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 .
This action is in response to the amendment filed 12 December 2024. Claims 1-20 have been amended. Claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 3, 4, 5, 8, 9, 11, 12, 13, 14, 15, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 2010/0023150 A1) in view of Meess et al. (US 2018/0130377 A1) and further in view of Wang et al. (US 2019/0321905 A1).
Claim 1. Cai discloses a system, comprising:
one or more pieces of welding equipment previously used to complete a plurality of … welds, a weld gun is selected (P 0026);
processing circuitry; and memory circuitry comprising: computer readable instructions which, when executed, cause the processing circuitry to: determine weld characteristics of the plurality of … welds based on the sensor data, the optimization of the design is determined based on the location and number of welds for a work piece (P 0020) welding standards include spot weld spacing and the distance a weld point must be from certain features on the work piece (P 0031) the input variables can also be the number of welds (P 0035) to optimize the design variables of total number of welds (P 0036) performance evaluations are carried out through virtual vehicle simulations, given a total number of welds (P 0037);
identify how many of the plurality of … welds were used to assemble a single part, the work piece may be a unitary piece (P 0020) to optimize the design variables of total number of welds, physical location of each weld point and weld density (P 0036, 0057),
a user interface configured to provide an output representative of how many of the plurality of … welds were used to assemble the single part, performance evaluations are carried out through virtual vehicle simulations, given an initial weld design (i.e., having total number of welds N, and the initial locations of each of the welds being X), computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046) the work piece geometry and an initial weld design are set as inputs to the method, represented by a simulated model, using a Computer-Aided-Design (CAD) tool (P 0024) Computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the results of the simulation provided as a schematic representation of the method of weld design (P 0049) .
Cai does not disclose, previously completed welds; a plurality of sensors positioned in, on, or proximate the one or more pieces of welding equipment, the plurality of sensors being configured to capture sensor data during completion of the plurality of previously completed welds, as disclosed in the claims. However, in the same field of invention, Meess discloses positions sensors on the welding gun may be used to calibrate the depth of the image, including, but are not limited to, magnetic sensors, optical sensors, acoustics sensors, and the like, which are sensed using an appropriate sensing system to allow for the positioning of the welding gun to be determined (P 0045) during a welding operation, the position of the welding tool is tracked (P 0066) tracking data is generated to create an image stream showing the welding environment with visual cues (P 0082) tracked parameters can be compared to upper and lower target thresholds for the type of welding process, the type and orientation of the weld joint, the type of materials, etc, based on testing of similar prior welds (P 0087) stored target weld data can be that of a successful prior welding run by the user based on computer modeling for the specific type of weld and/or testing of similar prior welds (P 0092) the virtual weld object can be based on a user's previous weld history in order to, e.g., compare the current weld with a previous weld (P 0110). Meess further discloses information for the custom part, device, component, etc. is also used by the logic processor-based subsystem for workpiece recognition and auto-calibration of the system (P 0068) to determine which acceptable welding process parameters used for any given welding exercise using the object recognition module operatively to train the system to recognize known rigid body objects and configured with information for recognizing custom parts (P 0069) upon recognition of a known rigid body, position and orientation are calculated relative to the camera origin and the “trained” rigid body orientation (P 0078) an image with the recognized coupon/workpiece and weld path is calibrated to the welding environment, the user can confirm that the coupon/workpiece recognition device auto-calibrated the coupon/workpiece properly, wherein the recognition device can auto-identify a range or welding joint types (P 0113). Therefore, considering the teachings of Cai and Meess, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine a plurality of sensors positioned in, on, or proximate the one or more pieces of welding equipment, the plurality of sensors being configured to capture sensor data during completion of the plurality of previously completed welds with the teachings of Cai with the motivation to allow a user of Cai to specify parameters for the sequence analysis to provide information to a welder to perform a proper weld based on the welder’s skill level (Meess: P 0004, 0005) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Cai does not disclose the identification being based on a machine learning analysis of at least some of the weld characteristics of the plurality of … welds, as disclosed in the claims. However, in the same field of invention, Wang discloses for detecting welding defects (P 0002) using machine learning to detect defective spectrum patterns (P 0100). Therefore, considering the teachings of Cai, Meess and Wang, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine the identification being based on a machine learning analysis of at least some of the weld characteristics of the plurality of … welds with the teachings of Cai and Meess with the motivation to allow a user of Cai to specify parameters for the sequence analysis to provide information to a welder to perform a proper weld based on the welder’s skill level (Meess: P 0004, 0005) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim 2. Cai, Meess and Wang disclose the system of claim 1, and the combination of Cai in view of Meess discloses wherein the analyzing comprises: determining a hypothesis, wherein the hypothesis comprises a hypothetical number of previously completed welds used to assemble the single part, optimization of the weld design includes determining the physical location of each weld point, the optimal number of weld points and may include other factors (P 0020) the input variables can also be the number of welds N associated with either a welding flange, a welding zone, or the entire welding structure (P 0035), determining a likelihood that the hypothesis is correct by testing the hypothesis via a correlation technique, or other machine learning technique, applied to the one or more weld characteristics, Design of Experiments (DOE) is a methodology for setting up a set of experiments in which all input variables are varied in a systematic manner, for the purpose of determining the correlation between input variables and to predict results or output (P 0033), determining whether the likelihood is above a threshold, and in response to determining that the likelihood is above the threshold, determining the hypothetical number of previously completed welds is how many of the plurality of previously completed welds were used to assemble the single part, performance evaluations are carried out through virtual vehicle simulations, given an initial weld design (i.e., having total number of welds N, and the initial locations of each of the welds being X), computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046) the design variable to be optimized is the total number of welds (N) (P 0036) such that the Optimized Weld Design satisfies threshold performance requirements (P 0047). Meess has been combined with Cai to reject limitations directed to previously completed welds per the rejection of Claim 1.
Claim 3. Cai, Meess and Wang disclose the system of claim 2, and the combination of Cai in view of Wang discloses wherein the machine learning analysis further comprises testing a different hypothesis in response to determining the likelihood is below the threshold, each input variable is varied between a high and a low level (P 0033) or between three levels and the output observed for resultant changes (P 0034).
Claim 4. Cai, Meess and Wang disclose the system of claim 2, and the combination of Cai in view of Meess discloses wherein testing the hypothesis comprises searching for a repeating pattern of the hypothetical number of previously completed welds within the plurality of previously completed welds based on a sub-analysis of the weld characteristics via the correlation technique or the other machine learning technique, optimization of the weld design includes determining the physical location of each weld point and the optimal number of weld points (P 0020) three performance indices are formulated and response surfaces for each are generated by perturbing the initial location of the each (P 0050) of (three) weld points (P 0050) the optimal location of a weld is determined (P 0052) a response surface can also be made for multiple welds, such as any two weld points having the initial location (which leads to a two-variable response surface), or all three weld points together (which leads to a three-variable response surface), and also for a clustered, multiple weld points together, where the weld points within the cluster have a fixed relative location so that the number of design variables can be reduced (P 0056). The repeating pattern is the optimum number and location of weld points that are recognized to be at fixed relative locations within a cluster. Meess has been combined with Cai to reject limitations directed to previously completed welds per the rejection of Claim 1 and Wang has been combined for machine learning.
Claim 5. Cai, Meess and Wang disclose the system of claim 1, and the combination of Cai in view of Meess and Wang discloses wherein the machine learning analysis comprises: performing a clustering analysis of the plurality of previously completed welds based on the weld characteristics by separating the plurality of previously completed welds into one or more groups based on similarities or differences between each weld of the plurality of previously completed welds, weld positions on the interface that lie within the boundaries of the response surface produce results within the acceptable limits for that performance index, while weld positions outside the boundaries of the response surface produce results outside the acceptable limits (P 0053) response surfaces are generated for the second weld point for the three performance indices selected (P 0054) a response surface can also be made for multiple welds, such as any two weld points having the initial location (which leads to a two-variable response surface), or all three weld points together (which leads to a three-variable response surface), and also for a clustered, multiple weld points together, where the weld points within the cluster have a fixed relative location so that the number of design variables can be reduced (P 0056), wherein how many of the plurality of previously completed welds were used to assemble the single part is determined based on a quantity of the one or more groups and an extent to which each group is distinct from the rest of the one or more groups, the optimized weld design is configured to have a minimum number of welds, and concurrently satisfy the predetermined manufacturing requirements and the performance factors (P 0004) performance evaluations are carried out through virtual vehicle simulations, given an initial weld design (i.e., having total number of welds N, and the initial locations of each of the welds being X), computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046) . Meess has been combined with Cai to reject limitations directed to previously completed welds per the rejection of Claim 1 and Wang has been combined for machine learning.
Claim 8. Cai, Meess and Wang disclose the system of claim 1, and Meess discloses a welding equipment including a power supply (P 0008). Therefore, considering the teachings of Cai and Meess, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine further comprising a welding-type power supply comprising the processing circuitry, the memory circuitry, and power conversion circuitry configured to convert input power to welding-type output power with the teachings of Cai and Meess with the motivation to provide information to a welder to perform a proper weld based on the welder’s skill level (Meess: P 0004, 0005).
Claim 9. Cai, Meess and Wang disclose the system of claim 1, and Meess discloses positions sensors on the welding gun may be used to calibrate the depth of the image, including, but are not limited to, magnetic sensors, optical sensors, acoustics sensors, and the like, which are sensed using an appropriate sensing system to allow for the positioning of the welding gun to be determined. (P 0045) and Wang discloses the sensors can be infrared sensor, visible light sensors, or ultraviolet sensors (P 0031). Therefore, considering the teachings of Cai, Meess and Wang, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the one or more pieces of welding equipment comprises a welding-type power supply, a work clamp, a welding torch, a wire feeder, or a gas supply, or the plurality of sensors comprise one or more current sensors, voltage sensors, magnetic field sensors, resistance sensors, wire feed speed sensors, gas flow sensors, clamping sensors, infrared sensors, radiographic sensors, torque sensors, temperature sensors, or humidity sensors with the teachings of Cai, Meess and Wang with the motivation to allow a user of Cai to specify parameters for the sequence analysis to provide information to a welder to perform a proper weld based on the welder’s skill level (Meess: P 0004, 0005) and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Claim(s) 11, 13, 14, 15, 18, 19 is/are directed to method claim(s) similar to the system claim(s) of Claim(s) 1, 3, 4, 5, 8, 9 and is/are rejected with the same rationale.
Claim(s) 12 is/are directed to method claim(s) similar to the system claim(s) of Claim(s) 2 and is/are rejected with the same rationale. Cai further discloses in response to determining that the likelihood is above the threshold, constructing a model of the part based on the hypothesis, the Optimized Weld Design is subject to a threshold requirement for performance that is considered feasible only if the response surfaces for each performance index (described below) exceed a threshold value (P 0023). Wang has been combined with Cai for machine learning.
Claim(s) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 2010/0023150 A1) in view of Meess et al. (US 2018/0130377 A1) and Wang et al. (US 2019/0321905 A1) and further in view of Yano (US 2014/0152054 A1).
Claim 6. Cai, Meess and Wang disclose the system of claim 1, and Cai discloses wherein the machine learning memory circuitry comprises computer readable instructions which, when executed, further cause the processing circuitry to: identifying a plurality of potential initial welds, or potential final welds, having weld characteristics similar to typical weld characteristics of initial welds, or final welds, via a rule based technique or other machine learning technique, the method may include selecting an initial weld design as an input to the method (P 0006) weld positions on the interface that lie within the boundaries of the response surface produce results within the acceptable limits for that performance index, while weld positions outside the boundaries of the response surface produce results outside the acceptable limits (P 0053). Cai does not disclose determining a typical number of intervening welds between the plurality of potential initial welds, or potential welds, wherein how many of the plurality of previously completed welds were used to assemble the single part is determined based on the typical number of intervening welds, as disclosed in the claims. Cai discloses the work piece geometry and an initial weld design are set as inputs to represent a simulated model (P 0024) the weld design has two weld points, x1 and x2. Here, (x1)0 and (x2)0 are the one-dimensional coordinates for the initial positions of the first and second weld points, N is fixed at two. Delta one and two (Δ1 and Δ2) are the amount by which the initial weld design is perturbed. Each weld position is varied between three levels and the output is observed for resultant changes. The response surface is a plot of the output or response with respect to the positions of the two weld points, x1 and x2 (P 0034) the design variable to be optimized is the total number of welds (N) (P 0036) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046). However, in the same field of invention, Yano discloses the number of welding points between the extended portion and the upper back cross-sectional portion is increased because the extended portion and the upper back cross-sectional portion are joined by spot welding through the opening of the inner coupling portion to improve the coupling strength between the extended portion and the upper back cross-sectional portion (P 0021) a welding point is placed between a first welding point and a second welding point (P 0069). Therefore, considering the teachings of Cai, Meess, Wang and Yano, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine determining a typical number of intervening welds between the plurality of potential initial welds, or potential welds, wherein how many of the plurality of previously completed welds were used to assemble the single part is determined based on the typical number of intervening welds with the teachings of Cai, Meess and Wang with the motivation to endure optimum placement of the weld points for maximum strength (Yano: P 0021).
Claim(s) 16 is/are directed to method claim(s) similar to the system claim(s) of Claim(s) 6 and is/are rejected with the same rationale.
Claim(s) 7, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 2010/0023150 A1) in view of Meess et al. (US 2018/0130377 A1) and Wang et al. (US 2019/0321905 A1) and further in view of Daniel (US 2017/0053557 A1).
Claim 7. Cai, Meess and Wang disclose the system of claim 1, and Cai discloses wherein the memory circuitry comprises computer readable instructions which, when executed, further cause the processing circuitry to: construct a model of the single part based on how many of the plurality of previously completed welds were used to assemble the single part, the work piece geometry and an initial weld design are set as inputs to represent a simulated model (P 0024) the weld design has two weld points, x1 and x2. Here, (x1)0 and (x2)0 are the one-dimensional coordinates for the initial positions of the first and second weld points, N is fixed at two. Delta one and two (Δ1 and Δ2) are the amount by which the initial weld design is perturbed. Each weld position is varied between three levels and the output is observed for resultant changes. The response surface is a plot of the output or response with respect to the positions of the two weld points, x1 and x2 (P 0034) the design variable to be optimized is the total number of welds (N) (P 0036) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046), the model comprising a neural net, a statistical model, or a data set collection, such as a Computer-Aided-Design (CAD) model (P 0024). Cai does not explicitly disclose identify one or more parts assembled by the plurality of previously completed welds based on the model, as disclosed in the claims. That is, Cai discloses that the model of the part or work piece is an input to the virtual design of experiment algorithm, but the part or work piece is not identified by the model. Cai discloses the work piece may be a unitary piece or a multi-component piece such as a sub-assembly, an assembly, or a whole vehicle structure (P 0020) the manufacturing feasibility work space is defined as satisfying one or more predetermined manufacturing requirements for the work piece (P 0021) other components may be a different part of the work piece, the fixture, robot or any other component (P 0028). While the manufacturing feasibility workspace is not properly the same as the part of work piece, the manufacturing feasibility workspace is directly dependent on the part or work piece. In the same field of invention, Daniel discloses the weld sequencing processor may determine the shape and size of an assembly by processing an image of the assembly provided by the imaging device and comparing the determined shape and size with potential matching parts in a database of profiles (P 0028) a next welding operation is identified by the assembly that is currently being welded and may identify the sequence of welds; the particular part number of the assembly that is being welded and the welds are identified (P 0035). That is, a part and/or assembly is associated with a particular sequence of welds in advance of the welding operation. Therefore, considering the teachings of Cai, Meess and Daniel, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine identify one or more parts assembled by the plurality of previously completed welds based on the model with the teachings of Cai and Meess with the motivation to better determine the proper optimum number and location of weld points in Cai based on the work piece (Daniel: P 0003-0005).
Claim(s) 17 is/are directed to method claim(s) similar to the system claim(s) of Claim(s) 7 and is/are rejected with the same rationale.
Claim(s) 10, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 2010/0023150 A1) in view of Meess et al. (US 2018/0130377 A1) and Wang et al. (US 2019/0321905 A1) and further in view of Falde et al. (US 2017/0072496 A1).
Claim 10. Cai and Meess disclose the system of claim 1, but Cai does not disclose wherein the machine learning analysis comprises a first machine learning analysis, and the memory circuitry comprises computer readable instructions which, when executed, further cause the processing circuitry to:
identify, from the plurality of previously completed welds, and based on a second machine learning analysis of the one or more weld characteristics of the plurality of previously completed welds, a first set of previously completed consecutive welds that were used to assemble a first part, and a second set of previously completed consecutive welds that were used to assemble a second part,
the first part and the second part being of a same part type as the single part, the first set of previously completed consecutive welds having a first set size equal to how many of the plurality of previously completed welds were used to assemble the single part, the second set of previously completed consecutive welds having a second set size equal to the first set size, and
output, via the user interface, a graphical depiction of the plurality of previously completed welds showing when the first set of previously completed consecutive welds were completed, and when the second set of previously completed consecutive welds were completed, relative to when a remainder of the plurality of previously completed welds were completed, as disclosed in the claims. However, Cai discloses the work piece geometry and an initial weld design are set as inputs to the method, represented by a simulated model, using a Computer-Aided-Design (CAD) tool (P 0024) the weld design has two weld points, x1 and x2. Here, (x1)0 and (x2)0 are the one-dimensional coordinates for the initial positions of the first and second weld points, N is fixed at two. Delta one and two (Δ1 and Δ2) are the amount by which the initial weld design is perturbed. Each weld position is varied between three levels and the output is observed for resultant changes. The response surface is a plot of the output or response with respect to the positions of the two weld points, x1 and x2 (P 0034) performance evaluations are carried out through virtual vehicle simulations, given an initial weld design (i.e., having total number of welds N, and the initial locations of each of the welds being X), computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046) the results of the simulation provided as a schematic representation of the method of weld design (P 0049) first, second and third weld points each have an initial location, the optimized weld design for the weld points are determined and optimal locations for the weld points are determined (P 0052, 0054, 0055) a response surface can also be made for multiple welds, such as any two weld points having initial locations (which leads to a two-variable response surface), or all three weld points together (which leads to a three-variable response surface), a response surface can also be made for a clustered, multiple weld points together, where the weld points within the cluster have a fixed relative location so that the number of design variables can be reduced (P 0056). That is, an initial weld design and the effect of perturbing the weld points from the initial design are compared to determine a performance evaluation, where clusters of weld points can be evaluated together. Meess discloses as a user is executing a weld, attribute indicators are changed to indicate if an attribute deviates outside threshold values from a target (P 0088) optimum value (P 0102) a weld sequence and corresponding attributes of individual welds are indicated by graphical indicia (P 0109). Meess discloses a user is notified if the welds are outside of a target threshold. But neither Cai nor Meess disclose that welds on two parts, or work pieces, are compared.
Cai discloses a first sheet metal is spot welded to a second sheet metal (P 0049). Meess discloses where the entire joint between two workpieces is not welded but only certain portions, visual cues can aid the user in identifying the start position by having a marker such as, e.g., a green spot at the start position and a second marker such as, e.g., a red spot at the stop position (P 0089). However, the two work pieces in each of Cai and Meess are not of the same type.
In the same field of invention, Falde discloses a mechanism for automated learning of weights and limits is provided wherein a user of the algorithm can select a second good weld (or part) as a second reference, which is compared with the first reference and is used as to compute effective limits (P 0034). Therefore, considering the teachings of Cai, Meess, Wang and Falde, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the machine learning analysis comprises a first machine learning analysis, and the memory circuitry comprises computer readable instructions which, when executed, further cause the processing circuitry to:
identify, from the plurality of previously completed welds, and based on a second machine learning analysis of the one or more weld characteristics of the plurality of previously completed welds, a first set of previously completed consecutive welds that were used to assemble a first part, and a second set of previously completed consecutive welds that were used to assemble a second part,
the first part and the second part being of a same part type as the single part, the first set of previously completed consecutive welds having a first set size equal to how many of the plurality of previously completed welds were used to assemble the single part, the second set of previously completed consecutive welds having a second set size equal to the first set size, and
output, via the user interface, a graphical depiction of the plurality of previously completed welds showing when the first set of previously completed consecutive welds were completed, and when the second set of previously completed consecutive welds were completed, relative to when a remainder of the plurality of previously completed welds were completed with the teachings of Cai, Meess and Wang with the motivation to provide for the selection (e.g., user selection) of a representative weld of known good quality which is used as a future reference for all corresponding welds of that specific type and/or class (Falde: P 0011).
Claim(s) 20 is/are directed to method claim(s) similar to the system claim(s) of Claim(s) 10 and is/are rejected with the same rationale.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed 7/28/2025, with respect to the rejections under 35 USC 101 as being directed to an abstract idea without significantly more or being integrated into a practical application have been fully considered and are persuasive. The rejections of Claims 1-20 have been withdrawn.
The independent claims are now directed to a plurality of sensors capturing data during completion of previously completed welds, and determining weld characteristics of the previously completed welds based on the sensor data. Since the data that is analyzed is required to be captured by sensors, the data is beyond the observation of a human.
Applicant's arguments filed 7/28/2025 have been fully considered but they are not persuasive.
Regarding the amendments to the independent claims, Cai discloses:
one or more pieces of welding equipment previously used to complete a plurality of … welds, a weld gun is selected (P 0026);
processing circuitry; and memory circuitry comprising: computer readable instructions which, when executed, cause the processing circuitry to: determine weld characteristics of the plurality of … welds based on the sensor data, the optimization of the design is determined based on the location and number of welds for a work piece (P 0020) welding standards include spot weld spacing and the distance a weld point must be from certain features on the work piece (P 0031) the input variables can also be the number of welds (P 0035) to optimize the design variables of total number of welds (P 0036) performance evaluations are carried out through virtual vehicle simulations, given a total number of welds (P 0037);
identify how many of the plurality of … welds were used to assemble a single part, the work piece may be a unitary piece (P 0020) to optimize the design variables of total number of welds, physical location of each weld point and weld density (P 0036, 0057),
a user interface configured to provide an output representative of how many of the plurality of … welds were used to assemble the single part, performance evaluations are carried out through virtual vehicle simulations, given an initial weld design (i.e., having total number of welds N, and the initial locations of each of the welds being X), computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046) the work piece geometry and an initial weld design are set as inputs to the method, represented by a simulated model, using a Computer-Aided-Design (CAD) tool (P 0024) Computer-aided engineering (CAE), which is the use of software tools and computer simulations in engineering, may be used to run the virtual performance assessments (P 0037) the results of the simulation provided as a schematic representation of the method of weld design (P 0049) .
Regarding the limitation:
previously completed welds; a plurality of sensors positioned in, on, or proximate the one or more pieces of welding equipment, the plurality of sensors being configured to capture sensor data during completion of the plurality of previously completed welds,
Meess discloses positions sensors on the welding gun may be used to calibrate the depth of the image, including, but are not limited to, magnetic sensors, optical sensors, acoustics sensors, and the like, which are sensed using an appropriate sensing system to allow for the positioning of the welding gun to be determined (P 0045) during a welding operation, the position of the welding tool is tracked (P 0066) tracking data is generated to create an image stream showing the welding environment with visual cues (P 0082) tracked parameters can be compared to upper and lower target thresholds for the type of welding process, the type and orientation of the weld joint, the type of materials, etc, based on testing of similar prior welds (P 0087) stored target weld data can be that of a successful prior welding run by the user based on computer modeling for the specific type of weld and/or testing of similar prior welds (P 0092) the virtual weld object can be based on a user's previous weld history in order to, e.g., compare the current weld with a previous weld (P 0110). Meess further discloses information for the custom part, device, component, etc. is also used by the logic processor-based subsystem for workpiece recognition and auto-calibration of the system (P 0068) to determine which acceptable welding process parameters used for any given welding exercise using the object recognition module operatively to train the system to recognize known rigid body objects and configured with information for recognizing custom parts (P 0069) upon recognition of a known rigid body, position and orientation are calculated relative to the camera origin and the “trained” rigid body orientation (P 0078) an image with the recognized coupon/workpiece and weld path is calibrated to the welding environment, the user can confirm that the coupon/workpiece recognition device auto-calibrated the coupon/workpiece properly, wherein the recognition device can auto-identify a range or welding joint types (P 0113).
The applicant argues:
Given that Cai does not disclose identifying how many previously completed welds were used to assemble a single part based on an analysis of at least some weld characteristics of the previously completed welds, it stands to reason that Cai also docs not disclose that the analysis of the at least some weld characteristics of the previously completed welds is a machine learning analysis, as set forth in claims 1-10.
The examiner respectfully disagrees. Cai discloses the work piece geometry and an initial weld design are set as inputs to represent a simulated model (P 0024) the weld design has two weld points, x1 and x2. Here, (x1)0 and (x2)0 are the one-dimensional coordinates for the initial positions of the first and second weld points, N is fixed at two. Delta one and two (Δ1 and Δ2) are the amount by which the initial weld design is perturbed. Each weld position is varied between three levels and the output is observed for resultant changes. The response surface is a plot of the output or response with respect to the positions of the two weld points, x1 and x2 (P 0034) the design variable to be optimized is the total number of welds (N) (P 0036) the initial weld design includes a fixed number of weld points, N, and the method can be repeated with another initial weld design containing fewer or more weld points N. For each initial weld design, N can also change according to the specification of the vDOE, since N is also design variable. Thus the variables for the initial weld design will be (N,X), or (X) when N is fixed (P 0046).
Since Cai performs a virtual design of experiment, and the design variable to be optimized is the total number of welds (N), then Cai does take into account the number of welds to be optimized. Meess discloses the virtual weld object can be based on a user's previous weld history in order to, e.g., compare the current weld with a previous weld. And, while not relied upon to reject the subject limitation, Wang analyzes existing welds with sensors, i.e. previously completed welds.
Applicant’s arguments with respect to claim(s) 1, 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the limitation:
the identification being based on a machine learning analysis of at least some of the weld characteristics of the plurality of … welds,
The examiner has combined new prior art reference Wang to reject this limitation. Wang discloses for detecting welding defects (P 0002) using machine learning to detect defective spectrum patterns (P 0100).
Conclusion
Any inquiry concerning this communication should be directed to JOHN M HEFFINGTON at telephone number (571)270-1696.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN M HEFFINGTON whose telephone number is (571)270-1696. The examiner can normally be reached on Monday through Friday from 9:30 am to 5:30 pm Eastern.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar B Paula, can be reached at telephone number 571-272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/J.M.H/Examiner, Art Unit 2145 12/27/2025/CHAU T NGUYEN/Primary Examiner, Art Unit 2145