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 .
Responsive to the communication dated: 9/1/2026.
Claims 1 – 20 are presented for examination.
Priority
ADS dated 7/25/2023 claims foreign priority to EU 22306107.8 dated 7/25/2022.
Information Disclosure Statement
IDS dated 8/21/2023, 9/12/2025, 9/1/2026 have been reviewed. See attached.
Drawings
The drawings dated 7/25/2023 have been reviewed. They are accepted.
Specification
The abstract dated 7/25/2023 has 99 words, 11 lines, and no legal phraseology. The abstract is accepted.
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 1 – 20 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 the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 13, 17 recite both “the product” and “the manufacturing product” while there is only antecedent bases for “a manufacturing product.” This introduces confusion because it is unclear as to whether “the product” and “the manufacturing product” are referring to the same element. Because the claim positively recites “the manufacturing product” is must be referring to “a manufacturing product.” Accordingly, there is not antecedent bases of the recited “the product.”
Dependent claims 2 – 12, 14 – 16, 18 - 20 are rejected due to their dependency.
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 under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Claim 1.
STEP 1: Yes. The claim recites “a computer-implemented method”
STEP 2A PRONG ONE: YES. The claim recites:
“… for designing a manufacturing product, the method comprising:
Obtaining a set of materials for manufacturing the product;
Obtaining a set of use and/or manufacturing constraints for the manufacturing product;
Obtaining specifications indicating an extent of compatibility of one or more reference materials with the constraints; and
Determining an optimal subset of the set of materials for manufacturing the product, the determining including classifying the materials with respect to compatibility with the constraints and based on the obtained specifications” which is a mental process of observing a set of alternatives, constraints, and compatibilities and making a judgement.
STEP 2A PRONG TWO: NO.
While the claim recites a “computer-implemented” method, this is merely a general recitation of computer used merely as a tool to execute the abstract idea (i.e., mental method of making a judgement).
While the claim recites that the method is for designing “a manufacturing product” designing is merely the mental processes of imagining a desired scenario where the scenario is described as “a manufacturing product.” By describing the scenario, these elements merely link the imagined scenario to a field of use called manufacturing products. Such limitations are not indicative of a practical application.
While the claim recites “Obtaining a set of materials for manufacturing the product; Obtaining a set of use and/or manufacturing constraints for the manufacturing product; Obtaining specifications indicating an extent of compatibility of one or more reference materials with the constraints” these elements merely gather information required to make a decision. The set of materials, constraints, and specifications may simply be lists obtained, for example, by hearing or seeing a written list. Indeed, engineers use, for example, reference books that contain lists of materials along with specifications indicating the extent the material properties. Accordingly, an engineer may be told to select a material with a melting point above 300 degree Faren height and by flipping through the reference book they choose, for example, steel which has a melting point between 2500 and 2,800 degrees F.
While the claim indicates that the obtained information is related to “materials”, this merely links the information to a filed of use. This is not indicative of a practical application.
STEP 2B: NO.
While the claim recites a computer implemented method a general recitation of a computer-implemented method is not significantly more than the abstract idea.
While the claim recites to “obtain” information about materials, the data gathering is recited at a high degree of generality. This is not significantly more than the abstract idea.
Accordingly, the claim elements when viewed individually and as a whole are not significantly more than the abstract idea itself.
Claim 13. The limitations of claim 13 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Additionally, the recitation of “a non-transitory computer-readable data storage medium having recorded thereon a computer program having instructions for performing a method for designing a manufacturing product, the method comprising:” merely recites general computer components and merely executing an abstract idea with a general computer is not a practical application and it is not significantly more than the abstract idea itself.
Claim 17. The limitations of claim 17 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claims. Additionally, while the claim recites “A computer system comprising: A processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to:” merely recites general computer components and merely executing an abstract idea with a general computer is not a practical application and it is not significantly more than the abstract idea itself.
Claims 2, 14, 18 while the claim recites “wherein the classification includes learning a Multiple Criteria Decision Aiding sorting model configured to take as input a set of materials and outputting an optimal subset of materials, the learning being based on the set of materials, on the constraints, and on the specifications” these elements are mathematical in nature and accordingly recite an abstract idea. Therefore, these claims merely recite that the mental process of making a decision/judgement is informed by an abstract idea of a mathematical calculation. This is not a practical application because even if the mental decision/opinion is improved in some way by the mathematical calculation an improvement may not be to an abstract idea itself. Further, these mathematical elements do not provide elements which are significantly more than the abstract idea as they are part of the abstract idea itself.
Claims 3, 15, 19 while the claims recite “wherein the specifications form a learning set of the model” this is merely descriptive of the information and is not a practical application as these elements to not rely upon or use the mental decision/judgement. They are also not significantly more than the abstract idea as these elements merely describe information and a set of data used for learning is well understood routine and conventional.
Claims 16, 20 while the claims recite “wherein the model includes one or more non-compensatory sorting (NCS) modesl” these elements are mathematical in nature and accordingly recite an abstract idea. Therefore, these claims merely recite that the mental process of making a decision/judgement is informed by an abstract idea of a mathematical calculation. This is not a practical application because even if the mental decision/opinion is improved in some way by the mathematical calculation an improvement may not be to an abstract idea itself. Further, these mathematical elements do not provide elements which are significantly more than the abstract idea as they are part of the abstract idea itself.
Claim 4. While the claim recites “Wherein the model includes one or more non-compensatory sorting (NCS) models” these elements are mathematical in nature and accordingly recite an abstract idea. Therefore, these claims merely recite that the mental process of making a decision/judgement is informed by an abstract idea of a mathematical calculation. This is not a practical application because even if the mental decision/opinion is improved in some way by the mathematical calculation an improvement may not be to an abstract idea itself. Further, these mathematical elements do not provide elements which are significantly more than the abstract idea as they are part of the abstract idea itself.
Claim 5. While the claim recites “Wherein the learning includes encoding learning clauses based on the constraints, the encoding using a SAT-based encoding” these elements are mathematical in nature and accordingly recite an abstract idea. Therefore, these claims merely recite that the mental process of making a decision/judgement is informed by an abstract idea of a mathematical calculation. This is not a practical application because even if the mental decision/opinion is improved in some way by the mathematical calculation an improvement may not be to an abstract idea itself. Further, these mathematical elements do not provide elements which are significantly more than the abstract idea as they are part of the abstract idea itself.
Claim 6. While the claim recites “Wherein the specification include incompatible specifications, and the learning includes finding a compromise between the incompatible specifications” these elements merely describe the information that is part of the extra solution data gathering. This is not a practical application of the abstract idea. Further, incompatible specifications merely indicate that some of the requirements are incompatible and humans often make mental decision in the face of conflicting requirements. Therefore, having to make a decision given incompatible requirements is not significantly more than the abstract idea. Human are capable of making tradeoffs and compromises.
Claim 7. While the claim recites “Wherein the learning includes encoding learning clauses based on the constraints, the encoding using a MaxSAT-based encoding” these elements are mathematical in nature and accordingly recite an abstract idea. Therefore, these claims merely recite that the mental process of making a decision/judgement is informed by an abstract idea of a mathematical calculation. This is not a practical application because even if the mental decision/opinion is improved in some way by the mathematical calculation an improvement may not be to an abstract idea itself. Further, these mathematical elements do not provide elements which are significantly more than the abstract idea as they are part of the abstract idea itself.
Claim 8. While the claim recites “Wherein the finding a compromise includes iteratively modifying the learning set until reaching an extent of compatibility between the specifications” this merely indicates that the method is iterative and humans are capable of iterating various options until they find one that works. This is a common form of mental decision making and is therefore not significantly more than the abstract idea itself.
Claim 9. While the claim recites “Wherein the incompatible specifications are obtaining from different users” this is merely part of the information gathering and humans commonly get information from more than one person while making a decision.
Claim 10. While the claim recites “Wherein the specifications are obtaining from different users” this is merely part of the information gathering and humans commonly get information from more than one person while making a decision.
Claim 11. While the claim recites “Wherein the one or more constraints are latent constraints” this merely describes the concept of heuristic type decision where the underlying reasoning may be obscure to a human but a human nevertheless is able to make a decision based on a heuristic allowing a human to reach a decision given incomplete or unknown information.
Claim 12. While the claim recites “Further comprising: selecting one or more materials within the determined optimal subset; and using the selected one or more materials for manufacturing the product” this is merely a general statement to apply the decision “for manufacturing.” Such elements are not sufficient application and are not significantly more than the abstract idea.
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.
Claims 1 – 8, 12 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ipek_2012 (An expert system based material selection approach to manufacturing, Materials and Design, 2012) in view of Tlili_2021 (Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations, European Journal of Operational Research, 2021).
Claim 1. Ipek_2012 makes obvious “A computer-implemented method for designing a manufacturing product (Abstract: “selection of proper materials for a diverse mechanism is one of the hardest tasks in the design and product improvements in various industrial applications. materials play a vital and important function during the entire design and manufacturing process… need to identify and select suitable materials with specific functionalities in order to attain the preferred output with the minimum cost… this paper tries to solve the materials selection problem by means of an expert system approach to manufacturing…”; page 332 section 3: “an expert system is an interactive computer-based decision tool that employes both facts and heuristics to answer hard decision problems founded on the knowledge obtained from an expert, the expert system method has been applied to numerous problems of arrangement, design, and diagnostics…”; page 333 section 4: “… in this study an expert system has been developed to offer suitable materials among different materials for the product, classification of goods and materials, suitable features for the products and the choice of materials are given…”), the method comprising: obtaining a set of materials for manufacturing the product (Fig. 1: “materials knowledge base”; Table 4: “list of candidate materials and their compositions”;; page 338: “… candidate materials for bumpers, flywheels and implant materials that include both currently used and promising metallic materials. In fact 35 different materials are taken as alternative materials for 10 dissimilar case studies… the candidate materials and their compositions are presented in Table 4…”); Obtaining a set of use and/or manufacturing constraints for the manufacturing product (Table 1: “products and material properties necessary for products”); Obtaining specifications indicating an extent of compatibility of one or more reference materials with the constraints (Table 5: “properties of candidate materials…”); and determining an optimal subset of the set of materials for manufacturing the product, the determining including [selecting] the materials with respect to compatibility with the constraints and based on the obtained specifications (page 338 – 339: “… a means of selecting the suitable automotive part as well as body implant materials… steps involved in the expert system method… in case of selecting materials for bumper, it is seen from Tables 1 and 3 that PP, HDPE and PMMA polymeric materials are preferred due to meeting the required properties of high impact resistance, lightness (lower density), formability, higher corrosion resistance and cheapness… while choosing the materials for flywheels, it is seen from Tables 1 and 3 that cast iron, high strength steel, GFRPs (glass fiber reinforced plastics) and CFRs (Carbon fiber reinforced plastics) composite materials are favored due to providing the essential properties of strength, formability, vibration absorption and lower cost…”).
Ipek_2012 teaches to select materials from a list of alternatives, and while a selection clearly results a selected group and an unselected group, and while grouping items may properly be found to make classification of alternatives into a selected classification group and an unselected classification group obvious to those of ordinary skill in the art, Ipek_2012 does not recite the word “classifying” in relation to such a grouping.
Tlili_2021, however, makes obvious “classifying” (abstract: “the non-compensatory sorting model aims at assigning alternatives evaluated on multiple criteria to one of the predefined ordered categories…”; page 981 section 2.1 Basic notations: “multicriteria sorting aims at assigning alternatives to one of the predefined ordered categories…”; page 983 section 3 learning an NCS model from data: “… assignment as a function mapping a subset of reference alternatives… to the ordered set of categories…”).
Ipek_2012 and Tlili_2021 are analogous art because they are from the same field of endeavor called decision support models. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ipek_2012 and Tlili_2021. The rationale for doing so would have been that Ipek_2012 teaches to have a decision support system with an inference mechanism (section 4.3, Fig. 1) that groups items. Tlili_2021 teaches an inference engine that groups items by classifying them where the inference engine is learned by use of a set of reference assignments in infer and extend assignments if items to groups of classifications (page 983 section 3) and further teaches to extend models such as the non-compensatory sorting model with SAT-based encoding and MaxSAT-based encoding as a way to deal with noise (page 987) and that during learning SAT and MaxSAT languages are relevant and efficient (page 1004). Therefore, it would have been obvious to combine the materials selection decision framework that uses inference models of Ipek_2012 with the learned inference models of Tlili_2021 for the benefit of having relevant and efficient means of learning and making inferences foe decision support to obtain the invention as specified in the claims.
Claim 13. The limitations of claim 13 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Additionally, Tlili_2021 makes obvious the further limitations of “a non-transitory computer-readable data storage medium having recorded thereon a computer program having instructions for performing a method for designing a manufacturing product, the method comprising:” (page 989 section 6.1.2: “this experimental study is run on a laptop with Windows 10 (64 bit) equipment with an Intel (R) Xeon (R) CPU E5-1620 V4 @3.5GHZ and 32 GB of RAM… instances are written in a file in DIMACS format, and passed to a command line SAT solver – CryptoMiniSat 5.0.1… instances are passed to a command line MaxSAT solver QmaxSAT in the required format…”).
Also, Ipek_2012 makes obvious “a non-transitory computer-readable data storage medium having recorded thereon a computer program having instructions for performing a method for designing a manufacturing product, the method comprising:” (page 332: “… select suitable materials from the database that contains more than 100 thousand materials… expert systems are computer programs… an expert system is an interactive computer-based decision tool…”).
Claim 17. The limitations of claim 17 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claims. Additionally, Tlili_2021 makes obvious the further limitations of “A computer system comprising: A processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to:” (page 989 section 6.1.2: “this experimental study is run on a laptop with Windows 10 (64 bit) equipment with an Intel (R) Xeon (R) CPU E5-1620 V4 @3.5GHZ and 32 GB of RAM… instances are written in a file in DIMACS format, and passed to a command line SAT solver – CryptoMiniSat 5.0.1… instances are passed to a command line MaxSAT solver QmaxSAT in the required format…”).
Also, Ipek_2012 makes obvious “A computer system comprising: A processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to:” (page 332: “… select suitable materials from the database that contains more than 100 thousand materials… expert systems are computer programs… an expert system is an interactive computer-based decision tool…”).
Claims 2, 14, 18 Ipek_2012 makes obvious “wherein the classification includes learning a Multiple Criteria Decision Aiding sorting model configured to take as input a set of [reference assignments] and outputting an optimal subset of [reference assignments], the learning being based on the set of [reference assignments], on the constraints, and on the specifications”(page 983 section 3 “… a set of reference assignments is given… the aim is to extend these assignment with an NCS model… an assignment as a function mapping a subset of reference alternatives… to ordered set of categories… these reference alternatives highlight values of interest on each criterion…”; page 987: “… learning examples (reference alternatives together with their assignement…” EXAMINER NOTE: the reference assignment is training set that includes a set of materials).
Additionally, Ipek_2013 teaches a subset of “materials” (page 338 – 339: “… a means of selecting the suitable automotive part as well as body implant materials… steps involved in the expert system method… in case of selecting materials for bumper, it is seen from Tables 1 and 3 that PP, HDPE and PMMA polymeric materials are preferred due to meeting the required properties of high impact resistance, lightness (lower density), formability, higher corrosion resistance and cheapness… while choosing the materials for flywheels, it is seen from Tables 1 and 3 that cast iron, high strength steel, GFRPs (glass fiber reinforced plastics) and CFRs (Carbon fiber reinforced plastics) composite materials are favored due to providing the essential properties of strength, formability, vibration absorption and lower cost…”).
Therefore, in combination, Ipek_2012 and Tlili_2021 make obvious “wherein the classification includes learning a Multiple Criteria Decision Aiding sorting model configured to take as input a set of materials [reference assignments] and outputting an optimal subset of materials [reference assignments], the learning being based on the set of materials [reference assignments], on the constraints, and on the specifications.”
Claims 3, 15, 19 Ipek_2012 makes obvious “wherein the specifications form a learning set of the model” (page 983 section 3 “… a set of reference assignments is given… the aim is to extend these assignment with an NCS model… an assignment as a function mapping a subset of reference alternatives… to ordered set of categories… these reference alternatives highlight values of interest on each criterion…”; page 987: “… learning examples (reference alternatives together with their assignement…” EXAMINER NOTE: the reference assignment is training set that includes a set of materials).
Claims 4, 16, 20 Tlili_2021 make obvious “wherein the model includes one or more non-compensatory sorting (NCS) models” (title: “learning non-compensatory sorting models using efficient SAT/MaxSAT formulations”).
Claim 5. Tlili_2021 make obvious “Wherein the learning includes encoding learning clauses based on the constraints, the encoding using a SAT-based encoding” (title: “learning non-compensatory sorting models using efficient SAT/MaxSAT formulations”).
Claim 6. Tlili_2021 make obvious “Wherein the specification includes incompatible specifications, and the learning includes finding a compromise between the incompatible specifications” (page 987: “… we formulate the relaxed optimization problem of finding the subset of learning examples… a soft constraint approach, using the language of weighted MaxSAT. This framework, derived from the SAT framework, is based on a conjunction of clauses… the soft clauses we are ready to see violated, as opposed to hard clauses that remain mandatory… allowing to specify lexicographically ordered goals in an additive framework…” EXAMINER NOTE: hard clauses are those that must be obeyed while soft clauses are those with a preference to satisfy as much as possible paired with a penalty/weight if broken. The encoding finds assignments that satisfy all hard clauses while minimizing the penalty of broken soft clauses. Accordingly, the learning finds a compromise between incompatible clauses/specifications.).
Claim 7. Tlili_2021 make obvious “Wherein the learning includes encoding learning clauses based on the constraints, the encoding using a MaxSAT-based encoding” (page 987 section 5 “MaxSAT…”; page 1003 section 7: “… we extend the two SAT problems using MaxSAT language… we propose two MaxSAT programs to compute the model’s parameters from noisy preference information…”
Appendix B. MaxSAT…”).
Claim 8. Tlili_2021 make obvious “Wherein the finding a compromise includes iteratively modifying the learning set until reaching an extent of compatibility between the specifications” (page 979: “… it should be highlighted that the construction of the learning set and the NCS model often results from a sequence of interactions between the DM and the analyst rather than in a one-step interaction…”).
Claim 12. Ipek_2013 makes obvious “Further comprising: selecting one or more materials within the determined optimal subset; and using the selected one or more materials for manufacturing the product” (abstract: “… design and manufacturing process… expert system approach to manufacturing…”; page 332: “… materials selection in manufacturing…”; section 5.2.2: “a manufacturer producing flywheels makes thousands of fly-wheels per day…”; section 5.2.4: “the flywheel material should be selected… also be economical… costs incurred in the manufacturing process…”; section 7: “the selection of appropriate materials… expert system based selection of materials approach for manufacturing…”).
Claims 9, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ipek_2012 in view of Tlili_2021 in view of Pedgley_2009 (Influence of Stakeholders on Industrial Design Materials and Manufacturing Selection, 2009).
Claim 9. Pedgley_2009 makes obvious “Wherein the incompatible specifications are obtaining from different users” (page 1: “… influence of project stakeholders on industrial designers’ selection of product materials and manufacturing processes… four-way stakeholder description of materials and manufacturing selection in industrial design, spanning: users, clients, manufacturing/vendors and designers/design team members. The practical influence of each stakeholder on materials and manufacturing decisions… establishes creativity in the selection of product materials and manufacturing processes as cleverly attending to stakeholder influence, and distinctly not to unconstrained freethinking or self-centered decision-making… a fusion of… perspectives…”; page 7: “… the target price-point could be too tight that simply opting for a slightly higher grade of material incurs an unacceptable cost… a design brief will contain an emphasis to opt for one material or manufacturing process rather than another…”; page 10: “… ideas of product form… was found to create a significant challenge for manufacturability… forced to fit the working constraints of manufacturing processes… product form usually have a complexity implication for tooling… and therefore an increased cost implication that must be justified…”; page 11: “a requirement for industrial designers to consider economics of scale… for example, an injection moulded part (high volume automated process, low part cost) would not usually be combined with a metal spun part (batch manufacture manual or semi-automated process, high part cost). Such mismatches place strain on supply chain, and are usually not commercially viable…”
EXAMINER NOTE: The paper outlines a comprehensive framework encompassing four primary stakeholder groups for material selection: users, clients, manufacturers/vendors, and the design team. It explores how each stakeholder provides specific requirements (e.g., clients focusing on product cost, manufacturers on tooling and tolerances, and users on utility and surface finishes). This paper explicitly details that these stakeholder inputs do not represent unconstrained decision-making. Instead, they introduce competing and often incompatible criteria—such as technical performance requirements pulling against user experience and financial constraints—forcing the product designer to navigate these tensions to reach a viable compromise.)
Ipek_2012 in view of Pedgley_2009 are analogous art because they are from the same field of endeavor called material selection decision-making. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ipek_2012 in view of Pedgley_2009. The rationale for doing so would have been that Ipek_2012 teaches to have an expert system decision making framework that takes input from an expert decision maker. Pedgley_2009, however, teaches that there are many stakeholders with competing requirements and all of their requirements should be considered in order to make the best decision for material selection. Therefore, it would have been obvious to combine Ipek_2012 in view of Pedgley_2009 for the benefit of making material selections that are capable of meeting all the requirements along the design and manufacturing value chain to obtain the invention as specified in the claims.
Claim 10. Pedgley_2009 makes obvious “Wherein the specifications are obtaining from different users” (page 1: “… influence of project stakeholders on industrial designers’ selection of product materials and manufacturing processes… four-way stakeholder description of materials and manufacturing selection in industrial design, spanning: users, clients, manufacturing/vendors and designers/design team members. The practical influence of each stakeholder on materials and manufacturing decisions… establishes creativity in the selection of product materials and manufacturing processes as cleverly attending to stakeholder influence, and distinctly not to unconstrained freethinking or self-centered decision-making… a fusion of… perspectives…”; page 7: “… the target price-point could be too tight that simply opting for a slightly higher grade of material incurs an unacceptable cost… a design brief will contain an emphasis to opt for one material or manufacturing process rather than another…”; page 10: “… ideas of product form… was found to create a significant challenge for manufacturability… forced to fit the working constraints of manufacturing processes… product form usually have a complexity implication for tooling… and therefore an increased cost implication that must be justified…”; page 11: “a requirement for industrial designers to consider economics of scale… for example, an injection moulded part (high volume automated process, low part cost) would not usually be combined with a metal spun part (batch manufacture manual or semi-automated process, high part cost). Such mismatches place strain on supply chain, and are usually not commercially viable…”
EXAMINER NOTE: The paper outlines a comprehensive framework encompassing four primary stakeholder groups for material selection: users, clients, manufacturers/vendors, and the design team. It explores how each stakeholder provides specific requirements (e.g., clients focusing on product cost, manufacturers on tooling and tolerances, and users on utility and surface finishes). This paper explicitly details that these stakeholder inputs do not represent unconstrained decision-making. Instead, they introduce competing and often incompatible criteria—such as technical performance requirements pulling against user experience and financial constraints—forcing the product designer to navigate these tensions to reach a viable compromise.)
Ipek_2012 in view of Pedgley_2009 are analogous art because they are from the same field of endeavor called material selection decision-making. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ipek_2012 in view of Pedgley_2009. The rationale for doing so would have been that Ipek_2012 teaches to have an expert system decision making framework that takes input from an expert decision maker. Pedgley_2009, however, teaches that there are many stakeholders with competing requirements and all of their requirements should be considered in order to make the best decision for material selection. Therefore, it would have been obvious to combine Ipek_2012 in view of Pedgley_2009 for the benefit of making material selections that are capable of meeting all the requirements along the design and manufacturing value chain to obtain the invention as specified in the claims.
Claim 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ipek_2012 in view of Tlili_2021 in view of Minoungou_2021 (Learning MR-Sort Models from Non-Monotone Data, July 20, 2021).
Claim 11. Minoungou_2021 makes obvious “Wherein the one or more constraints are latent constraints” (Introduction: “… in this paper we consider multiple criteria sorting problems in which alternatives evaluated on several criteria are to be assigned to one of the pre-defined ordered categories… non-compensatory sorting model… a particular case of NCS… MR-sort… it is usual to elicit the sorting model parameters indirectly from a set of assignment examples, i.e., a set of alternatives with the associated desired category. Such a preference learning approach has been developed… in which the evaluation of alternatives on criteria does not necessarily induce monotone preference… Example 1… the sorting model should be inferred from the data, even if the way to account for the substance A level is unknown… example 2… the last criteria should be taken into account, but does not know if it should be maximized, minimized… the goal is to simultaneously learn the classifier parameters and the preference direction for the last criterion…”; page 5: “… this learning approach… has been previously considered in the literature. In particular… inv-MR-sort problem with latent preference directions, i.e., considering criteria whose preference direction, in terms of gain/cost, is not known beforehand…”; page 12: “… the algorithm is able to accurately restore new assignment examples based on the learned models (0.93 on average up to 9 criteria) and remains relatively efficient with the number of criteria with unknown preference directions…”; section 7: “… model with criteria that can either be of type… (iii) single-peak or (iv) single-valley criteria…”
EXAMINER NOTE: at page 5, the author, while summarizing previous learning approaches uses the phrase “latent preference directions” and defines latent preference directions as a preference direction is unknown beforehand. The author further provides example 1 and example 2, in the introduction, in which preference direction is unknown before learning occurs but that the learning learns all the classifier parameters – including those, the preference direction of which, is unknown prior to the learning but was latent in the learning set. The preference direction is a “constraint” because it is a selection criteria used in the classification.
Traditional algorithms for learning NCS parameters assume that preferences on criteria are strictly monotone (i.e., monotonically increasing or decreasing). However, real-world training sets often reflect human choices bound by latent preference directions or non-monotone constraints (such as single-peaked or single-valley preferences) where optimal targets exist, but are completely unobserved/latent in the data. The authors present a Mixed-Integer Linear Programming (MILP) optimization framework designed to infer the sorting rules directly from these training assignments. The algorithm is unique because it is capable of training the model parameters while simultaneously discovering the hidden underlying constraints (uncovering whether a criterion acts as a gain, cost, single-peaked, or single-valley boundary) directly from the data.)
Tlili_2021 and Minoungou_2021 are analogous art because they are from the same field of endeavor called multiple criteria sorting. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Tlili_2021 and Minoungou_2021. The rationale for doing so would have been that Tlili_2021 teaches learning non-compensatory sorting models based on training sets of reference assignments based on decision set preferences. Minougou_2021 teaches learning non-compensatory sorting models where the training sets of reference assignments include unknown and intrinsic constraints and that many real-world problems have such constraints. Therefore, it would have been obvious to combine Tlili_2021 and Minoungou_2021 for the benefit of learning sorting models for real-world situations where preference direction is unknown (i.e., non-monotone) to obtain the invention as specified in the claims.
Conclusion
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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