DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is responsive to the amendments filed 03/31/2026. Claims 1-13 and 16-22 are pending in this application. As directed, claims 2 and 21 have been amended; claims 14-15 cancelled; claims 1, 3, 5, 7, 9-10, 12 and 16-19 have been withdrawn. Accordingly, claims 2, 4, 6, 8, 11, 13, 20-22 are examined as follow.
With respect to Specification Objections: Applicant’s amendments to the Specification have overcome the Specification Objections set forth in the Non-Final Office Action dated 01/27/2026.
With respect to 35 U.S.C. 112 Claim Rejections: Applicant’s amendments to the Claims have overcome the 35 U.S.C. 112(b) Claim Rejections set forth in the Non-Final Office Action dated 01/27/2026.
Response to Arguments
With respect to 35 U.S.C. 101 Claim Rejections: Applicant(s)’ arguments filed 03/31/2026 have been fully considered but they are not persuasive for the following reasons:
Applicant alleged that the amended claim 2 would overcome the 35 U.S.C. 101 Claim Rejections set forth in the Non-Final Office Action dated 01/27/2026, see details on pages 11-12 of the Remarks dated 03/31/2026.
Applicant’s arguments have been fully considered but they are not persuasive for the following reasons:
Firstly, with respect to Applicant’s argument that the claims are not directed to an abstract idea because the claims predict defects in a 3D-printing process using specific criteria and data, Examiner respectfully disagrees because the focus of the claim (i.e., “generating, by a processor, a first mathematical model and a second mathematical model, the first mathematical model relating input information to intermediate output information, the input information including items of a material of the built object, a welding condition, and a welding track, the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, a feature amount of a shape of a molten pool when each weld bead is formed, and a bead height or bead width of each weld bead, and the second mathematical model relating the intermediate output information to output information including defect information of the built object”) is on selecting certain information and analyzing it. These observations or evaluations are simply mathematical concepts (e.g., algorithms, spatial relationships, geometry). When given its broadest reasonable interpretation in light of the disclosure, it is simply selection and mathematical manipulation of data. Merely selecting information for collection and analysis does nothing significant to differentiate a process from an abstract idea. The additional limitation(s) of “creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; inputting, by the processor, the input information including the items of the material of the built object, the welding condition and the welding track into the database, and searching, by the processor, the database to obtain the defect information of the built object; and presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, wherein each item of the input information includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of pieces of individual defect information corresponding to the individual intermediate values, and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual defect information by the second mathematical model” are recited at a high level of generality. The additional limitation(s) merely are used to perform the abstract idea, and are merely invoked as tools of performing generic functions. The further limitation(s) are considered insignificant extra-solution activities to the judicial exception. The limitation(s) of “processor” and “3D printer” represent no more than mere instructions to apply the judicial exception on generic devices, and can be viewed as nothing more than an attempt to link the use of the judicial exception to the technological environment. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 224-26 (2014).
Secondly, with respect to Applicant’s argument that the claim improves the quality of a physical manufactured object by preventing defects before fabrication, the Examiner respectfully disagrees. While the claim 2 recites defect information that may be used in connection with a manufacturing process, the claim itself does not recite any step that changes, controls, or improves the operation of the additive manufacturing device. Rather, the claim generates information regarding potential defects and presents that information. Collecting information, analyzing information, and presenting the results of the analysis do not constitute a technological improvement. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). The alleged improvement resides in the information generated by the first and the second mathematical models, not in any improvement to the 3D printer or welding head.
Additionally, with respect to Applicant’s argument that the recited material information, welding conditions, and welding track are tied to a physical process, the Examiner respectfully disagrees. The recited parameters are data that are used as inputs to the mathematical models. Even if these input data are obtained from physical process, the use of real-world data as inputs to an abstract analysis does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Furthermore, with respect to Applicant’s argument that claim 2 recites a specific first mathematical model and second mathematical model that calculate intermediate physical states and predict defect information, the Examiner respectfully disagrees. The claim recites the mathematical models functionally and at a high level of generality. The claim does not recite any specific mathematical algorithm, model architecture, machine-learning technique, or improvement to mathematical processing technology. Rather, the claim broadly recites generating models that relate inputs to intermediate outputs and intermediate outputs to defect information. Such relationships constitute mathematical concepts and mathematical manipulation of data. Limiting the calculations to temperature history, molten pool shape, bead height, bead width, or defect information does not change the character of the claim, which remains directed to mathematical analysis.
Lastly, with respect to Applicant’s amendments reciting “presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer”, the Examiner respectfully disagrees that the amendment integrates the judicial exception into a practical application. The additional limitation merely presents the results of the mathematical analysis to a 3D printer. The claim does not require the 3D printer to modify any manufacturing parameter, alter any tool path, or adjust any operating condition based on the presented information. Accordingly, the limitation amounts to no more than outputting or displaying information in a particular technological environment. The recited 3D printer merely serves as a recipient of information generated by the abstract idea. Such a limitation is insufficient to integrate the judicial exception into a practical application. See MPEP 2106.05(f). Accordingly, with respect to Applicant’s argument that the presently claimed subject matter is clearly not purely performed on a generic computer, the Examiner respectfully disagrees. Step 2 considers whether the claim provides limitations which amount to “significantly more” than the recited judicial exception. The claim as a whole does not provide any meaningful limitations which amount to significantly more than the mathematical concepts of claim 2. The limitation(s) of “processor” and “3D printer” are recited in a manner that is well understood, generic and conventional, and are just nominal or tangential additions to the claim. The additional recitation of “to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer” does not impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. As explained previously, the additional limitation merely presents the results of the mathematical analysis to the 3D printer, and the claim does not require the 3D printer to modify any manufacturing parameter, alter any tool path, or adjust any operating condition based on the presented information. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Therefore, the claim as a whole does not provide meaningful limitations which amount to significantly more than the mathematical concepts of claim 2 and does not state an inventive concept. Looking at the elements as a combination does not add anything more than the elements analyzed individually.
The Examiner therefore maintains that claims 2, 4, 6, 8, 11, 13, 20-22 are rejected under 35 USC 101 as detailed herein.
With respect to 35 U.S.C. 103 Claim Rejections: Applicant(s)’ arguments filed 03/31/2026 have been fully considered but are moot based on new ground(s) of rejection necessitated by amendments. Specifically, the newly cited prior art Baturynska et al. (NPL, “Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework”, Published 2018, newly cited) has been added to this Office Action to teach the newly amended limitations “the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer” as recited in the independent claim 2 (lines 20-23).
Claim Objections
Claims 2, 4, 6, 8, 11, 13, 20-22 are objected to because of the following informalities:
Claim 2 recites the limitation “the quality” in line 22. This limitation should be changed to “quality” since there is no “quality” recited previously.
Claims 4, 6, 8, 11, 13, 20-22 are objected by virtue of their dependence on claim 2.
Appropriate correction is required.
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 2, 4, 6, 8, 11, 13, 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) are directed to a method and recite(s) judicial exceptions as explained in the Step 2A, Prong 1 analysis below. The judicial exceptions are not integrated into a practical application as explained in the Step 2A, Prong 2 analysis below. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained in the Step 2B analysis below.
Independent Claim 2:
A defect occurrence prediction method for predicting occurrence of a defect when a built object is manufactured by additive manufacturing, in a desired shape, weld beads formed by melting and solidifying a filler metal fed from a welding head, the method comprising: respectively generating, by a processor, a first mathematical model and a second mathematical model, the first mathematical model relating input information to intermediate output information, the input information including items of a material of the built object, a welding condition, and a welding track, the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, a feature amount of a shape of a molten pool when each weld bead is formed, and a bead height or bead width of each weld bead, and the second mathematical model relating the intermediate output information to output information including defect information of the built object; creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; inputting, by the processor, the input information including the items of the material of the built object, the welding condition and the welding track into the database, and searching, by the processor, the database to obtain the defect information of the built object; and presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, wherein each item of the input information includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of pieces of individual defect information corresponding to the individual intermediate values, and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual defect information by the second mathematical model.
Step
Analysis
1: Statutory Category?
Yes. Claim 2 recites a series of steps and therefore, is a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes)?
Yes. Claim 2 recites “respectively generating, by a processor, a first mathematical model and a second mathematical model, the first mathematical model relating input information to intermediate output information, the input information including items of a material of the built object, a welding condition, and a welding track, the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, a feature amount of a shape of a molten pool when each weld bead is formed, and a bead height or bead width of each weld bead, and the second mathematical model relating the intermediate output information to output information including defect information of the built object; creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; inputting, by the processor, the input information including the items of the material of the built object, the welding condition and the welding track into the database, and searching, by the processor, the database to obtain the defect information of the built object; and presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, wherein each item of the input information includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of pieces of individual defect information corresponding to the individual intermediate values, and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual defect information by the second mathematical model”.
The focus of the claim (i.e., “generating, by a processor, a first mathematical model and a second mathematical model, the first mathematical model relating input information to intermediate output information, the input information including items of a material of the built object, a welding condition, and a welding track, the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, a feature amount of a shape of a molten pool when each weld bead is formed, and a bead height or bead width of each weld bead, and the second mathematical model relating the intermediate output information to output information including defect information of the built object”) is on selecting certain information and analyzing it. These observations or evaluations are simply mathematical concepts (e.g., algorithms, spatial relationships, geometry). When given its broadest reasonable interpretation in light of the disclosure, “generating, by a processor, a first mathematical model and a second mathematical model, the first mathematical model relating input information to intermediate output information, the input information including items of a material of the built object, a welding condition, and a welding track, the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, a feature amount of a shape of a molten pool when each weld bead is formed, and a bead height or bead width of each weld bead, and the second mathematical model relating the intermediate output information to output information including defect information of the built object” is simply selection and mathematical manipulation of data. Merely selecting information for collection and analysis does nothing significant to differentiate a process from an abstract idea.
Thus, the claim recites an abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim does not recite any additional elements that would integrate the judicial exception into a practical application.
The additional limitation(s) of “creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; inputting, by the processor, the input information including the items of the material of the built object, the welding condition and the welding track into the database, and searching, by the processor, the database to obtain the defect information of the built object; and presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, wherein each item of the input information includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of pieces of individual defect information corresponding to the individual intermediate values, and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual defect information by the second mathematical model” are recited at a high level of generality. The additional limitation(s) merely are used to perform the abstract idea, and are merely invoked as tools of performing generic functions. The further limitation(s) are considered insignificant extra-solution activities to the judicial exception. The limitation(s) of “processor” and “3D printer” represent no more than mere instructions to apply the judicial exception on generic devices, and can be viewed as nothing more than an attempt to link the use of the judicial exception to the technological environment. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 224-26 (2014).
Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application.
2B: Claim provides an Inventive Concept?
No.
Step 2 considers whether the claim provides limitations which amount to “significantly more” than the recited judicial exception. The claim as a whole does not provide any meaningful limitations which amount to significantly more than the mathematical concept of claim 1.
The limitation(s) of “creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; inputting, by the processor, the input information including the items of the material of the built object, the welding condition and the welding track into the database, and searching, by the processor, the database to obtain the defect information of the built object; and presenting, by the processor, the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, wherein each item of the input information includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of pieces of individual defect information corresponding to the individual intermediate values, and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual defect information by the second mathematical model” are recited in a manner that is well understood, generic and conventional. The additional recitation(s) do not impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation(s) are at a high level of generality and are just a nominal or tangential addition to the claim. The limitation(s) are at best the equivalent of merely adding the words “apply it” to the judicial exception. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more.
Therefore, the claim as a whole does not provide meaningful limitations which amount to significantly more than the mathematical concept of claim 1 and does not state an inventive concept. The limitation(s) are just a nominal or tangential addition to the claim. Looking at the elements as a combination does not add anything more than the elements analyzed individually.
Applicant’s disclosure does not provide evidence that the additional element(s) recited in claim 2 (i.e., the claim element(s) in addition to the abstract idea) is sufficient to amount to significantly more than the abstract idea itself. This issue is explained by the Federal Circuit, as follows:
It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention “significantly more” than that ineligible concept. In Alice, the Supreme Court held that claims directed to a computer-implemented scheme for mitigating settlement risks claimed a patent-ineligible abstract idea. 134 S.Ct. at 2352, 2355—56. Some of the claims at issue covered computer systems configured to mitigate risks through various financial transactions. Id. After determining that those claims were directed to the abstract idea of intermediated settlement, the Court considered whether the recitation of a generic computer added “significantly more” to the claims. Id. at 2357. Critically, the Court did not consider whether it was well-understood, routine, and conventional to execute the claimed intermediated settlement method on a generic computer. Instead, the Court only assessed whether the claim limitations other than the invention’s use of the ineligible concept to which it was directed were well-understood, routine and conventional. Id. at 2359-60. BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1290 (2018) (emphases added).
Therefore, independent claim 2 is ineligible.
Dependent Claim 4:
Step
Analysis
1: Statutory Category?
Yes. Claim 4 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 4 is ineligible.
Dependent Claim 6:
Step
Analysis
1: Statutory Category?
Yes. Claim 6 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 6 is ineligible.
Dependent Claim 8:
Step
Analysis
1: Statutory Category?
Yes. Claim 8 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 8 is ineligible.
Dependent Claim 11:
Step
Analysis
1: Statutory Category?
Yes. Claim 11 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 11 is ineligible.
Dependent Claim 13:
Step
Analysis
1: Statutory Category?
Yes. Claim 13 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 13 is ineligible.
Dependent Claim 20:
Step
Analysis
1: Statutory Category?
Yes. Claim 20 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 20 is ineligible.
Dependent Claim 21:
Step
Analysis
1: Statutory Category?
Yes. Claim 21 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 21 is ineligible.
Dependent Claim 22:
Step
Analysis
1: Statutory Category?
Yes. Claim 22 recites a series of steps and therefore, falls under a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 2 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim 22 is ineligible.
Therefore, when considering the combination of elements and the claimed invention as a whole, claims 2, 4, 6, 8, 11, 13, 20-22 are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 4, 6, 8, 11, 13, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al. (U.S. Pub. No. 2019/0227525 A1, previously cited) in view of Baturynska et al. (NPL, “Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework”, Published 2018, newly cited), Lee et al. (KR 20120053123 A, previously cited), and further in view of Seong et al. (KR 20190037954 A, Published 04/08/2019, previously cited).
Regarding claim 2, Mehr discloses a defect occurrence prediction method for predicting occurrence of a defect when a built object is manufactured by additive manufacturing, in a desired shape (Mehr Abstract discloses: “Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.”), weld beads formed by melting and solidifying a filler metal fed from a welding head (Mehr Par.0052 discloses: “In one preferred embodiment, the additive manufacturing processes and systems to which the disclosed defect classification and adaptive control methods may be applied is laser-metal wire deposition. The central process in laser-metal wire deposition is the generation of beads of deposited material (a plurality of which may be required to form a single layer) using a high power laser source and additive material in the form of metal wire (Heralić (2012), Monitoring and Control of Robotized Laser Metal-Wire Deposition, Ph.D. Thesis, Department of Signals and Systems, Chalmers University of Technology, Göteborg, Sweden). The laser generates a melt pool on the substrate material, into which the metal wire is fed and melted, forming a metallurgical bound with the substrate. By moving the laser processing head and the wire feeder, i.e., the deposition (or welding) tool, relative to the substrate a bead is formed during solidification.”), the method comprising:
respectively generating, by a processor (“processor”, Mehr Par.0004) (Mehr Par.0004 discloses: “the method is implemented using a single integrated system comprising a deposition apparatus, a sensor, and a processor.”), a first mathematical model (process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101) and a second mathematical model (“machine learning algorithm”, Mehr Pars.0007 & 0101) (Mehr Par.0007 discloses: “Disclosed herein are methods for automated classification of object defects, the methods comprising: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a)”), the first mathematical model (process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101) relating input information (“input process control parameters”, Mehr Par.0004) to intermediate output information (“simulation data”, Mehr Par.0004) (Mehr Par.0004 discloses: “In some embodiments, the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters”), the input information (“input process control parameters”, Mehr Par.0004) including items of a material of the built object (“a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), a welding condition (welding condition includes “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004), and a welding track (“a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004) (Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”), the intermediate output information (“simulation data”, Mehr Par.0004) including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information (“input process control parameters”, Mehr Par.0004) (Mehr Par.0004 discloses: “In some embodiments, the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters”, and Mehr Par.0103 discloses: “Examples of deposition process parameters that may be estimated using FEA analysis (or other simulation techniques) include, but are not limited to, a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material”), a feature amount of a shape of a molten pool when each weld bead is formed (Mehr Par.0014 discloses: “FIGS. 4A-C provide examples of FEA simulation data for modeling of a laser-metal wire deposition melt pool. FIG. 4A: isometric view of color-encoded three dimensional FEA simulation data for the liquid fraction of material in the melt pool being deposited by a laser-metal wire deposition process. FIG. 4B: cross-sectional view of the FEA simulation data for the liquid fraction of material in the melt pool. FIG. 4C: cross-sectional view of color-encoded three dimensional FEA simulation data for the static temperature of the material in the melt pool.” and as shown in Figs.4A-4C), and the second mathematical model (“machine learning algorithm”, Mehr Pars.0007 & 0101) relating the intermediate output information (“simulation data”, Mehr Par.0004) to output information including defect information of the built object (“classification of detected object defects”, Mehr Par.0007) (Mehr Par.0007 discloses: “Disclosed herein are methods for automated classification of object defects, the methods comprising: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a)”; furthermore, Mehr Par.0133 discloses: “Machine learning algorithms for defect detection and classification: Any of a variety of machine learning algorithms may be used in implementing the disclosed automated object defect detection and classification methods.”);
creating, by the processor (“processor”, Mehr Par.0004), a database (Mehr Par.0006 discloses input data and output data are stored in computer memory device) indicating a correspondence between the input information (“input process control parameters”, Mehr Par.0004) and the output information (“classification of detected object defects”, Mehr Par.0007) by using the first mathematical model (process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101) and the second mathematical model (“machine learning algorithm”, Mehr Pars.0007 & 0101) (Mehr Par.0006 discloses: “the system further comprises a computer memory device within which machine learning algorithm software, sensor data from the one or more process characterization sensors, predicted or adjusted values of one or more process control parameters, the training data set, or any combination thereof, is stored.”);
inputting, by the processor (“processor”, Mehr Par.0004), the input information (“input process control parameters”, Mehr Par.0004) including the items of the material of the built object (“a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), the welding condition (welding condition includes “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004) and the welding track (“a location of a deposition apparatus as a function of time (a tool path)”, Mehr Par.0004) (Mehr Par.0004 discloses: “In some embodiments, the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters”, and Mehr Par.0004 further discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”, and Mehr Par.0027 discloses: “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of object design geometries that are the same as or different from the object”)
presenting, by the processor (“processor”, Mehr Par.0004), the obtained defect information of the built object (Mehr Par.0027 discloses: “provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a)”) to a 3D printer (3D printer as indicated by Mehr Pars.0047-0048), wherein
each item (each of (i) material of the built object (“a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), (ii) welding condition (welding condition includes “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004), and (iii) welding track (“a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004)) of the input information (“input process control parameters”, Mehr Par.0004) includes a plurality of input subitems that are mutually different ((i) material of the built object (subitems: “a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), (ii) welding condition (welding condition includes subitems: “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004), and (iii) welding track (subitems: “a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004)) (Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”),
the intermediate output information (“simulation data”, Mehr Par.0004) includes individual intermediate values (results obtained from simulation, Mehr Par.0004) corresponding to the input subitems ((i) material of the built object (subitems: “a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), (ii) welding condition (welding condition includes subitems: “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004), and (iii) welding track (subitems: “a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004)) (Mehr Par.0004 discloses the process simulation data comprises predicted parameter as a function of a set of specified input process control parameters, specifically, Mehr Par.0004 discloses: “the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters.”),
the output information (“classification of detected object defects”, Mehr Par.0007) includes a plurality of pieces of individual defect information corresponding to the individual intermediate values (results obtained from simulation, Mehr Par.0004) (Mehr Par.0143 discloses: “a machine learning algorithm is used to explore a range of input values for one or more process control parameters during process simulation and/or actual part fabrication, and generates a learned model that maps input process control parameters to process outcomes under a variety of different process and environmental conditions.”, and Mehr Par.0179 discloses: “Examples of suitable input data streams include, but are not limited to, process simulation data (e.g., FEA simulation data), process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, as well as a list of process control parameters that may be adjusted to implement next step actions to achieve a target (or future) fabrication state. This data is fed to the ANN, which in many cases has been previously trained using one or more training data sets comprising process simulation data, process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, from previous fabrication runs of the same or different types of parts. The hidden or intermediate layers of the ANN act as trained feature extractors, while the output layer in the example of FIG. 10 provides a determination of a predicted future build state. As noted above, the ANN model is trained to predict future build state based on current build state and a set of actions. Once the ANN model has been developed (i.e., the model can map current state and process parameters to a future state) it's use can be extended to the determination of a set of process control parameter adjustments for the next N states.”), and
in the generating of the first mathematical model (process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101) and the second mathematical model (“machine learning algorithm”, Mehr Pars.0007 & 0101), the input subitems ((i) material of the built object (subitems: “a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004), (ii) welding condition (welding condition includes subitems: “a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus”, “an angle of overhang in an intended geometry”, “a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus”, Mehr Par.0004), and (iii) welding track (subitems: “a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004)) (Mehr Par.0004 discloses the process simulation data comprises predicted parameter as a function of a set of specified input process control parameters, specifically, Mehr Par.0004 discloses: “the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters.”) are respectively related to the individual intermediate values (results obtained from simulation, Mehr Par.0004) by the first mathematical model (process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101) (Mehr Par.0004 discloses the process simulation data comprises predicted parameter as a function of a set of specified input process control parameters, specifically, Mehr Par.0004 discloses “the process simulation data comprises a prediction of a bulk or peak temperature of a deposited material, a cooling rate of a deposited material, a chemical composition of a deposited material, a segregation state of constituents in a deposited material, a geometrical property of a deposited material, an intensity of heat flux out of a material during deposition, an electromagnetic emission from a deposition material, an acoustic emission from a deposition material, or any combination thereof, as a function of a set of specified input process control parameters.”), and the individual intermediate values (results obtained from simulation, Mehr Par.0004) are respectively related to the individual defect information by the second mathematical model (“machine learning algorithm”, Mehr Pars.0007, 0101) (Mehr Par.0143 discloses: “a machine learning algorithm is used to explore a range of input values for one or more process control parameters during process simulation and/or actual part fabrication, and generates a learned model that maps input process control parameters to process outcomes under a variety of different process and environmental conditions.”, Mehr Par.0179 discloses: “Examples of suitable input data streams include, but are not limited to, process simulation data (e.g., FEA simulation data), process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, as well as a list of process control parameters that may be adjusted to implement next step actions to achieve a target (or future) fabrication state. This data is fed to the ANN, which in many cases has been previously trained using one or more training data sets comprising process simulation data, process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, from previous fabrication runs of the same or different types of parts. The hidden or intermediate layers of the ANN act as trained feature extractors, while the output layer in the example of FIG. 10 provides a determination of a predicted future build state. As noted above, the ANN model is trained to predict future build state based on current build state and a set of actions. Once the ANN model has been developed (i.e., the model can map current state and process parameters to a future state) it's use can be extended to the determination of a set of process control parameter adjustments for the next N states.”).
Mehr does not explicitly disclose:
presenting the obtained defect information of the built object to the 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer;
the intermediate output information including a bead height or bead width of each weld bead;
searching, by the processor, the database to obtain the defect information of the built object
Baturynska teaches an additive manufacturing method (see Baturynska Abstract, Section 1.Introduction, and Section 2.Additive Manufacturing) comprising:
presenting the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer (Baturynska teaches using combined FEM and Machine Learning to predict detect information before beginning manufacturing the built object in order to prevent defects from being formed in the built object to improve quality of the built object that is manufactured by additive manufacturing with the 3D printer. To be more specific, Baturynska on page 228, left column, second paragraph teaches: “Combination of FEM and machine learning can provide possibility to simulate process (FEM), predict or optimize process parameters to achieve desired mechanical properties (Machine Learning), and then test predicted process parameters by testing them on developed models for process simulation (FEM).”; Baturynska on page 231, right column, paragraph 3 teaches: “Machine learning allows optimizing process/material parameters by prediction of desired engineering properties of product”, and last paragraph teaches: “Design of Experiments can be applied for better understanding of which process and material parameters (their combination) have the most impact on mechanical properties. Then, results from this analysis can be used partly as an input for mathematical models to be solved by FEM and partly as an input in Machine Learning algorithm. The latter one can be coupled with the mathematical model developed for finite element analysis as a fitness function in ML techniques.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention modify the method of Mehr, by adding the teaching of presenting the obtained defect information of the built object to a 3D printer before beginning manufacturing the built object in order to prevent defects from being formed in the built object, thereby improving the quality of the built object that is manufactured by additive manufacturing with the 3D printer, as taught by Baturynska, in order to obtain information indicative of potential defects and quality issues, such as porosity, shrinkage, density variations and other undesirable build characteristics, before manufacturing the object, thereby allowing process parameters to be optimized in advance to reduce the likelihood of producing a defective part, and also reduce costly trial-and-error builds, improve part quality, and increase manufacturing efficiency.
Mehr in view of Baturynska does not explicitly teach:
the intermediate output information including a bead height or bead width of each weld bead; and
searching, by the processor, the database to obtain the defect information of the built object
Lee teaches a welding simulation method (Lee Abstract) comprising:
the intermediate output information including a bead height or bead width of each weld bead (it is noted that the intermediate output information is interpreted as the simulation output, as cited and explained above; in this case, Lee Translated Document on page 3 – paragraph 7 teaches: “In the step of receiving the sample input data, the materials of various base materials, input voltage, welding torch speed, contact tip to work distance (CTWD), welding rod feeding speed (Wire) Enter the sample welding conditions such as Feed Rate (WFR). The welding simulation program calculates a basic sample bead shape by performing a fluid and thermal flow analysis simulation on the sample input data (S12). The basic sample bead shape here includes bead height, bead width, bead depth, and the like. In addition, numerical analysis techniques such as finite difference method or finite element method may be used to perform fluid and thermal flow simulation.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention modify the method of Mehr in view of Baturynska, by adding the teaching of the intermediate output information including a bead height or bead width of each weld bead, as taught by Lee, in order to obtain the bead profile to predict and ensure the weld’s structural integrity, mechanical strength, and performance since the weld bead profile directly affects fatigue life, residual stresses, and distortion.
Mehr in view of Baturynska and Lee does not teach:
searching, by the processor, the database to obtain the defect information of the built object
Seong teaches a defect occurrence prediction method for predicting occurrence of a defect when a built object is manufactured by additive manufacturing, the method comprising:
searching, by the processor (deformation amount predicting means 30, Seong Fig.2 & Translated Document on page 4 – 2nd paragraph), the database (database 40, Seong Fig.2 & Translated Document on page 4 – 2nd paragraph) to obtain the defect information of the built object (Seong Translated Document on page 4 – 2nd paragraph teaches: “The deformation amount predicting means 30 preferably searches the database 40 set in advance based on the welding information and the degree of constraint to calculate the expected welding deformation amount and shrinkage amount in all the welding portions. Here, the predetermined database 40 includes information on the relationship between the material property and the constraint degree of the member, the deformation amount depending on the welding condition, and the shrinkage amount as a result of the experiment and the analysis.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention modify the method of Mehr in view of Baturynska and Lee, by adding the teachings of searching, by the processor, the database to obtain the defect information of the built object, as taught by Seong, in order to save processing time and cost, and also ensure quality control, process efficiency, and consistency.
Regarding claim 4, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein information regarding the material in the input information (“input process control parameters”, Mehr Par.0004) includes information regarding a type of the filler metal (“a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used”, Mehr Par.0004) (Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof”).
Regarding claim 6, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein information regarding the welding condition in the input information (“input process control parameters”, Mehr Par.0004) includes information regarding at least one of a welding current, a welding voltage, a travel speed, a width of a pitch between adjacent welding tracks, an interpass time of moving from a specific welding track to another welding track among a plurality of the welding tracks, a target position of the welding head, a welding position of the welding head, and a speed of feeding the filler metal when each weld bead is formed, or a combination thereof (in this case, Mehr discloses a target position of the welding head because Mehr Par.0004 discloses a direction of displacement for a deposition apparatus; specifically, Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”).
Regarding claim 8, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 4, Mehr also discloses
wherein information regarding the welding condition in the input information (“input process control parameters”, Mehr Par.0004) includes information regarding at least one of a welding current, a welding voltage, a travel speed, a width of a pitch between adjacent welding tracks, an interpass time of moving from a specific welding track to another welding track among a plurality of the welding tracks, a target position of the welding head, a welding position of the welding head, and a speed of feeding the filler metal when each weld bead is formed, or a combination thereof (in this case, Mehr discloses a target position of the welding head because Mehr Par.0004 discloses a direction of displacement for a deposition apparatus; specifically, Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”).
Regarding claim 11, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses:
wherein the welding track (“a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004) includes a partial welding track corresponding to an element shape obtained by cutting out a part of an entire shape of the built object (it is noted that the welding track is interpreted as simulation data obtained from process simulation tools including finite element analysis (FEA), finite volume analysis (FVA), finite difference analysis (FDA), computational fluid dynamics (CFD), and the like, or any combination thereof, Mehr Pars.0004 & 0101, and as cited and explained in the rejection of claim 2 above; furthermore, Mehr Par.0030 discloses: “process simulation tools such as finite element analysis (FEA) may be used to simulate the process for fabricating an object or a specific portion thereof”; it is noted that since Mehr discloses the FEA is used to simulate a specific portion, thus, the welding track includes a partial welding track corresponding to an element shape obtained by cutting out a part of an entire shape of the built object).
Regarding claim 13, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein the output information (“classification of detected object defects”, Mehr Par.0007) includes information regarding at least one of a defect size, a defect shape, a spatter generation amount, and presence or absence of defect occurrence (Mehr discloses defect shape because Mehr Par.0118 discloses: “The solidification process may also cause metallurgical defects such as pores and cracks to form in the deposited layer.”, and Mehr Par.0181 discloses: “a machine learning model that correlates process control parameters (e.g., laser power, feed rate, travel speed, etc.) and result of the deposition process (e.g., the shape of melt pool, defects in the melt pool, etc.)”).
Regarding claim 20, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein information regarding the welding track (“a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction”, Mehr Par.0004) in the input information (“input process control parameters”, Mehr Par.0004) includes information regarding at least one of passes forming each weld bead, the number of passes, an order of forming each weld bead, and a cross-sectional shape of each weld bead (in this case, Mehr discloses an order of forming each weld bead because Mehr Par.0004 discloses: “the one or more process control parameters to be predicted or controlled comprise a rate of material deposition, a rate of displacement for a deposition apparatus, a rate of acceleration for a deposition apparatus, a direction of displacement for a deposition apparatus, a location of a deposition apparatus as a function of time (a tool path), an angle of a deposition apparatus with respect to a deposition direction, an angle of overhang in an intended geometry, an intensity of heat flux into a material during deposition, a size and shape of a heat flux surface, a flow rate and angle of shielding gas flow, a temperature of a baseplate, an ambient temperature control during a deposition process, a temperature of a deposition material prior to deposition, a current or voltage setting in a resistive heating apparatus, a voltage frequency or amplitude in an inductive heating apparatus, a choice of deposition material, a ratio by volume or a ratio by weight of deposition materials if more than one deposition material is used, or any combination thereof.”).
Regarding claim 21, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein at least one of the first mathematical model or the second mathematical model (“machine learning algorithm”, Mehr Pars.0007 & 0101) (It is noted that the limitation “at least one of the first mathematical model or the second mathematical model” is in alternative form; therefore, only one of these was required during examination. In this case, Mehr Pars.0007 & 0101 teaches the second mathematical model is machine learning algorithm) is a learned model obtained by machine-learning of a relation between the input information and the output information (Mehr Par.0004 discloses: “the machine learning algorithm randomly chooses values within a specified range for each of a set of one or more process control parameters, and incorporates the resulting process simulation data, process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, into the training data set to improve a learned model that maps process control parameter values to process outcomes.”, Mehr Par.0143 discloses: “a machine learning algorithm is used to explore a range of input values for one or more process control parameters during process simulation and/or actual part fabrication, and generates a learned model that maps input process control parameters to process outcomes under a variety of different process and environmental conditions.”, and Mehr Par.0179 discloses: “Examples of suitable input data streams include, but are not limited to, process simulation data (e.g., FEA simulation data), process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, as well as a list of process control parameters that may be adjusted to implement next step actions to achieve a target (or future) fabrication state. This data is fed to the ANN, which in many cases has been previously trained using one or more training data sets comprising process simulation data, process monitoring or characterization data, in-process inspection data, post-build inspection data, or any combination thereof, from previous fabrication runs of the same or different types of parts. The hidden or intermediate layers of the ANN act as trained feature extractors, while the output layer in the example of FIG. 10 provides a determination of a predicted future build state. As noted above, the ANN model is trained to predict future build state based on current build state and a set of actions. Once the ANN model has been developed (i.e., the model can map current state and process parameters to a future state) it's use can be extended to the determination of a set of process control parameter adjustments for the next N states.”).
Regarding claim 22, Mehr in view of Baturynska, Lee and Seong teaches the method set forth in claim 2, Mehr also discloses
wherein an input range of the input information (“input process control parameters”, Mehr Par.0004) is restricted to a range limited based on a predetermined condition (Mehr Par.0004 discloses: “the machine learning algorithm randomly chooses values within a specified range for each of a set of one or more process control parameters, and incorporates the resulting process simulation data, process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, into the training data set to improve a learned model that maps process control parameter values to process outcomes.”)
Conclusion
The following prior art(s) made of record and not relied upon is/are considered pertinent to Applicant’s disclosure.
Dheeradhada et al. (U.S. Pub. No. 2020/0147889 A1) discloses a method that leverages machine learning to build predictive model(s) for specific objectives for materials utilized in an additive manufacturing process, such as relating additive machine parameters to defect concentration, materials behavior, mechanical behavior, or build efficiency.
Woytowitz et al. (U.S. Pub. No. 2020/0156323 A1) discloses methods for printing a three-dimensional (3D) object, comprising processing a computer model of the 3D object to generate a strength or stress profile of the computer model. Printing of the 3D object can be simulated using the tool path to yield a simulated 3D object. The simulated 3D object may be analyzed to determine whether the simulated 3D object meets a threshold. If the simulated 3D object meets the threshold, printing instructions comprising the tool path may be generated for use by a 3D printer to print the 3D object.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THAO UYEN TRAN-LE/Examiner, Art Unit 3761 06/10/2026
/JUSTIN C DODSON/Primary Examiner, Art Unit 3761
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