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 .
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 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.
Response to Amendment
Applicant’s amendment filed on 13 January 2026 has been entered. Claims 11, 11, and 14 are amended. Claim 2 has been canceled. Claims 1 and 3-15 are pending.
Applicant's arguments with respect to the rejection(s) of Claims 1-15 under 35 U.S.C. 102(a)(1) as being anticipated by Lappas et al. (hereafter Lappas - US 20180095450) have been fully considered but they are not persuasive.
Regarding claim 1, Applicant has contended that Lappas does not anticipate “determining neighbor points for each point of an input point could.” The Examiner does not agree. Lappas teaches “point clouds may be generated from an image of a 3D object (e.g., using any suitable 2D and/or 3D scanning technology and methodology).” (para. 0149). Therefore, Lappas teaches neighbor points for the input cloud could be determined from an image of a 3D object.
Further, regarding claim 1 and 14, Applicant contended Lappas does not anticipate "generating edges between each point and corresponding neighbor points." Examiner does not agree. Lappas teaches generating markers for data analysis using k-nearest neighbors algorithm (para. 0140). Furthermore, “the one or more markers are ridges, edges, borders, rims and/or boundaries along a surface of the 3D object…” and “the one or more markers include (or be transformed into) one or more point-clouds” (para. 0149). Therefore, Lappas teaches amended claims 1 and 14.
Regarding claims 1, 11, and 14, Applicant has contended Lappas does not anticipate "storing a record of each edge" because storing positions of the nominal markers locations in the coordinate system is not storing a record of an edge. Examiner does not agree. Lappas teaches storing positions of the nominal marker locations in the coordinate system of the geometric model (para. 0167). The markers are edges (see para. 0149). Therefore, Lappas teaches storing a record of each edge.
Further, regarding claim 11, Applicant has contended Lappas does not anticipate "wherein the edges are lines between each point and corresponding neighbor points in the input point cloud." Examiner does not agree. Lappas teaches the markers may be edges and lines corresponding to a point cloud (para. 0149).
Claim Rejections - 35 USC § 102
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.
Claims 1 and 3-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lappas et al. (hereafter Lappas – US 20180095450).
Claim 1 recites “a method.” Lappas teaches such a method, as will be shown.
Lappas teaches (Figs. 1-14) a method, comprising:
determining neighbor points for each point of the input point cloud (see para. 0261, neighbor points for the markers or edges are determined from the input point cloud of the geometric model).
generating edges between each point and corresponding neighbor points (para. 0140 and 0149, markers for data analysis using k-nearest neighbors algorithm, the markers being edges);
storing a record of each edge (para. 0167, markers are edges, records of which are stored);
predicting a point cloud (point cloud for a simulated object is predicted, para. 0259, “The one or more simulations can consider the physics model and (e.g., applied to) the geometric model of the requested object. A simulated object can be formed (e.g., 1408) considering (e.g., based on) the one or more simulations.”) that indicates a predicted object deformation using a machine learning model (para. 0261) and edges (see para. 0261, markers are edges, as described in para. 0149) determined from an input point cloud (geometric model 1402 is from an input point cloud, see para. 0167 and 0259).
Regarding Claim 3, Lappas teaches (Figs. 1-14) the method of claim 1, further comprising determining a local value for each of the edges (see para. 0064, local value determined by “thermo-mechanical analysis comprises at least one of a thermal expansion of the three-dimensional object, a thermal conductivity of the three-dimensional object, an estimated thermo-plastic deformation of the three-dimensional object”).
Regarding Claim 4, Lappas teaches (Figs. 1-14) the method of claim 3, further comprising determining a combination of the local value and a global value for each of the edges (see para. 0238-0239, global value determined by heat capacity, or thermal mass).
Regarding Claim 5, Lappas teaches (Figs. 1-14) the method of claim 4, wherein the local value indicates local neighborhood information to simulate a thermal diffusion effect (see para. 0064) and the global value indicates global information to simulate a global thermal mass effect (see para. 0238-0239).
Regarding Claim 6, Lappas teaches (Figs. 1-14) the method of claim 4, further comprising determining an edge feature based on the combination for each of the edges (see para. 0259, edge features based on different physics models to predict deformation).
Regarding Claim 7, Lappas teaches (Figs. 1-14) the method of claim 6, wherein predicting the point cloud comprises convolving the edge features to predict the point cloud (see para. 0259, edge features based on different physics models are convolved to predict the point cloud of the simulated object).
Regarding Claim 8, Lappas teaches (Figs. 1-14) the method of claim 1, wherein the machine learning model is trained with first point clouds from three-dimensional (3D) object models and second point clouds from scanned objects (see para. 0261).
Regarding Claim 9, Lappas teaches (Figs. 1-14) the method of claim 1, wherein the machine learning model comprises edge convolution layers (see para. 0264, learning module is used to adjust the physics model (or any component thereof, and/or associated corrected geometric model) over a period of a forming operation (e.g., in real time). In some embodiments, adjusting in real time comprises adjusting the physics model (or any component thereof, and/or associated corrected geometric model) during the forming of a single layer (or multiple layers)).
Regarding Claim 10, Lappas teaches (Figs. 1-14) the method of claim 1, wherein the predicted object deformation is based on thermal diffusion in three-dimensional (3D) printing (see para. 0064).
Claim 11 recites “an apparatus.” Lappas teaches such an apparatus, as will be shown.
Lappas teaches (Figs. 1-14) an apparatus, comprising:
a memory 1102;
a processor 1106 in electronic communication with the memory, wherein the processor is to:
generate a graph by determining edges for each point of an input point cloud (para. 0149);
wherein the edges are lines between each point and corresponding neighbor points in the input point cloud (para. 0149);
store a record of each edge (para. 0167, records of markers, which are edges/lines are stored);
determine an edge feature for each of the edges of the graph (see para. 0259-0260, physics models are used to determine edge features); and predict, based on the edge features, an object deformation resulting from three-dimensional (3D) printing of an object model (see para. 0259-0260, The one or more simulations can consider the physics model and (e.g., applied to) the geometric model of the requested object. A simulated object can be formed (e.g., 1408) considering (e.g., based on) the one or more simulations), wherein the predicted object deformation is indicated by a point cloud (the predicted object deformation of the simulated object would be point cloud).
Regarding Claim 12, Lappas teaches (Figs. 1-14) the apparatus of claim 11, wherein the processor is to predict the object deformation using a machine learning model that comprises layers to convolve the edge features (see para. 0264).
Regarding Claim 13, Lappas teaches (Figs. 1-14) the apparatus of claim 12, wherein the processor is to determine the input point cloud from a 3D object model (see para. 0149).
Claim 14 recites “a non-transitory tangible computer-readable medium storing executable code.” Lappas teaches such a non-transitory tangible computer-readable medium storing executable code, as will be shown.
Lappas teaches (Figs. 1-14) a non-transitory tangible computer-readable medium storing executable code, comprising:
code to cause a processor to convert an input point cloud into a graph based on determining neighbor points for each point of the input point cloud (para. 0149 and 0140 neighbor points would be determined for use with k-nearest neighbors algorithm (k-NN)); and generating edges between each point and corresponding neighbor points (para. 0140 and 0149, markers for data analysis using k-nearest neighbors algorithm, the markers being edges);
code to cause the processor to store a record of each edge (para. 0167, records of markers, which are edges/lines are stored); and
code to cause the processor to use a machine learning model to predict, based on the graph, three-dimensional (3D) printing object deformation as a point cloud (para. 0259-0260).
Regarding Claim 15, Lappas teaches (Figs. 1-14) the computer-readable medium of claim 14, wherein determining the neighbor points comprises determining a set of nearest neighbor points relative to a point of the input point cloud (see para. 0140, neighbor points are used with k-nearest neighbors algorithm (k-NN)), wherein the input point cloud corresponds to a 3D object model for 3D printing (para. 0141).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BUI whose telephone number is (571) 272-0685. The examiner can normally be reached on 7:30 AM - 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Courtney Heinle can be reached on (571) 270-3508. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ANDREW THANH BUI/Examiner, Art Unit 3745
/COURTNEY D HEINLE/Supervisory Patent Examiner, Art Unit 3745
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