DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/11/2026 has been entered.
Claims 1, 2, 5, 6, and 25-40 have been presented for examination based on the application filed on 3/11/2026.
Claims 3-4, 7-24 are cancelled.
Claims 37-40 are new.
Claim(s) 25-30, 32-33, & 36-37 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20190054700 A1 by Chandar; Arjun et al.
Claim(s) 1, 2, 5-6, 39, 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210299753 A1 by HARTMANN; Christoph et al., in view of US 20190054700 A1 by Chandar; Arjun et al.
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190278255 A1 by de Pena; Alejandro Manuel et al.
Claim(s) 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190329499 A1 by Parangi; Abraham Lawrence.
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20180307209 A1 by Chin; Ricardo et al.
Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190377843 A1 by Chen; Yanzhi et al.
Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190377843 A1 by Chen; Yanzhi et al.
This action is made Non-Final.
Response to Arguments
Applicant’s argument regarding chamber volume, bounding box and object are considered. These concepts are core of any additive manufacturing system which would yield a practical 3D printed material. The core argument on remarks Pg.16 is:
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Examiner respectfully disagrees as Chandar provides individual axis based compensation and also volumetric compensation as shown in ¶[0066]:
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Remarks Pg. 17:
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Examiner further points to Chandar ¶[0064]:
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Examiner finds applicant’s arguments unpersuasive as Chandar accounts for volumetric compensation as detailed above.
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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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 25-30, 32-33, & 36-37 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20190054700 A1 by Chandar; Arjun et al.
Regarding Claim 25 (Updated 5/13/2026)Chandar teaches A non-transitory computer-readable data storage medium storing program code (Chandar : [0076]-[0078]) executable by a processor to perform processing (Chandar: [0070] [0076]-[0078]) comprising:
receiving production object model data defining a production object that an additive manufacturing apparatus is to physically generate (Chandar: [0005]) , wherein the production object model data specifies: a production object volume (Chandar : [0005] having volume computation for the production & nominal model) having first and second dimensional lengths along first and second axes, respectively (Chandar: Fig.9B & [0059] showing measurements in 3 axis from 5 real models [0007][0008]; [0041] "... The tools and procedures developed are intended to be easily generalizable to address dimensional errors in all three (breadth, depth, and height) directions...."; Emphasis is that Chandar teaches individual axis measurements and deviation explicitly for each location in XYZ build envelope; Also see Fig.8A-8B [0055]-[0057]);, and which portions within the production object volume are solid (Chandar: [0005] "... During the slicing step, the enclosed volume described in the STL file is sliced into a set of finite-thickness planar layers. Slice thickness refers to the nominal thickness of these layers. In order to conserve material and shorten cycle time, the internal volume of 3D printed parts are typically supported by lattice-like infill structures instead of being filled completely. In the case of FDM or FFF, these infill structures are also generated during the slicing step and incorporated into the G-code file. These internal features are difficult to measure after printing and are ideally measured during the 3D printing process. The slicer packages used can usually calculate the [solid] volume of molten material to extrude over each print move based on the length of the print move, thickness of the layer, and extrusion width...."; [0050] [0058] [0062]-[0066] discussing solid volume and errors associated with solid volume of the production; [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber):
receiving a production position within a fabrication chamber at which the additive manufacturing apparatus is to physically generate the production object (Chandar: Fig.9A [0058]-[0061] [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." ),
wherein the production position specifies coordinates of a location within the fabrication chamber corresponding to a single reference point of the production object volume (Chandar: Fig.9A [0058]-[0061] [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate [a single reference point] has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." );
providing, as input to an inference model (Chandar: [0038] "... This disclosure enables modelling the accuracy error of the printer and providing compensation methods and models [as inference model] to improve the dimensional accuracy of 3D printers...."; [0061] use of machine learning) , the production position within the fabrication chamber at which the additive manufacturing apparatus is to physically generate the production object (Chandar: [0058]-[0061]; Fig.9A [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...."); and receiving, as output from the inference model, first and second dimensional compensations for the first and second axes, respectively, based on the production position (Chandar: Fig.9A [0058]-[0061] [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." );
respectively, applying the first and second dimensional compensations to the first and second dimensional lengths of the production object model data for the production object, to generate modified production object model data (Chandar: Fig.7 & Fig. 10; [0049]-[0064] discussing X ([0058] x-compensation) Y([0058] y-compensation) and Z ([0058] z-compensation) dimensional compensation) ; and
causing the additive manufacturing apparatus to physically generate the production object at the production position by selectively fusing build material together in accordance with the modified production object model data (Chandar: Fig.7 [0049]-[0054] & Fig. 10 as in [0061]-[0067]).
Regarding Claim 26 (Updated 5/13/2026)
Chandar teaches wherein the first dimensional compensation includes either or both of a first scaling factor (Chandar : [0036]) and a first offset factor, such that the first dimensional length of the production object volume is scaled by the first scaling factor and/or is offset by the first offset factor (Chandar: [0045] "... This strategy involves first capturing the systematic component of layer thickness variation as a function of various process parameters (error mapping and modelling), then applying a geometric offset based on the deviation predicted by the model (error compensation)...."; Fig.3; [0036] "... An important limitation of such implementations, which apply shrinkage compensation as a uniform scaling transform, is that they can only reliably correct the spatially uniform component of dimensional deviation due to shrinkage. For example, spatially varying dimensional deviations due to viscoelastic effects cannot be fully corrected by a uniform scaling correction—intermediate features will remain in incorrect positions even if the scaling results in the correct overall part size...."; such scaling is corrected by error mapping and modeling and deviations/compensation are corrected as discussed in [0045] [0058]-[0060]; [0064] discusses offsets; first dimension can be X/Y axis compensation/correction)) , and wherein the second dimensional compensation includes either or both of a second scaling factor and a second offset factor such that the second dimensional length of the production object model volume is scaled by the second scaling factor and/or is offset by the second offset factor (Chandar: [0036], [0045] showing scaling and offset compensation using compensation model in one axis can be applied to second axis/dimension as discussed in [0058] "... Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." ; second dimension can be Y or Z axis ) .
Regarding Claim 27 (Updated 5/13/2026)
Chandar teaches wherein the processing further comprises selecting the inference model from a plurality of inference models based on: an environmental condition in which the additive manufacturing apparatus is to physically generate the production object at the production position (Chandar: [0058]-[0061], Fig.7 [0049]-[0054] & Fig. 10 as in [0061]-[0067]).); aspects of the additive manufacturing apparatus itself; a composition of the build material from which the additive manufacturing apparatus is to physically generate the production object (Chandar: [0040] "... This may include a model that effects the various inputs to a 3D printer and printed part parameters on the layer thickness variation for different qualified printer materials....");a cooling profile of the production object upon the build material having been selectively fused together in accordance with the modified object production object model data (Chandar: [0029]; shinkage assocated with cooling of the 3D model in [0060]) ;and/or a print mode of the additive manufacturing apparatus in accordance with which the production object is physically generated (Chandar Also see Fig.3 and [0036], [0038] as sources of variation & therefore the training data sets for the model being varied).
Regarding Claim 28 (Updated 5/13/2026)
Chandar teaches wherein the production object model data defines a solid production proportion of the production object volume that is to be solid (Chandar: [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber), wherein the solid production proportion is also provided as input to the inference model (Chandar: [0058]-[0062] see nominal dimension 204 as solid production proportions in [0060]-[0061] being modified by the compensation [0058]-[0061]), and wherein the first and second dimensional compensations received as output from the inference model are further based on the solid production proportion (Chandar: Fig.[0058]) in addition to being based on the production position (Chandar: Fig.[0062]"... the invention includes methods of providing 3D printing error compensation which introduce geometric offsets that depend on location within the work volume....").
Regarding Claim 29 (Updated 5/13/2026)
Chandar teaches wherein the production object volume also provided as input to the inference model (Chandar: [0058]-[0061] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber), and wherein the first and second dimensional compensations received as output from the inference model are further based on the production object volume (Chandar: [0059] Fig.7 to 9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber (as build envelope [0041][0050][0058]) used for inference (compensation) model) in addition to being based on the production position (Chandar: [0062]"... the invention includes methods of providing 3D printing error compensation which introduce geometric offsets that depend on location within the work volume....")..
Regarding Claim 30 (Updated 5/13/2026)
Chandar teaches wherein the production object data defines a solid production proportion of the production object volume (Chandar: proportion as scaling and offset in [0036] [0064] [0045][0062], [0064]; and [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber; production object data can be the G-Code from the CAD model [0005], [0035], [0060-[0067]) the production object is to occupy within the fabrication chamber based on the production object model data is also provided as input to the inference model (Chandar: [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber), and wherein the first and second dimensional compensations received as output from the inference model are further based on the solid production proportion (Chandar: [0058]-[0061] dimensional compensation is based on solid production models detailed in Fig.8A-8B which have proportions as offsets and scaling detailed in [0036], [0064], [0045], [0062], [0064]; proportion based on volume [0050]
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; [0058]:
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) in addition to being based on the production object volume (Chandar : [0065] "... Modifying the coordinates of the destination positions described in G-code allows direct application of error compensation that directly translates to changes in nozzle position and the volume of material extruded.... This allows a large number of small adjustments to be distributed across the part volume, as opposed to a small number of “lumped” adjustments, thereby increasing conformance of compensated part geometry to the original design intent geometry. ") and on the production position (Chandar: [0062]"... the invention includes methods of providing 3D printing error compensation which introduce geometric offsets that depend on location within the work volume...."; [0066] "... this disclosure may be readily generalized to a true volumetric error compensation solution that addresses systematic errors in all six kinematic degrees of freedom...." ).
Regarding Claim 32
Chandar teaches wherein providing as input to the inference model the production position and receiving as output from the inference model the first and second dimensional compensations comprise: applying an algorithm realizing the inference model to the production position to determine the first and second dimensional compensations (Chandar: [0058]) .
Regarding Claim 33
Chandar teaches wherein providing as input to the inference model the production position and receiving as output from the inference model the first and second dimensional compensations comprise: applying the inference model to the production position to determine the first and second dimensional compensations (Chandar: [0058]), wherein the inference model is a curve fitting model and polyharmonic data-fitting model (Chandar: [0056]-[0057]) , or a data regression model (Chandar: Abstract; [0009][0050][0060]) .
Regarding Claim 36
Chandar teaches wherein the processor is part of a device different from the additive manufacturing apparatus, such that the device as opposed to the additive manufacturing apparatus performs the processing (Chandar: Fig.11 [0041] "... The tools and procedures developed are intended to be easily generalizable to address dimensional errors in all three (breadth, depth, and height) directions. In addition, these tools and procedures are designed to be hardware-agnostic (i.e., easily applicable to 3D printers other than G-code based FDM systems).” ).
Regarding Claim 37 (New)
Chandar teaches wherein the single reference point of the production object volume to which the location within the fabrication chamber specified by the coordinates of the production position (Chandar: [0050] " …In the provided method, an error mapping artifact (see FIGS. 8A and 8B) is produced and inspected using a coordinate measuring machine to determine height errors at 81 discrete points in the machine's work envelope.... A polynomial regression model is fitted to this measured data and used to generate height error predictions at each point in the volume of novel parts. ..." showing volume associated with each point in the build envelope (fabrication chamber); also see [0058]; The production object volume is taught as part volume in [0062] "... The spatial error models generated through these processes may dictate the amount of compensation or offset to be applied at each point within the part volume...." ) corresponds is a center point of the production object volume (Chandar: Exemplary Fig.2B shows a point V (centering aspect) in the part volume and [0065] shows the "... The main advantage of applying error compensation directly to the G-code [point based adjustment] is the degree of fine adjustment it allows. Compensation applied at the G-code level is not subjected to tessellation or discretization errors, unlike adjustments applied prior to slicing. In fact, the smallest dimensional adjustment possible using this compensation approach is limited only by the motion control resolution of the hardware involved (e.g., a single step of a stepper motor). This allows a large number of small adjustments to be distributed across the part volume, as opposed to a small number of “lumped” adjustments, thereby increasing conformance of compensated part geometry to the original design intent geometry....") , such that the production object is physically generated by the additive manufacturing apparatus such that the center point of the production object volume is at the production position within the fabrication chamber (Chandar: [0065] part volume within the build envelope as disclosed in [0058]) .
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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 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 5-6, 39, 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210299753 A1 by HARTMANN; Christoph et al., in view of US 20190054700 A1 by Chandar; Arjun et al.
Regarding Claims 1 (Updated 5/13/2026)
Hartmann teaches (Claim 1) A method for generating objects with an additive manufacturing apparatus by fusing build material within a fabrication chamber (Hartmann: [0010][0028] where additive manufacturing by fusing building material is taught as selective laser melting process for additive manufacturing) , the method comprising:
causing an additive manufacturing apparatus (Hartmann: [0010][0028] where additive manufacturing by fusing building material is taught as selective laser melting process for additive manufacturing) to physically generate a plurality of training objects in different positions within a fabrication chamber (Hartmann: Fig.1 showing a plurality of motor vehicle components 1 which have been produced additively [0020] ) by selectively fusing build material together in accordance with object model data for each training object (Hartmann: [0028] claimed selectively fusing is mapped to selective laser melting process) ;
measuring, for each training object, first (Hartmann: [0023] "... [0023] In a fourth method step 8, an actual geometry, assigned to the respective production positions, of the motor vehicle components 1 is ascertained by means of a detection device. ..." [0031] "... During an ongoing series, that is to say in the case of the additive manufacturing of the plurality of motor vehicle components 1 being performed multiple times in succession, it is possible, upon every measurement of the actual geometry, for any process variations that arise to be corrected by virtue of the respective compensation being recalculated...." – Although explicit enumeration of first and second dimension/axis is missing, an actual geometry measurement would necessitate first and second dimension at least) ;
a position of the training object within the fabrication chamber during physical generation(Hartmann: [0010]"... each of the motor vehicle components is assigned the production position thereof and linked, in an assignment rule, to the actual geometry ascertained for the production position...." )
generating an inference model based on the plurality of training objects (Hartmann: [0023] "... Subsequently, in a comparison 9, the respective actual geometry [which is position dependent geometry of part in the fabrication chamber as in [0010]] is compared with the setpoint geometry [CAD model geometry as in [0007]] in a production-position-related manner, and in a fifth method step 10, a deviation between the setpoint geometry and the actual geometry is determined...." ; [0024] "... [0024] In a sixth method step 12, a compensation of the deviation between the setpoint geometry and the actual geometry is performed. The compensation of the deviation between the setpoint geometry and the actual geometry results in an adaptation 13 of the production geometry in a manner dependent on the compensation. Here, the adaptation 13 of the production geometry is performed in each case in a production-position-related manner [Emphasis is made that compensation is done based position of component in the production chamber]...."); [0007] "...The setpoint geometry for the plurality of motor vehicle components may for example be predefined by means of a CAD model...." ; [0010]"... each of the motor vehicle components is assigned the production position thereof and linked, in an assignment rule, to the actual geometry ascertained for the production position...." - here the inference model is taught as compensation computation that compensates the geometry based on actual position of each of the production position);
receiving production object model data defining a production object that the additive manufacturing apparatus is to physically generate (Hartmann: [0028] "... For this purpose, firstly, a CAD model of the metal part to be manufactured is created by computer. ..." [0023] "... Subsequently, in a comparison 9, the respective actual geometry [which is position dependent geometry of part in the fabrication chamber as in [0010]] is compared with the setpoint geometry [production object model data as CAD model geometry as in [0007]]…”), wherein the production object model data specifies:
a production object volume having first and second dimensional lengths along the first and second axes, respectively (Hartmann: [0007] "...The setpoint geometry [the set point geometry would be the dimensions in first & second axes defining the CAD model] for the plurality of motor vehicle components may for example be predefined by means of a CAD model...." ; Also see [0023][0031]) , and
(Hartmann: Although this would be obvious as this decides how much material is needed, the computation of solid volume is not explicitly discussed in Hartmann) ;
receiving a production position within the fabrication chamber at which the additive manufacturing apparatus is to physically generate the production object (Hartmann: [0006]"... In a manner dependent on the predefined setpoint geometry, a production geometry assigned to a respective production position within a tool for additive manufacturing is predefined. The tool is in particular an additive manufacturing installation....") ,
wherein the production position specifies coordinates of a location within the fabrication chamber corresponding to a single reference point of the production object volume (Hartmann: [0007] "... This means that the setpoint geometry is predefined uniformly for each of the motor vehicle components, wherein the respective production geometries of the individual motor vehicle components may vary in a manner dependent on the respective production position [This specifies coordinates of a location within the fabrication chamber where the production geometries are replicated in the production chamber from the set point geometry/CAD model] of the respective motor vehicle component in the tool. The respective production geometries are derived from the predefined setpoint geometry....") ;
providing, as input to the inference model, the production position within the fabrication chamber at which the additive manufacturing apparatus is to physically generate the production object (Hartmann: Fig.1 showing physically generating the production object at plurality of production positions; [0023] "... Subsequently, in a comparison 9, the respective actual geometry [which is position dependent geometry of part in the fabrication chamber as in [0010]] is compared with the setpoint geometry [CAD model geometry as in [0007]] in a production-position-related manner, and in a fifth method step 10, a deviation between the setpoint geometry and the actual geometry is determined...." [0007] "...The setpoint geometry for the plurality of motor vehicle components may for example be predefined by means of a CAD model...." ;); and
receiving, as output from the inference model, (Hartmann: [0024] "... [0024] In a sixth method step 12, a compensation of the deviation between the setpoint geometry and the actual geometry is performed. The compensation of the deviation between the setpoint geometry and the actual geometry results in an adaptation 13 of the production geometry in a manner dependent on the compensation. Here, the adaptation 13 of the production geometry is performed in each case in a production-position-related manner [Emphasis is made that compensation is done based position of component in the production chamber]...."); [0007] "...The setpoint geometry for the plurality of motor vehicle components may for example be predefined by means of a CAD model...." ; [0010]"... each of the motor vehicle components is assigned the production position thereof and linked, in an assignment rule, to the actual geometry ascertained for the production position...." [0028]-[0029]);
applying (Hartmann: [0030]-[0032] "... Respective distortion-compensated production of further motor vehicle components 1 with the production-position-related adapted production geometries can be implemented with particularly short development times....") ; and
causing the additive manufacturing apparatus to physically generate the production object at the production position in accordance with the modified production object model data (Hartmann: [0030]-[0032]) .
Hartmann does not explicitly teach generating a training dataset comprising for each training object:
a first dimensional inaccuracy between the measured first dimension of the training object as has been physically generated and a corresponding first dimension of the training object as specified by the object model data in accordance with which the training object has been physically generated;
a second dimensional inaccuracy[[,]] between the measured second dimension of the training object as has been physically generated and a corresponding second dimension of the training object as specified by the object model data in accordance with which the training object has been physically generated; and
receiving, as output from the inference model,
applying
Chandar complements Hartmann by primarily teaching measuring, for each training object, first and second dimensions of the training object as has been physically generated, the first and second dimensions respectively measured along first and second axes (Chandar: Fig.9B & [0059] showing measurements in 3 axis from 5 real models [0007][0008]; [0041] "... The tools and procedures developed are intended to be easily generalizable to address dimensional errors in all three (breadth, depth, and height) directions...."; Emphasis is that Chandar teaches individual axis measurements and deviation explicitly for each location in XYZ build envelope; Also see Fig.8A-8B [0055]-[0057]);
Chandar complements Hartmann by primarily teaching generating a training dataset comprising for each training object (Chandar: [0059]-[0060] disclosing training dataset; [0061] details use of machine learning; Fig.9A & [0058] also illudes to fact that the measurement and compensation generation is position based [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. [Examiner Note: This is position based measurement and compensation, complementing Hartmann’s mapping the first causing limitation in claim 1. This is the major point of argument presented that combination including does not do position based compensation] Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation...."):
a first dimensional inaccuracy between the measured first dimension of the training object as has been physically generated and a corresponding first dimension of the training object as specified by the object model data in accordance with which the training object has been physically generated (Chandar: [0032] "... The use of the Grey Taguchi method allows combining the goals of reducing inaccuracies of all dimensions into a single objective known as the grey relation grade. Maximizing this relation provides optimal factor level settings for dimensional accuracy. A lower layer thickness ultimately reduces the percentage change in height of the part whilst also increasing errors in the x and y dimensions...."; [0038] "... Accuracy refers to how close the measured value is to the nominal or true expected value. In the case of z-dimensional accuracy this refers to the error in height between the actual measured part and the nominal height set by the slicer. Precision refers to how close the measured values are to each other, i.e. the variation between two similar parts..." ; - dimensional inaccuracies in any one of the x, y or z could be first dimensional inaccuracy; [0041]-[0043]"... [0041] At least two approaches are disclosed herein. One approach is map height error in all three dimensions across the build envelope. By mapping the error, a 3D compensation model can be formulated and tested using a software based compensation method to try and increase the part height accuracy of the 3D printer...."; [0058]-[0061] as cited before);
a second dimensional inaccuracy between the measured second dimension of the training object as has been physically generated and a corresponding second dimension of the training object as specified by the object model data in accordance with which the training object has been physically generated (Chandar: [0032] "... The use of the Grey Taguchi method allows combining the goals of reducing inaccuracies of all dimensions into a single objective known as the grey relation grade. Maximizing this relation provides optimal factor level settings for dimensional accuracy. A lower layer thickness ultimately reduces the percentage change in height of the part whilst also increasing errors in the x and y dimensions...."; [0038] "... Accuracy refers to how close the measured value is to the nominal or true expected value. In the case of z-dimensional accuracy this refers to the error in height between the actual measured part and the nominal height set by the slicer. Precision refers to how close the measured values are to each other, i.e. the variation between two similar parts..." ; - dimensional inaccuracies in any one of the x, y or z could be second dimensional inaccuracy; [0041]-[0043] "... [0041] At least two approaches are disclosed herein. One approach is map height error in all three dimensions across the build envelope. By mapping the error, a 3D compensation model can be formulated and tested using a software based compensation method to try and increase the part height accuracy of the 3D printer...." [0058]-[0061] as cited before); and
Chandar complements Hartmann by also teaching (i.e. taught in Hartmann also) a position of the training object within the fabrication chamber during physical generation (Chandar: [0058]-[0059] & Fig.9A-9Ba position as XYZ measurement location within the build envelope (as chamber) ).
Chandar complements Hartmann by also teaching (i.e. taught in Hartmann also) generating an inference model (Chandar : [0038] "... This disclosure enables modelling the accuracy error of the printer and providing compensation methods and models [as inference model] to improve the dimensional accuracy of 3D printers...."; [0061] use of machine learning) based on the plurality of training objects (Chandar:[0059] "... [0059] With reference now to FIG. 9B, a diagram of a 3D visualization of standard deviation of predicted height errors across five real models derived from repeat measurements of a single artifact is shown...." [0058] "... First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation...." ) ;
Chandar complements Hartmann by also teaching (i.e. taught in Hartmann also) providing, as input to the inference model, a production position within the fabrication chamber at which a production object is to be physically generated (Chandar: Fig.9A [0058]-[0061] [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." );
Chandar complements Hartmann by also teaching (i.e. taught in Hartmann also) receiving, as output from the inference model, first and second dimensional compensations for the first and second axes, respectively, based on the production position (Chandar: Fig.9A [0058]-[0061] [0058] "... For example, FIG. 9A illustrates the technique for applying a Z-compensation at each XYZ coordinate. First, each XYZ coordinate in the 3D printer's build envelope is identified 150. Next, each XYZ coordinate is processed using the error compensation model 152. Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." );
Chandar complements Hartmann by also teaching (i.e. taught in Hartmann also) applying the first and second dimensional compensations to production object model data for the production object along the first and second axes, respectively, to generate modified production object model data (Chandar: Fig.7 & Fig. 10; [0049]-[0054]); and causing the additive manufacturing apparatus to physically generate the production object at the production position in accordance with the modified production object model data (Chandar: Fig.7 [0049]-[0054] & Fig. 10 as in [0061]-[0067]).
Chandar complements Hartmann by also teaching a production object volume having first and second dimensional lengths along the first and second axes, respectively (Chandar: [0042] x-y dimension for the production object [0041] z dimension for the production object, all associated with accuracy in context of nominal dimensions as described in [0007]; Also see [0058]) , and
which portions within the production object volume are solid (Chandar: [0005] "... During the slicing step, the enclosed volume described in the STL file is sliced into a set of finite-thickness planar layers. Slice thickness refers to the nominal thickness of these layers. In order to conserve material and shorten cycle time, the internal volume of 3D printed parts are typically supported by lattice-like infill structures instead of being filled completely. In the case of FDM or FFF, these infill structures are also generated during the slicing step and incorporated into the G-code file. These internal features are difficult to measure after printing and are ideally measured during the 3D printing process. The slicer packages used can usually calculate the [solid] volume of molten material to extrude over each print move based on the length of the print move, thickness of the layer, and extrusion width...."; [0050] [0058] [0062]-[0066] discussing solid volume and errors associated with solid volume of the production; [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Chandar to Hartmann to provide further details of the how the training data is gathered for each of the XYZ axis and specific location explicitly (Chandar: [0058]-[0061] ) to get uniform parts, complementing position based compensation/inference model. The motivation to combine would have been that Chandar complements Hartmann in the field of correcting the model/mesh to yield consistent parts manufactured en-masse in hundreds/thousands at same time using additive manufacturing by fusing building material (Chandar: Fig.2D, Fig.3, Fig.5, Fig.6A, Fig.10; Hartmann: [0010][0028]), and are analogous to the claimed subject matter in the same field of invention.
Regarding Claims 2 (Updated 5/13/2026)
Chandar teaches claim 1 wherein the first dimensional compensation includes either or both of a first scaling factor (Chandar : [0036]) and a first offset factor (Chandar: [0045] "... This strategy involves first capturing the systematic component of layer thickness variation as a function of various process parameters (error mapping and modelling), then applying a geometric offset based on the deviation predicted by the model (error compensation)...."; Fig.3) , such that the first dimensional length of the production object volume is scaled by the first scaling factor and/or is offset by the first offset factor (Chandar: [0036] "... An important limitation of such implementations, which apply shrinkage compensation as a uniform scaling transform, is that they can only reliably correct the spatially uniform component of dimensional deviation due to shrinkage. For example, spatially varying dimensional deviations due to viscoelastic effects cannot be fully corrected by a uniform scaling correction—intermediate features will remain in incorrect positions even if the scaling results in the correct overall part size...."; such scaling is corrected by error mapping and modeling and deviations/compensation are corrected as discussed in [0045] [0058]-[0060]; [0064] discusses offsets; first dimension can be X/Y axis compensation/correction) , and wherein the second dimensional compensation includes either or both of a second scaling factor and a second offset factor such that the second dimensional length of the production object model volume is scaled by the second scaling factor and/or is offset by the second offset factor (Chandar: [0036], [0045] showing scaling and offset compensation using compensation model in one axis can be applied to second axis/dimension as discussed in [0058] "... Lastly, each XYZ coordinate has a Z-compensation attributed to it 154. This process can also be generalized to X-compensation and Y-compensation. This process can be completed using three similar techniques or reduced to one more sophisticated technique which computes simultaneously...." ; second dimension can be Y or Z axis) .
Regarding Claim 5
Chandar teaches wherein the plurality of generated training objects comprises either or both of: one or more instance of a first training object and one or more instance of a second training object, wherein the first and second training objects have different length: width: height ratios (Chandar: Fig.8A-8B; [0050]-[0058] showing different cylinders with different length/width/height ratios) ; and a plurality of instances of a third training object and a plurality of instances of a fourth training object, wherein the third and fourth training objects have different solid proportions (Chandar: Fig.8A-8B; [0050]-[0058] showing different cylinder instances in different proportions at different positions).
Regarding Claim 6
Chandar and Hartmann and Parangi teach claim 1 wherein the training dataset is a first training dataset and a plurality of training data sets including the first training set are generated for: different environmental conditions, different object generation apparatus, different object generation material compositions (Chandar: [0040] "... This may include a model that effects the various inputs to a 3D printer and printed part parameters on the layer thickness variation for different qualified printer materials....") , different object cooling profiles, and different print modes (Chandar: [0029] "... The variation can be explained by the variability in cooling time between layers 14 at different heights of the part 12.... Less control of cooling rates in the FDM process can cause unpredictable viscoelastic effects in the part 12 causing it to be inaccurate. …" , Also see Fig.3 and [0036], [0038] as sources of variation & therefore the training data sets for the model being varied hence and in Hartmann: [0020] source of variation and therefore the training data sets; [0029] "...Predicting dimensional deviations that arise during the additive manufacturing 7 of the plurality of motor vehicle components 1 by simulation is extremely challenging, and such a prediction is highly dependent on a quality of a respective simulation model that is used, in particular on material and process input variables, and calculation methods used....";) .
Regarding Claim 22 (Cancelled)Regarding Claim 23 (Cancelled)
Regarding Claim 24 (Cancelled)
Regarding Claim 39 (New)
Chandar teaches wherein the production object model data defines a solid production proportion of the production object volume that is to be solid (Chandar: [0005] "... The slicer packages used can usually calculate the volume of molten material to extrude over each print move based on the length of the print move, thickness of the layer, and extrusion width...."; proportion also as scaling and offset in [0036] [0064] [0045][0062], [0064]; and [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber) , wherein the solid production portion is also provided as input to the inference model model (Chandar: [0059] Fig.9A-9B; Fig.8A-8B [0055]-[0056] as solid proportion of an object volume showing the pillar in the chamber), and wherein the first and second dimensional compensations received as output from the inference model are based on the solid production proportion (Chandar : [0041] "... One approach is map height error in all three dimensions across the build envelope.... The second accuracy approach tackles height dimensional accuracy by modeling variations in layer thickness along the height of a part… The tools and procedures developed are intended to be easily generalizable to address dimensional errors in all three (breadth, depth, and height) directions…" [0058]-[0061] dimensional compensation is based on solid production models detailed in Fig.8A-8B which have proportions as offsets and scaling detailed in [0036], [0064], [0045], [0062], [0064]-[0065]) in addition to being based on the production position (Chandar: [0062] "...the invention includes methods of providing 3D printing error compensation which introduce geometric offsets that depend on location within the work volume. Such methods may correct both spatially uniform and spatially varying dimensional errors, as long as they are systematic in nature. The spatial error models generated through these processes may dictate the amount of compensation or offset to be applied at each point within the part volume....") .
Regarding Claim 40 (New)
Chandar teaches wherein the production object volume is also provided as input to the inference model (Chandar: [0005] "... The slicer packages used can usually calculate the volume of molten material to extrude over each print move based on the length of the print move, thickness of the layer, and extrusion width....";), and wherein the first and second dimensional compensations received as output from the inference model are based on the production object volume (Chandar : [0041] "... One approach is map height error in all three dimensions across the build envelope.... The second accuracy approach tackles height dimensional accuracy by modeling variations in layer thickness along the height of a part… The tools and procedures developed are intended to be easily generalizable to address dimensional errors in all three (breadth, depth, and height) directions…" [0058]-[0061] dimensional compensation is based on solid production models detailed in Fig.8A-8B which have proportions as offsets and scaling detailed in [0036], [0064], [0045], [0062], [0064]-[0065]) in addition to being based on the production position (Chandar: [0062] "...the invention includes methods of providing 3D printing error compensation which introduce geometric offsets that depend on location within the work volume. Such methods may correct both spatially uniform and spatially varying dimensional errors, as long as they are systematic in nature. The spatial error models generated through these processes may dictate the amount of compensation or offset to be applied at each point within the part volume....") .
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Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190278255 A1 by de Pena; Alejandro Manuel et al.
Regarding Claim 31 Teachings of Chandar are shown in the parent claim 25. Chandar does not specifically appear to teach
de Pena teaches looking up the production position within a look-up table realizing the inference model to determine the first and second dimensional compensations (de Pena : [0050] ) .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of dePena to Chandar to further use lookup for dimensional correction based on each axis (Chandar: [0058]; dePena: [0050] "... If such distortions can be quantified they may be included in the characteristic data 106 and used by the transformation module to perform a suitable geometrical transformation module to compensate for any distortion. In one example the characteristic data may include a lookup table....") because Chandar already has volumetric presentation in Fig.9B which may be easily converted to lookup table as disclosed in dePean for the same purpose in the same technological field of compensating 3D printing for distortions, as in the current claim.
Claim(s) 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190329499 A1 by Parangi; Abraham Lawrence.Regarding Claim 34
Teachings of Chandar are shown in the parent claim 25. Chandar does not specifically appear to teach wherein the inference model is a neural network.
Parangi teaches The non-transitory computer-readable data storage medium of claim 25, wherein the inference model is a neural network (Parangi : Fig.1 element 102 as Fully Convolutional Neural Network leading to correction field/corrected mesh/corrected part; [0066] "... [0066] Returning to FIG. 1A, at act 162, a predicted error field is generated responsive to execution of act 160. At act 172, a correction field is generated using the predicted error field. At act 174, the correction field is used to generate a corrected mesh...."; ); [0029]-[0041] detailing how the error (field) is determined between the model and actual part data leading to correction field).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Parangi to Chandar to further detail machine learning model disclosed in Chandar (Chandar: [0061]) to be neural network model (as mapped above). The motivation to combine would have been that Parangi complements Chandar in the field of correcting the model/mesh to yield consistent parts manufactured en-masse in hundreds/thousands at same time using additive manufacturing by fusing building material (Parangi: [0055]; Chandar: Fig8A-8B – [0006], [0038] consistancy), and are analogous to the claimed subject matter in the same field of invention.
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20180307209 A1 by Chin; Ricardo et al.Regarding Claim 35
Teachings of Chandar are shown in the parent claim 25. Chandar does not specifically appear to teach wherein the processor is part of the additive manufacturing apparatus, such that the additive manufacturing apparatus performs the processing.
Chin teaches wherein the processor is part of the additive manufacturing apparatus, such that the additive manufacturing apparatus performs the processing (Chin: Fig.1 [0018] "... a control system 118 [as processor] manages operation of the printer 100 to fabricate the object 112 according to a three-dimensional model using a fused filament fabrication process or the like...." The control system includes a camera 150 & [0054]; [0055] "... still images from the camera 150 may also or instead be used to dynamically correct a print process, or to visualize where and how automated or manual adjustments should be made,..."; correction/compensation model are discussed in [0071]-[0091] in all 3 dimensions).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Chin to Chandar to provide further details of system that integrates the processing and correction/modification of the model in print system (Chin: Fig.1). The motivation to combine would have been that Chin complements Chandar as it performs modifications in 3 Axis as Chandar and preforms such within the printer (Chin: Fig.1 [0018] "... a control system 118 [as processor] manages operation of the printer 100 to fabricate the object 112 according to a three-dimensional model using a fused filament fabrication process or the like...." The control system includes a camera 150 & [0054]; [0055] "... still images from the camera 150 may also or instead be used to dynamically correct a print process, or to visualize where and how automated or manual adjustments should be made,..."; correction/compensation model are discussed in [0071]-[0091] in all 3 dimensions; Chandar: Fig.2D, Fig.3, Fig.5, Fig.6A, Fig.10;), and are analogous to the claimed subject matter in the same field of invention.
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Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over US 20190054700 A1 by Chandar; Arjun et al., in view of US 20190377843 A1 by Chen; Yanzhi et al.
Regarding Claim 37
Teachings of Chandar are shown in the parent claim 25. Chandar does not specifically teach corner alignment.
Chen teaches The non-transitory computer-readable data storage medium of claim 25, wherein the single reference point of the production object volume to which the location within the fabrication chamber specified by the coordinates of the production position corresponds is a given corner point of the production object volume (Chen: Figs.1-2 [0043]-[0045] discussing corner and corner alignment), such that the production object is physically generated by the additive manufacturing apparatus such that the given corner point of the production object volume is at the production position within the fabrication chamber (Chen: Figs.1-2[0044] "... The skeleton can be determined by comparing the nominal model to the deformations in the target AM product during various stages of a previous fabrication run. At operation 808, a deformed skeleton model to be used to produce the target AM product is generated. The deformed skeleton model can be determined by calculating differences in the volume and shape features (e.g., edges, corners, planar alignments, etc.)....") .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Chen to Chandar to provide further details of system that integrates the processing and correction/modification of the model in print system (Chen: Fig.8). The motivation to combine would have been that Chin complements Chandar as it performs modifications in 3 Axis as Chandar and preforms such within the printer (Chen: Fig.8 element 810, 816; Chandar: Fig.2D, Fig.3, Fig.5, Fig.6A, Fig.10;), and are analogous to the claimed subject matter in the same field of invention of model compensation for additive manufacturing.
Relevant Prior Art of Record
US Patent No. 119838321 by Shepherd; Matthew A. et al. discloses alignment within build volume 103 subdivided into smaller volumes where the print objects are centered 136 and corner 138 aligned (See Fig. 2 Col.8 Lines 33-43). Claims 37 & 38 may be additionally rejected with this prior art.
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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, May 14, 2026
1 PE2E Search string L128 (MLTD).
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