DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are presented for examination.
Claim Interpretation
Office personnel are to give claims their "broadest reasonable interpretation" in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541,550-551(CCPA 1969). See *also In re Zletz, 893 F.2d 319,321-22, 13 USPQ2d 1320, 1322(Fed. Cir. 1989) ("During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow").... The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed.... An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
Claims recite "and/or". The claims reciting "and/or" were interpreted as “or”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 11 recites the limitation "the determined optimal set" in line(s) 2. There is insufficient antecedent basis for this limitation in the claim. While there is "determining of the optimal subset" anteceding this limitation in the claim, there is no "determined optimal set" anteceding this limitation in the claim.
Dependent claims inherit the defect of the claim from which they depend.
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 1-20 are rejected because the claimed invention is directed to a judicial exception without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes)
Independent claim 1, Step 2A, Prong One: claim recites:
for 3D printing planning… determining an optimal subset of the set of spare parts to be 3D printed, the determining including optimizing one or more objective manufacturing functions under the 3D printing constraints and based on the reference set
The limitations are substantially drawn to mental concepts. The limitations, as drafted and under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power Group1 (Electric Power hereinafter): “Information… is an intangible”.
As to the limitations "3D printing planning", these limitations, as drafted and under a broadest reasonable interpretation "can be performed in the human mind or by a human using a pen and paper". These activities can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper. The specification reads (underline emphasis added):
"The reference set may for example be provided by a user (e.g., an employee of a company ordering the manufacturing of the provided set of spare parts or an operator of the one or more factories). This allows the user to intervene in the 3D printing planning process and to guide the optimization, so that the optimal subset ultimately meets the user's requirements. In other words, this provides flexibility in the 3D printing planning where the user is provided with the possibility of guiding the optimization" (see page 9, line 25 to page 10, line 1).
As to the limitations "determining an optimal subset of the set of spare parts to be 3D printed, the determining including optimizing one or more objective manufacturing functions under the 3D printing constraints and based on the reference set", these limitations, as drafted and under a broadest reasonable interpretation "can be performed in the human mind or by a human using a pen and paper". Determinations are mental in nature. These activities can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper. The specification reads (underline emphasis added):
"the one or more objective manufacturing functions. The optimization of the latter function(s) indeed leads to the optimal subset. This allows to prioritize the spare parts to 3D print not only based on 3D constraints, but also in order to optimize one or more objective manufacturing functions, which capture one or more manufacturing objectives. In other words, this allows to prioritize the spare parts to 3D print based on the 3D constraints but also for achieving one or more manufacturing goals (e.g., reducing the carbon footprint caused by the manufacturing process) captured by the objective function(s). These/this objective manufacturing goal(s) and the optimization with respect to them/it allow to objectively balance the need for spare parts 3D printing with the practical limitations thereof, by discriminating spare part portfolios from others with respect to the manufacturing goal(s)" (see page 9, lines 4-15).
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 1, Step 2A, Prong two: The claim recites the additional element computer-implemented as performing generic computer functions routinely used in computer applications.
As to the limitations “obtaining: a set of spare parts to be manufactured in one or more factories comprising 3D printers and other manufacturing machines; 3D printing constraints, the 3D printing constraints including: one or more constraints each representing a 3D printing constraint and/or a mechanical constraint for a spare part, and one or more 3D printing capacity constraints for the one or more factories; and a reference set of one or more spare parts each classified either as compatible with the 3D printing constraints or as non-compatible with the 3D printing constraints”, these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, claim 1 recites the additional element computer-implemented. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mathematical or mental algorithm has not been held by the courts to be enough to qualify as “significantly more”. The implementation on a computing system is described in the specification (underline emphasis added):
"A typical example of computer-implementation of a method is to perform the method with a system adapted for this purpose. The system may comprise a processor coupled to a memory and a graphical user interface (GUI)" (see page 34, line 29 to page 35, line 29).
As discussed with respect to Step 2A, claim 1 recites data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
Taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Independent claims 13 and 17, Step 2A Prong One: These claims recite substantially the same elements as claim 1 and are rejected for the same reasons above.
Independent claims 13 and 17, Step 2A Prong two and 2B: As to the further additional elements a computer-readable data storage medium and a processor coupled to a memory, they are interpreted as drawn to a generic computer. (See Independent claim 1, Step 2B above).
Dependent claims, Prong One: The claim limitations further the mental concepts of their independent claims. (See Independent claims, Step 2A, Prong One above).
As to the limitations “2/14/18… wherein the optimization includes learning a Multiple Criteria Decision Aiding sorting model configured to take as input an input set of spare parts and to output an optimal subset of spare parts, the learning being based on the set of spare parts, on the 3D printing constraints, on the one or more objective manufacturing functions, and on the reference set", "3/15/19… wherein the reference set forms a learning set of the model", and "4/16/20… wherein the model includes a Non-Compensatory Sorting model", learning is mental in nature. See for example in the Specification (underline emphasis added):
"The concept of Multiple Criteria Decision Aiding sorting model is known per se. The model is constrained by the 3D printing constraints and thus forms a constrained Multiple Criteria Decision Aiding sorting model. The sorting model comprises parameters which are inferred through the learning. The learning in other words comprises inferring the parameters, this inference being the result of the optimization" (see page 18, lines 19-25).
As to the limitations "7… wherein the determining of the optimal subset includes a preliminary step of verifying consistency the reference set with the 3D printing constraints and modifying the reference set as long as the reference set is inconsistent" and "8… wherein the modification of the reference set is performed by a user", these limitations, as drafted and under a broadest reasonable interpretation "can be performed in the human mind or by a human using a pen and paper". Determinations and verifications are mental in nature. These activities can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining/verifying (judgments), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Dependent claims, Step 2A, Prong two:
As to the limitations "5… wherein the learning includes encoding learning clauses based on the 3D printing constraints and on the one or more objective manufacturing functions, the encoding using a SAT-based encoding", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
As to the limitations “6… wherein the reference set is provided by a user" and "9… wherein the determining of the optimal subset includes, from a user, obtaining one or more target values for the one or more objective manufacturing functions”, these limitations describe the concept of “mere data gathering”. (See Independent claims, Step 2A, Prong Two above).
As to the limitations “11… establishing a 3D printing plan for the one or more factories based on the determined optimal set" and "12… 3D printing of the optimal set in the one or more factories based on the established 3D printing plan", they represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims, Step 2B:
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
As discussed with respect to Step 2A, claims recite data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2). As to the limitations “11… establishing a 3D printing plan for the one or more factories", the "establishing" is described in the specification (underline emphasis added):
'Establishing the 3D printing plan may include assigning 3D printing tasks to the 3D printers of the one or more factories, i.e., allocating the manufacturing of the spare parts of the optimal set to the different 3D printers of the one or more factories so that the 3D printers may then 3D print the optimal set of spare parts according to this allocation' (see page 7, lines 2-6).
The claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-11 and 13-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bertolini et al., (Bertolini hereinafter), "A TOPSIS-based approach for the best match between manufacturing technologies and product specifications", taken in view of Tlili et al., (Tlili hereinafter), "Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations". (See IDS dated 10/07/2022).
As to claim 1, Bertolini discloses a computer-implemented method for 3D printing planning (see "use TOPSIS in the technology selection problem by distinguishing two main analysis. The first refers to the general technology selection problem, where the manufacturing technologies were derived from technical literature. The second refers to the design of the specific application of TOPSIS to our case study, i.e. the identification of values and weights to populate the TOPSIS matrixes and scales… These analyses result in the definition of the alternatives suitable for manufacturing the product and the criteria of interest to the match between the same technologies and the product" in page 13, 1st paragraph; " 3D PRINTING" in page 16, Figure 4), comprising: obtaining: a set of spare parts to be manufactured in one or more factories comprising 3D printers and other manufacturing machines; 3D printing constraints, the 3D printing constraints including: one or more constraints each representing a 3D printing constraint and/or a mechanical constraint for a spare part, and one or more 3D printing capacity constraints for the one or more factories (see "selection of the product family to be analysed, in agreement with the cross-functional group at FBC. The choice fell upon a product characterised by high-quality requirements, relatively stringent rules to be followed for compliance with food and beverage regulations, and a high utilisation rate (i.e. many of those products are manufactured and assembled per year). We selected the filling-valves and their components, which belong to the filling machines… it is characterised by a wide variety of parts with high requirements (e.g. tolerances and roughness)… After selecting the product family, we identified the technologies to be considered, clustering them by different technology processes (Table 6). These specific technologies were considered for the following goals: (i) investigating whether AM technologies could be used in FBC productions and (ii) achieving the best match between products and production processes. Thus, we identified the mechanical components that implement the fluid delivery mechanisms, both statics and driving" in page 16, 1st paragraph; and "3D printers and other manufacturing machines" in page 16:
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); and a reference set of one or more spare parts each classified either as compatible with the 3D printing constraints or as non-compatible with the 3D printing constraints (see "set the matrix Xnxm. The alternatives are the technologies under investigation, while the criteria are the ‘drivers’ addressing the match between the performances of the technology and the product specifications… The first step concerns the criteria and alternatives selection. In a manufacturing-technology selection, these criteria are the ‘drivers’ that describe both the characteristics of the parts belonging to the family identified and the performances of the technologies that can be used to produce them. The identification of the criteria is a consequence of the identification of the product family or, more generally, of the parts to be manufactured. However, the choice of the technologies to be considered for the study must be consistent with the product/part specifications" in page 13, last paragraph); and determining an optimal subset (see "To achieve synergetic effect from both technology and economics validation, criteria (i.e. the drivers of our analysis) need to consider both technological and economic aspects, as well as to link the one to the other, when defining the PIS and NIS. An example could be the use of optimisation algorithms to define the loss function that leads the driver selection towards the PIS and NIS" in page 24, last line to page 25, line 3) of the set of spare parts to be 3D printed (see "judgement is based on the distance between the performance of the technology and the required specification benchmark based on the part design. It is important to consider mandatory features of the dummy bottle part as well as its whole manufacturing process, paying attention to the reworks that follow the manufacturing of the raw part" in page 20, 3rd paragraph; and in page 16:
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)…
Bertolini does not disclose, but Tlili discloses the determining including optimizing one or more objective manufacturing functions under the 3D printing constraints and based on the reference set (see "formulate the relaxed optimization problem of finding the subset of learning examples (reference alternatives together with their assignment) correctly restored of maximum cardinality with a soft constraint approach" in page 987, col. 2, 2nd paragraph; "For optimization approaches, we translate the assignment into a Boolean satisfaction problem, described by sets of variables and clauses and an objective function" in page 989, 5th paragraph).
Bertolini and Tlili are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Tlili with Bertolini, because Tlili discloses "two popular variants of the Non-Compensatory Sorting model… the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions" (see page 979, abstract), and as a result, Tlili reports that "for real world decision problems, assuming that the number of reference assignments is ∼100 examples, we can consider two types of applications: an application that involves a large number of criteria (|N|>∼12) and therefore the separation-based representation seems better as it is faster and generalizes better than the first one, and an application that involves a limited number of criteria (|N|<∼10), in this case, the coalition-based representation is slightly faster and generalizes less than the separation-based one" (see page 1003, last paragraph to page 1004, 1st paragraph).
As to claim 2, Bertolini does not disclose, but Tlili discloses wherein the optimization (see "We formulate the relaxed optimization problem of finding the subset of learning examples (reference alternatives together with their assignment) correctly restored of maximum cardinality with a soft constraint approach" in page 987, col. 2, 2nd paragraph) includes learning a Multiple Criteria Decision Aiding sorting model (see "we are interested in a specific sorting procedure: the Non-Compensatory Sorting (NCS) model" in page 979, col. 1, last paragraph; "Learning an NCS model" in page 1005, 1st & 2nd paragraphs – Examiner notes that as per dependent claim 4, "the model includes a Non-Compensatory Sorting model") configured to take as input an input set of spare parts and to output an optimal subset of spare parts, the learning being based on the set of spare parts (see "inverse Non-Compensatory Sorting problem… takes as input a set of assignment examples, and computes (whenever it exists) an NCS sorting model which is consistent with this preference information. In other words, Inv-NCS learns the NCS parameters that perfectly match a set of desired outputs (assignment examples)" in page 979, last paragraph), on the 3D printing constraints (see "We formulate the relaxed optimization problem of finding the subset of learning examples (reference alternatives together with their assignment) correctly restored of maximum cardinality with a soft constraint approach" in page 987, col. 2, 2nd paragraph), on the one or more objective manufacturing functions (see "For optimization approaches, we translate the assignment into a Boolean satisfaction problem, described by sets of variables and clauses and an objective function" in page 989, 5th paragraph), and on the reference set (see "For optimization approaches, we introduce a proportion µ of assignment errors in the learning set" in page 989, 2nd paragraph).
Bertolini and Tlili are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Tlili with Bertolini, because Tlili discloses "two popular variants of the Non-Compensatory Sorting model… the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions" (see page 979, abstract), and as a result, Tlili reports that "for real world decision problems, assuming that the number of reference assignments is ∼100 examples, we can consider two types of applications: an application that involves a large number of criteria (|N|>∼12) and therefore the separation-based representation seems better as it is faster and generalizes better than the first one, and an application that involves a limited number of criteria (|N|<∼10), in this case, the coalition-based representation is slightly faster and generalizes less than the separation-based one" (see page 1003, last paragraph to page 1004, 1st paragraph).
As to claim 3, Bertolini does not disclose, but Tlili discloses wherein the reference set forms a learning set of the model (see "For optimization approaches, we introduce a proportion µ of assignment errors in the learning set" in page 989, 2nd paragraph).
Bertolini and Tlili are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Tlili with Bertolini, because Tlili discloses "two popular variants of the Non-Compensatory Sorting model… the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions" (see page 979, abstract), and as a result, Tlili reports that "for real world decision problems, assuming that the number of reference assignments is ∼100 examples, we can consider two types of applications: an application that involves a large number of criteria (|N|>∼12) and therefore the separation-based representation seems better as it is faster and generalizes better than the first one, and an application that involves a limited number of criteria (|N|<∼10), in this case, the coalition-based representation is slightly faster and generalizes less than the separation-based one" (see page 1003, last paragraph to page 1004, 1st paragraph).
As to claim 4, Bertolini does not disclose, but Tlili discloses wherein the model includes a Non-Compensatory Sorting model (see "The SAT formulation based on coalitions aims at learning both NCS parameters… from a set of assignment examples" in page 984, col. 1, last paragraph).
Bertolini and Tlili are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Tlili with Bertolini, because Tlili discloses "two popular variants of the Non-Compensatory Sorting model… the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions" (see page 979, abstract), and as a result, Tlili reports that "for real world decision problems, assuming that the number of reference assignments is ∼100 examples, we can consider two types of applications: an application that involves a large number of criteria (|N|>∼12) and therefore the separation-based representation seems better as it is faster and generalizes better than the first one, and an application that involves a limited number of criteria (|N|<∼10), in this case, the coalition-based representation is slightly faster and generalizes less than the separation-based one" (see page 1003, last paragraph to page 1004, 1st paragraph).
As to claim 5, Bertolini does not disclose, but Tlili discloses wherein the learning includes encoding learning clauses based on the 3D printing constraints and on the one or more objective manufacturing functions, the encoding using a SAT-based encoding (see "The SAT formulation based on coalitions aims at learning both NCS parameters… from a set of assignment examples" in page 984, col. 1, last paragraph).
Bertolini and Tlili are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Tlili with Bertolini, because Tlili discloses "two popular variants of the Non-Compensatory Sorting model… the Non-Compensatory Sorting model with a unique profile and the Non-Compensatory Sorting model with a unique set of sufficient coalitions" (see page 979, abstract), and as a result, Tlili reports that "for real world decision problems, assuming that the number of reference assignments is ∼100 examples, we can consider two types of applications: an application that involves a large number of criteria (|N|>∼12) and therefore the separation-based representation seems better as it is faster and generalizes better than the first one, and an application that involves a limited number of criteria (|N|<∼10), in this case, the coalition-based representation is slightly faster and generalizes less than the separation-based one" (see page 1003, last paragraph to page 1004, 1st paragraph).
As to claim 6, Bertolini discloses wherein the reference set (see "set the matrix Xnxm. The alternatives are the technologies under investigation, while the criteria are the ‘drivers’ addressing the match between the performances of the technology and the product specifications… The first step concerns the criteria and alternatives selection. In a manufacturing-technology selection, these criteria are the ‘drivers’ that describe both the characteristics of the parts belonging to the family identified and the performances of the technologies that can be used to produce them. The identification of the criteria is a consequence of the identification of the product family or, more generally, of the parts to be manufactured. However, the choice of the technologies to be considered for the study must be consistent with the product/part specifications" in page 13, last paragraph) is provided by a user (see "selection of the product family to be analysed… we identified the mechanical components that implement the fluid delivery mechanisms" in page 16, 1st paragraph).
As to claim 7, Bertolini discloses wherein the determining of the optimal subset includes a preliminary step of verifying consistency the reference set with the 3D printing constraints (see "drivers are allocated in accordance with the product family for which the technology selection is developed, i.e. the design specification of products provide preliminary criteria that must be respected when selecting the manufacturing technology" in page 18, 1st paragraph) and modifying the reference set (see "Drivers d1 and d2 refer to the lower limits of the tolerances and roughness required to process the specific product or part. Drivers d3 and d4 are related to lead times, both for prototyping and production. Drivers d5–d9 refer to the technological constraints of the process" in page 18, last paragraph) as long as the reference set is inconsistent (see "3.4 The final rank To define the ranking of the alternatives, TOPSIS firstly computes the distance 𝑠𝑖+ between each alternative criterion 𝑡𝑖𝑗… Once the relative closeness to the positive ideal solution c𝑖+is computed for each alternative i, the alternatives can be ranked in descending order. The best-in-class solution, which as mentioned is a compromise solution between PIS and NIS, is ranked first" in page 12, next to last & last paragraphs).
As to claim 8, Bertolini discloses wherein the modification of the reference set (see "modification" as "reengineering", "The aim of the engineering/reengineering process identifies the alternative (the n* technologies to be investigated), and the identification of the product family (q parts) along with the identified technologies settles the criteria (m drivers describing both technologies and parts)" in page 14, 1st paragraph) is performed by a user (see "selection of the product family to be analysed… we identified the mechanical components that implement the fluid delivery mechanisms" in page 16, 1st paragraph).
As to claim 9, Bertolini discloses wherein the determining of the optimal subset includes, from a user, obtaining one or more target values for the one or more objective manufacturing functions (see "criteria are the ‘drivers’ addressing the match between the performances of the technology and the product specifications… The first step concerns the criteria and alternatives selection. In a manufacturing-technology selection, these criteria are the ‘drivers’ that describe both the characteristics of the parts belonging to the family identified and the performances of the technologies that can be used to produce them. The identification of the criteria is a consequence of the identification of the product family or, more generally, of the parts to be manufactured. However, the choice of the technologies to be considered for the study must be consistent with the product/part specifications" in page 13, last paragraph).
As to claim 10, Bertolini discloses wherein the determining of the optimal subset includes several optimizations of the one or more objective manufacturing functions, and wherein before each optimization, the one or more target values are obtained and/or the one or more target values are modified based on a result of a previous optimization (see "3.4 The final rank To define the ranking of the alternatives, TOPSIS firstly computes the distance 𝑠𝑖+ between each alternative criterion 𝑡𝑖𝑗… Once the relative closeness to the positive ideal solution c𝑖+is computed for each alternative i, the alternatives can be ranked in descending order. The best-in-class solution, which as mentioned is a compromise solution between PIS and NIS, is ranked first" in page 12, next to last & last paragraphs).
As to claim 11, Bertolini discloses establishing a 3D printing plan for the one or more factories based on the determined optimal set (see "use TOPSIS in the technology selection problem by distinguishing two main analysis. The first refers to the general technology selection problem, where the manufacturing technologies were derived from technical literature. The second refers to the design of the specific application of TOPSIS to our case study, i.e. the identification of values and weights to populate the TOPSIS matrixes and scales… These analyses result in the definition of the alternatives suitable for manufacturing the product and the criteria of interest to the match between the same technologies and the product" in page 13, 1st paragraph; " 3D PRINTING" in page 16, Figure 4).
As to claims 13-20, these claims recite a computer-readable storage medium and a computer system for performing the method of claims 1-4. Bertolini discloses "Once the cost-effectiveness drivers are defined, it is possible to use the model in different simulations" (see page 23, last paragraph) for performing a method that teaches claims 1-4. Therefore, claims 13-20 are rejected for the same reasons given above.
Claim 12 is rejected under 35 U.S.C. 103(a) as being unpatentable over Bertolini taken in view of Tlili as applied to claim 11 above, and further in view of Junji Sato, (Sato hereinafter), U.S. Pre–Grant publication 20170277148.
As to claim 12, Bertolini and Tlili do not disclose, but Sato discloses 3D printing of the optimal set in the one or more factories based on the established 3D printing plan (see "[0062]… the part forming control unit 323 uses the model data acquired in S606 to request execution of the forming to the 3D printer control application").
Bertolini, Tlili, and Sato are analogous art because they are related to solutions for manufacturing processes and operations.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Sato with Bertolini and Tlili, because Sato discloses that a "[0081] … part management unit 317 acquires an error code indicating that an error has occurred in a 3D printer and, in a case where a part corresponding to the error code is formable in the 3D printer, instructs to form the part", and as a result, Sato reports that "[0082]… the part management unit 317 can instruct to form the part in consideration of timing for replacing the part on the basis of the counter information regarding the part. For example, counter information regarding a print head may be the number of times of stacking and a time period used for forming it. The forming of a part may be instructed before the lifetime of the part ends in consideration of preset durability times or a preset time period. The part management unit 317 may instruct the forming by predicting possible failures of a part in consideration of not only the counter information regarding the part but also status information regarding the part. For example, even in a case where the time period used for forming a print head is shorter than a preset time period, a failure may be discovered from status information regarding the print head so that a print head to replace may be formed before an error occurs actually".
Conclusion
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
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