DETAILED ACTION
1. This office action is in responsive to the applicant’s arguments filed on 1/6/26.
2. The present application is being examined under the first inventor to file provisions of the AIA .
3. Claims 1-24 are currently pending.
4. Claims 1-2, 7-8, 11, 17-18 and 20 are amended. Claims 4-6, 10, 14, 16 are previously presented.
5. Claims 3, 9, 12-13, 15 and 19 are original. Claims 23-24 are new.
Response to Arguments
Response: 35 U.S.C. § 101
6. Examiner Response:
Applicant’s arguments, see pages 11-23, filed 1/16/26, with respect to the 35 U.S.C. 101 rejections have been fully considered and are persuasive. The 35 U.S.C. 101 rejections of claims 1-22 has been withdrawn.
Response: 35 U.S.C. § 103
7. Applicants argue:
The applicant argues that the Suppapitarm reference doesn’t teach the recent amendment to the limitation of claim 1 that states “executing, via one or more software programs, a plurality of iterations of a divergent search algorithm to generate a plurality of initial frames by adding, during each iteration of the divergent search algorithm, a random plurality of nodes to the input frame and interconnecting each of the random plurality of nodes with all nodes included in the plurality of nodes to generate a corresponding initial frame included in the plurality of initial frames” (Remarks: pages 23-24)
7. Examiner Response:
The examiner notes that on Pg. 176 4th paragraph of the Suppapitarm reference it states “Figure 10 shows the evolution of the trade-off surface in two-objective projections (mean
deflection vs. frontal projected area and mean deflection vs. system mass). Although during
the search designs with as many as 19 nodes were generated, no design with more than nine
nodes was ever archived, suggesting that simple (from a manufacturing viewpoint) designs
can readily be found without the need for compromise with respect to the other objectives.”. On Pg. 177, 1st – 2nd paragraph of the Suppapitarm reference it states “Figure 10(c) shows clear trade-offs between the other objectives in the Pareto-optimal designs archived at the end of the search. Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations). All of these have a conventional riding position (are 'normal-postured'), which is unsurprising given that the search was initiated from such a configuration (Fig. 9). To avoid confusion with the tube members, the rider's legs are not shown in these (and subsequent similar) images. The thicknesses of the lines representing the frame members are proportional to the outside diameter of the tubes for the example of that topology shown. The number of designs of the same topology, but with differing member sizes and lengths, in the archive at this stage are reported in the figure.”. The examiner notes that in Figs. 10 and 11 and the paragraph shown above of the Suppapitarm reference, it shows how different nodes are added for the different iterations that are conducted. The different designs as shown in Fig. 11 of the Suppapitarm reference, further shows that there are a plurality of frames, where the position of the rider is different. Also, the examiner considers the simple designs that had no more than nine nodes that were archived, to be the plurality of initial frames since these designs are designs that are generated off of the initial design shown in Fig. 9 of the Suppapitarm reference, see Suppapitnarm et al. (Pg. 167, 2nd paragraph and 1st bullet “The topology modification rules are as follows: • Rule I: The topology is modified by adding, etc.”.
8. Examiner Response:
The examiner’s response regarding the applicant’s arguments to the newly added claims are shown below.
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 for establishing a background for determining obviousness under 35
U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-9, 11 and 13-24 is/are rejected under 35 U.S.C. 103 as being unpatentable
over online reference Conceptual design of bicycle frames by multiobjective shape annealing, written by Suppapitnarm et al. in view of Sato et al. (US 2016/0092629).
With respect to claim 1, Suppapitnarm et al. discloses “A method for generating one or more designs for a structural frame” as [Suppapitnarm et al. (Pg. 165, sec. 1 Introduction, 2nd paragraph, “This paper explores these ideas further through the development of a multiobjective shape annealing approach applied to the design of bicycle frames”)];
“receiving an input frame and an optimization objective that indicates a design goal for the one or more designs” as [Suppapitnarm et al. (Pg. 170, sec. 2.4 Framework Structure, “Figure 4 presents a flow chart of the optimization process with the frame grammar introduced at the design generation phase, 1st bullet “When a new solution has been generated using the grammar rules, its structural performance is analyzed (objectives and constraints are evaluated).”, Suppapitnarm et al. Pg. 175, sec. 4.1 Case Study 1: Design for Economy and Efficiency, 1st paragraph, objectives 1-4, “In this first study, we seek to compare different concepts on the basis of the following objectives:, etc.”, Suppapitnarm et al. Pg. 176 2nd paragraph, “The optimization was started from a design with a minimal number of connections, as shown in Figure 9.”, Fig. 4)];
“wherein the input frame includes a plurality of node” as [Suppapitnarm et al. (Pgs. 170-171 sec. 2.4 Framework Structure, last bullet “First, a shape is randomly selected from the current design, etc.”, The examiner considers the frame including a plurality of nodes to be the input frame, since the frame is used for the bicycle design.)];
“based on the input frame, generating a set of multiple candidate frames by: executing, via one or more software programs, a plurality of iterations of a divergent search algorithm to generate a plurality of initial frames by adding, during each iteration of the divergent search algorithm, a random plurality of nodes to the input frame and interconnecting each of the random plurality of nodes with all nodes included in the plurality of nodes to generate a corresponding initial frame included in the plurality of initial frames” as [Suppapitnarm et al. (Pg. 167, 2nd paragraph and 1st bullet “The topology modification rules are as follows: • Rule I: The topology is modified by adding, etc.”, Pg. 176, 2nd – 4th paragraph “The optimization was started from a design with a minimal number of connections, etc.”, Suppapitnarm et al. Pg. 177, 1st – 2nd paragraph “Figure 10(c) shows clear trade-offs between the other objectives in the Pareto-optimal designs archived at the end of the search. Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations). All of these have a conventional riding position (are 'normal-postured'), which is unsurprising given that the search was initiated from such a configuration (Fig. 9). To avoid confusion with the tube members, the rider's legs are not shown in these (and subsequent similar) images. The thicknesses of the lines representing the frame members are proportional to the outside diameter of the tubes for the example of that topology shown. The number of designs of the same topology, but with differing member sizes and lengths, in the archive at this stage are reported in the figure.”, Figs. 9-11, The examiner considers the simple designs that had no more than nine nodes that were archived, to be the plurality of initial frames since these designs are designs that are generated off of the initial design shown in Fig. 9 of the Suppapitarm reference. Also, the examiner notes that in Fig. 10 and the paragraph shown above of the Suppapitarm reference, it shows how different nodes are added for the different iterations that are conducted. Further, the different designs as shown in Fig. 11 of the Suppapitarm reference, further shows that there are a plurality of frames, where the position of the rider is different.)];
“executing, via the one or more software programs, a convergent search algorithm to optimize each initial frame included in the plurality of initial frames by merging one or more pairs of nodes that are within a threshold distance of one another and are included in at least one of the plurality of nodes or the random plurality of nodes to generate a plurality of optimized initial frames” as [Suppapitnarm et al. (Pg. Pg. 176, 2nd paragraph “The optimization was started from a design with a minimal number of connections, etc. and Suppapitnarm et al. Pgs. 177-178 last paragraph “Figure 11 shows the five optimal topologies identified during the early stages of the search, etc.”. The search that is being initiated is from the configuration of Fig. 9 that shows the merging of the nodes. Also, Fig. 11 shows the nodes of Fig. 9 are merged to get the designs that are shown. Further, the examiner notes that adding a member joining two randomly selected nodes of the existing structure, shows that there’s a distance between the nodes that are interconnected as shown in Fig. 1 of the Suppapitnarm et al. reference.)];
“and adding, to the set of multiple candidate frames, one or more optimized initial frames included in the plurality of optimized initial frames that meet a quality threshold” as [Suppapitnarm et al. (Pg. 177, 2nd paragraph, “Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations).”, Fig. 11, The examiner considers the quality threshold to be the design with the same topology but different member sizes and lengths as shown in Fig. 11 of the Suppapitarm reference, since there is a determination of which designs are used after a number of iterations)];
“based on the optimization objective and the set of multiple candidate frames, selecting a subset of candidate frames from the set of multiple candidate frames” as [Suppapitnarm et al. (Pg. 178, 1st paragraph “Figure 12 shows the eight optimal topologies identified after 50,000 iterations.”, Fig. 12, The examiner considers the designs selected in Fig. 12 as being the designs selected, since those designs superseded the designs generated from 5000 iterations)];
“and for each candidate frame included in the subset of candidate frames: generating an iterative solution based on the candidate frame” as [Suppapitnarm et al. (Pg. 178, 1st paragraph “Figure 12 shows the eight optimal topologies identified after 50,000 iterations.”, Fig. 12, The examiner the designs after 50,000 iterations as being the iterative solution based on the candidate frame, since the designs shown in Fig. 12 of the Suppapitarm reference are based on the designs shown in Fig. 11 of the Suppapitarm reference which are generated from 5,000 iterations.)];
“determining a quality factor for the iterative solution frame that enables a quantitative comparison with respect to the optimization objective of the iterative solution with other iterative solutions” as [Suppapitnarm et al. (Pg. 178, 1st paragraph “Figure 12 shows the eight optimal topologies identified after 50,000 iterations.”, Fig. 12, With there being a comparison of the different designs which showed that the optimal topologies identified after 5,000 iterations are superseded, shows there’s a comparison and a quality factor is determined, since a quality factor is based on a comparison)];
“executing, via the one or more software programs, the convergent search algorithm to iteratively modify the iterative solution to generate a plurality of different iterative solutions frames having a plurality of quality factors to converge to a solution frame that corresponds to a local minimum for the quality factor” as [Suppapitnarm et al. (Pg. 179, 1st – 2nd paragraph, “Figure 13 shows the 11 optimal topologies identified when the search, etc.”, Fig. 13, The examiner considers the designs of Fig. 13 to be the executing of the convergent search algorithm to modify the iterative solutions, where the designs shown in Fig. 13 of the Suppapitarm reference are modified from the designs shown in Fig. 12 of the Suppapitarm reference. Also, the examiner considers the optimal topologies to be the optimization, since the optimal topologies are optimization of the frame of the bicycle to generate different designs.)];
“wherein a plurality of solution frames produced for the candidate frames included in the subset of candidate frames includes a global solution frame that corresponds to a global minimum for the quality factor.” as [Suppapitnarm et al. (Pg. 169, sec. 2.3.4 Returns to Base, 1st paragraph, “In a traditional single objective SA implementation the 'return to base' (restart) option retrieves the best solution found and continues the search from there. In our MOSA implementation when a return to base occurs a solution is retrieved from the archive (which contains the best solutions found))];
While Suppapitnarm et al. teaches applying a multiobjective shape annealing approach to the design of bicycle frames, Suppapitnarm et al. does not explicitly disclose “A computer-implemented method for generating one or more computer-aided designs for a structural frame, and generate a first computer-aided design based on the solution frame”
Sato et al. discloses “A computer-implemented method for generating one or more designs for a structural frame” as [Sato et al. (paragraph [0065] “Subsequently, as illustrated in FIG. 2, based on user operation, the CPU 14 creates three-dimensional CAD data of the design object (the intake duct in the first example) by using the design parameters P1 to P11 stored in the design-parameter storage region 134 (step S3)”)];
“and generate a first software computer-aided design based on the solution frame” as [Sato et al. (paragraph [0065] “Subsequently, as illustrated in FIG. 2, based on user operation, the CPU 14 creates three-dimensional CAD data of the design object (the intake duct in the first example) by using the design parameters P1 to P11 stored in the design-parameter storage region 134 (step S3)”)];
Suppapitnarm et al. and Sato et al. are analogous art because they are from the same field
endeavor of design optimization.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Suppapitnarm et al. of applying a multiobjective shape annealing approach to the design of bicycle frames by incorporating a computer-implemented method for generating one or more designs for a structural frame; and generate a first software computer-aided design based on the solution frame as taught by Sato et al. for the purpose of designing aircrafts that have shapes that require both aerodynamic characteristics and stealth characteristics.
Suppapitnarm et al. in view of Sato et al. teaches a computer-implemented method for generating one or more designs for a structural frame and generate a first software fcomputer-aided design based on the solution frame
The motivation for doing so would have been because Sato et al. teaches that by designing aircrafts that have shapes that require both aerodynamic characteristics and stealth characteristics using a designing parameter setting step and an analyzing step, the ability to reduce the cost and labor can be accomplished (Sato et al. (paragraph [0012] – [0013])).
With respect to claim 2, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “mutating the input frame to generate the plurality of initial frames” as [Suppapitnarm et al. (Pg. 167, 2nd paragraph Rules 1-4, “The topology modification rules are as follows: Rule 1: The topology is modified by adding a member joining two randomly selected nodes of the existing structure. As a result, the total number of nodes does not change, but the total number of members in the structure increases by one, etc.”, Suppapitnarm et al. Pg. 170, sec. 2.4 Framework Structure, last bullet, “First, a shape is randomly selected from the current design. Shapes that are recognized by the frame grammar ( and hence are eligible for rule applications) can consist of as few as two, etc.”, Suppapitnarm et al. Pg. 171, 1st and 2nd bullet, “Second, an eligible (for the selected shape) topology modification rule is applied. Not all topology modification rules are always eligible, depending on the connections within the selected shape and between the selected shape and the remainder of the frame. • If there is more than one eligible rule for the selected shape, the rule to be applied is randomly chosen from these candidates. Similarly, if the chosen rule entails the removal of a member and there is more than one eligible candidate, the member to be removed is chosen randomly.”, Suppapitnarm et al. Pg. 171, 5th bullet “If no new designs of the current topology are added to the archive for Narc-max consecutive iterations, or if Neon-max consecutive new designs violate the structural and geometrical constraints defined, a new frame topology is generated and this process is repeated.”)];
“and for each initial frame of the plurality of initial frames, optimizing the initial frame via the convergent search algorithm based on the optimization objective.” as [Suppapitnarm et al. (Pg. 168, 2nd paragraph, “The MOSA method searches for a set of non-dominated solutions, i.e. solutions for which no other solution is better with respect to all requirements, and provides the designer with information about the various trade-offs between the design objectives. Multiobjective simulated annealing does this by iteratively comparing the quality (objectives) of each new solution with that of all the solutions in the established archive (the record of all non-dominated solutions found).”, Suppapitnarm et al. Pg. 171 4th bullet “Once a new frame topology has been generated and accepted, shape and sizing optimization is performed by perturbing the design variables of this topology using the shape and size modification rules and accepting proposed changes using the MOSA acceptance Criteria, etc.”, Suppapitnarm et al. Pgs. 177-178, last paragraph, “Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations). All of these have a conventional riding position (are 'normal-postured'), which is unsurprising given that the search was initiated from such a configuration (Fig. 9). To avoid confusion with the tube members, the rider's legs are not shown in these (and subsequent similar) images. The thicknesses of the lines representing the frame members are proportional to the outside diameter of the tubes for the example of that topology shown. The number of designs of the same topology, but with differing member sizes and lengths, in the archive at this stage are reported in the figure.”)];
With respect to claim 3, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Sato et al. further discloses “wherein the convergent search algorithm comprises a gradient-based optimization algorithm.” as [Sato et al. (paragraph [0072] “In this case, the CPU 14 may update the design parameters P1 to P11 until the analytical results of the CFD analysis and the RCS analysis satisfy the design conditions. However, it is preferable that the design parameters P1 to P11 be updated while being optimized by utilizing either one of an optimizing method, such as a gradient method or a genetic algorithm”)];
With respect to claim 4, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein determining the quality factor for the iterative solution comprises quantifying a physical characteristic of the iterative solution, wherein the physical characteristic is associated with the optimization objective.” as [Suppapitnarm et al. (Pg. 168, 2nd paragraph, “The MOSA method searches for a set of non-dominated solutions, i.e. solutions for which no other solution is better with respect to all requirements, and provides the designer with information about the various trade-offs between the design objectives. Multiobjective simulated annealing does this by iteratively comparing the quality (objectives) of each new solution with that of all the solutions in the established archive (the record of all non-dominated solutions found).”, Suppapitnarm et al. Pg. 170, 1st bullet “When a new solution has been generated using the grammar rules, its structural performance is analyzed (objectives and constraints are evaluated)”, Suppapitnarm et al. Pg. 175, sec. 4.1 Case Study 1: Design for Economy and Efficiency, 1st paragraph, 1-4 objectives, “A good bicycle design simultaneously meets a number of different performance criteria, including economic, structural, aerodynamic and ergonomic criteria. In this first study, we seek to compare different concepts on the basis of the following objectives: 1. The mass of the system must be minimized, reflecting both material cost and practical performance considerations, etc.”)];
With respect to claim 6, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 4 above, and Suppapitnarm et al. further discloses “performing a finite element analysis on the iterative solution to generate the value, for each beam included in the iterative solution, that is associated with the physical characteristic.” as [Suppapitnarm et al. (Pg. 174, sec. 3.2 Loading Cases, “Three loading cases (starting, speeding and braking) are considered in this study. For each case, loads are imposed at the three application points shown in Figure 6. Because the topology and geometry of the system are changed during the optimization process, the loads at these points are varied to ensure that equivalent conditions are maintained for all designs [33, 34]. Table III gives details of the three loading cases used. Structural analysis is performed using FElt, a freeware finite element analysis system [35].”, Suppapitnarm et al. Pg. 175, 1st paragraph “Geometrically feasible designs generated by the grammar are also subjected to a validation process that checks performance or structural constraints through the finite element analysis within the optimization model”)];
With respect to claim 7, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein generating the set of multiple candidate frames by executing the plurality of iterations of the divergent search algorithm comprises adding at least one of a new node and a new beam to a geometry of one of the input frame or an initial frame included in the plurality of initial frames that is derived from the input frame.” as [Suppapitnarm et al. (Pg. 167, 2nd paragraph Rules 1-3, “The topology modification rules are as follows: Rule 1: The topology is modified by adding a member joining two randomly selected nodes of the existing structure. As a result, the total number of nodes does not change, but the total number of members in the structure increases by one, etc.”, Suppapitnarm et al. Pg. 167, 4th paragraph, “The motivation behind the development of this particular grammar has been its application to the design of bicycle frames [20]. The topology modification rules are based on those in Shea's truss grammar, but they have been reformulated in such a way that the truss grammar's triangulation constraint has been relaxed. Figure 2 shows examples of the effects of the application of these rules on some simple frame structures.”, Pg. 176, 2nd – 4th paragraph “The optimization was started from a design with a minimal number of connections, etc.”, Fig. 10, The examiner notes that in Fig. 10 and the paragraph shown above of the Suppapitarm reference, it shows how different nodes are added for the different iterations that are conducted.)];
With respect to claim 8, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein generating the set of multiple candidate frames by executing the divergent search algorithm comprises making at least one change to a topology of either the input frame or an initial frame included in the plurality of initial frames that is derived from the input frame to generate a second initial frame, wherein the change is determined via a process that is not restricted to selection based on a quality metric of the input frame, the initial frame, or the second initial frame.” as [Suppapitnarm et al. (Pg. 167, 2nd paragraph Rules 1-3, “The topology modification rules are as follows: Rule 1: The topology is modified by adding a member joining two randomly selected nodes of the existing structure. As a result, the total number of nodes does not change, but the total number of members in the structure increases by one, etc.”, Suppapitnarm et al. Pg. 170, sec. 2.4 Framework Structure, last bullet, “First, a shape is randomly selected from the current design. Shapes that are recognized by the frame grammar ( and hence are eligible for rule applications) can consist of as few as two, etc.”, Suppapitnarm et al. Pg. 171, 1st and 2nd bullet, “Second, an eligible (for the selected shape) topology modification rule is applied. Not all topology modification rules are always eligible, depending on the connections within the selected shape and between the selected shape and the remainder of the frame. • If there is more than one eligible rule for the selected shape, the rule to be applied is randomly chosen from these candidates. Similarly, if the chosen rule entails the removal of a member and there is more than one eligible candidate, the member to be removed is chosen randomly.”, Suppapitnarm et al. Pg. 171, 5th bullet “If no new designs of the current topology are added to the archive for Narc-max consecutive iterations, or if Neon-max consecutive new designs violate the structural and geometrical constraints defined, a new frame topology is generated and this process is repeated.”)];
With respect to claim 9, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein the optimization objective is based on at least one physical characteristic of the structural frame.” as [Suppapitnarm et al. (Pg. 168, 2nd paragraph, “The MOSA method searches for a set of non-dominated solutions, i.e. solutions for which no other solution is better with respect to all requirements, and provides the designer with information about the various trade-offs between the design objectives. Multiobjective simulated annealing does this by iteratively comparing the quality (objectives) of each new solution with that of all the solutions in the established archive (the record of all non-dominated solutions found).”, Suppapitnarm et al. Pg. 170, 1st bullet “When a new solution has been generated using the grammar rules, its structural performance is analyzed (objectives and constraints are evaluated)”, Suppapitnarm et al. Pg. 175, sec. 4.1 Case Study 1: Design for Economy and Efficiency, 1st paragraph, 1-4 objectives, “A good bicycle design simultaneously meets a number of different performance criteria, including economic, structural, aerodynamic and ergonomic criteria. In this first study, we seek to compare different concepts on the basis of the following objectives: 1. The mass of the system must be minimized, reflecting both material cost and practical performance considerations, etc.”)];
With respect to claim 11, Sato et al. discloses “A non-transitory computer readable medium storing instructions that, when executed by a processor” as [Sato et al. [0037] “The display unit 12 includes a display (not illustrated) and displays various types of information on the display based on a display signal received from the CPU 14.”, Sato et al. [0038] “The storage unit 13 is a memory constituted of, for instance, a random access memory (RAM) and a read-only memory (ROM). The storage unit 13 stores various types of programs and data and also functions as a working area of the CPU 14.”, Fig. 1)];
The other limitations of the claim recite the same substantive limitations as claim 1 shown above, and are rejected using the same teachings.
With respect to claim 13, the combination of Suppapitnarm et al. and Sato et al. discloses the medium of claim 11 above, and Suppapitnarm et al. further discloses “wherein receiving the input frame comprises receiving an initial geometry having an initial number of nodes and an initial number of beams.” as [Suppapitnarm et al. (Pg. 176, 2nd paragraph, “The optimization was started from a design with a minimal number of connections, as shown in Figure 9.”, Fig. 9)];
With respect to claim 14, the combination of Suppapitnarm et al. and Sato et al. discloses the medium of claim 13 above, and Suppapitnarm et al. further discloses “wherein at least one solution frame included in the plurality of solution frames has a number of nodes that is different from the initial number of nodes” as [Suppapitnarm et al. (Pg. 176, 4th paragraph, “Figure 10 shows the evolution of the trade-off surface in two-objective projections (mean deflection vs. frontal projected area and mean deflection vs. system mass). Although during the search designs with as many as 19 nodes were generated, no design with more than nine nodes was ever archived, suggesting that simple (from a manufacturing viewpoint) designs can readily be found without the need for compromise with respect to the other objectives”, Fig. 10)];
With respect to claim 15, the combination of Suppapitnarm et al. and Sato et al. discloses the medium of claim 11 above, and Suppapitnarm et al. further discloses “wherein receiving the input frame comprises receiving constraint information associated with the structural frame” as [Suppapitnarm et al. (Pg. 174 sec. 3.3 Constraints, 1st- 6th bullet, “Various geometric constraints are imposed to ensure the feasibility of each design generated: For stability reasons [36], the projection of the head tube must pass behind the centre of the front wheel and in front of the contact patch.”)];
With respect to claim 16, the combination of Suppapitnarm et al. and Sato et al. discloses the medium of claim 15 above, and Suppapitnarm et al. further discloses “wherein the constraint information includes at least one of a static physical load on the structural frame, a dynamic physical load on the structural frame, a thermal load on the structural frame, or a proscribed region within which neither a node nor a beam of the structural frame can be located.” as [Suppapitnarm et al. (Pg. 174 sec. 3.2 Loading Cases, “Three loading cases (starting, speeding and braking) are considered in this study. For each case, loads are imposed at the three application points shown in Figure 6. Because the topology and geometry of the system are changed during the optimization process, the loads at these points are varied to ensure that equivalent conditions are maintained for all designs [33, 34].”, Suppapitnarm et al. Pg. 175, 3rd bullet “In addition, to account for the effects of any lateral and moment loadings, the calculation
of the maximum bending stress, ϭB, in members under compression is modified using the
Perry-Robertson formula [37], etc.”)];
With respect to claim 17, the claim recites the same substantive limitations as claim 7 shown above, and is rejected using the same teachings.
With respect to claim 18, the claim recites the same substantive limitations as claim 8 shown above, and is rejected using the same teachings.
With respect to claim 19, the claim recites the same substantive limitations as claim 9 shown above, and is rejected using the same teachings.
With respect to claim 20, Suppapitnarm et al. discloses “A system” as [Suppapitnarm et al. (Pg. 172, sec. 3.1 Control Variables, “Each design concept is represented by the fundamental layout positions (key elements) of the system”)];
Sato et al. discloses “a memory that stores instructions; and a processor that is coupled to the memory” as [Sato et al. [0037] “The display unit 12 includes a display (not illustrated) and displays various types of information on the display based on a display signal received from the CPU 14.”, Sato et al. [0038] “The storage unit 13 is a memory constituted of, for instance, a random access memory (RAM) and a read-only memory (ROM). The storage unit 13 stores various types of programs and data and also functions as a working area of the CPU 14.”, Fig. 1)];
The other limitations of the claim recite the same substantive limitations as claim 1 shown above, and are rejected using the same teachings.
With respect to claim 21, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein executing the convergent search algorithm to iteratively modify the iterative solution comprises determining one or more modifications to the iterative solution based on a derivative of the quality factor for the plurality of different iterative solutions.”. as [Suppapitnarm et al. (Pg. 178, 1st full paragraph “Figure 12 shows the eight optimal topologies identified after 50,000 iterations”, Fig. 12, The different designs of Fig. 12 demonstrates that the iterative solution is modified)];
With respect to claim 22, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein adding the random plurality of nodes to the input frame comprises adding a random number of nodes at a plurality of random locations with respect to the input frame” as [Suppapitnarm et al. (Pg. 167, 1st bullet “Rule I: The topology is modified by adding a member joining two randomly selected, etc.”)];
With respect to claim 23, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 1 above, and Suppapitnarm et al. further discloses “wherein, during each iteration of the divergent search algorithm, the random plurality of nodes are added to the input frame at random locations within a bounding box associated with the input frame, and interconnecting each of the random plurality of nodes comprises adding a plurality of beams to interconnect each of the random plurality of nodes with all other nodes included in the random plurality of nodes and with all nodes included in the plurality of nodes to generate the corresponding initial frame.” as [Suppapitnarm et al. (Pg. 177, 2nd paragraph, “Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations). All of these have a conventional riding position (are 'normal-postured'), which is unsurprising given that the search was initiated from such a configuration (Fig. 9). To avoid confusion with the tube members, the rider's legs are not shown in these (and subsequent similar) images. The thicknesses of the lines representing the frame members are proportional to the outside diameter of the tubes for the example of that topology shown. The number of designs of the same topology, but with differing member sizes and lengths, in the archive at this stage are reported in the figure.”, Fig. 11, The examiner notes that Fig. 11 of the Suppapitnarm et al. reference shows random nodes are added at random locations within a bounding box, since the position of the rider changes. Also, the examiner considers the length of the bike to be the bounding box, since it’s the perimeter of the bike, see paragraph [0022] of the specification)];
With respect to claim 24, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 23 above, and Suppapitnarm et al. further discloses “wherein executing, via the one or more software programs, the convergent search algorithm comprises merging all pairs of nodes that are within the threshold distance of one another from among the plurality of nodes and the random plurality of nodes, and wherein the one or more pairs of nodes are merged by, for each pair of nodes included in the one or more pairs of nodes, removing the pair of nodes, adding a new node located at a centroid of the pair of nodes, and connecting endpoints of beams formerly connected to the pair of nodes to the new node.” as [Suppapitnarm et al. (Pg. 177, 2nd paragraph, “Figure 11 shows the five optimal topologies identified during the early stages of the search (after 5000 iterations). All of these have a conventional riding position (are 'normal-postured'), which is unsurprising given that the search was initiated from such a configuration (Fig. 9). To avoid confusion with the tube members, the rider's legs are not shown in these (and subsequent similar) images. The thicknesses of the lines representing the frame members are proportional to the outside diameter of the tubes for the example of that topology shown. The number of designs of the same topology, but with differing member sizes and lengths, in the archive at this stage are reported in the figure.”, Fig. 11, The examiner notes that Fig. 11 of the Suppapitnarm et al. reference shows how the nodes that connect the tubes are merged and removed. This is shown how the rider is in different riding positions while on the bike)];
Claim(s) 5 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Suppapitnarm et al. in view of Sato et al. online reference Rationalization of trusses generated via layout optimization, written by He et al.
With respect to claim 5, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 4 above, and Suppapitnarm et al. discloses “quantifying a physical characteristic of the solution frame comprises a value associated with the physical characteristic for a beam included in the solution frame” as [Suppapitnarm et al. (Pg. 175, sec. 4.1 Case Study 1: Design for Economy and Efficiency, 1st paragraph, 1st objective, “A good bicycle design simultaneously meets a number of different performance criteria, including economic, structural, aerodynamic and ergonomic criteria. In this first study, we seek to compare different concepts on the basis of the following objectives: 1. The mass of the system must be minimized, reflecting both material cost and practical performance considerations, etc.”)];
While the combination of Suppapitnarm et al. and Sato et al. teaches quantifying a physical characteristic of the solution frame comprises a value associated with the physical characteristic for a beam included in the solution frame, Suppapitnarm et al. and Sato et al. do not explicitly disclose “wherein quantifying a physical characteristic of the solution frame comprises summing a value associated with the physical characteristic for a beam included in the solution frame with the value for each other beam included in the solution frame.”
He et al. discloses “wherein quantifying a physical characteristic of the solution frame comprises summing a value associated with the physical characteristic for a beam included in the solution frame with the value for each other beam included in the solution frame.” as [He et al. (Pg. 2, sec. 2 Rationalization of layout optimization solutions using joint lengths, 1st – 3rd paragraph, “The first rationalization technique considered is one proposed by Parkes (1975). According to his formulation, a notional joint length, s, is added to the length of each bar, etc.”, Eqns. 1a – 1c)];
Suppapitnarm et al., Sato et al. and He et al. are analogous art because they are from the
same field endeavor of design optimization.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Suppapitnarm et al. and Sato et al. of quantifying a physical characteristic of the solution frame comprises a value associated with the physical characteristic for a beam included in the solution frame by incorporating wherein quantifying a physical characteristic of the solution frame comprises summing a value associated with the physical characteristic for a beam included in the solution frame with the value for each other beam included in the solution frame as taught by He et al. for the purpose of generating a truss using numerical layout optimization.
Suppapitnarm et al. in view of Sato et al. in further view of He et al. teaches wherein quantifying a physical characteristic of the solution frame comprises summing a value associated with the physical characteristic for a beam included in the solution frame with the value for each other beam included in the solution frame.
The motivation for doing so would have been because He et al. teaches that generating a truss using numerical layout optimization, an efficient way of identifying (near) optimal truss topologies for a variety of problem types can be accomplished (He et al (Pg. 14, sec. 5 Conclusions, 1st paragraph, “Numerical layout optimization provides, etc.”)).
With respect to claim 10, the combination of Suppapitnarm et al. and Sato et al. discloses the method of claim 9 above.
While the combination of Suppapitnarm et al. and Sato et al. teaches an optimization objective being based on at least one physical characteristic of the structural frame, Suppapitnarm et al. and Sato et al. do not explicitly disclose “wherein the at least one physical characteristic of the structural frame comprises a value for one element of the structural frame summed with a respective value for each other element of the structural frame.”
He et al. discloses “wherein the at least one physical characteristic of the structural frame comprises a value for one element of the structural frame summed with a respective value for each other element of the structural frame.” as [He et al. (Pg. 2, sec. 2 Rationalization of layout optimization solutions using joint lengths, 1st – 3rd paragraph, “The first rationalization technique considered is one proposed by Parkes (1975). According to his formulation, a notional joint length, s, is added to the length of each bar, etc.”, Eqns. 1a – 1c)];
Suppapitnarm et al., Sato et al. and He et al. are analogous art because they are from the
same field endeavor of design optimization.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Suppapitnarm et al. and Sato et al. of an optimization objective being based on at least one physical characteristic of the structural frame by incorporating wherein the at least one physical characteristic of the structural frame comprises a value for one element of the structural frame summed with a respective value for each other element of the structural frame as taught by He et al. for the purpose of generating a truss using numerical layout optimization.
Suppapitnarm et al. in view of Sato et al. in further view of He et al. teaches wherein the at least one physical characteristic of the structural frame comprises a value for one element of the structural frame summed with a respective value for each other element of the structural frame.
The motivation for doing so would have been because He et al. teaches that generating a truss using numerical layout optimization, an efficient way of identifying (near) optimal truss topologies for a variety of problem types can be accomplished (He et al (Pg. 14, sec. 5 Conclusions, 1st paragraph, “Numerical layout optimization provides, etc.”)).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Suppapitnarm et al. in view of Sato et al. online reference A Structural topology design method based on principal stress line, written by Kwok et al.
With respect to claim 12, the combination of Suppapitnarm et al. and Sato et al. discloses the medium of claim 11 above.
While the combination of Suppapitnarm et al. and Sato et al. teaches an optimization objective being based on at least one physical characteristic of the structural frame, Suppapitnarm et al. and Sato et al. do not explicitly disclose “wherein the input frame comprises a fixed support that is constrained to a pre-defined location, a node that is not constrained to a specific location, and one beam that is directly coupled to at least one of the fixed support and the node.”
Kwok et al. discloses “wherein the input frame comprises a fixed support that is constrained to a pre-defined location, a node that is not constrained to a specific location, and one beam that is directly coupled to at least one of the fixed support and the node.” as [Kwok et al. (Pg. 28, sec. 5.4 Bicycle frame structure, 1st paragraph, “Fig. 18 shows a test case using a bicycle frame structure [44]. The given load simulates the situation of a person sitting on the frame and holding the front handles. Therefore, two loads, one in the middle pointing down and one on the left with both x and y directions, are applied. The domain is fixed at two points on the bottom to simulate the centers of the wheels. After performing the FEA on the design domain, the optimal regions can be computed for the design problem. The two points of load are T -point and S−-point, and the two points of support are S+- and S−-points, respectively. There is one local maximum point at the top of the domain between two points of load. PSLs were traced from the two points of load consecutively in the load phase during the initial structure generation. Accordingly, the connectivity between the loads and supports was found. The initial structure has six members and five joints.”, Pg. 26, sec. 5.1 Cantilever structure, 1st paragraph “The classic Michell cantilever structure is the case of supporting a single-load with two fixed supports as shown in Fig. 4. We have shown the growth process of the Michell cantilever structure in Fig. 10.”)];
Suppapitnarm et al., Sato et al. and Kwok et al. are analogous art because they are from
the same field endeavor of design optimization.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Suppapitnarm et al. and Sato et al. of an optimization objective being based on at least one physical characteristic of the structural frame by incorporating wherein the input frame comprises a fixed support that is constrained to a pre-defined location, a node that is not constrained to a specific location, and one beam that is directly coupled to at least one of the fixed support and the node as taught by Kwok et al. for the purpose developing a novel design platform for structural topology optimization.
Suppapitnarm et al. in view of Sato et al. in further view of Kwok et al. teaches wherein the input frame comprises a fixed support that is constrained to a pre-defined location, a node that is not constrained to a specific location, and one beam that is directly coupled to at least one of the fixed support and the node.
The motivation for doing so would have been because Kwok et al. teaches that developing a novel design platform for structural topology optimization, the ability to develop an initial structure generation algorithm that can connect a given load to given supports for different kinds of domains and set-ups can be accomplishe (Kwok et al. (Pg. 20, left col., 1st paragraph, #1-3, “We develop a novel design platform, etc.)).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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