AI-BASED PRINTED CIRCUIT BOARD INSPECTION SYSTEMS AND APPLICATIONS

§101 §102 §103 §112

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 . Response to Arguments Applicant's arguments filed 20 August 2026 have been fully considered but they are not persuasive. Claims 1, 4-14 and 16-23 are pending in this application and have been considered below. Claims 2-3 and 15 are canceled by the applicant. Argument: The applicant argues the Office has not shown that Eichler teaches or suggests all of the features of claim 1, at least as amended herein. For instance, Applicant submits Eichler has not been shown to teach or suggest at least the features of "updating one or more parameters associated with the AOI system based at least on the one or more differences to reduce incorrect predictions by the AOI system," as amended claim 1 recites. Applicant submits that Eichler generally describes using a classifier as a secondary verification mechanism for determining whether defects identified by an AOI system represent true errors or pseudo errors. See Eichler, Abstract, [0034], and [0075]. For example, Eichler describes using the classifier output to determine whether a PCBA proceeds to a subsequent production step or is routed for further inspection. See id. However, Applicant respectfully submits that such verification of an AOI result is different from using differences between defects identified by the machine learning model(s) and defects predicted by the AOI system to update parameters of the AOI system itself to reduce incorrect predictions by the AOI system, as amended claim 1 recites. Response: At the level of detail in the current claims, Eichler teaches all of the claim limitations. To the extent Eichler differs from the inventive concept, the broadest reasonable interpretation of the claims as currently written includes the teachings of Eichler. Applicants are reminded that the specification cannot be imported into the claims, and details from the specification that differ from the teachings of Eichler need to be incorporated into the claims. US Patent Publication 2025 0225639 A1, (Eichler et al.) shows the limitation “indicating at least a number of incorrect predictions of one or more Automated Optical Inspection (AOI) machines, the number of incorrect predictions determined based at least on the one or more defects ("turning to FIG. 4, in order to reduce the effort for manual inspection a false call reduction is envisioned which is also scalable, i.e., can be used for different inspection types. Inspection types, may be given by specific set of numerical inspection results (=AOI system outputs) Therein the different numerical inspection results may be obtained by different imaging techniques (when capturing the one or more images of the PCBA) and/or by different image processing algorithms and/or by measuring different object in the image of the PCBA, e.g., measuring different components of the PCBA. For example, a numerical measurement result may be obtained by comparing grey values of pixels in the image(s). The inspection types may be accompanied by or may be equivalent to an error report which is generated and/or output by an AOI system. Since the inspection types may vary according to the configuration of the AOI system there is need to provide a solution for reducing the falls call rate which covers a plurality of inspection types," paragraph [0035]).” In addition, Eichler teaches using a neural network in paragraph [0051], specifically “In FIG. 6 a neural network is shown but another machine learning model such as a random forest may be used as a classifier. The input features of the classifier are obtained from an AOI system. Specifically, the input features of the classifier are obtained by reducing the dimensionality, e.g., using PCA, of a feature vector. As explained herein, the feature vector is a data construct that comprises a plurality of sections, wherein each section corresponds to an inspection type of the AOI system, preferably all inspection types of the AOI system.” And further in paragraph [0052] “In FIG. 7 the confusion matrix of the classifier is shown. based on the confusion matrix the number of true negative, TN, false negative, FN, false positives, FP, and true positives TP can be determined. Based on these results the metrics for the optimization function, cf. above min (k*slip_ rate)+max (test_effort_reduction), of the classifier can be calculated and the training and optimization of the classifier can be performed” where one of the inspection types is a prediction. Thus, at the level of detail shown in the current claims, Eichler anticipates the claims. Information Disclosure Statement The IDS dated 17 October 2024 that has been previously considered remains placed in the application file. Claim Interpretation Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). Claims 5, 6, 10, 13, 16, 17 and 20 recite “at least one of.” Since “at least one of” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history. 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. Newly added Claim 23 is rejected under 35 U.S.C. § 112(b) 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 23 recites the trademark/trade name “Representational State Transfer (REST) application programming interface (API).” Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. § 112(b). See Ex parte Simpson, 218 U.S.P.Q. 1020, 1021, 1022 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a programming interface and, accordingly, the identification/description is indefinite. Claim Rejections - 35 USC § 101 Claims 1, 12 and 18 have been amended. The rejection of Claims 1- 20 under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more is withdrawn. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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 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. Claims 1, 4-14 and 16-21 (all claims except 22-23) are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by US Patent Publication 2025 0225639 A1, (Eichler et al.). References are listed in the Notice of Cited References when they were first cited. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. Claim 1 Regarding Claim 1, Eichler et al. disclose a method ("Automatic optical inspection, AOI, is an inspection method for PCBAs," paragraph [0004])comprising: applying, to one or more machine learning models executed using one or more first microservices ("The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]), image data representing an image of at least a portion of a printed circuit board (PCB) ("one or more images of the PCBA are captured and image processing of the one or more images is performed" paragraph [0026]), (PCB), the portion of the PCB identified as including one or more predicted defects determined using an Automated Optical Inspection (AOI) system("The image processing may be based on a greyscale comparison of the image pixels, e.g., compared to a golden image. Thereby geometric properties such as length, area, volume or angle, and/or brightness or color of at least one object in one or more images of the PCBA may be obtained as numerical measurement results," paragraph [0075] and "An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); obtaining, using the one or more machine learning models ("The machine learning model may serve as a classifier which provides a binary label for a given test object, e.g., a region of interest of an image of a PCBA," paragraph [0033]), output data indicating at least one or more defects associated with the PCB ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); determining one or more differences between the one or more defects and the one or more predicted defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); generating, using one or more second microservices, visualization data indicating information associated with at least the one or more defects ("Finally, the classifier is applied and the error class may be output and may be used for controlling, e.g., the conveyor belt accordingly and/or is visualized on a display and/or an (operator) dashboard as well," paragraph [0053]); and causing presentation of the visualization data on a graphical user interface displayed using one or more client devices ("Finally, the classifier is applied and the error class may be output and may be used for controlling, e.g., the conveyor belt accordingly and/or is visualized on a display and/or an (operator) dashboard as well," paragraph [0053]); and updating one or more parameters associated with the AOI system based at least on the one or more differences to reduce incorrect predictions by the AOI system ("Again, as before, the obtained numerical measurement results are entered into at least one section of the feature vector in a step S2. The numerical measurement results correspond to measurement variables of an inspection type and/or wherein different inspection types comprise different measurement variables. Then in a step S6, which may be part of the step S3 for selecting the input variable from the feature vector, the feature vector is mapped onto input features. Therein, the mapping may be based on a principal component analysis of the feature vector," paragraph [0075]). Claim 4 Regarding Claim 4, Eichler et al. disclose the method of claim 1, wherein the applying of the image data to the one or more machine learning models comprises sending, from one or more third microservices, at least the image data to the one or more first microservices ("The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]). Claim 5 Regarding Claim 5, Eichler et al. disclose the method of claim 1, further comprising storing, using one or more third microservices ("The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]), at least one of the output data or the visualization data in a database accessible to the one or more third microservices ("may label the PCBA either as false calls or as real errors and may store these results in a quality database as described in the above in connection with FIG. 1," paragraph [0043]). Claim 6 Regarding Claim 6, Eichler et al. disclose the method of claim 1, further comprising: applying, to one or more second machine learning models executed using the one or more first microservices, second image data representing a second image of at least a second portion of the PCB ("The machine learning model may serve as a classifier which provides a binary label for a given test object, e.g., a region of interest of an image of a PCBA," paragraph [0033] where one region teaches other regions); and obtaining, using the one or more second machine learning models, second output data indicating at least one or more second defects associated with the PCB ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]). Claim 7 Regarding Claim 7, Eichler et al. disclose the method of claim 6, wherein the one or more second machine learning models are trained to predict a different type of PCB defect than the one or more machine learning models ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060] and "The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]). Claim 8 Regarding Claim 8, Eichler et al. disclose the method of claim 1, wherein the visualization data is further indicative of one or more metrics, the one or more metrics having one or more values calculated based at least on the one or more defects associated with the PCB ("a standardization step is carried out by taking statistical metrics like "min, max, avg, quantiles" and "value counts" for each numerical measurement result and metadata category, respectively," paragraph [0044]). Claim 9 Regarding Claim 9, Eichler et al. disclose the method of claim 1, wherein the one or more machine learning models execute on one or more resources associated with one or more nodes hosting one or more containers associated with the one or more first microservices ("The apparatus 25 may comprise a software application which is executed by the apparatus' processor 22 and memory 21. The Software application may be based on microservices where each microservice executes a dedicated function. For example, a microservice may execute a single one the steps as described in the above in particular with respect to FIGS. 9 to 13," paragraph [0082]). Claim 10 Regarding Claim 10, Eichler et al. disclose the method of claim 1, wherein the one or more defects associated with the PCB include at least one of: one or more defective components ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060] ); one or more incorrect components; one or more missing components ("comparison between a finding of the object-based analysis and stored assembly information for the PCB is proposed to identify missing components," paragraph [0005]); one or more soldering defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); one or more plating defects; one or more component placement defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); one or more warpage defects; or one or more residual defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]). Claim 11 Regarding Claim 11, Eichler et al. disclose the method of claim 1, wherein the output data further indicates at least one or more confidence scores corresponding to the one or more defects associated with the PCB, and the visualization data further indicates second information associated with the one or more confidence scores ("The classifier may provide as output an indicator indicating either "true error" or "pseudo error". The output of the classifier may be used as an inspection result," paragraph [0065] where pseudo error teaches confidence score, and "FIG. 12 shows an illustration of exemplary steps according to a fourth embodiment. In a step S9, the numerical measurement results are augmented with metadata. For example, they may be stored together or may be otherwise associated. The metadata may relate to one or more components of the PCBA, one or more inspection types and/or an inspection result of the one or more inspection types," paragraph [0077]). Claim 12 Regarding Claim 12, Eichler et al. disclose a system("Automatic optical inspection, AOI, is an inspection method for PCBAs," paragraph [0004]) comprising: one or more processors ("an apparatus 25 comprising an interface 20, as well as a processor 22 and a memory 21," paragraph [0082]) to: obtain, using one or more first services ("The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]) to process one or more images depicting one or more printed circuit boards (PCBs) ("one or more images of the PCBA are captured and image processing of the one or more images is performed" paragraph [0026]), data indicating one or more defects associated with the one or more PCBs ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); generate, using one or more second services to analyze the data, one or more metrics indicating at least a number of incorrect predictions of one or more Automated Optical Inspection (AOI) machines, the number of incorrect predictions determined based at least on the one or more defects("turning to FIG. 4, in order to reduce the effort for manual inspection a false call reduction is envisioned which is also scalable, i.e., can be used for different inspection types. Inspection types, may be given by specific set of numerical inspection results (=AOI system outputs) Therein the different numerical inspection results may be obtained by different imaging techniques (when capturing the one or more images of the PCBA) and/or by different image processing algorithms and/or by measuring different object in the image of the PCBA, e.g., measuring different components of the PCBA. For example, a numerical measurement result may be obtained by comparing grey values of pixels in the image(s). The inspection types may be accompanied by or may be equivalent to an error report which is generated and/or output by an AOI system. Since the inspection types may vary according to the configuration of the AOI system there is need to provide a solution for reducing the falls call rate which covers a plurality of inspection types," paragraph [0035]); and update one or more parameters of the one or more AOI machines based at least on ("These reference values (and/and/or an associated error class) may be used for training the classifier. Hence, in a step S8, the classifier may be trained (based) on reference values of the numerical measurement results associated with the error class," paragraph [0076] where a reference value is a parameter) a determination that the number of incorrect predictions meets or exceeds a threshold ("a standardization step is carried out by taking statistical metrics like "min, max, avg, quantiles" and "value counts" for each numerical measurement result and metadata category, respectively," paragraph [0044]). Claim 13 Regarding Claim 13, Eichler et al. disclose the system of claim 12, wherein the one or more defects associated with the one or more PCBs include at least one of: one or more defective components ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060] ); one or more component placement defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); one or more incorrect components; one or more missing components ("comparison between a finding of the object-based analysis and stored assembly information for the PCB is proposed to identify missing components," paragraph [0005]); one or more soldering defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]); one or more plating defects; one or more warpage defects; or one or more residual defects ("An AOI system is furthermore capable of providing flags and quantifying the defects such as Missing lead, Offset, Rotation, Polarity, Upside down, OCV, Solder filet, Billboarding, Lifted Lead, Lifted Body, Tombstone, Bridging and more," paragraph [0060]). Claim 14 Regarding Claim 14, Eichler et al. disclose the system of claim 12, wherein the one or more first services correspond to one or more containerized microservices ("The Software application may be based on microservices where each microservice executes a dedicated function," paragraph [0082]) running one or more machine learning models ("The machine learning model may serve as a classifier which provides a binary label for a given test object, e.g., a region of interest of an image of a PCBA," paragraph [0033]), the one or more defects associated with the one or more PCBs obtained based at least on applying, to the one or more machine learning models, image data representing the one or more images("one or more images of the PCBA are captured and image processing of the one or more images is performed" paragraph [0026]). Claim 16 Regarding Claim 16, Eichler et al. disclose the system of claim 12, the one or more processors further to: identify at least one machine that contributed to manufacturing a number of the one or more PCBs ("The numerical measurement results of the AOI system result from analyzing the given one or more images of the PCBA and for PCBA-component-specific details. This image processing determines typical patterns of failures, e.g., due to assembly and/or soldering, and transforms them into numerical measurement results," paragraph [0044]); determine, based at least on the one or more metrics, that a subset of the one or more defects associated with the number of the one or more PCBs meets or exceeds a threshold ("The numerical measurement results of the AOI system result from analyzing the given one or more images of the PCBA and for PCBA-component-specific details. This image processing determines typical patterns of failures, e.g., due to assembly and/or soldering, and transforms them into numerical measurement results," paragraph [0044]); and update one or more second parameters of the machine based at least on the subset of the one or more defects meeting or exceeding the threshold ("Again, as before, the obtained numerical measurement results are entered into at least one section of the feature vector in a step S2. The numerical measurement results correspond to measurement variables of an inspection type and/or wherein different inspection types comprise different measurement variables. Then in a step S6, which may be part of the step S3 for selecting the input variable from the feature vector, the feature vector is mapped onto input features. Therein, the mapping may be based on a principal component analysis of the feature vector," paragraph [0075]). Claim 17 Regarding Claim 17, Eichler et al. disclose the system of claim 12, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine ("The software application may further output or initiate or trigger control commands for controlling a conveyor (belt) and/or may display the inspection result of the classifier on a display. Accordingly, the software application is computer implemented and is operative to perform the steps as described herein, in particular relating to FIG. 9 to 13," paragraph [0082]); a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations ("The test simulates the normal circumstances in which the PCBA will operate. Power and simulated signals run through the PCBA in this test while testers monitor the PCBA' s electrical characteristics," paragraph [0027]); Claim 18 Regarding Claim 18, Eichler et al. disclose at least one processor ("an apparatus 25 comprising an interface 20, as well as a processor 22 and a memory 21," paragraph [0082]) comprising: processing circuitry to update one or more parameters of an Automated Optical Inspection (AOI) machine based at least on a determination that a number of incorrect predictions of the AOI machine meets or exceeds a threshold ("Again, as before, the obtained numerical measurement results are entered into at least one section of the feature vector in a step S2. The numerical measurement results correspond to measurement variables of an inspection type and/or wherein different inspection types comprise different measurement variables. Then in a step S6, which may be part of the step S3 for selecting the input variable from the feature vector, the feature vector is mapped onto input features. Therein, the mapping may be based on a principal component analysis of the feature vector," paragraph [0075]), the number of incorrect predictions determined based at least on a number of defects associated with a printed circuit board (PCB), the number of the defects detected using one or more machine learning models ("turning to FIG. 4, in order to reduce the effort for manual inspection a false call reduction is envisioned which is also scalable, i.e., can be used for different inspection types. Inspection types, may be given by specific set of numerical inspection results (=AOI system outputs) Therein the different numerical inspection results may be obtained by different imaging techniques (when capturing the one or more images of the PCBA) and/or by different image processing algorithms and/or by measuring different object in the image of the PCBA, e.g., measuring different components of the PCBA. For example, a numerical measurement result may be obtained by comparing grey values of pixels in the image(s). The inspection types may be accompanied by or may be equivalent to an error report which is generated and/or output by an AOI system. Since the inspection types may vary according to the configuration of the AOI system there is need to provide a solution for reducing the falls call rate which covers a plurality of inspection types," paragraph [0035]) to process image data used by the AOI machine ("one or more images of the PCBA are captured and image processing of the one or more images is performed" paragraph [0026]) to generate the incorrect predictions. Claim 19 Regarding Claim 19, Eichler et al. disclose the processor of claim 18, wherein the determination that the number of incorrect predictions of the AOI machine meets or exceeds the threshold is based at least on computing one or more metrics corresponding to at least the defects detected ("a standardization step is carried out by taking statistical metrics like "min, max, avg, quantiles" and "value counts" for each numerical measurement result and metadata category, respectively," paragraph [0044])using the one or more machine learning models ("The machine learning model may serve as a classifier which provides a binary label for a given test object, e.g., a region of interest of an image of a PCBA," paragraph [0033]) and one or more predicted defects of the AOI machine, the one or more predicted defects including the incorrect predictions ("The classifier may provide as output an indicator indicating either "true error" or "pseudo error". The output of the classifier may be used as an inspection result," paragraph [0065] where pseudo error teaches confidence score, and "FIG. 12 shows an illustration of exemplary steps according to a fourth embodiment. In a step S9, the numerical measurement results are augmented with metadata. For example, they may be stored together or may be otherwise associated. The metadata may relate to one or more components of the PCBA, one or more inspection types and/or an inspection result of the one or more inspection types," paragraph [0077]). Claim 20 Regarding Claim 20, Eichler et al. disclose the processor of claim 18, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine("The software application may further output or initiate or trigger control commands for controlling a conveyor (belt) and/or may display the inspection result of the classifier on a display. Accordingly, the software application is computer implemented and is operative to perform the steps as described herein, in particular relating to FIG. 9 to 13," paragraph [0082]); a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations ("The test simulates the normal circumstances in which the PCBA will operate. Power and simulated signals run through the PCBA in this test while testers monitor the PCBA' s electrical characteristics," paragraph [0027]); Claim 21 Regarding Claim 21, Eichler et al. disclose the method of claim 1, further comprising: identifying a cause of at least one incorrect prediction by the AOI system ("The numerical measurement results of the AOI system result from analyzing the given one or more images of the PCBA and for PCBA-component-specific details. This image processing determines typical patterns of failures, e.g., due to assembly and/or soldering, and transforms them into numerical measurement results," paragraph [0044]); and altering at least one of the one or more parameters based at least on the cause "Again, as before, the obtained numerical measurement results are entered into at least one section of the feature vector in a step S2. The numerical measurement results correspond to measurement variables of an inspection type and/or wherein different inspection types comprise different measurement variables. Then in a step S6, which may be part of the step S3 for selecting the input variable from the feature vector, the feature vector is mapped onto input features. Therein, the mapping may be based on a principal component analysis of the feature vector," paragraph [0075]). 1st 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claim 22 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2025 0225639 A1, (Eichler et al.) in view of US Patent Publication 2024 0160194 A1, (Bakhshmand et al.). The references are listed in a PTO-892 from the Office Action in which they are first used. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. Claim 22 Regarding Claim 22, Eichler et al. teach the method of claim 1, as noted above. Eichler et al. is not relied upon to explicitly teach all of visual indicators. However, Bakhshmand et al. teach wherein generating the visualization data comprises modifying the image to include a visual indicator identifying a location of at least one of the one or more defects and textual information identifying a type of the at least one defect ("An approved true detection may be labelled in the user interface 2100a using a unique colour indicator (e.g. a red colour indicator). For example, certain information (e.g. defect ID, centroid location, etc.) may be displayed in text near the defect or bounding box for the defect and such text may be coloured or highlighted in a colour," paragraph [0363]). Therefore, taking the teachings of Eichler et al. and Bakhshmand et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Inspection of Printed Circuit Board Assemblies” as taught by Eichler et al. to use “Manufacturing Quality Control using Automated Visual Inspection” as taught by Bakhshmand et al., showing that Eichler et al. and Bakhshmand et al. are analogous art because both are automated inspection of Printed Circuit Boards (PCBs). The suggestion/motivation for combination is that, “Accordingly, there is a need for an improved system and method for automated visual inspection that overcomes at least some of the disadvantages of existing systems and methods.” as noted by the Bakhshmand et al. disclosure in paragraph [0008], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that users will want to know where on the PCB the defect exists; and/or because doing so merely combines prior art elements according to known methods to yield predictable results. 2nd Claim Rejections - 35 USC § 103 Claim 23 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2025 0225639 A1, (Eichler et al.) in view of US Patent Publication 2021 0049827 A1, (Lebaredian et al.) (Applicant: Nvidia Corporation). The references are listed in a PTO-892 from the Office Action in which they are first used. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. Claim 23 Regarding Claim 23, Eichler et al. teach the system of claim 12, as noted above. Eichler et al. is not relied upon to explicitly teach all of a REST API. However, Lebaredian et al. teach the one or more processors further to: receive, from at least one of the one or more AOI machines via a Representational State Transfer (REST) application programming interface (API), image data representing at least one of the one or more images ("To do so, the state manager 612 may implement functionality of a Web Socket server, a Representational State Transfer (REST) Hooks server, a WebHooks server, a Pub-Sub server, or other state-based management solution. In embodiments, a bidirectional stateful-protocol may be used. For example, sessions between the client(s) 106 and the content management systems 604 may be implemented over persistent WebSocket connections. States that are maintained(e.g., logged and tracked) by the state manager 612 for connections to a content management system 604 may include those of authentication, as well as the set of subscriptions for the publish/subscribe model and their corresponding version identifiers ( e.g., eta gs)," paragraph [0129]); and trigger processing of the image data using the one or more first services ("assign jobs or tasks to various servers and/or instances within a same or different content management system 604 (e.g., for load balancing purposes). This may include the state manager 612 passing any of the various state data associated with the job to those servers," paragraph [0129]). Therefore, taking the teachings of Eichler et al. and Lebaredian et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Inspection of Printed Circuit Board Assemblies” as taught by Eichler et al. to use Representational State Transfer (REST) as taught by Lebaredian et al., showing that Eichler et al. and Lebaredian et al. are analogous art because both are electronic content management. The suggestion/motivation for combination is that, “The present disclosure relates to approaches for cloud-centric platforms for collaboration and connectivity on 3D virtual environments.” as noted by the Lebaredian et al. disclosure in paragraph [0005], which also motivates combination because the combination would predictably have a lower degree of difficulty in exchanging results between hardware platforms as there is a reasonable expectation that both results and data will need to be exchanged for further analysis and review; and/or because doing so merely combines prior art elements according to known methods to yield predictable results. Reference Cited The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. US Patent Publication 2020 0160083 A1 to Zhu et al. discloses method for surface defect inspection that includes recording an optical image of a surface including a defect; converting the optical image including the defect into a heat map; extracting a region of interest including the defect from the heat map; and comparing the region of interest including the defect from the heat map to a binary classification model using a sliding window based voting mechanism to determine if the defect is greater than or less than a threshold failure value. US Patent Publication 2024 0385121 A1 to Bakhshmand discloses a camera for acquiring an inspection image of the target article and an AI visual inspection computing device for detecting defects or anomalies in the target article. The device includes a communication interface for receiving the inspection image acquired by the camera, an adaptive ROI segmentation module for processing the inspection image using an ROI segmentation model to generate a masked inspection image in which regions not of interest are masked, an image analysis module for receiving the masked inspection image and analyzing the masked inspection image using an image analysis model to generate output data indicating presence of the defects or anomalies detected by the image analysis model, wherein analysis of the masked inspection image is limited to non-masked regions of interest ("Roi’s"), and an output interface for displaying the output data. Conclusion THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEATH E WELLS whose telephone number is (703)756-4696. The examiner can normally be reached Monday-Friday 8:00-4:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Heath E. Wells/Examiner, Art Unit 2664 Date: 14 October 2026