DETAILED ACTION
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 .
Specification
The disclosure is objected to because of the following informalities:
para. 0006 recites “However, these solutions do not eliminate the risk and costs of late-stage defect discovery, and such systems are often complex to operate and require laborious calibrations with any changeover” and should be amended, e.g., to “However, these solutions do not eliminate the risk and costs of late-stage defect discovery, and such systems are often complex to operate and require laborious calibrations with [[any changeover]]many changeovers” [e.g., as a known source of downtime; para. 0003: “The associated downtime and financial cost of reworks can add up to two or three times the original predicted project cost. The lack of a reliable and scalable technology is the main deterrent to real-time welding inspection.”].
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Appropriate correction is required.
Claim Interpretation
“deviation” is used by the claims to indicate conventional elements known in the art [i.e., an unexpected (deviant) measured value (incoming data) resulting from a process deviation (source) [para. 0014: “The process deviation may comprise one or more of: a current or voltage interruption; a wire feed interruption; a protective gas flow interruption; burn-through; oil, grease, moisture, dust, oxidation, or rust on the surface of the weld; and a welding arc or laser interruption.”; para. 0046: “Embodiments of the disclosure are directed at methods of monitoring welding process quality in real-time. For example, according to embodiments of the disclosure, quality indicators of the welding process may be monitored in real-time, and any deviations in the quality indicators may be automatically detected. The quality indicators may be based on incoming data. For example, with a video stream as input, the system may automatically distinguish particular regions of interest in the video stream, such as regions in images of the video stream showing melt spatter, arc, and base metal.”]
“normally” with regards to the progression of a welding operation, is used by the claims to indicate conventional structure known in the art [e.g., comparison with a threshold, and determination of being within an acceptable level of deviation with the threshold; para. 0026: “Determining that the welding operation is not progressing normally may comprise: comparing the one or more welding quality signals to one or more thresholds; and determining, based on the comparison, that the welding operation is not progressing normally”]
Claim Objections
Claim 14:
Applicant is advised that should claim 13 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). In this case, claim 13 already requires wherein the regions of interest comprise of one or more of: a melt spatter region; a welding arc or laser region; a melt pool region; and a weld seam region.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5, 12, and 21 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 5:
The recitation of “a quality index” renders the claim indefinite because it is unclear if this is intended to be distinct from the quality index recited in claim 1. In view of “during a training phase” and “training welding operations”/“training region of interest”, claim 5 will be interpreted as reciting “a training quality index”
Claim 21:
The recitation of “a quality index” and “one or more welding quality signals” renders the claim indefinite because it is unclear if this is intended to be distinct from the quality index and one or more welding quality signals recited in claim 1. In view of “during a training phase” and “training welding operations”/“training region of interest”, claim 21 will be interpreted as reciting “a training quality index” and “one or more training welding quality signals”
Claim 12:
“wherein determining the quality index comprises: identifying one or more similarities between the first matrix and the second matrix; and determining the quality index based on the one or more similarities.”
The term “similarities” is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In this case, it is unclear to what extent a first matrix may differ from a second matrix while still falling within the metes and bounds of the claim [e.g., how many elements of the matrices may differ, and to what extent may each element differ]. The claims are being interpreted as an adaptation of conventional analysis/pattern recognition applied to historical data related to process operations and outcomes thereof.
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.
Claim 1 (drawn to a process: A computer-implemented method of monitoring a quality of a welding operation), claim 24 (drawn to A system for monitoring a quality of a welding operation, comprising: a welding device, one or more cameras, one or more processors specifically configured for carrying out the said process), claim 25 (drawn to A non-transitory computer-readable medium having stored thereon computer program code configured, when executed by one or more processors, to perform a method; i.e., the method of claim 1), and claim 26 (drawn to A computer-implemented method of monitoring a quality of a welding operation creating a weld) are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception, specifically an abstract idea (mental process of monitoring a welding operation and determining/identifying) without significantly more.
Step 1
The claimed inventions are directed to statutory subject matter as the claims are directed to a process.
Step 2A, Prong One
The recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper [reviewing images/sensor data, comparing new data with old data, determining indexes (e.g., assigning a rating/grade), generating signals (e.g., pass/fail), determining/identifying a source of a deviation (e.g., a known probable cause of an unexpected/deviant sensed value of a well-understood variable based on historic recorded values/experience); see MPEP 2106.04(a)(2) subsection (III)].
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the independent claims above recite steps for monitoring a welding operation, resulting in generating one or more welding quality signals indicative of the quality of the welding operation, and identifying, based on the comparison, a source of the deviation of the one or more welding quality signals from the one or more normal welding quality signals. The limitations “computer-implementation” and “processors” are recited at a high-level of generality and amount to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “obtaining video”/“one or more cameras” and “stored…signals” amount to no more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Further, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). In this case, elements of general computer are being used to implement the abstract idea of monitoring a welding operation.
Regarding dependent claims 2-23, the limitations of claims 2-23 further define the limitations already indicated as being directed to the abstract idea, e.g., Examiner notes that while claim 2 recites “based on the identification of the source of the deviation of the one or more welding quality signals, adjusting one or more parameters of the welding operation so as to adjust a progression of the welding operation”, this seems to be the high-level appending of a well-understood, routine, and conventional activity known to the industry [i.e., computerized automatic control of a welding device according to, e.g., parameters that determined by a user, wherein the parameters are entered into the computer by the user so as to control a welding operation].
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.
Claims 1-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kurpiewski (US 20220324060 A1; IN 202121017108 - 4/12/21).
Regarding claim 1,
Kurpiewski teaches:
A computer-implemented method of monitoring a quality of a welding operation [para. 0008: “A method is also provided and includes receiving, with a computer having a processor and memory, weld parameter data generated during a welding process by a welder to join at least two parts with a weld, inputting, with the computer, the received weld parameter data to a data analytics model to generate at least one predicted weld quality parameter, comparing, with the computer, the at least one predicted weld quality parameter with a weld quality parameter threshold, and generating, with the computer, output indicating at least one of the at least one predicted weld quality parameter and a result of the comparison between the at least one predicted weld quality parameter and the weld quality parameter threshold.”; para. 0091-92: “Closed-Loop Corrections - As discussed in detail above, the quality monitoring systems and methods of the present disclosure utilize data analytics and machine learning algorithms and models to predict or estimate the weld quality for a given weld based on weld parameters that are determined, generated, and/or sensed during the weld process. In addition to predicting or estimating weld quality, the quality monitoring systems and methods of the present disclosure can include systems and methods for making closed-loop corrections to the weld process to adjust operational characteristics of the weld process for subsequent welds. For example, to adjust and increase weld quality, the quality monitoring systems and methods of the present disclosure can include closed-loop corrections to make adjustments to the electrical energy supplied by the power supply 18 to the ultrasonic converter 12, to make adjustments to the movements of, and force applied by, the horn 16, and/or to adjust any other controllable aspects of the weld process.”], the method comprising:
Kurpiewski further teaches that the types of inputted weld parameters that are determined, generated, and/or sensed during the weld process is not limited [para. 0056: “…While specific weld quality parameters are discussed in the examples above, any methodology for weld quality assessment can be used. For example, the quality monitoring systems and methods of the present disclosure can predict or estimate weld quality by predicting or estimating any of the following weld quality parameters:”, see paras. 0057-90]
obtaining video of the welding operation in progress [para. 0064: “Automatic Optical Inspection (AOI)/Video image processing. Customers take digital photos or video of the weld for record keeping and quality control.”; para. 0108: “ The sensors 42, for example, can include one or more temperature sensors, laser vibrometer, position measurement sensors, stress/strain sensors, an impact sensor and/or a camera.”];
processing the video to identify regions of interest in the video, each region of interest corresponding to a portion of an image in the video [i.e., regions corresponding to conventional weld quality elements, e.g., the weld itself (bubbles, strands), excess material, cracks, other visual features, etc.; para. 0065: “Appearance. Visual inspection by human or machine is often used to check weld quality. Inspectors look for bubbles, strands, excess weld material (aka "flash"), weld gaps (under-welding), cracks, stress lines, burn marks, and other visual features. In metal welding, customers specifically look for discoloration, a sign of oxidation. Microscopes and magnification can be used.”];
for each region of interest, determining a quality index by processing the portion of the image corresponding to the region of interest [e.g., a predicted weld quality parameter for each corresponding weld quality element; para. 0009: “Another system is provided and includes a computer having a processor and memory configured to receive weld parameter data generated during a welding process by a welder to join at least two parts with a weld, filter the weld parameter data by removing anomalies in the weld parameter data based on comparing the weld parameter data with at least one threshold, for any anomalies in the weld parameter data, generate output indicating that the weld parameter data was rejected, input the filtered weld parameter data to a data analytics model to generate at least one predicted weld quality parameter, compare the predicted weld quality parameter with a weld quality parameter threshold, and generate output indicating at least one of: the at least one predicted weld quality parameter and a result of the comparison between the at least one predicted weld quality parameter and the weld quality parameter threshold.”];
based on the quality indices determined for the regions of interest, generating one or more welding quality signals indicative of the quality of the welding operation [i.e., the result of the comparison between the predicted parameter and corresponding threshold];
determining, based on the one or more welding quality signals, that the welding operation is not progressing normally [i.e., the resulting weld will not be of sufficient quality, e.g., a particular weld quality element is not within an expected range; para. 0034: “For example, the quality monitoring systems and methods can process the various inputs and generate and output a predicted value indicating the quality of the weld strength, such as peel strength, pull/tensile strength, burst pressure, appearance, etc. The predicted value can then be compared to a predetermined threshold to determine whether the welded part has a sufficient weld strength/quality. Additionally or alternatively, the quality monitoring system can generate an output based on the comparison of the predicted value with the predetermined threshold to indicate whether the predicted weld quality metric or measurement is sufficient, based on whether the particular weld followed the expected process and/or achieved some measurement of quality characteristic above or below the predetermined threshold.”];
in response to determining that the welding operation is not progressing normally, comparing the one or more welding quality signals to one or more stored welding quality signals [Kurpiewski teaches that it is known in the art to incorporate data analytics (i.e., stored welding quality signals) and machine learning algorithms and models; para. 0092], wherein each stored welding quality signal is associated with a respective source of a deviation [i.e., weld quality correlates in a known manner with welding parameters, e.g., a stored value of supplied electrical energy for welding and the corresponding quality analyzed and stored therewith; para. 0011: “Another system is provided and includes a computer having a processor and memory configured to: receive weld parameter training data that includes weld parameters and weld quality data associated with the weld parameters”; para. 0034: “The present disclosure includes quality monitoring systems and methods that monitor and receive data from the welder. For example, the monitored and received weld parameter data and/or graphs can include data with respect to electrical current, voltage, power, frequency, time, distance/depth, force, velocity, and/or other data parameters generated or used by the welder during the welding process.”] of the stored welding quality signal from one or more normal welding quality signals associated with one or more welding operations that progressed normally [e.g., data from previous actual processes, or training processes (as is known in the data analytics/machine learning arts); paras. 0050-138]; and
identifying, based on the comparison, a source of the deviation of the one or more welding quality signals from the one or more normal welding quality signals [i.e., the method identifying that an adjustment to a specific welding parameter is to be made, the parameter associated with a source of the abnormal weld quality parameter, e.g., electrical energy needs to be adjusted; para. 0092].
Regarding claim 2, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
further comprising, based on the identification of the source of the deviation of the one or more welding quality signals, adjusting one or more parameters of the welding operation so as to adjust a progression of the welding operation [i.e., adjustments to electrical energy supplied].
Regarding claim 3, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein obtaining the video comprises capturing, using one or more cameras, images of the welding operation in progress [para. 0034].
Regarding claim 4, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein processing the video to identify the regions of interest comprises:
extracting one or more features from images in the video;
comparing the extracted one or more features to one or more stored features, wherein the one or more stored features are extracted from images obtained from one or more other welding operations and comprising the regions of interest; and
identifying, based on the comparison, the regions of interest in the video of the welding operation [e.g., temperature as an extracted feature/parameter data; para. 0063].
Regarding claim 5, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
further comprising, during a training phase prior to the welding operation:
obtaining video of each of multiple training welding operations;
for each training welding operation, processing the video of the training welding operation to identify training regions of interest in the video, each training region of interest corresponding to a portion of an image in the video of the training welding operation; and
for each training region of interest, determining a quality index based on a quality of the training welding operation associated with the training region of interest [a training mode; para. 0011; para. 0045].
Regarding claim 6, Kurpiewski teaches the method of claim 5.
Kurpiewski also teaches:
wherein determining the quality index comprises:
comparing the processed portion of the image corresponding to the region of interest to one or more processed portions of the images corresponding to the training regions of interest; and
based on the comparison and based on one or more quality indices determined for the training regions of interest, determining the quality index for the region of interest [i.e., the corresponding steps of claim 1 applied to training data of claim 5].
Regarding claim 7, Kurpiewski teaches the method of claim 5.
Kurpiewski also teaches:
wherein, during at least one of the training welding operations, a process deviation is introduced into the at least one of the training welding operations [i.e., combinations of different values of input parameters result in welds of varying qualities, e.g., distinct actual welding processes and distinct training welding processes have different welding parameters, and thus result in corresponding different weld qualities; para. 0043: “The predicted weld quality parameter is then compared with the actual weld quality parameter for the associated inputted weld parameters and the data analytics model 404 can then be adjusted, modified, and/or tuned based on the difference between the predicted weld quality parameter(s) and the actual weld quality parameter(s). The learning or training process 402 then continues on to a next set of inputted weld parameters from the training data 400.”].
Regarding claim 8, Kurpiewski teaches the method of claim 7.
Kurpiewski also teaches:
wherein the process deviation comprises one or more of:
a current or voltage interruption [para. 0034];
a wire feed interruption [a known, controllable weld parameter in the art; paras. 0091-92];
a protective gas flow interruption [a known, controllable weld parameter in the art; paras. 0091-92];
burn-through [para. 0065];
oil, grease, moisture, dust, oxidation, or rust on the surface of the weld [para. 0085: “Particulate measuring. Measuring for dust or weight (parts per million or particles per volume)more likely in clean rooms, air ducts, microfluidics.”; para. 0089: “Contamination. Inspecting the weld for unusual substances, introduction of foreign material, or excess material (for example, welding tooth paste tubes, the inspection process was to look for toothpaste on the outside of the tube).”]; and
a welding arc or laser interruption [a known, controllable weld parameter in the art; paras. 0091-92].
Regarding claim 9, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein processing the video to identify the regions of interest comprises:
inputting the video to a trained neural network; and identifying the regions of interest using the trained neural network [para. 0033: “Additionally or alternatively, the systems and methods of the present disclosure can utilize data analytics and machine learning algorithms or models categorized as unsupervised learning algorithms or models, such as AI neural network algorithms or models”].
Regarding claim 10, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein determining the quality index comprises:
processing the portion of the image corresponding to the region of interest;
comparing the processed portion of the image corresponding to the region of interest to a stored processed portion of an image, wherein the stored processed portion of the image corresponds to a region of interest identified in the video of one or more other welding operations; and
based on the comparison, determining the quality index for the region of interest [e.g., images of the weld region and regions adjacent the weld are processed and compared to historic/training data].
Regarding claim 11, Kurpiewski teaches the method of claim 10.
Kurpiewski also teaches:
wherein:
processing the portion of the image corresponding to the region of interest comprises converting the portion of the image corresponding to the region of interest to a first matrix representing the region of interest; and
comparing the processed portion of the image to the stored processed portion of the image comprises comparing the first matrix to a second matrix representing the region of interest corresponding to the stored processed portion of the image [matrices, and their use, being known elements/tools in the art; para. 0034: “In addition, a matrix profiling anomalies method can be used to search weld graph data for anomalies, as discussed in further detail below.”; paras. 0105-137].
Regarding claim 12, Kurpiewski the method of claim 11.
Kurpiewski also teaches:
wherein determining the quality index comprises:
identifying one or more similarities between the first matrix and the second matrix; and
determining the quality index based on the one or more similarities [i.e., comparing matrices with data and identifying anomalies; paras. 0105-137].
Regarding claim 13, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein the regions of interest comprise one or more of:
a melt spatter region corresponding to a portion in each of one or more images in the video, wherein the portion shows melt spatter occurring during the welding operation [flash; para. 0065];
a welding arc or laser region corresponding to a portion in each of one or more images in the video, wherein the portion shows a welding arc or laser produced during the welding operation [a known, controllable, visual weld parameter in the art; para. 0065; paras. 0091-92];
a melt pool region corresponding to a portion in each of one or more images in the video, wherein the portion shows a melt pool formed during the welding operation [visual inspection of the weld in progress; para. 0065]; and
a weld seam region corresponding to a portion in each of one or more images in the video, wherein the portion shows a weld seam formed during the welding operation [visual inspection of a seam of a completed weld; para. 0065].
Regarding claim 14, Kurpiewski teaches the method of claim 13.
Kurpiewski, for the same reasons as presented in the rejection under 35 U.S.C. 102(a)(1) of claim 13 above, also teaches:
wherein the regions of interest consist of one or more of:
the melt spatter region;
the welding arc or laser region;
the melt pool region; and
the weld seam region.
Regarding claim 15, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
further comprising, during the welding operation:
obtaining welding process data associated with the welding operation; and
determining one or more process quality indices for the welding process data,
wherein generating the one or more welding quality signals is further based on the one or more process quality indices [para. 0034: “The present disclosure includes quality monitoring systems and methods that monitor and receive data from the welder. For example, the monitored and received weld parameter data and/or graphs can include data with respect to electrical current, voltage, power, frequency, time, distance/depth, force, velocity, and/or other data parameters generated or used by the welder during the welding process.”].
Regarding claim 16, Kurpiewski teaches the method of claim 15.
Kurpiewski also teaches:
wherein determining the one or more process quality indices comprises:
comparing the welding process data to stored welding process data, wherein the stored welding process data is associated with one or more other welding operations; and
based on the comparison, determining the one or more process quality indices [see claim 1, wherein the welding process data of claim 15 is included as data input into the quality monitoring system, so as to make adjustments to the process; paras. 0091-92].
Regarding claim 17, Kurpiewski teaches the method of claim 15.
Kurpiewski also teaches:
further comprising, during a training phase prior to the welding operation:
obtaining sets of welding process data associated with multiple training welding operations; and
for each set of welding process data associated with a given one of the training welding operations, determining a process quality index based on a quality of the training welding operation associated with the welding process data [a training mode; para. 0011; para. 0045].
Regarding claim 18, Kurpiewski teaches the method of claim 17.
Kurpiewski also teaches:
wherein determining the one or more process quality indices comprises:
comparing the welding process data associated with the welding operation to the welding process data associated with the training welding operations; and
based on the comparison and based on the one or more process quality indices determined for the welding process data associated with the training welding operations, determining the one or more welding process quality indices for the welding process data associated with the welding operation [i.e., the corresponding steps of claim 1 applied to training data of claim 17].
Regarding claim 19, Kurpiewski teaches the method of claim 15.
Kurpiewski also teaches:
wherein the welding process data comprises data relating to one or more of:
audio captured during the welding operation using one or more audio sensors [a known methodology for weld quality assessment; para. 0056];
a colour of a welding arc or laser produced during the welding operation [para. 0065];
heat emitted or dissipated during the welding operation [para. 0063];
a current used for producing a welding arc or laser during the welding operation [para. 0034]; and
a voltage used for producing a welding arc or laser during the welding operation [para. 0034].
Regarding claim 20, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein determining that the welding operation is not progressing normally comprises:
comparing the one or more welding quality signals to one or more thresholds; and determining, based on the comparison, that the welding operation is not progressing normally [i.e., comparison with a predetermined threshold, such that e.g., a determination is made that the electrical energy needs to be adjusted].
Regarding claim 21, Kurpiewski teaches the method of claim 1.
Kurpiewski teaches:
further comprising, during a training phase prior to the welding operation, generating the one or more stored welding quality signals by:
obtaining video of each of one or more training welding operations, wherein at least one of the one or more training welding operations did not progress normally;
for each training welding operation, processing the video of the training welding operation to identify training regions of interest in the video, each training region of interest corresponding to a portion of an image in the video of the training welding operation;
for each training region of interest, determining a quality index based on a quality of the training welding operation associated with the training region of interest;
based on the quality indices determined for the training regions of interest, generating, for each of the training welding operations, one or more welding quality signals; and
storing the generated one or more welding quality signals to thereby form the one or more stored welding quality signals [a training mode; para. 0011; para. 0045].
Regarding claim 22, Kurpiewski teaches the method of claim 1.
Kurpiewski also teaches:
wherein generating the one or more welding quality signals comprises generating one or more strings of numbers, wherein each number is based on the quality index determined for one of the regions of interest [e.g., a matrix of numbers (para. 0034), corresponding to the string of numbers corresponding to inputted weld parameter data, quality data, and/or predicted data].
Regarding claim 23, Kurpiewski teaches the method of claim 15.
Kurpiewski also teaches:
wherein generating the one or more welding quality signals comprises generating one or more strings of numbers, wherein each number is based on one of the process quality indices determined for the welding process data [e.g., a matrix of numbers (para. 0034), corresponding to the string of numbers corresponding to inputted weld parameter data, quality data, and/or predicted data].
Regarding claim 24,
Kurpiewski, for the same reasons as presented in the rejection under 35 U.S.C. 102(a)(1) of claim 1 above, also teaches:
A system for monitoring a quality of a welding operation, comprising:
a welding device for performing a welding operation [ultrasonic welder system 10/11];
one or more cameras positioned so as to capture video of the welding operation when in progress; and
one or more processors communicative with the one or more cameras and configured, during the welding operation, to:
obtain, from the one or more cameras, video of the welding operation in progress;
process the video to identify regions of interest in the video, each region of interest corresponding to a portion of an image in the video;
for each region of interest, determine a quality index by processing the portion of the image corresponding to the region of interest;
based on the quality indices determined for the regions of interest, generate one or more welding quality signals indicative of the quality of the welding operation;
determine, based on the one or more welding quality signals, that the welding operation is not progressing normally;
in response to determining that the welding operation is not progressing normally, compare the one or more welding quality signals to one or more stored welding quality signals, wherein each stored welding quality signal is associated with a respective source of a deviation of the stored welding quality signal from one or more normal welding quality signals associated with one or more welding operations that progressed normally; and
identify, based on the comparison, a source of the deviation of the one or more welding quality signals from the one or more normal welding quality signals.
Regarding claim 25,
Kurpiewski, for the same reasons as presented in the rejection under 35 U.S.C. 102(a)(1) of claim 1 above, also teaches:
A non-transitory computer-readable medium [para. 0149] having stored thereon computer program code configured, when executed by one or more processors, to perform a method comprising:
obtaining video of a welding operation in progress;
processing the video to identify regions of interest in the video, each region of interest corresponding to a portion of an image in the video;
for each region of interest, determining a quality index by processing the portion of the image corresponding to the region of interest;
based on the quality indices determined for the regions of interest, generating one or more welding quality signals indicative of the quality of the welding operation;
determining, based on the one or more welding quality signals, that the welding operation is not progressing normally;
in response to determining that the welding operation is not progressing normally, comparing the one or more welding quality signals to one or more stored welding quality signals, wherein each stored welding quality signal is associated with a respective source of a deviation of the stored welding quality signal from one or more normal welding quality signals associated with one or more welding operations that progressed normally; and
identifying, based on the comparison, a source of the deviation of the one or more welding quality signals from the one or more normal welding quality signals.
Regarding claim 26,
Kurpiewski, for the same reasons as presented in the rejection under 35 U.S.C. 102(a)(1) of claim 1 above, also teaches:
A computer-implemented method of monitoring a quality of a welding operation creating a weld, the method comprising:
obtaining video of the welding operation in progress;
processing the video to identify regions of interest in the video, each region of interest corresponding to a portion of an image in the video, wherein the portion of the image does not include the weld [e.g., excess weld material (aka "flash") in regions adjacent the target weld; para. 0065];
for each region of interest, determining a quality index by processing the portion of
the image corresponding to the region of interest;
based on the quality indices determined for the regions of interest, generating one
or more welding quality signals indicative of the quality of the welding operation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hsu (US 20170032281 A1) – teaches detecting audio as a known method for weld quality assessment [para. 0091]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE J EVANGELISTA whose telephone number is (571)272-6093. The examiner can normally be reached Monday - Friday, 9am - 5pm EST.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Edward F Landrum can be reached at (571) 272-5567. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THEODORE J EVANGELISTA/Examiner, Art Unit 3761 /EDWARD F LANDRUM/Supervisory Patent Examiner, Art Unit 3761
