume) of the geometric model with tessellations (mesh) densities that are greater than a predetermined density. In some embodiments, the orientation of the markers with respect to a surface ( or volume) of the geometric model is controlled. For example, in some embodiments, a marker is oriented (e.g., substantially) normal with respect to surface location of the geometric model. In some embodiment, the geometric model with the model markers, is further processed by altering the geometric model to a tessellated version (i.e., having tessellations (e.g., surface mesh)). FIGS. 19A-19C show perspective views of an example geometric model of the requested object 1900 (e.g., computer aided design (CAD) drawing) and associated model markers. FIG. 19A shows geometric model 1900 having a requested geometry. FIG. 19B shows the geometric model after model markers 1902 (e.g., hemispherical recesses) are added to surfaces of the geometric model. FIG. 19C shows the geometric model with markers converted to tessellated versions 1904 (a surface mesh). An object can be formed ( e.g., printed) using instructions ( e.g., printing instructions) that consider (e.g., based on) the geometric model. Any suitable system and associated forming process( es) can be used to form the object, such as described herein. The instructions (e.g., printing instructions) can include specifics related to the forming process, e.g., including instructions for the forming of multiple layers during the forming process, as described herein…”)
MARIAMPILLAI teach aligning the plurality of meshes using an iterative closest point algorithm. ([0127] MARIAMPILLAI “…Referring again to the example flow chart shown in FIG. 11, surface topology imaging is performed using structured light imaging system 171 to produce structured light data set 161. In the present example, an iterative closest point (ICP) variant is then used to register the surface topology dataset 161 to the one or more partial surfaces 131, generating a rigid body transform. It will be understood that the term ICP represents a wide class of registration algorithms. In general, such algorithms are based on finding the nearest neighbour for each point in two datasets and calculating a cost function for those two points. The cost function could be a simple function, such as the squared distance, or a more complex function. The phrase "ICP variant" refers to one of the many implementations of the ICP class of registration algorithms…”)
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over
LAPPAS et al., U.S. Patent Application Publication 2018/0095450 A1 (hereinafter ‘LAPPAS’) in view of
MARIAMPILLAI et al., U.S. Patent Application Publication 2016/0275703 A1 (hereinafter ‘MARIAMPILLAI’) further in view of
Lösch, M., et al. “Feature Selection for Human Activity Recognition Using Feature Taxonomies and User Comments” [2008] (hereinafter ‘Lösch’).
Regarding Claim 12: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein the design shape of the part includes
LAPPAS teaches not deforming the selected surfaces during the step of applying the pre-process transformation. ([0140] LAPPAS “…Differences between the dimensions can then be used to predict what portions of an object are most likely to deform (and/or an overall deformation of the object) due to the forming process. In some cases, the differences can include differences in an expected density (e.g., porosity), material consistency, metallurgical shape (e.g., and their distribution), and/or other aspects of an object. As described above, in some embodiments, the geometric model includes one or more model markers ( e.g., protrusions, recesses and/or deletions) that result in corresponding physical markers of the formed object…”)
LAPPAS and MARIAMPILLAI does not appear to explicitly disclose
a selection of at least one surface for which deformation is not desired, and,
However, Lösch teaches a selection of at least one surface for which deformation is not desired, and, (Fig. 7 Lösch “Fig. 7. Example for interactive feature selection GUI: the full line around the body models denotes to mark all related features for use in the selection, dotted rectangles denote areas to be ignored…”)
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LAPPAS, MARIAMPILLAI, and Lösch are analogous art because they are from the same field of endeavor, computer modeling and simulation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the not deforming the selected surfaces during the step of applying the pre-process transformation as disclosed by LAPPAS and MARIAMPILLAI by a selection of at least one surface for which deformation is not desired as disclosed by Lösch.
One of ordinary skill in the art would have been motivated to make this modification in order to not require specific feature selection as discussed on pg. 1 right col 4th paragraph “The integration of elaborated feature selection methods in activity recognition which is the main focus of this paper is a usual part of activity systems, but there are also approaches which do not employ an explicit feature selection step. Some systems are designed to make use of only a small number of features which are used completely ([3], [4]), or the systems are too specific to a special application area, therefore no selection of features is necessary…”
Regarding Claim 13: LAPPAS and MARIAMPILLAI teach The method of claim 9 further comprising
LAPPAS teaches not deforming … during the step of adjusting the pre-process shape according to the volumetric deformation map. (LAPPAS [0022] “…In some embodiments, the estimated thermally induced change comprises an estimated volumetric change in at least a portion of the three-dimensional object. In some embodiments, the estimated thermally induced change comprises an estimated expansion or an estimated contraction in at least a portion of the three-dimensional object…”)
LAPPAS and MARIAMPILLAI does not appear to explicitly disclose
the selected surfaces
However, Lösch teaches the selected surfaces (Fig. 7 Lösch “Fig. 7. Example for interactive feature selection GUI: the full line around the body models denotes to mark all related features for use in the selection, dotted rectangles denote areas to be ignored…”)
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LAPPAS, MARIAMPILLAI, and Lösch are analogous art because they are from the same field of endeavor, computer modeling and simulation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the not deforming during the step of adjusting the pre-process shape according to the volumetric deformation map as disclosed by LAPPAS and MARIAMPILLAI by the selected surfaces as disclosed by Lösch.
One of ordinary skill in the art would have been motivated to make this modification in order to not require specific feature selection as discussed on pg. 1 right col 4th paragraph “The integration of elaborated feature selection methods in activity recognition which is the main focus of this paper is a usual part of activity systems, but there are also approaches which do not employ an explicit feature selection step. Some systems are designed to make use of only a small number of features which are used completely ([3], [4]), or the systems are too specific to a special application area, therefore no selection of features is necessary…”
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over
LAPPAS et al., U.S. Patent Application Publication 2018/0095450 A1 (hereinafter ‘LAPPAS’) in view of
MARIAMPILLAI et al., U.S. Patent Application Publication 2016/0275703 A1 (hereinafter ‘MARIAMPILLAI’) further in view of
Wakai et al., “Microstructural Evolution and Anisotropic Shrinkage in Constrained Sintering and Sinter Forging” [2012] (hereinafter ‘Wakai’).
Regarding Claim 14: LAPPAS and MARIAMPILLAI teach The method of claim 9 wherein
LAPPAS and MARIAMPILLAI does not appear to explicitly disclose
the pre-process transformation includes applying an anisotropic scaling process.
However, Wakai teaches the pre-process transformation includes applying an anisotropic scaling process (Pg. 2393 left col 1st paragraph Wakai “…Many sintering bodies shrink in an anisotropic manner, for example, when non-spherical particles have been compacted in uniaxial pressing, injection molding, extrusion, and tape casting. The anisotropic shrinkage is characterized by measuring the ratio of axial to radial dimensions during free sintering. We consider herein the quasi-equilibrium sintering to study the microstructural evolution in free sintering of anisotropic material with tetragonal symmetry. The quasiequilibrium sintering is a hypothetical sintering process in which pores shrink while keeping equilibrium shapes. For given qðtÞ and c/a(t) at time t, strain rates e_iðtÞ are calculated from sintering stress tensor and macroscopic viscosity tensor…”)
LAPPAS, MARIAMPILLAI, and Wakai are analogous art because they are from the same field of endeavor, computer modeling and simulation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the applying a pre-process transformation to the design shape to create a pre-process shape of the part as disclosed by LAPPAS and MARIAMPILLAI by the pre-process transformation includes applying an anisotropic scaling process as disclosed by Wakai.
One of ordinary skill in the art would have been motivated to make this modification in order to predict the evolution of the sintering as discussed in the abstract of Wakai “…This model is able to predict both the evolution of the anisotropic microstructure during sintering, and also the effect of the local micro-structure on anisotropic shrinkage…”
Conclusion
Claims 9-20 are rejected.
Claims 1-8 are withdrawn per election.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN E JOHANSEN whose telephone number is (571)272-8062. The examiner can normally be reached M-F 9AM-3PM.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187
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