DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/08/2024, 07/18/2024, 07/18/2024, 12/08/2025 and 03/11/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1, 36, and 53 is objected to because of the following informalities:
In claim 1, Line 9 the term “perform the steps of” should be changed to “perform for typographical/grammar issues to avoid clarity issues to prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 36, Line 10 the term “perform the steps of” should be changed to “perform for typographical/grammar issues to avoid clarity issues to prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 53, Line 9 the term “perform the steps of” should be changed to “perform for typographical/grammar issues to avoid clarity issues to prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 9, 36, and 53, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “an optics assembly configured to” [Line 4].
Claim 1; “a recoater configured to” [Line 7].
Claim 9; “recoating the build surface with the recoater” [Line 2-3].
Claim 36; recites the limitation, “an optics assembly configured to” [Line 4].
Claim 36; “recoating the build surface with the recoater” [Line 8].
Claim 53; recites the limitation, “an optics assembly configured to” [Line 4].
Claim 53; a recoater configured to” [Line 7].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 9, 36, and 53:
“an optics assembly” (Fig. 2, #226 and #228. Paragraph [0059]- the optics assembly may include beam forming optics such as lenses 226 and 228 (which may be individual lenses, lens arrays, and/or combined macrolenses), mirrors 230, and/or any other appropriate type of optics disposed along the various optical paths between the optical fibers and the build surface 210 which may shape and direct the laser energy within the optics assembly. (Wherein an optics assembly has the structure associated with it of a pair of lenses or mirrors).)).
“a recoater” (Fig. 3, #312. Paragraph [0061]- as the recoater traversers the build surface of the build plate, it deposits a precursor material 302a, such as a powder, onto the build plate and smooths the surface to provide a layer of precursor material with a predetermined thickness on top of the underlying volume of fused and/or unfused precursor material deposited during prior formation steps. (wherein the recoater has structure associated with it of a device used to spread and smooth material over a surface).).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12-22, 24-25, 43, and 68 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more nor an integration of the judicial exceptions into a practical application. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion). The claimed invention simply performs obtaining of images and See analysis below for more details.
Regarding Independent Claim 12 and its dependent claims 13-25,
Step 1 Analysis: Claim 12 is directed to a method, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 12 recites, in part:
“and predicting formation of potential part defects in the current layer prior to fusing based at least in part on the images.”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
“predicting formation of potential part defects in the current layer prior to fusing based at least in part on the images” is a step a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, etc.) and evaluate them to make a determination/identification of a defect;
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing, wherein the plurality of layers include at least one previous layer and a current layer;”
The additional elements “obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing, wherein the plurality of layers include at least one previous layer and a current layer;” include steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc] .
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 12 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 13-22 and 24-25 do not provide elements that overcome the deficiencies of the independent claim 12.
Moreover, claim 13 recites, in part,
“providing information related to the images to a trained statistical defect prediction model to predict the formation of the potential part defects in the current layer”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the providing or inputting of data into a ML model.
Accordingly, the dependent claims 13 are not patent eligible under 101.
Moreover, claim 14 recites, in part,
“identifying recoating defects in the images”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the identification of defects within a image.
Accordingly, the dependent claims 14 are not patent eligible under 101.
Moreover, claim 15 recites, in part,
“generating binary masks based at least in part on the identified recoating defects”
“providing the binary masks to the trained statistical defect prediction model”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the identification of regions to of recoating defects in a image and providing the regions.
Accordingly, the dependent claims 15 are not patent eligible under 101
Moreover, claim 16 recites, in part,
“identifying one or more portions of the image with light intensities greater than a threshold light intensity”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the identification of regions that are brighter than the rest of the image.
Accordingly, the dependent claims 16 are not patent eligible under 101
Moreover, claim 17 recites, in part,
“identifying contiguous groups of pixels of the images with intensities greater than the threshold intensity”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the identification of regions that are brighter than the rest of the image.
Accordingly, the dependent claims 17 are not patent eligible under 101
Moreover, claim 18 recites, in part,
“obtaining a plurality of fusing energy maps”
Which is insignificant extra-solution/post-solution activities of data gathering
“omitting identified recoating defects associated with fusing energies less than a threshold energy.”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing not including defects in a region seen as low energy in the energy map.
Accordingly, the dependent claims 18 are not patent eligible under 101
Moreover, claim 19 recites, in part,
“controlling one or more operations of the additive manufacturing system based at least in part on the predicted formation of the potential part defects.”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing manually stopping or shifting the additive manufacturing system in a case were a defect is seen or predicted.
Accordingly, the dependent claims 19 are not patent eligible under 101
Moreover, claim 20 recites, in part,
“the one or more operations includes at least one selected from scraping and recoating the build surface.”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing scraping or recoating when deemed necessary.
Accordingly, the dependent claims 20 are not patent eligible under 101
Moreover, claim 21 recites, in part,
“outputting the predicted formation of the potential part defects to a user.”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing pointing to or saying that there is a predicted defect to someone.
Accordingly, the dependent claims 21 are not patent eligible under 101
Moreover, claim 22 recites, in part,
“blurring the images.”
Which is a insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc].
Accordingly, the dependent claims 22 are not patent eligible under 101
Moreover, claim 23 recites, in part,
“comprising fusing the precursor material with one or more laser energy pixels to form one or more parts on the build surface.”
This would overcome the 101 because of “fusing the precursor material with one or more laser energy pixels”.
Accordingly, the dependent claims 23 is patent eligible under 101
Moreover, claim 24 recites, in part,
“A non-transitory computer readable memory including instructions that when executed by at least one processor performs the method of claim 12.”
Which is recited at a high level of generality (i.e. as a processor performing executing instructions stored, a memory storing instruction program, a computer to have computer components executing the instructions of the invention, a non-transitory computer readable medium performing storing instructions, generic devices [interface, screen, camera, sensor, etc.], etc.) such that they/it amount(s) to no more than mere instructions to apply the exception using generic components, devices, additional elements.
Accordingly, the dependent claims 24 are not patent eligible under 101
Moreover, claim 25 recites, in part,
“A part manufactured using the method of claim 12.”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing the method of claim 12 to create a part.
Accordingly, the dependent claims 25 are not patent eligible under 101
Regarding Independent Claim 43,
Step 1 Analysis: Claim 43 is directed to a method, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 43 recites, in part:
“identifying the recoating defects based at least in part on light intensities in the image”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
“identifying the recoating defects based at least in part on light intensities in the image” is a step a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, etc.) and evaluate them to make a determination/identification of a defect;
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface”
The additional elements “obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface” include steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc].
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 43 does not comply with the requirements of 35 USC 101.
Regarding Independent Claim 68,
Step 1 Analysis: Claim 68 is directed to a method, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 68 recites, in part:
“predicting formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images and the one or more fusing energy maps.”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
“predicting formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images and the one or more fusing energy maps” is a step a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, etc.) and evaluate them to make a determination/identification of a defect;
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface”
“obtaining one or more fusing energy maps including information related to energy applied to different portions of one or more layer of precursor material on a build surface of an additive manufacturing system”
The additional elements “obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface” and “obtaining one or more fusing energy maps including information related to energy applied to different portions of one or more layer of precursor material on a build surface of an additive manufacturing system” include steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc [acquiring data/information, transmitting data/info., outputting data/information, displaying data/info., converting data/info., generating data/info., etc].
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 68 does not comply with the requirements of 35 USC 101.
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 of this title, 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.
Claims 1-4, 8, 10, 12-15, 19, 21, and 23-25 are rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Vernet et al. (US 20230264268 A1) hereafter referenced as Vernet.
Regarding claim 1, Hwang teaches an additive manufacturing system comprising (Fig. 5, Paragraph [0028]- Hwang discloses an exemplary embodiment may employ an off-axis imaging system for monitoring solidification quality during additive manufacturing environment, which is used to record the real-time laser scanning process.):
a build surface (Fig. 5, Paragraph [0037]- Hwang discloses the powder delivery apparatus 90 is configured to distribute a plurality of powder materials 60 from powder hopper onto the build plate 50 to be sintered or melted during the additive manufacturing process for fabricating at least one part.);
one or more laser energy sources (Fig. 5, Paragraph [0037]- Hwang discloses the one or more laser sources 65 configured to sinter or melt of a distributed powder layer 61.);
an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface (Fig. 5, Paragraph [0037]- Hwang discloses the laser source is systemically coupled to a pair of mirrors 70.sub.n which are controlled by a scanner, and that facilitates focusing laser beam on a target area of the applied powder layer 61.);
a photosensitive detector configured to image at least a portion of the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the image recording device includes any suitable types of camera, such as a charged-couple device (CCD) camera, a complementary metal-oxide semiconductor (CMOS) camera, an infrared (IR) camera, or a pyrometer, and so on.);
a recoater configured to deposit sequential layers of precursor material on the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the powder deliver apparatus 90, also referred as a re-coater, moves along with p-axis of the build station 95, which deposits a plurality of layers 61 on the build plate 50.);
and at least one processor configured to perform the steps of (Fig. 13a, Paragraph [0051]- Hwang discloses the controller 800, such as computer, includes a plurality of components, such as a processor 815, a memory 805, an input-output (I/O) connector 810, and a network interface 820):
obtain images of a plurality of layers of precursor material on the build surface using the photosensitive detector (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.),
wherein the plurality of layers include at least one previous layer and a current layer (Fig. 7, Paragraph [0044]- Hwang discloses following the performing second error detection process step 545, the method may end 570 if there is no remaining layer to fabricate, or may process the next layer to step 560 and iterating (wherein since the process iterates there it would take a current layer image on the second iteration and a previous layer image on the first iteration).);
and predict formation of potential part defects in the current layer based at least in part on the images (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Although Hwang explicitly teaches obtain images of a plurality of layers of precursor material on the build surface using the photosensitive detector, and predict formation of potential part defects in the current layer based at least in part on the images. Hwang is silent to explicitly teach obtain images of a plurality of layers of precursor material on the build surface prior to fusing using the photosensitive detector, and predict formation of potential part defects in the current layer prior to fusing based at least in part on the images.
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Regarding claim 2, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 1,
Hwang further teaches wherein the at least one processor is configured to provide information related to the images to a trained statistical defect prediction model to predict the formation of the potential part defects in the current layer (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Regarding claim 3, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 2,
Hwang further teaches wherein the at least one processor is configured to identify recoating defects in the images (Fig. 8, Paragraph [0045]- Hwang discloses Once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520. For example, one or more defect may be detected by calculating changes of contrast to ensure the applied powder layer is evenly distributed, or calculating dimension of less distributed or unapplied area of the applied powder layer. In another exemplary embodiment, the predictive model optionally may perform if any contour is seen in an unevenly applied powder layer (wherein the uneven application of powder is seen as a recoating defect).).
Regarding claim 4, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 3,
Hwang further teaches wherein the at least one processor is configured to generate binary masks based at least in part on the identified recoating defects (Figs. 11b and 11d, Paragraph [0049]- Hwang discloses FIG. 11b is a processed image of FIG. 11a for efficient and selective analysis of the solidified portion only, thereby to save processing time and transmitting data size, as described above. FIG. 11c is an example image captured after applying a powder layer following the process shown in FIG. 11a. FIG. 11d is a processed image of FIG. 11c for selectively emphasizing the defectively applied portion of the powder layer by detecting remaining solidified part from the applied layer powder (wherein Fig. 11b and 11d show binary mask images).),
and provide the binary masks to the trained statistical defect prediction model (Fig. 9, Paragraph [0047]- Hwang discloses when the computer-vision based system processes the captured image, cut out the unsolidified powder portion and selectively segment transmit the solidified portion only for extracting feature-set to save processing time and process it effectively, as shown in FIG. 11b. After then, identify the level of solidification quality using a predictive model, step 539.).
Regarding claim 8, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 1,
Hwang further teaches wherein the at least one processor is configured to control one or more operations of the additive manufacturing system based at least in part on the predicted formation of the potential part defects (Fig. 8, Paragraph [0045]- Hwang discloses the controller 800 may utilize one or more computer-vision based systems which may require an optional GPU 870 to process heavy data in real-time as shown in FIG. 13b. Once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Regarding claim 10, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 1,
Hwang further teaches wherein the at least one processor is configured to output the predicted formation of the potential part defects to a user (Fig. 12b, Paragraph [0046]- Hwang discloses when a decision is made for correcting the defect, the controller then gives an alert to correct identified issue displayed on the coupled screen, step 521, as shown in FIG. 12b, or may return step 530 if adjustment is not required.).
Regarding claim 12, Hwang teaches a method for predicting part defects during an additive manufacturing process (Fig. 1, Paragraph [0031]- Hwang discloses FIG. 1 is a block diagram of an embodiment illustrating a framework for training 125 data-set to generate a predictive model 200 and to obtain an expected label 45 via applying at least one preprocessed new image to the predictive model),
the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.),
wherein the plurality of layers include at least one previous layer and a current layer (Fig. 7, Paragraph [0044]- Hwang discloses following the performing second error detection process step 545, the method may end 570 if there is no remaining layer to fabricate, or may process the next layer to step 560 and iterating (wherein since the process iterates there it would take a current layer image on the second iteration and a previous layer image on the first iteration).);
and predicting formation of potential part defects in the current layer based at least in part on the images (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Although Hwang explicitly teaches obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system; and predicting formation of potential part defects in the current layer based at least in part on the images Hwang is silent to explicitly teach obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing; and predicting formation of potential part defects in the current layer prior to fusing based at least in part on the images
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Regarding claim 13, Hwang in view of Vernet explicitly teaches the method of claim 12,
Hwang further teaches wherein predicting the formation of the potential part defects includes providing information related to the images to a trained statistical defect prediction model to predict the formation of the potential part defects in the current layer (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Regarding claim 14, Hwang in view of Vernet explicitly teaches the method of claim 13,
Hwang further teaches further comprising identifying recoating defects in the images (Fig. 8, Paragraph [0045]- Hwang discloses Once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520. For example, one or more defect may be detected by calculating changes of contrast to ensure the applied powder layer is evenly distributed, or calculating dimension of less distributed or unapplied area of the applied powder layer. In another exemplary embodiment, the predictive model optionally may perform if any contour is seen in an unevenly applied powder layer (wherein the uneven application of powder is seen as a recoating defect).).
Regarding claim 15, Hwang in view of Vernet explicitly teaches the method of claim 14,
Hwang further teaches further comprising generating binary masks based at least in part on the identified recoating defects (Figs. 11b and 11d, Paragraph [0049]- Hwang discloses FIG. 11b is a processed image of FIG. 11a for efficient and selective analysis of the solidified portion only, thereby to save processing time and transmitting data size, as described above. FIG. 11c is an example image captured after applying a powder layer following the process shown in FIG. 11a. FIG. 11d is a processed image of FIG. 11c for selectively emphasizing the defectively applied portion of the powder layer by detecting remaining solidified part from the applied layer powder (wherein Fig. 11b and 11d show binary mask images).),
and providing the binary masks to the trained statistical defect prediction model (Fig. 9, Paragraph [0047]- Hwang discloses when the computer-vision based system processes the captured image, cut out the unsolidified powder portion and selectively segment transmit the solidified portion only for extracting feature-set to save processing time and process it effectively, as shown in FIG. 11b. After then, identify the level of solidification quality using a predictive model, step 539.).
Regarding claim 19, Hwang in view of Vernet explicitly teaches the method of claim 12,
Hwang further teaches further comprising controlling one or more operations of the additive manufacturing system based at least in part on the predicted formation of the potential part defects (Fig. 8, Paragraph [0045]- Hwang discloses the controller 800 may utilize one or more computer-vision based systems which may require an optional GPU 870 to process heavy data in real-time as shown in FIG. 13b. Once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Regarding claim 21, Hwang in view of Vernet explicitly teaches the method of claim 12,
Hwang further teaches further comprising outputting the predicted formation of the potential part defects to a user (Fig. 12b, Paragraph [0046]- Hwang discloses when a decision is made for correcting the defect, the controller then gives an alert to correct identified issue displayed on the coupled screen, step 521, as shown in FIG. 12b, or may return step 530 if adjustment is not required.).
Regarding claim 23, Hwang in view of Vernet explicitly teaches the method of claim 12,
Hwang further teaches further comprising fusing the precursor material with one or more laser energy pixels to form one or more parts on the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the system 95 shown as Powder Bed Fusion (PBF) system. In some implementations, the described embodiment herein is described with reference to a PBF system, wherein the disclosure also applies to other types of additive manufacturing systems including, but not limited to, selective laser melting (SLM), direct metal laser sintering (DMLS), electron beam melting (EBM).).
Regarding claim 24, Hwang in view of Vernet explicitly teaches the method of claim 12
Hwang further teaches a non-transitory computer readable memory including instructions that when executed by at least one processor performs (Fig. 13b, Paragraph [0064]- Hwang discloses the memory 805 which may include a data repository (or database), may be embodied in any number of forms, including within any computer or other machine-readable data storage medium, memory device or other storage or communication device for storage or communication of information, currently known or which becomes available in the future, including, but not limited to, a memory integrated circuit (“IC”), or memory portion of an integrated circuit (such as the resident memory within a processor 815), whether volatile or non-volatile, whether removable or non-removable, including without limitation RAM, FLASH, DRAM, SDRAM, SRAM, MRAM, FeRAM, ROM, EPROM or E2PROM, or any other form of memory device, such as a magnetic hard drive, an optical drive, a magnetic disk or tape drive, a hard disk drive, other machine-readable storage or memory media such as a floppy disk, a CDROM, a CD-RW, digital versatile disk (DVD) or other optical memory, or any other type of memory, storage medium, or data storage apparatus or circuit, which is known or which becomes known, depending upon the selected embodiment. The memory 805 may be adapted to store various look up tables, parameters, coefficients, other information and data, programs or instructions (of the software of the present invention), and other types of tables such as database tables.).
Regarding claim 25, Hwang in view of Vernet explicitly teaches the method of claim 12
Hwang further teaches a part manufactured using the method of claim 12 (Fig. 13a, Paragraph [0051]- Hwang discloses this may enable to obtain an optimum environment to manufacture a desired part in good quality.).
Claims 5-6, 11, 16-17, 22, and 36 are rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Vernet et al. (US 20230264268 A1) hereafter referenced as Vernet and Nwoke et al. (US 20240119579 A1) hereafter referenced as Nwoke.
Regarding claim 5, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 3,
Hwang in view of Vernet fails to explicitly teach wherein the at least one processor is configured to identify one or more portions of the image with light intensities greater than a threshold light intensity to identify the recoating defects.
However, Nwoke explicitly teaches wherein the at least one processor is configured to identify one or more portions of the image with light intensities greater than a threshold light intensity to identify the recoating defects (Fig. 1, Paragraph [0061]- Nwoke discloses ones of the voxels 106 in which an absolute value of the z-score 118 is above a threshold value 120 of the z-score 118 are identified as high z-score, which indicate a high variance from normal and an area of interest for further analysis.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Nwoke wherein the at least one processor is configured to identify one or more portions of the image with light intensities greater than a threshold light intensity to identify the recoating defects.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein the at least one processor is configured to identify one or more portions of the image with light intensities greater than a threshold light intensity to identify the recoating defects.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 6, Hwang in view of Vernet and Nwoke explicitly teaches the additive manufacturing system of claim 5,
Hwang in view of Vernet fails to explicitly teach wherein the at least one processor is configured to identify contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size to identify the recoating defects.
However, Nwoke explicitly teaches wherein the at least one processor is configured to identify contiguous groups of pixels (Fig. 1, Paragraph [0067]- Nwoke discloses the at least one clustering parameter 128 includes a spatial dimension 132 (FIG. 3) between each one of the voxels 106 and an adjacent one of the voxels 106. As an example, the spatial dimension 132 is a minimum distance in the X-direction, Y-direction, or Z-direction from one voxel 106 to another voxel 106 that makes the voxels 106 neighbors.) of the images with intensities greater than the threshold intensity (Fig. 1, Paragraph [0064]- Nwoke discloses thus, All the voxels 106 with the z-scored 118 having an absolute value greater than the threshold value 120 (e.g., extremely high z-scores) are identified and collected. Any of the voxels 106 with the z-scored 118 having an absolute value less than the threshold value 120 (e.g., low or normal z-scores) are ignored. This process greatly reduces the population of voxels 106 that are available for clustering and enables implementation of a clustering algorithm 162 feasible by a computing device (e.g., computer 150 shown in FIG. 3). The clustering algorithm 162 utilizes the clustering parameters 128 as criteria for what makes the voxels 106 neighbors and, thus, to determine which voxels 106 are clustered to form the cluster 124.) and a size greater than a threshold size to identify the recoating defects (Fig. 3, Paragraph [0080]- Nwoke discloses the cluster-boundary parameter 122 includes or takes the form of a volume 140 (FIG. 3) of the cluster 124. In these examples, the parameter threshold 126 includes or takes the form of a minimum volume 142 (FIG. 3) of the defect 100. Generally, the minimum volume 142 of the defect 100 is the smallest volume that would qualify as a defect and anything smaller than the minimum volume 142 would be within manufacturing tolerances and, thus, not a defect.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Nwoke wherein the at least one processor is configured to identify contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size to identify the recoating defects.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein the at least one processor is configured to identify contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size to identify the recoating defects.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 11, Hwang in view of Vernet explicitly teaches the additive manufacturing system of claim 1,
Hwang in view of Vernet fails to explicitly teach wherein the at least one processor is configured to blur the images.
However, Nwoke explicitly teaches wherein the at least one processor is configured to blur the images (Fig. 10, Paragraph [0051]- Nwoke discloses in one or more examples, according to the method 1000, the step of (block 1012) calculating the average value 112 for the intensity 108 of each one of the voxels 106 includes a step of performing a three-dimensional gaussian smoothing (e.g., blur) operation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Nwoke wherein the at least one processor is configured to blur the images.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein the at least one processor is configured to blur the images.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 16, Hwang in view of Vernet explicitly teaches the method of claim 14,
Hwang in view of Vernet fails to explicitly teach wherein identifying the recoating defects is based at least in part on identifying one or more portions of the image with light intensities greater than a threshold light intensity.
However, Nwoke explicitly teaches wherein identifying the recoating defects is based at least in part on identifying one or more portions of the image with light intensities greater than a threshold light intensity (Fig. 1, Paragraph [0061]- Nwoke discloses ones of the voxels 106 in which an absolute value of the z-score 118 is above a threshold value 120 of the z-score 118 are identified as high z-score, which indicate a high variance from normal and an area of interest for further analysis.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Nwoke wherein identifying the recoating defects is based at least in part on identifying one or more portions of the image with light intensities greater than a threshold light intensity.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein identifying the recoating defects is based at least in part on identifying one or more portions of the image with light intensities greater than a threshold light intensity.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 17, Hwang in view of Vernet and Nwoke explicitly teaches the method of claim 16,
Hwang in view of Vernet fails to explicitly teach wherein identifying the recoating defects includes identifying contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size.
However, Nwoke explicitly teaches wherein identifying the recoating defects includes identifying contiguous groups of pixels (Fig. 1, Paragraph [0067]- Nwoke discloses the at least one clustering parameter 128 includes a spatial dimension 132 (FIG. 3) between each one of the voxels 106 and an adjacent one of the voxels 106. As an example, the spatial dimension 132 is a minimum distance in the X-direction, Y-direction, or Z-direction from one voxel 106 to another voxel 106 that makes the voxels 106 neighbors.) of the images with intensities greater than the threshold intensity (Fig. 1, Paragraph [0064]- Nwoke discloses thus, All the voxels 106 with the z-scored 118 having an absolute value greater than the threshold value 120 (e.g., extremely high z-scores) are identified and collected. Any of the voxels 106 with the z-scored 118 having an absolute value less than the threshold value 120 (e.g., low or normal z-scores) are ignored. This process greatly reduces the population of voxels 106 that are available for clustering and enables implementation of a clustering algorithm 162 feasible by a computing device (e.g., computer 150 shown in FIG. 3). The clustering algorithm 162 utilizes the clustering parameters 128 as criteria for what makes the voxels 106 neighbors and, thus, to determine which voxels 106 are clustered to form the cluster 124.) and a size greater than a threshold size (Fig. 3, Paragraph [0080]- Nwoke discloses the cluster-boundary parameter 122 includes or takes the form of a volume 140 (FIG. 3) of the cluster 124. In these examples, the parameter threshold 126 includes or takes the form of a minimum volume 142 (FIG. 3) of the defect 100. Generally, the minimum volume 142 of the defect 100 is the smallest volume that would qualify as a defect and anything smaller than the minimum volume 142 would be within manufacturing tolerances and, thus, not a defect.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Nwoke wherein identifying the recoating defects includes identifying contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein identifying the recoating defects includes identifying contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 22, Hwang in view of Vernet teaches the method of claim 12,
Hwang in view of Vernet fails to explicitly teach further comprising blurring the images.
However, Nwoke explicitly teaches further comprising blurring the images (Fig. 10, Paragraph [0051]- Nwoke discloses in one or more examples, according to the method 1000, the step of (block 1012) calculating the average value 112 for the intensity 108 of each one of the voxels 106 includes a step of performing a three-dimensional gaussian smoothing (e.g., blur) operation.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Nwoke further comprising blurring the images.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein further comprising blurring the images.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Regarding claim 36, Hwang teaches an additive manufacturing system comprising (Fig. 5, Paragraph [0028]- Hwang discloses an exemplary embodiment may employ an off-axis imaging system for monitoring solidification quality during additive manufacturing environment, which is used to record the real-time laser scanning process.):
a build surface (Fig. 5, Paragraph [0037]- Hwang discloses the powder delivery apparatus 90 is configured to distribute a plurality of powder materials 60 from powder hopper onto the build plate 50 to be sintered or melted during the additive manufacturing process for fabricating at least one part.);
one or more laser energy sources (Fig. 5, Paragraph [0037]- Hwang discloses the one or more laser sources 65 configured to sinter or melt of a distributed powder layer 61.);
an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface (Fig. 5, Paragraph [0037]- Hwang discloses the laser source is systemically coupled to a pair of mirrors 70.sub.n which are controlled by a scanner, and that facilitates focusing laser beam on a target area of the applied powder layer 61.);
one or more lights configured to illuminate the build surface (Fig. 5, Paragraph [0036]- Hwang discloses a system 95 includes a build platform 55 configured to fixture a build plate 50 during manufacturing and unloading process, one or more light sources 85.sub.n…);
a photosensitive detector configured to image at least a portion of the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the image recording device includes any suitable types of camera, such as a charged-couple device (CCD) camera, a complementary metal-oxide semiconductor (CMOS) camera, an infrared (IR) camera, or a pyrometer, and so on.);
a recoater configured to deposit sequential layers of precursor material on the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the powder deliver apparatus 90, also referred as a re-coater, moves along with p-axis of the build station 95, which deposits a plurality of layers 61 on the build plate 50.);
and at least one processor configured to perform the steps of (Fig. 13a, Paragraph [0051]- Hwang discloses the controller 800, such as computer, includes a plurality of components, such as a processor 815, a memory 805, an input-output (I/O) connector 810, and a network interface 820):
obtain an image of at least a portion of the current layer the current layer using the photosensitive detector (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.);
Although Hwang explicitly teaches obtain an image of at least a portion of the current layer the current layer using the photosensitive detector Hwang is silent to explicitly teach obtain an image of at least a portion of the current layer prior to fusing the current layer using the photosensitive detector.
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; one or more lights configured to illuminate the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Hwang in view of Vernet fails to explicitly teach identify recoating defects in the current layer based at least in part on light intensities in the image.
However, Nwoke explicitly teaches identify recoating defects in the current layer based at least in part on light intensities in the image (Fig. 1, Paragraph [0061]- Nwoke discloses ones of the voxels 106 in which an absolute value of the z-score 118 is above a threshold value 120 of the z-score 118 are identified as high z-score, which indicate a high variance from normal and an area of interest for further analysis. Further in Fig. 1, paragraph [0060]- Nwoke discloses neighboring voxels 106, each having an extreme value for the z-score 118 can be grouped together to represent areas of nonconforming density within the powder manufactured component 102, such as potential defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; one or more lights configured to illuminate the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Nwoke identify recoating defects in the current layer based at least in part on light intensities in the image.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein identify recoating defects in the current layer based at least in part on light intensities in the image.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Claims 9 and 20 are rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Vernet et al. (US 20230264268 A1) hereafter referenced as Vernet, Kenworthy et al. (US 20220088685 A1) hereafter referenced as Kenworthy, and Deaton, JR et al. (US 20190143409 A1) hereafter referenced as Deaton.
Regarding claim 9, Hwang in view of Vernet teaches the additive manufacturing system of claim 8,
Hwang in view of Vernet fails to explicitly teach wherein the one or more operations includes at least one selected from scraping.
However, Kenworthy explicitly teaches wherein the one or more operations includes at least one selected from scraping (Fig. 1, Paragraph [0031]- Kenworthy discloses in the case of detected defects in the build piece, for example, the system (such as a controller of the 3D printer) may modify the operation of the 3D printer to, e.g., mitigate the defect (e.g., physically remove the defect, such as by drilling or scraping out an inclusion, adjust printer parameters to correct the defect, such as increasing laser power locally applied to an area in which a void was detected in the current layer or a previous layer, e.g., to re-fuse the area of the defect), flag the defect (e.g., to inform a post-processing treatment such as hot isostatic pressing (HIP), drilling out the defect and filling in the hole afterwards, etc.), or the system may modify the operation by simply ending the print job (thus saving time and energy), or take other corrective measures.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Kenworthy wherein the one or more operations includes at least one selected from scraping.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein the one or more operations includes at least one selected from scraping.
The motivation behind the modification would have been to allow for higher quality pieces to be created, since both Hwang and Kenworthy are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Kenworthy’s system allows for the quality of pieces created to be enhanced. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Kenworthy et al. (US 20220088685 A1) Paragraph [0028].
Hwang in view of Vernet and fails to explicitly teach recoating the build surface with the recoater.
However, Deaton explicitly teaches recoating the build surface with the recoater (Fig. 6, Paragraph [0057]- Deaton discloses after defect 264 in structure 258 is detected, the repair process is initiated. In the exemplary embodiment, the repair process includes recoating the current layer of powder with a supplemental layer of powder before a supplemental scan path is commenced.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet and Kenworthy of having an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Deaton recoating the build surface with the recoater.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein recoating the build surface with the recoater.
The motivation behind the modification would have been to allow for better correction of defects detected, since both Hwang and Deaton are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Deaton’s system allows for the remedying of defects detected based on scanning. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Deaton et al. (US 20190143409 A1) Paragraph [0004].
Regarding claim 20, Hwang in view of Vernet teaches the method of claim 19,
Hwang in view of Vernet fails to explicitly teach wherein the one or more operations includes at least one selected from scraping.
However, Kenworthy explicitly teaches wherein the one or more operations includes at least one selected from scraping (Fig. 1, Paragraph [0031]- Kenworthy discloses in the case of detected defects in the build piece, for example, the system (such as a controller of the 3D printer) may modify the operation of the 3D printer to, e.g., mitigate the defect (e.g., physically remove the defect, such as by drilling or scraping out an inclusion, adjust printer parameters to correct the defect, such as increasing laser power locally applied to an area in which a void was detected in the current layer or a previous layer, e.g., to re-fuse the area of the defect), flag the defect (e.g., to inform a post-processing treatment such as hot isostatic pressing (HIP), drilling out the defect and filling in the hole afterwards, etc.), or the system may modify the operation by simply ending the print job (thus saving time and energy), or take other corrective measures.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet of having a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Kenworthy wherein the one or more operations includes at least one selected from scraping.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein the one or more operations includes at least one selected from scraping.
The motivation behind the modification would have been to allow for higher quality pieces to be created, since both Hwang and Kenworthy are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Kenworthy’s system allows for the quality of pieces created to be enhanced. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Kenworthy et al. (US 20220088685 A1) Paragraph [0028].
Hwang in view of Vernet and fails to explicitly teach recoating the build surface with the recoater.
However, Deaton explicitly teaches recoating the build surface with the recoater (Fig. 6, Paragraph [0057]- Deaton Jr. discloses after defect 264 in structure 258 is detected, the repair process is initiated. In the exemplary embodiment, the repair process includes recoating the current layer of powder with a supplemental layer of powder before a supplemental scan path is commenced.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Vernet and Kenworthy of having a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing with the teachings of Deaton recoating the build surface with the recoater.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein recoating the build surface with the recoater.
The motivation behind the modification would have been to allow for better correction of defects detected, since both Hwang and Deaton are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Deaton’s system allows for the remedying of defects detected based on scanning. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Deaton et al. (US 20190143409 A1) Paragraph [0004].
Claim 26 is rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Vernet et al. (US 20230264268 A1) and Gupta et al. (US 20200393813 A1) hereafter referenced as Gupta.
Regarding claim 26, Hwang teaches a method of training a recoating defect detection statistical model (Fig. 1, Paragraph [0031]- Hwang discloses FIG. 1 is a block diagram of an embodiment illustrating a framework for training 125 data-set to generate a predictive model 200 and to obtain an expected label 45 via applying at least one preprocessed new image to the predictive model, and FIG. 2 is a block diagram of an embodiment illustrating for collecting data sources to be used in machine learning system.),
the method comprising: obtaining training data (Fig. 3, paragraph [0033]- Hwang discloses step 115, generating training data-set with curves 20 from preprocessed image in sequence 10 and corresponding build parameter values 15, in some instances, preprocessed captured images in sequence may be input data in addition to the curves.),
wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers and associated with forming the plurality of parts (Fig. 3, paragraph [0033]- Hwang discloses the method begins, step 100, as described above, with collecting data sources 30 obtaining from preprocessed captured images at predetermined setting in sequence layer by layer 105, job files 5, build parameters 15, inspection output of solidification quality level and corresponding layers thereof 25, step 115, generating training data-set with curves 20 from preprocessed image in sequence 10 and corresponding build parameter values 15, in some instances, preprocessed captured images in sequence may be input data in addition to the curves.);
generating a trained statistical defect prediction model using the training data (Paragraph [0033]- Hwang discloses the solidification level based on inspection output is labeled with training data-set, step 120, and training a machine learning algorithm 40 to generate a predictive model 200, step 125.);
Although Hwang explicitly teaches wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers and associated with forming the plurality of parts Hwang in view of Gupta is silent to explicitly teach wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers prior to fusing and associated with forming the plurality of parts.
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang a method of training a recoating defect detection statistical model, the method comprising: obtaining training data, wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers prior to fusing and associated with forming the plurality of parts with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Hwang in view of Vernet is silent to explicitly teach storing the trained statistical defect prediction model on non-transitory computer readable memory for subsequent use.
However, Gupta explicitly teaches storing the trained statistical defect prediction model on non-transitory computer readable memory for subsequent use (Fig. 5, Paragraph [0051]- Gupta discloses memory unit 542 can provide the control processor with local cache memory and storage memory to store, for example, material property prediction model(s) 546 and data records 548.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Gupta a method of training a recoating defect detection statistical model, the method comprising: obtaining training data, wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers prior to fusing and associated with forming the plurality of parts with the teachings of Gupta storing the trained statistical defect prediction model on non-transitory computer readable memory for subsequent use.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein storing the trained statistical defect prediction model on non-transitory computer readable memory for subsequent use.
The motivation behind the modification would have been to improve production quality, since both Hwang and Gupta are both systems that predict and correct defects using machine learning. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Gupta’s system allows for better part quality control. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Gupta et al. (US 20200393813 A1) Paragraph [0010].
Claim 43 is rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Nwoke et al. (US 20240119579 A1) hereafter referenced as Nwoke.
Regarding claim 43, Hwang teaches a method of detecting recoating defects on a build surface of an additive manufacturing system (Fig. 1, Paragraph [0031]- Hwang discloses FIG. 1 is a block diagram of an embodiment illustrating a framework for training 125 data-set to generate a predictive model 200 and to obtain an expected label 45 via applying at least one preprocessed new image to the predictive model),
the method comprising: obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.);
Hwang fails to explicitly teach identifying the recoating defects based at least in part on light intensities in the image.
However, Nwoke explicitly teaches identifying the recoating defects based at least in part on light intensities in the image (Fig. 1, Paragraph [0061]- Nwoke discloses ones of the voxels 106 in which an absolute value of the z-score 118 is above a threshold value 120 of the z-score 118 are identified as high z-score, which indicate a high variance from normal and an area of interest for further analysis. Further in Fig. 1, paragraph [0060]- Nwoke discloses neighboring voxels 106, each having an extreme value for the z-score 118 can be grouped together to represent areas of nonconforming density within the powder manufactured component 102, such as potential defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang a method of detecting recoating defects on a build surface of an additive manufacturing system, the method comprising: obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface with the teachings of Nwoke identifying the recoating defects based at least in part on light intensities in the image.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein identifying the recoating defects based at least in part on light intensities in the image.
The motivation behind the modification would have been to allow for a more efficient system, since both Hwang and Nwoke are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Nwoke’s system allows for improvement of time efficiency and reduces error. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Nwoke et al. (US 20240119579 A1) Paragraph [0003].
Claims 53 and 68 are rejected under 35 U.S.C 103 as being unpatentable over Hwang et al. (US 20190283333 A1) hereafter referenced as Hwang in view of Pavan et al. (US 20200038953 A1) hereafter referenced as Pavan and Vernet et al. (US 20230264268 A1) hereafter referenced as Vernet.
Regarding claim 53, Hwang teaches an additive manufacturing system comprising (Fig. 5, Paragraph [0028]- Hwang discloses an exemplary embodiment may employ an off-axis imaging system for monitoring solidification quality during additive manufacturing environment, which is used to record the real-time laser scanning process.):
a build surface (Fig. 5, Paragraph [0037]- Hwang discloses the powder delivery apparatus 90 is configured to distribute a plurality of powder materials 60 from powder hopper onto the build plate 50 to be sintered or melted during the additive manufacturing process for fabricating at least one part.);
one or more laser energy sources (Fig. 5, Paragraph [0037]- Hwang discloses the one or more laser sources 65 configured to sinter or melt of a distributed powder layer 61.);
an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface (Fig. 5, Paragraph [0037]- Hwang discloses the laser source is systemically coupled to a pair of mirrors 70.sub.n which are controlled by a scanner, and that facilitates focusing laser beam on a target area of the applied powder layer 61.);
a photosensitive detector configured to image at least a portion of the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the image recording device includes any suitable types of camera, such as a charged-couple device (CCD) camera, a complementary metal-oxide semiconductor (CMOS) camera, an infrared (IR) camera, or a pyrometer, and so on.);
a recoater configured to deposit sequential layers of precursor material on the build surface (Fig. 5, Paragraph [0036]- Hwang discloses the powder deliver apparatus 90, also referred as a re-coater, moves along with p-axis of the build station 95, which deposits a plurality of layers 61 on the build plate 50.);
and at least one processor configured to perform the steps of (Fig. 13a, Paragraph [0051]- Hwang discloses the controller 800, such as computer, includes a plurality of components, such as a processor 815, a memory 805, an input-output (I/O) connector 810, and a network interface 820):
obtain one or more images of the one or more layers using the photosensitive detector (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.);
and predict formation of potential part defects in a current layer based at least in part on the one or more images (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Hwang fails to explicitly teach obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; and the one or more fusing energy maps.
However, Pavan explicitly teaches obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface (Fig. 1, Paragraph [0027]- Pavan discloses the energy density map is used to identify critical sections (e.g., sections with potential for errors when building, which may be referred to as a non-conformity) of the object (e.g., areas or volumes), or the probability that a section has an error or non-conformity.);
and the one or more fusing energy maps (Fig. 1, Paragraph [0027]- Pavan discloses the energy density map is used to identify critical sections (e.g., sections with potential for errors when building, which may be referred to as a non-conformity) of the object (e.g., areas or volumes), or the probability that a section has an error or non-conformity. Further in Fig. 1, Paragraph [0048]- Pavan discloses if the amount of energy per volume of the region is above a second threshold energy amount (different than the first threshold energy amount), then the region may include a possible defect and be labeled a critical region.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Pavan obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; and the one or more fusing energy maps.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; and the one or more fusing energy maps.
The motivation behind the modification would have been to allow for better control of additive manufacturing, since both Hwang and Pavan are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Pavan’s system allows for better control allowing for higher quality products to be produced. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Pavan et al. (US 20200038953 A1) Paragraph [0027].
Although Hwang in view of Pavan explicitly teaches obtain one or more images of the one or more layers using the photosensitive detector; and predict formation of potential part defects in a current layer based at least in part on the one or more images. Hwang in view of Pavan is silent to explicitly obtain one or more images of the one or more layers prior to fusing using the photosensitive detector; and predict formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Pavan an additive manufacturing system comprising: a build surface; one or more laser energy sources; an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface; a photosensitive detector configured to image at least a portion of the build surface; a recoater configured to deposit sequential layers of precursor material on the build surface; and at least one processor configured to perform the steps with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Regarding claim 68, Hwang teaches a method for predicting part defects during an additive manufacturing process (Fig. 5, Paragraph [0028]- Hwang discloses an exemplary embodiment may employ an off-axis imaging system for monitoring solidification quality during additive manufacturing environment, which is used to record the real-time laser scanning process.),
the method comprising: obtaining one or more images of the one or more layers using a photosensitive detector (Fig. 6a-c, Paragraph [0043]- Hwang discloses FIGS. 6a to 6c are examples of captured images illustrating detected errors from applied powder layer(s) during additive manufacturing process according to an exemplary embodiment. As shown in FIGS. 6a and 6b, some characteristic patterns, such as rapid contrast changes and horizontal scratch patterns derived from something on the re-coater 90 (i.e., powder delivery apparatus) blades 91.sub.n, are represented in the captured images after applying the powders on the build plate. The captured image then would be processed through computer-vision based system, and then applied to a first error detecting process 515 as shown in FIG. 7.);
and predicting formation of potential part defects in a current layer based at least in part on the one or more images (Fig. 8, Paragraph [0045]- Hwang discloses once the preprocessed image applied to a predictive model 200, then it identifies any defect of the applied powder layer from the extracted data-set (i.e., the preprocessed images), step 519, thereby determines any corrective actions to be followed, step 520.).
Hwang fails to explicitly teach obtaining one or more fusing energy maps including information related to energy applied to different portions of one or more layer of precursor material on a build surface of an additive manufacturing system; and the one or more fusing energy maps
However, Pavan explicitly teaches obtaining one or more fusing energy maps including information related to energy applied to different portions of one or more layer of precursor material on a build surface of an additive manufacturing system (Fig. 1, Paragraph [0027]- Pavan discloses the energy density map is used to identify critical sections (e.g., sections with potential for errors when building, which may be referred to as a non-conformity) of the object (e.g., areas or volumes), or the probability that a section has an error or non-conformity.);
and the one or more fusing energy maps (Fig. 1, Paragraph [0027]- Pavan discloses the energy density map is used to identify critical sections (e.g., sections with potential for errors when building, which may be referred to as a non-conformity) of the object (e.g., areas or volumes), or the probability that a section has an error or non-conformity. Further in Fig. 1, Paragraph [0048]- Pavan discloses if the amount of energy per volume of the region is above a second threshold energy amount (different than the first threshold energy amount), then the region may include a possible defect and be labeled a critical region.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining one or more images of the one or more layers prior to fusing using a photosensitive detector with the teachings of Pavan obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; and the one or more fusing energy maps.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; and the one or more fusing energy maps.
The motivation behind the modification would have been to allow for better control of additive manufacturing, since both Hwang and Pavan are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Pavan’s system allows for better control allowing for higher quality products to be produced. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Pavan et al. (US 20200038953 A1) Paragraph [0027].
Although Hwang in view of Pavan explicitly teaches the method comprising: obtaining one or more images of the one or more layers using a photosensitive detector and predicting formation of potential part defects in a current layer based at least in part on the one or more images. Hwang in view of Pavan is silent to explicitly the method comprising: obtaining one or more images of the one or more layers prior to fusing using a photosensitive detector and predicting formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images.
However, Vernet explicitly teaches prior to fusing (Fig. 4, Paragraph [0061]- Vernet discloses the coating defect detection step as described in FIG. 4 takes place between the coating step, A, and the fusion step, C, and makes it possible to ensure that the fusion step, C, will not be performed on a layer of powder having defects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hwang in view of Pavan a method for predicting part defects during an additive manufacturing process, the method comprising: obtaining one or more images of the one or more layers prior to fusing using a photosensitive detector with the teachings of Vernet prior to fusing.
Wherein having Hwangs’s system for predicting defects in additive manufacturing process wherein prior to fusing.
The motivation behind the modification would have been to allow for the process to be implemented on simple equipment, since both Hwang and Vernet are both systems that predict and correct defects in an additive manufacturing process. Wherein Hwang’s system wherein improved the accuracy and efficiency of defect detection, while Vernet’s system ease of integration with simple equipment. Please see Hwang et al. (US 20190283333 A1), Paragraph [0054] and Vernet et al. (US 20230264268 A1) Paragraph [0009-10].
Allowable Subject Matter
Claims 7 and 18 along with their dependent claims respectively, are therefrom objected to as being dependent upon rejected base claim, claims 1 and 12, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims and to overcome the claim objections and 101 rejections.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 7, the prior arts fail to explicitly teach, omitting identified recoating defects associated with fusing energies less than a threshold energy, as claimed in claim 7.
Regarding claim 18, the prior arts fail to explicitly teach, omitting identified recoating defects associated with fusing energies less than a threshold energy, as claimed in claim 18.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
KATAYAMA et al. (US 20230298327 A1)- A final determination result is derived in accordance with target data, in consideration of determination results given by determining sections. An information processing device includes: a reliability determining section that determines, in accordance with an inspection image, reliabilities of determination results given by determining sections each configured to determine a given determination matter in accordance with the inspection image; and a comprehensive determination section configured to determine the given determination matter with use of the determination results and the reliabilities....................Please see Fig. 1. Abstract.
Eiríksson et al. (US 20200001541 A1)- An additive manufacturing apparatus for building a product according to a planned geometry by successive solidification of a radiation-curable fluid in a solidification layer extending in a vertical direction from a surface of the fluid to a surface of the product. The apparatus includes: a vat holding the fluid; a support holding the product; a mechanism to control feeding of fluid to the solidification layer; a curing radiation source to generate a 2D exposure pattern of curing radiation in the solidification layer. The exposure pattern is defined by a curing radiation pattern geometry and/or curing radiation intensity. A radiation sensor receives radiation from the solidification layer. The radiation sensor generates a sensor signal having information indicative of a solidification process status. A control system is connected to the feed control mechanism and the curing radiation source. The control system receives the sensor signal and responsive thereto adjusts parameters controlling the solidification in the solidification layer.....................Please see Fig. 1. Abstract.
Beckett et al. (US 20220042924 A1)- An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder......................Please see Fig. 1. Abstract.
TRAN et al. (US 20220143704 A1)- A monitoring system for in-situ identification of anomalies of a workpiece in a 3D printing manufacturing process is provided. The monitoring system includes an optical sensor having an optical path; an infrared sensor having an IR path; an optical device configured to merge the optical and the IR paths to obtain a merged optical path, which is arranged to be directed to the workpiece during a first stage of a 3D printing manufacturing process to obtain a first perception data; and a processor configured to identify anomalies of the workpiece based on the first perception data. A method is also provided. The method includes steps of: merging an optical path of an optical sensor and an infrared path of an IR sensor using an optical device to obtain a merged path; directing the merged path to the workpiece during a first stage of a 3D printing manufacturing process to obtain a first perception data; and identifying anomalies of the workpiece based on the first perception data.......................Please see Fig. 1. Abstract.
SEITA et al. (US 20210331399 A1)- There is provided a method of monitoring a powder bed process in additive manufacturing, using at least one processor. The method including: obtaining a first image of a powder bed layer from scanning the powder bed layer in a first scanning direction using a first contact image sensor, the powder bed layer being formed by a powder re-coater arm in the powder bed process and the first contact image sensor being attached to the powder re-coater arm; determining a focus level property of the first image; and detecting non-uniformities in the powder bed layer based on the focus level property of the first image. There is also provided a corresponding system for monitoring a powder bed process........................Please see Fig. 1. Abstract.
Bromberg et al. (US 20230398746 A1)- An additive manufacturing apparatus includes a process chamber having a length, a support extending along the length of the process chamber, a printing assembly, a vision system configured to image a dispensed binder pattern, and an electronic control unit communicatively coupled to the printing assembly, the first actuator, and the vision system. The electronic control unit is configured to cause the printing assembly to traverse the build zone in a forward or reverse direction while dispensing binder according to a programmed deposition pattern, receive image data from the vision system of the dispensed binder pattern resulting from the programmed deposition pattern, analyze the image data to determine whether there is an anomaly in the dispensed binder pattern, and in response to determining the anomaly in the dispensed binder pattern, adjust the programmed deposition pattern for a subsequent traversal of the printing assembly over the build zone to address the anomaly........................Please see Fig. 1. Abstract.
Jurg et al. (US 20210078076 A1)- An additive manufacturing system and method is provided for fabricating 3D objects (16) from successive layers (14) of material. The additive manufacturing system (10) has an energy projection assembly (20) for inputting energy (22) into a specified area within the layer (18) to consolidate the material; a plurality of image sensors (30, 32, 34), each of the image sensors having a corresponding field of view (35, 40, 42) covering at least part of the layer (18) of material, such that each of the fields of view at least partially overlap with the field of view of at least one other of the image sensors; and an image processor (56) to capture image data from each of the image sensors (30, 32, 34). The image processor (56) controls exposure times for each of the image sensors (30, 32, 34) and combines the image data from the image sensors to provide a single, spatially resolved image of the energy being input throughout the specified area for each layer (14) of material respectively for comparison against threshold data values to locate potential consolidation defects in the specified area........................Please see Fig. 1. Abstract.
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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