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 .
Claim Status
Claims 1, 3, 5-6, 8-12, and 15-17 have been amended. Claims 1-17 remain pending and are ready for examination.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03.
The claim is to a method, i.e. one of the statutory categories.
Step 2A prong one: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(11) and the October 2019 Update, a claim "recites" a judicial exception when the judicial exception is "set forth" or "described" in the claim.
The claim recites:
“(iv) fitting the test data to a second-order function that relates the at least one property to the one or more process parameters to determine coefficients of the one or more process parameters; “
If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. (Step 2A prong one: YES).
"(i) causing an additive manufacturing system to at least partially generate a plurality of test samples according to the design and the plurality of sets of test values;
(v) determining, based on the second-order function and the coefficients, optimal values for the one or more process parameters that result in a global optimum for the at least one property.”
These limitations recite concepts that can be practically performed in the human mind but for the recitation of generic computer components. Thus, the limitations fall into the “Mental Processes” grouping of abstract ideas. (Step 2A prong one: YES).
Step 2A prong two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section lll{A){2), 84 Fed. Reg. at 54-55.
This judicial exception is not integrated into a practical application because: Besides the abstract idea, the claim recites the additional limitations of:
“A method of determining optimal values of one or more process parameters for printing a part by additive manufacturing, the part having a design, the method comprising
(i) obtaining a plurality of sets of test values for the one or more process parameters;
(iii) obtaining, during or after generation of the plurality of test samples, test data indicative of respective measurements of at least one property of the test samples for respective sets of test values;”
The additive manufacturing system is a recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Thus, the limitation represents no more than mere instructions to apply the judicial exceptions on a computer.
The limitations “(i) obtaining a plurality of sets of test values for the one or more process parameters; (iii) obtaining, during or after generation of the plurality of test samples, test data indicative of respective measurements of at least one property of the test samples for respective sets of test values;” merely add insignificant extra-solution activity to the judicial exception because they claim mere data gathering.
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, additive manufacturing system does not affect this analysis. See MPEP 2106.05(1) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank lnt'I, 573 U.S. 208, 224-26 (2014).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception (Step 2A prong two: NO).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05
Regarding the additional elements:
The additive manufacturing system is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications. Thus, the limitation represents no more than mere instructions to apply the judicial exceptions on a computer. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic components as a tool to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
The limitations “(i) obtaining a plurality of sets of test values for the one or more process parameters; (iii) obtaining, during or after generation of the plurality of test samples, test data indicative of respective measurements of at least one property of the test samples for respective sets of test values;” represent mere instructions to apply a judicial exception and is recited at high level of generality. These limitation in the claim are thus insignificant extra-solution activity. This is also well-understood, routine, conventional activity (See MPEP 2106.05(d) – receiving or transmitting data over a network.). Bonakdar (US20220176457A1) discloses obtaining the final optimal parameter values. Further, Rankouhi (US 20210260829 A1) discloses obtaining different values of one or more parameter objectives for the component
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept (Step 2B: NO). The claim is not patent eligible.
Regarding claims 13 and 16-17, the claims have similar limitations as claim 1; moreover, claim 13 recites a system, claim 16 recites a method, claim 17 recites a non-transitory computer readable storage, which are generic computer components and do not practically integrate the invention nor amount to significantly more. The claims 13 and 16-17 are not patent eligible.
Regarding claims 2-12 and 14-15, there are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limit on the judicial exception. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, claims 2-12 and 14-15 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 6-8, 10-11, and 13-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou et al. (NPL: “A class of composite designs for response surface methodology” -hereinafter Georgiou) in view of Bonakdar et al. (US20220176457A1 -hereinafter Bonakdar).
Regarding Claim 1, Georgiou teaches a method of determining optimal values of one or more process parameters…, the part having a design (see page 1127, section 3.1, first paragraph; Georgiou: “The proposed method constructs a three-part design matrix X”), the method comprising:
obtaining a plurality of sets of test values for the one or more process parameters; (see page 1128, last paragraph; Georgiou: “Suppose we want to construct a composite response surface design with n runs and k factors”.)
…to at least partially generate a plurality of test samples according to the design and the plurality of sets of test values; (see 1129; Step 1; Georgiou: “Find a triple (nf , nc , na) of integers satisfying nf = 2ℓ > k, na ≥ k, and nc ≥ 0, such that the total run size n can be written as n = nf + nc + 2na.”)
obtaining, during or after generation of the plurality of test samples, test data indicative of respective measurements of at least one property of the test samples for respective sets of test values; (see 1129; Step 2-4; Georgiou: “Step 2. Select k factors from an existing balanced two-level design of nf runs. Call this nf × k part of the design F. Step 3. Select k factors from a known weighing design of order na, and multiply the constructed part by a or alternatively, select k factors from an orthogonal design OD(na; s1, s2, . . . , su) and suitably replace its u variables by elements from the set {0,±1,±a}. Call the resulting na × k design V. Step 4. Define an nc × k matrix consisting of nc center points and call this matrix C.”)
fitting the test data to a second-order function that relates the at least one property to the one or more process parameters to determine coefficients of the one or more process parameters; and (see page 1127, section 3.1, first paragraph; Georgiou: “The proposed method constructs a three-part design matrix X that will be suitable for fitting a second-order response surface as follows:” See 1129; Step 5: “Define the response surface design X as in Eq. (5).”)
determining, based on the second-order function and the coefficients, optimal values for the one or more process parameters that result in a global optimum for the at least one property. (see page 1130, last paragraph; Georgiou: “The properties of the constructed designs are further discussed and evaluated in terms of rotatability, blocking, and D-optimality under the full second-order model.” See page 1129, first paragraph: “Step 6. Set a = 1, and calculate the D-value (see Eq. (2)) of the design and its rotatability measure Q∗”) [D-value/ D-optimality reads on ‘optimal values’]
However, Georgiou does not explicitly teach: …for printing a part by additive manufacturing; causing an additive manufacturing system…;
Bonakdar from the same or similar field of endeavor teaches:
…for printing a part by additive manufacturing; (see [0015]; Bonakdar: “a method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material.”)
causing an additive manufacturing system…; (see [0016]; Bonakdar: “The method includes obtaining an initial set of process parameters and a set of target output material properties to be optimized, pertaining to an additive manufacturing (AM) process utilizing a specified process material.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Georgiou to include Bonakdar’s features of for printing a part by additive manufacturing and causing an additive manufacturing system. Doing so would maximize or minimize certain defined target output material properties of the printed parts. (Bonakdar, [0027])
Regarding Claim 6, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above; Bonakdar further teaches wherein the at least one property comprises one or more of: porosity, residual stress, mechanical properties, surface properties, electronic or magnetic related properties. (see [0027]; Bonakdar: “Examples of output material properties that may be optimized include, for example, density, hardness, surface roughness, tensile strength, mechanical yield strength, stiffness, fatigue life, among others.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 6.
Regarding Claim 7, the combination of Georgiou and Bonakdar teaches all the limitations of claim 6 above, Bonakdar further teaches wherein the mechanical properties comprise one or more of: yield stress and compressive stress;
and/or wherein the surface properties comprise surface roughness. (see [0027]; Bonakdar: “Examples of output material properties that may be optimized include, for example, density, hardness, surface roughness, tensile strength, mechanical yield strength, stiffness, fatigue life, among others.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 7.
Regarding Claim 8, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above, Bonakdar further teaches wherein the at least one process parameter includes one or more of: laser power, scanning speed, powder-bed temperature, hatch distance, layer thickness, post processing heat treatment time, and temperature. (see [0028]; Bonakdar: “The illustrated embodiment pertains to a laser powder-bed fusion (LPBF) process.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 8.
Regarding Claim 10, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above, Bonakdar further teaches wherein the additive manufacturing system is powder based. (see [0028]; Bonakdar: “The illustrated embodiment pertains to a laser powder-bed fusion (LPBF) process. However, aspects of the present invention are not limited to any specific AM process and may be applied to various other AM processes, including many different powder layer based and non-layer based AM processes.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 10.
Regarding Claim 11, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above, Georgiou further teaches wherein the second-order function is:
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where E(C, t) is the at least one property, ci, cii, and cij are the one or more parameters, and xo, xi, xii, and xij are the coefficients; and t is time. (see page 1124, section 1; Georgiou: “In these cases, the following second-order model can be used:
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84
532
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where y is the response vector, xi, xii = xi 2, and xij = xi xj are the model matrix columns corresponding to the main, pure-quadratic, and two-factor interaction effects respectively. β0, βi, βii, and βij are unknown constant coefficients corresponding to the intercept, main, pure-quadratic, and two-factor interaction effects, respectively, while ε is the error vector with components εj being i.i.d. N(0, σ2).)
Regarding Claim 13, the limitations in this claim is taught by the combination of Georgiou and Bonakdar as discussed connection with claim 1.
Regarding Claim 14, the combination of Georgiou and Bonakdar teaches all the limitations of claim 13 above, Bonakdar further teaches further comprising the additive manufacturing system. (see [0015]; Bonakdar: “a method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material.”)
Regarding Claim 15, the combination of Georgiou and Bonakdar teaches all the limitations of claim 13 above, Bonakdar further teaches wherein the storage comprises instructions for causing the at least one processor to cause the additive manufacturing system to print the part according to the design and the optimal values for the one or more process parameters. (see [0015]; Bonakdar: “a method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material… In the optimization phase, a second plurality of experimental print runs is executed, based on a second experiment design applied to said subset of significant process parameters.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 15.
Regarding Claim 16, Georgiou teaches a method of printing a part by an additive manufacturing system, the part having a design (see page 1127, section 3.1, first paragraph; Georgiou: “The proposed method constructs a three-part design matrix X”), the method comprising: determining optimal values of one or more process parameters… by a method according to claim 1; (see page 1130, last paragraph; Georgiou: “The properties of the constructed designs are further discussed and evaluated in terms of rotatability, blocking, and D-optimality under the full second-order model.” See page 1129, first paragraph: “Step 6. Set a = 1, and calculate the D-value (see Eq. (2)) of the design and its rotatability measure Q∗”) [D-value/ D-optimality reads on ‘optimal values’]
However, Georgiou does not explicitly teach: determining optimal values of one or more process parameters for the additive manufacturing system…; and causing the additive manufacturing system to print the part according to the design using the optimal values of the process parameters.
Bonakdar from the same or similar field of endeavor teaches:
determining optimal values of one or more process parameters for the additive manufacturing system…; (see [0015]; Bonakdar: “a method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material.”)
and causing the additive manufacturing system to print the part according to the design using the optimal values of the process parameters. (see [0015]; Bonakdar: “a method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material… In the optimization phase, a second plurality of experimental print runs is executed, based on a second experiment design applied to said subset of significant process parameters.”)
The same motivation to combine Georgiou and Bonakdar a set forth for Claim 1 equally applies to Claim 16.
Regarding Claim 17, the limitations in this claim is taught by the combination of Georgiou and Bonakdar as discussed connection with claim 1.
Claim(s) 2 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou in view of Bonakdar in view of Pekic (US20220203617A1 -hereinafter Pekic).
Regarding Claim 2, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein the method is performed for a single batch of raw material.
Pekic from the same or similar field of endeavor teaches wherein the method is performed for a single batch of raw material. (see [0083]; Pekic: “In the context of a single printer, a “batch” refers to a job file (containing a set of print instructions) that is repeated.” See [0188]: “Due to variability between batches of material, this database may also be used to store a manufacturer's batch ID or production runs (such as lot numbers).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Georgiou and Bonakdar to include Pekic’s features of for printing a part by additive manufacturing and causing an additive manufacturing system. Doing so would print with high accuracy and reduce manual efforts. (Pekic, [0003] and [0017])
Regarding Claim 5, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above; however, it does not explicitly teach: wherein the plurality of test samples are generated at different respective times.
Pekic from the same or similar field of endeavor teaches wherein the plurality of test samples are generated at different respective times. (see [0157]; Pekic: “. The process of developing print setting profiles for each material is distributed over a large network of printers that have provided hundreds of hours of concurrent testing.”)
The same motivation to combine Georgiou, Bonakdar, and Pekic a set forth for Claim 2 equally applies to Claim 5.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou in view of Bonakdar in view of Dheeradhada et al. (US20200147889A1 -hereinafter Dheeradhada).
Regarding Claim 3, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein step (ii) comprises partially generating one of the test samples, and step (iii) comprises obtaining test data for the partially generated test sample, such that the global optimum is obtained prior to completion of generation of the test sample.
Dheeradhada from the same or similar field of endeavor teaches wherein step (ii) comprises partially generating one of the test samples (see [0016]; Dheeradhada: “Such models intelligently sample the design space and iteratively improve their accuracy by using feedback from subsequent additively manufactured parts (wherein an additively manufactured part can be of a simple geometry, such as a pin, or may be of a complex geometry)”. See [0019]: “Referring again to FIG. 1, in some embodiments the test device 104 includes a test platform 105 for receiving a test sample (not shown), and measurement devices 106, 108, 110 to measure different attributes associated with the test sample.”), and step (iii) comprises obtaining test data for the partially generated test sample, such that the global optimum is obtained prior to completion of generation of the test sample. (see [0016]; Dheeradhada: “The machine learning assisted framework described herein results in a rapid optimization loop which satisfies a set of specific objectives while only requiring a limited number of build iterations, thus providing optimum additive process parameters under certain conditions (which conditions may include factors such as the environment, particle size distribution, re-coater material, and the like) quicker than conventional processes.” See [0036]: “For example, a specified objective may be defect concentration, wherein the goal is to fabricate a part having a defect concentration that is less than or equal to a particular defect concentration value. In this case of the objective being defect concentration, the machine learning model 408 itself may not be needed to determine if the defect concentration at an iteration of the process 400 satisfies the specified attributes/limits.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Georgiou and Pekic to include Dheeradhada’s features of comprising partially generating one of the test samples, and obtaining test data for the partially generated test sample, such that the global optimum is obtained prior to completion of generation of the test sample. Doing so would reduce the development cycles of additive manufacturing processes to reduce overall development time and to reduce associated costs. (Dheeradhada, [0005])
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou in view of Bonakdar in view of Dheeradhada in view of Pekic.
Regarding Claim 4, the combination of Georgiou, Bonakdar, and Dheeradhada teaches all the limitations of claim 3 above, Pekic further teaches wherein partially generating one of the test samples comprises printing one layer or a few layers of the test sample (see [0005]; Pekic: “By moving the print bed 105 along the y-axis and the hot end assembly 104 along the x- and z-axes, a 3D printed part 150 can be formed through extrusion of the printing material from the nozzle, depositing layer upon layer on the print bed 105 to build the printed part 150.”); and obtaining test data comprises measuring a temperature distribution of the partially generated test sample. (see [0116]; Pekic: “Data can be collected about material type, cooldown time, the threshold temperature at which parts release, the temperature at which parts are printed, and the first layer success rate of parts printed at each temperature. This data can be used to generate part removal profiles for each printing material.”)
The same motivation to combine Georgiou, Bonakdar, Dheeradhada, and Pekic a set forth for Claim 2 equally applies to Claim 4.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou in view of Bonakdar in view of Nazan et al. (NPL: “Process parameter optimization of 3D printer using Response Surface Method” -hereinafter Nazan).
Regarding Claim 9, the combination of Georgiou and Bonakdar teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein the optimal values are determined using a 3D surface plot.
Nazan from the same or similar field of endeavor teaches: wherein the optimal values are determined using a 3D surface plot. (see page 2295, left paragraph, second paragraph; Naxan: “Surface plot is a plot that shows the 3D model of contour plot which is shown in three axes as shown in Figure-8.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Georgiou and Bonakdar to include Nazan’s features of the optimal values are determined using a 3D surface plot. Doing so would obtain the best printing quality. (Nazan, page 2291, left column, last paragraph)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Georgiou in view of Bonakdar in view of Dey et al. (NPL: “D-Efficient Composite-type Second Order Designs via Computer Search” -hereinafter Dey).
Regarding Claim 12, the combination of Georgiou and Pekic teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein the plurality of sets of test values are obtained by orthogonal array composite design (OACD).
Dey from the same or similar field of endeavor teaches: wherein the plurality of sets of test values are obtained by orthogonal array composite design (OACD). (see page 34, second paragraph; Dey: “Xu et al. (2014) introduced a class of second order designs, called orthogonal array composite designs (OACD), as an alternative to the usual central composite designs. In OACD, the axial points of a central composite design are replaced by the runs of a 3-symbol orthogonal array.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Georgiou and Bonakdar to include Dey’s features of obtaining the plurality of sets of test values by orthogonal array composite design (OACD). Doing so would provide the best improvement in D-efficiency. (Dey, page 35, third paragraph)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Das et al. (US20140163717A1) discloses find the optimal settings for every new material, microstructure layout, deposit height, and sample size.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VI N TRAN whose telephone number is (571)272-1108. The examiner can normally be reached Mon-Fri 9:00-5:00.
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/V.N.T./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117