DETAILED ACTION
Claims 1-11 are presented for examination. This action is made in response to the communication filed on December 1, 2025.
Claims 1-3 are rejected under 35 USC 101 as ineligible.
Claim 1 is rejected under 35 USC 102 over Ganesh.
Claims 2 and 3 are rejected under 35 USC 103 over Ganesh in view of Cook.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Claims 4-11 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on December 1, 2025.
Formal Examiner Note
This is neither a rejection nor an objection, but the Applicant is advised, for the purpose of clarity, to modify the method steps from the form of “a [function] step of …” to the gerund form for clarity. If the Applicant agrees to do so, for the purpose of clarity in the claims, the record will reflect that this formal amendment is not for the purpose of overcoming any objections/rejections herein; accordingly, this formal amendment would not have any estoppel effect from the prosecution history.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
Claim 1 recites,
a press-forming analysis step of performing press-forming analysis using the analysis model data to calculate deformation of the elements for the first sheet, the second sheet and the weld bead when the tailored blank is press formed; and (Mental Evaluation, Mathematical Calculation – The analysis to calculate deformation is an evaluation, a mental process practically performable in the mind or with the aid of pen, paper, and/or a calculator, an abstract idea. The analysis to calculate deformation is also a mathematical calculation, a mathematical concept, an abstract idea.)
a bead-width designing step of deciding on a design value of a width of the weld bead for each of positions along a longitudinal direction of the weld bead in the tailored blank prior to press forming based on a press-forming-induced change in a width relating to the elements for the weld bead obtained through calculation at the press-forming analysis step, to generate bead-width design data indicating a relationship between a position along the longitudinal direction of the weld bead and the design value of the width of the weld bead. (Mental Evaluation – The designing to generate bead-width design data deformation is an evaluation, a mental process practically performable in the mind or with the aid of pen, paper, and/or a calculator, an abstract idea.)
Claim 1 recites mental processes and mathematical concepts, which are abstract ideas.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
Claim 1 recites the following additional limitations:
a model acquisition step of acquiring analysis model data representing a tailored blank having a first sheet and a second sheet joined together, the analysis model data containing elements for the first sheet, elements for the second sheet, and elements for a weld bead between an edge of the first sheet and an edge of the second sheet;
This is mere data gathering, similar to the MPEP 2106.05(g) examples: “e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent“ “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Mere data gathering is insignificant extra-solution activity and, under MPEP 2106.05(g), fails to integrate the abstract idea into a practical application.
Claim 1 fails to recite any additional limitations that integrate the abstract idea into a practical application.
Claim 1 is directed to the judicial exception.
Step 2B: Claim provides an Inventive Concept?
No.
Claim 1 recites the following additional limitations:
a model acquisition step of acquiring analysis model data representing a tailored blank having a first sheet and a second sheet joined together, the analysis model data containing elements for the first sheet, elements for the second sheet, and elements for a weld bead between an edge of the first sheet and an edge of the second sheet;
These additional limitations are well-understood, routine, and conventional (WURC) activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”. Because these additional limitations are WURC and are insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), these additional limitations fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claim 1 fails to provide any additional limitations that fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claim 1 is ineligible.
Dependent Claims
Claim 2
wherein: the model acquisition step, the press-forming analysis step and the bead-width designing step are repeatedly performed in two or more rounds; and (Repetition - This is a repetition of the limitations of claim 1, so it fails to confer eligibility for at least the same reasons as the limitations of claim 1)
the model acquisition step from a second round onward acquires analysis model data containing the elements for the weld bead with a width based on the bead width design data generated at the bead-width designing step in a previous round. (Repetition – This step essentially repeats the model acquisition step of claim 1 with updated data and fails to confer eligibility for at least the same reasons.)
Claim 2 fails to provide any additional limitations that confer eligibility.
Claim 2 is ineligible.
Claim 3
further comprising: a join-line-containing-model acquisition step for acquiring join-line-containing analysis model data representing a tailored blank having a first sheet and a second sheet joined together, the join-line-containing model data containing elements for the first sheet and elements for the second sheet and including a join line for the first and second sheets, the elements for the first sheet and the elements for the second sheet being in contact along the join line;
This is mere data gathering and WURC for the same reasons as the model acquisition step of claim 1.
a previous-press-forming analysis step for performing press-forming analysis using the join-line-containing analysis model data to calculate deformation of the elements for the first and second sheets when the tailored blank represented by the join-line-containing analysis model data is press formed; and
This merely qualifies the evaluation conducted in the press-forming analysis step, so it is an element of the mental process, an element of the abstract idea. Therefore, this element fails to provide any additional limitations to confer eligibility.
an initial value generation step for deciding on an initial value of the width of the weld bead corresponding to the join line based on a press-forming-induced change in a width relating to elements for the first and second sheets on both sides of the join line obtained through the calculation at the previous-press-forming analysis step to generate initial value data indicating a relationship between a position along the longitudinal direction of the weld bead and the initial value of the width of the weld bead,
(Mental Evaluation, Mathematical Calculation – The decision on an initial value based on data is an evaluation, a mental process practically performable in the mind or with the aid of pen, paper, and/or a calculator, an abstract idea. The analysis to calculate deformation is also a mathematical calculation, a mathematical concept, an abstract idea.)
This element fails to provide any additional limitations to confer eligibility.
wherein the model acquisition step acquires the analysis model data containing the elements for the weld bead with a width based on the initial value data generated at the initial value generation step.
This is merely a qualification of the model acquisition step and fails to confer eligibility for at least the same reasons as the model acquisition step.
Should it be found otherwise, this describes what data represents in the process, so it merely limits the abstract idea to a particular field of technology, so under MPEP 2106.05(h), it fails to confer eligibility.
Claim 3 is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1: Ganesh
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by NPL: “Determining the forming behavior of tailor welded blanks” by Ganesh et al. (Ganesh).
Claim 1
Regarding claim 1, Ganesh teaches:
A method of designing a width of a weld bead in a tailored blank, comprising: (Ganesh Page 13, First Column, Last Paragraph – Second Column, First Paragraph “The forming behavior of TWBs is influenced by several parameters, including thickness and strength differences between the sheets being welded; weld conditions such as weld properties, orientation, and location; number of welds; welding technique; and weld profile and microstructure. Predicting the TWB’s parameters in advance can help the fabricator determine its formability compared to that of unwelded base material. However, this prediction requires a lot of experimental and simulation trials for each case, which is time-consuming and resource-intensive” Page 13, Second Column, Third Paragraph “Researchers have been developing an expert system for TWBs that can predict their tensile, deep-drawing, and forming behaviors under varied base material and weld conditions using different formability tests and criteria and different material models. Expert systems might be used to determine the best material combinations and weld conditions for making successful TWBs, without the need for simulation and experimental trials” – This is a method of designing parameters, including width, of a weld bead in a tailor blank.)
a model acquisition step of acquiring analysis model data representing a tailored blank having a first sheet and a second sheet joined together, the analysis model data containing elements for the first sheet, elements for the second sheet, and elements for a weld bead between an edge of the first sheet and an edge of the second sheet; (Ganesh Page 13, First Column, First Paragraph “Tailor welded blanks (TWBs) (see Figure 1) are blanks with two or more sheets of different thicknesses, materials, or coatings welded in a single plane before forming. “ – The study concerns tailor welded blanks. TWBs that are formed by welds between two sheets, including the weld bead. Page 13, Third Column, Second-to-Last Paragraph – Page 14, First Paragraph “The tensile behavior, formability characteristics, and deep drawability of a TWB were simulated by standard formability tests. Different categories of industrial sheet parts were simulated, and an expert system was developed to predict their forming behavior. Figure 2 shows that the expert system was given inputs such as thickness ratio, strength ratio, weld orientation, weld location, weld properties, weld width, number of welds, and weld profile. Forming behavior, such as tensile behavior, deep drawability, and forming limit, was predicted. The expert system was updated with respect to base materials and formability prediction and criteria used for the prediction.” – All of these elements are used as inputs to the modeling. Page 14, Third Column, Expert System Development “For this work, the researchers used artificial neural network (ANN) to develop the expert system for predicting TWB forming behavior. ANN was trained to learn arbitrary nonlinear relationships between input and output parameters of TWBs, which can be used for obtaining deformation behavior of TWBs for any given input property combinations. The data required for ANN training was obtained from simulations using PAM STAMP® 2G, an elastoplastic finite element code.” – As part of the analysis to train the neural network, the entire system, including both of the sheets and the weld bead, are modeled using finite element analysis to determine final properties of part formed by pressing the tailored welded blank.
a press-forming analysis step of performing press-forming analysis using the analysis model data to calculate deformation of the elements for the first sheet, the second sheet and the weld bead when the tailored blank is press formed; and (Ganesh Page 14, Forming Properties That Can Be Predicted “This research work involved aluminum sheet base material and weld region properties as shown in Figure 3. Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. In the case of deep drawing of TWBs, a square-cup deep-drawing simulation was constructed as per the NUMISHEET ’93 benchmark specifications The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal. In the case of steel TWBs, the initial shape of the blank also was considered as input to the expert system, and the entire weld line profile was predicted during deep drawing. In this case, the expert system was able to predict the forming limit strains of the TWBs. – The press-forming analysis is conducted using simulation, where the elements undergo stress and the representative drawn model updates the geometries of the sheets and the weld bead of the TWB. Again, this is performed using finite element analysis.)
a bead-width designing step of deciding on a design value of a width of the weld bead for each of positions along a longitudinal direction of the weld bead in the tailored blank prior to press forming based on a press-forming-induced change in a width relating to the elements for the weld bead obtained through calculation at the press-forming analysis step, to generate bead-width design data indicating a relationship between a position along the longitudinal direction of the weld bead and the design value of the width of the weld bead. (Ganesh Page 15, Figure 4 and Page 14, Forming Properties That Can Be Predicted “Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. […] The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal.” – The system is trained to output a weld line movement (“bead-width design data indicating a relationships between a position along the longitudinal direction of the weld bead and thee design value of the width of the weld bead”) based on an input of weld bead width (W) (“a design value of a width of the weld bead for each of positions along a longitudinal direction of the weld bead in the tailored blank prior to press-forming”) based on the FEM simulation of the weld line movement. Page 13, Expert System for TWB Formability “Automotive sheet forming engineers can use an expert system to determine a TWB’s forming behavior. An expert system is an intelligent computer program which, like a human consultant, aims to deliver accurate suggestions for solving a problem at any level, such as during planning, designing, manufacturing, and quality control. Researchers have been developing an expert system for TWBs that can predict their tensile, deep-drawing, and forming behaviors under varied base material and weld conditions using different formability tests and criteria and different material models. Expert systems might be used to determine the best material combinations and weld conditions for making successful TWBs, without the need for simulation and experimental trials. – The critical weld line movement depends on the weld thickness, so, when the model is deployed for inference, the model will be used to determine bead widths (when the other parameters are determined) that yield the appropriate weld line movement.)
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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 2-3: Ganesh and Cook
Claim(s) 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Determining the forming behavior of tailor welded blanks” by Ganesh et al. (Ganesh) in view of NPL: “Iterative linear solvers as metaphor” by Cook (Cook).
Claim 2
Regarding claim 2, Ganesh teaches the features of claim 1 and further teaches:
wherein the model acquisition step, the press-forming analysis step and the bead-width designing step are repeatedly performed in two or more rounds; and (NOTE This is a mere repetition of the respective steps of the independent claim and is rejected for the same reasons as those steps.)
the model acquisition step from a second round onward acquires analysis model data containing the elements for the weld bead with a width based on the bead width design data bead-width . (Ganesh Page 15, Figure 4 and Page 14, Forming Properties That Can Be Predicted “Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. […] The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal.” – The system is trained to output a weld line movement (“bead-width design data indicating a relationships between a position along the longitudinal direction of the weld bead and thee design value of the width of the weld bead”) based on an input of weld bead width (W) (“a design value of a width of the weld bead for each of positions along a longitudinal direction of the weld bead in the tailored blank prior to press-forming”) based on the FEM simulation of the weld line movement. Page 13, Expert System for TWB Formability “Automotive sheet forming engineers can use an expert system to determine a TWB’s forming behavior. An expert system is an intelligent computer program which, like a human consultant, aims to deliver accurate suggestions for solving a problem at any level, such as during planning, designing, manufacturing, and quality control. Researchers have been developing an expert system for TWBs that can predict their tensile, deep-drawing, and forming behaviors under varied base material and weld conditions using different formability tests and criteria and different material models. Expert systems might be used to determine the best material combinations and weld conditions for making successful TWBs, without the need for simulation and experimental trials. – The critical weld line movement depends on the weld thickness, so, when the model is deployed for inference, the model will be used to determine bead widths (when the other parameters are determined) that yield the appropriate weld line movement. Also, See Ganesh FIG. 2 for iterative approaches to design.)
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Ganesh teaches that a model can be used to predict the properties of a tailored blank after being pressed based on an input of the weld bead width and other factors and asserts that the model is useful for determining the design parameters prior to pressing, but it does not specifically teach, but Ganesh in view of Cook teaches:
the model acquisition step from a second round onward acquires analysis model data containing the elements for the weld bead with a width based on the bead width design data generated at the bead-width designing step in a previous round. (Cook “Iterative methods start by taking a guess at the final solution. In some contexts, this guess may be fairly good. For example, when solving differential equations, the solution from one time step gives a good initial guess at the solution for the next time step. Similarly, in sequential Bayesian analysis the posterior distribution mode doesn’t move much as each observation arrives. Iterative methods can take advantage of a good starting guess while methods like Gaussian elimination cannot. Iterative methods take an initial guess and refine it to a better approximation to the solution. This sequence of approximations converges to the exact solution. In theory, Gaussian elimination produces an exact answer in a finite number of steps, but iterative methods never produce an exact solution after any finite number of steps. But in actual computation with finite precision arithmetic, no method, iterative or not, ever produces an exact answer. The question is not which method is exact but which method produces an acceptably accurate answer first. Often the iterative method wins.” – Cook discusses using a trial and error interactive approach to arrive at a solution. In combination with Ganesh, Cook teaches trying different values of weld bead width (and other parameters) to get a specific output of weld line movement.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the use of a rendered model for determining weld parameters of Ganesh by the trial and error method of determining a desired set of parameters of Cook because the person of ordinary skill in the art would be motivated by the stated desire in Ganesh to make a functional model to determine parameters for a mathematical system to look to Cook to utilize the model through trial and error to determine acceptably accurate design specifications, including a weld bead width, that often beats other methods of determination. (Ganesh Page 13, First Column, Last Paragraph – Second Column, Second Paragraph “The forming behavior of TWBs is influenced by several parameters, including thickness and strength differences between the sheets being welded; weld conditions such as weld properties, orientation, and location; number of welds; welding technique; and weld profile and microstructure. Predicting the TWB’s parameters in advance can help the fabricator determine its formability compared to that of unwelded base material. However, this prediction requires a lot of experimental and simulation trials for each case, which is time-consuming and resource intensive. Automotive sheet forming engineers can use an expert system to determine a TWB’s forming behavior. An expert system is an intelligent computer program which, like a human consultant, aims to deliver accurate suggestions for solving a problem at any level, such as during planning, designing, manufacturing, and quality control.; Cook Page 1, Third-Fourth Paragraphs “Iterative methods start by taking a guess at the final solution. In some contexts, this guess may be fairly good. For example, when solving differential equations, the solution from one time step gives a good initial guess at the solution for the next time step. Similarly, in sequential Bayesian analysis the posterior distribution mode doesn’t move much as each observation arrives. Iterative methods can take advantage of a good starting guess while methods like Gaussian elimination cannot. Iterative methods take an initial guess and refine it to a better approximation to the solution. This sequence of approximations converges to the exact solution. In theory, Gaussian elimination produces an exact answer in a finite number of steps, but iterative methods never produce an exact solution after any finite number of steps. But in actual computation with finite precision arithmetic, no method, iterative or not, ever produces an exact answer. The question is not which method is exact but which method produces an acceptably accurate answer first. Often the iterative method wins.”)
Claim 3
Ganesh teaches the features of claim 1, and further teaches:
a join-line-containing-model acquisition step for acquiring join-line-containing analysis model data representing a tailored blank having a first sheet and a second sheet joined together, the join-line-containing model data containing elements for the first sheet and elements for the second sheet and including a join line for the first and second sheets, the elements for the first sheet and the elements for the second sheet being in contact along the join line; (Ganesh Page 14, Third Column, Expert System Development “For this work, the researchers used artificial neural network (ANN) to develop the expert system for predicting TWB forming behavior. ANN was trained to learn arbitrary nonlinear relationships between input and output parameters of TWBs, which can be used for obtaining deformation behavior of TWBs for any given input property combinations. The data required for ANN training was obtained from simulations using PAM STAMP® 2G, an elastoplastic finite element code. – The system models the deformation of the tailored blank along the join-line during pressing with finite element analysis.
a previous-press-forming analysis step for performing press-forming analysis using the join-line-containing analysis model data to calculate deformation of the elements for the first and second sheets when the tailored blank represented by the join-line-containing analysis model data is press formed; and (Ganesh “Page 14, Forming Properties That Can Be Predicted “This research work involved aluminum sheet base material and weld region properties as shown in Figure 3. Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. In the case of deep drawing of TWBs, a square-cup deep-drawing simulation was constructed as per the NUMISHEET ’93 benchmark specifications The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal. In the case of steel TWBs, the initial shape of the blank also was considered as input to the expert system, and the entire weld line profile was predicted during deep drawing. In this case, the expert system was able to predict the forming limit strains of the TWBs. – The press-forming analysis is conducted using simulation, where the elements undergo stress and the representative drawn model updates the geometries of the sheets and the weld bead of the TWB. Again, this is performed using finite element analysis.)
an initial value generation step for deciding on an initial value of the width of the weld bead corresponding to the join line based on a press-forming-induced change in a width relating to elements for the first and second sheets on both sides of the join line obtained through the calculation at the -press-forming analysis step to generate initial value data indicating a relationship between a position along the longitudinal direction of the weld bead and the initial value of the width of the weld bead, (Ganesh Page 15, Figure 4 and Page 14, Forming Properties That Can Be Predicted “Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. […] The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal.” – The study determined, as shown in Figure 4, that different weld bead widths were selected to try on the basis of determining weld line movement, among other parameters. The entirety of the system is drawn, including the sheets and the weld bead, which define the weld line. This includes a calculation that uses the weld bead width at each position along the longitudinal length of the weld.)
wherein the model acquisition step acquires the analysis model data containing the elements for the weld bead with a width . (Ganesh Page 15, Figure 4 and Page 14, Forming Properties That Can Be Predicted “Seven significant TWB parameters were chosen as input to the expert system (see Figure 4) for deep-drawing behavior and tensile behavior prediction. The standard ASTM E646-98 sample was used for simulating the tensile behavior of TWBs. […] The tensile response of TWBs— namely, stress-strain curve, yield strength, ultimate tensile strength, uniform elongation, strain hardening exponent (n), and strength coefficient (K—was evaluated and predicted by the expert system. The deep-drawing behaviors monitored were: •Maximum punch force—obtained from force-progression data during deep-drawing simulation. •Maximum weld line movement— considered of practical importance, as the weld region ideally should be located in the safe region of the drawn cup. •Draw depth—obtained after cup failure was witnessed. •Draw-in profile—quantified by the dimensions DX, DY, and DD. The draw-in profile is important and can be related to anisotropic sheet properties and earring behavior of sheet metal.” – The study determined, as shown in Figure 4, that different weld bead widths were selected to try on the basis of determining weld line movement, among other parameters. The entirety of the system is drawn, including the sheets and the weld bead, which define the weld line. This includes a calculation that uses the weld bead width at each position along the longitudinal length of the weld.)
Ganesh teaches that a model can be used to predict the properties of a tailored blank after being pressed based on an input of the weld bead width and other factors and asserts that the model is useful for determining the design parameters prior to pressing, but it does not specifically teach, but Ganesh in view of Cook teaches:
an initial value generation step for deciding on an initial value of the width of the weld bead corresponding to the join line based on a press-forming-induced change in a width relating to elements for the first and second sheets on both sides of the join line obtained through the calculation at the previous based on the initial value data generated at the initial value generation step. (Cook “Iterative methods start by taking a guess at the final solution. In some contexts, this guess may be fairly good. For example, when solving differential equations, the solution from one time step gives a good initial guess at the solution for the next time step. Similarly, in sequential Bayesian analysis the posterior distribution mode doesn’t move much as each observation arrives. Iterative methods can take advantage of a good starting guess while methods like Gaussian elimination cannot. Iterative methods take an initial guess and refine it to a better approximation to the solution. This sequence of approximations converges to the exact solution. In theory, Gaussian elimination produces an exact answer in a finite number of steps, but iterative methods never produce an exact solution after any finite number of steps. But in actual computation with finite precision arithmetic, no method, iterative or not, ever produces an exact answer. The question is not which method is exact but which method produces an acceptably accurate answer first. Often the iterative method wins.” – Cook discusses using a trial and error interactive approach to arrive at a solution. In combination with Ganesh, Cook teaches trying different values of weld bead width (and other parameters) to get a specific output of weld line movement.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the use of a rendered model for determining weld parameters of Ganesh by the trial and error method of determining a desired set of parameters of Cook because the person of ordinary skill in the art would be motivated by the stated desire in Ganesh to make a functional model to determine parameters for a mathematical system to look to Cook to utilize the model through trial and error to determine acceptably accurate design specifications, including a weld bead width, that often beats other methods of determination. (Ganesh Page 13, First Column, Last Paragraph – Second Column, Second Paragraph “The forming behavior of TWBs is influenced by several parameters, including thickness and strength differences between the sheets being welded; weld conditions such as weld properties, orientation, and location; number of welds; welding technique; and weld profile and microstructure. Predicting the TWB’s parameters in advance can help the fabricator determine its formability compared to that of unwelded base material. However, this prediction requires a lot of experimental and simulation trials for each case, which is time-consuming and resource intensive. Automotive sheet forming engineers can use an expert system to determine a TWB’s forming behavior. An expert system is an intelligent computer program which, like a human consultant, aims to deliver accurate suggestions for solving a problem at any level, such as during planning, designing, manufacturing, and quality control.; Cook Page 1, Third-Fourth Paragraphs “Iterative methods start by taking a guess at the final solution. In some contexts, this guess may be fairly good. For example, when solving differential equations, the solution from one time step gives a good initial guess at the solution for the next time step. Similarly, in sequential Bayesian analysis the posterior distribution mode doesn’t move much as each observation arrives. Iterative methods can take advantage of a good starting guess while methods like Gaussian elimination cannot. Iterative methods take an initial guess and refine it to a better approximation to the solution. This sequence of approximations converges to the exact solution. In theory, Gaussian elimination produces an exact answer in a finite number of steps, but iterative methods never produce an exact solution after any finite number of steps. But in actual computation with finite precision arithmetic, no method, iterative or not, ever produces an exact answer. The question is not which method is exact but which method produces an acceptably accurate answer first. Often the iterative method wins.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “Spot-Weld Fatigue optimization” by Anderson et al. (Teaches different considerations for spot welds, including for tailored blanks)
NPL: “An inverse approach to the numerical design of the process sequence of tailored heat treated blanks” by Geiger et al. (Teaches modeling the effect of press-forming on a weld bead)
NPL: “Optimizing weld morphology and mechanical properties of laser welded Al-Si coated 22MnB5 by surface application of colloidal graphite” by Khan et al. (Teaches weld morphology optimization)
NPL: “Welding Methods and Forming Characteristics of Tailored Blanks (TBs)” by Miyazaki et al. (Teaches aspects of formation of tailored blanks)
NPL: “Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks” by Nagesh et al. (Teaches using neural networks to predict weld bead geometry)
NPL: “An Analytical Model to Predict Elongation of Tailor Welded Blanks [TWB]” by Patel et al. (Teaches predicting press forming effects based on the weld bead of a tailor welded blank)
NPL: “Forming of Tailor-Welded Blanks” by Saunders et al. (Teaches aspects of how tailor welded blanks are originally formed and pressed)
NPL: “Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis” by Xiong et al. (Teaches using a neural network to predict weld bead geometry)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188