CTNF 18/778,610 CTNF 98460 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The IDS dated 17 October 2024 has been considered and placed in the application file. 07-30-03-h AIA 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 § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process using images/ drawings (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.), and mathematical calculations for likelihood/ probability (e.g., - P(A) = f / N Where P(A) = Probability of an event (event A) occurring; f = Number of ways an event can occur (frequency); N = Total number of outcomes possible). This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such. According to the USPTO guidelines, a claim is directed to non-statutory subject matter if: STEP 1 : the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or STEP 2 : the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: STEP 2A (PRONG 1) : Does the claim recite an abstract idea, law of nature, or natural phenomenon? STEP 2A (PRONG 2) : Does the claim recite additional elements that integrate the judicial exception into a practical application? STEP 2B : Does the claim recite additional elements that amount to significantly more than the judicial exception? Using the two-step inquiry, it is clear that claims 1, 12 and 18 are directed to an abstract idea as shown below: STEP 1 : Do the claims fall within one of the statutory categories ? YES . Claims 1 are directed to a method, i.e., process, and claims 12 and 18 are directed to a system i.e., a machine. STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea ? YES , the claims are directed toward a mental process (i.e., abstract idea). With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas: Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion). The method in claim 1, for example, comprises a mental process that can be practicably performed in the human mind therefore, an abstract idea. Claim 1 recites: applying, to one or more machine learning models… generating, using one or more second microservices, visualization data… causing presentation of the visualization data on a graphical user interface… These limitations, as drafted, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person could present images to a person, then have them recognize defects and then circle the defects . The mere nominal recitation that the various steps are being executed by a processor (e.g., processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application ? NO , the claims do not recite additional elements that integrate the judicial exception into a practical application. With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application: an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition; an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; an additional element effects a transformation or reduction of a particular article to a different state or thing; and an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application: an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; an additional element adds insignificant extra-solution activity to the judicial exception; and an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use. Thus, Claims 1- 20 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Thus, since Claims 1, 12 and 18 are/is: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1, 12 and 18 are not eligible subject matter under 35 U.S.C 101. Similar analysis is made for the dependent claims 2-11, 13-17 and 19-20 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. Claims 1-20 (all claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2025 0225639 A1 , (Eichler et al.) . The references are listed in a PTO-892 from the Office Action in which they are first used. Claim 1 [AltContent: textbox (Eichler et al. Fig. 4, showing an AOI system inspecting PCB boards.)] PNG media_image1.png 531 652 media_image1.png Greyscale Regarding Claim 1 , Eichler et al. teach 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]) ; 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]) ; 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]) . It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Eichler et al. explicitly motivates doing so at least in paragraphs [0082], [0085] and [0058] including “It should be understood that the optimal machine learning model, or classifier, is of individual nature and depends on personal preferences about acceptance thresholds of the slip rate. In fact, this threshold may even vary for different types of components and related requirements such as safety, e.g., when producing safety-critical or less critical PCBAs).” and otherwise motivating experimentation and optimization. The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of system claim 12 and apparatus claim 18 while noting that the rejection above cites to both device and method disclosures. Claims 12 and 18 are mapped below for clarity of the record and to specify any new limitations not included in claim 1. Claim 2 Regarding claim 2, Eichler et al. teach the method of claim 1, further comprising: comparing, using one or more third microservices, the one or more defects associated with the PCB with one or more predicted defects associated with the PCB to determine one or more differences between the one or more defects and the one or more predicted defects, the one or more predicted defects determined using one or more Automated Optical Inspection (AOI) systems to analyze the PCB ("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 updating one or more parameters associated with the one or more AOI systems based at least on the one or more differences between the one or more defects and the one or more predicted defects ("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 3 Regarding claim 3, Eichler et al. teach the method of claim 1, wherein the at least the portion of the PCB depicted in the image corresponds to one or more predicted defects associated with the PCB, the one or more predicted defects determined using one or more Automated Optical Inspection (AOI) machines ("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 4 Regarding claim 4, Eichler et al. teach 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. teach 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. teach 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. teach 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. teach 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. teach 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. teach 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 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 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]) ; 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. teach 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. teach 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 corresponding to 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 update one or more parameters of one or more machines based at least on the one or more metrics ("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) . Claim 13 Regarding claim 13, Eichler et al. teach 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 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]) ; 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. teach 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 15 Regarding claim 15, Eichler et al. teach the system of claim 12, the one or more processors further to: determine, based at least on the one or more metrics ("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]) , that one or more differences between the one or more defects and one or more predicted defects associated with the one or more PCBs meet or exceed a threshold, the one or more predicted defects obtained using one or more automated optical inspection (AOI) machines to inspect the one or more PCBs ("Automatic optical inspection, AOI, is an inspection method for PCBAs," paragraph [0004]) ; and update one or more second parameters of the one or more AOI machines based at least on the one or more differences 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 16 Regarding claim 16, Eichler et al. teach the system of claim 12, the one or more processors further to: identify at least one machine of the one or more machines 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. teach 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]) ; and 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. teach 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 ("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]) 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. teach 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. teach 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]) ;and 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]) . Reference Cited 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. US Patent Publication 2024 0160194 A1 to Bakkhshmand et al. discloses providing an inspection image of the article to an object detection model trained to detect at least one defect type in an input image and generating object location data identifying a location of a detected object in the inspection image; comparing the inspection image to a golden sample image to identify an artifact in the inspection image corresponding to a difference between the inspection image and the golden sample image, wherein the artifact is defined by artifact location data describing a location of the artifact in the inspection image; and determining whether the artifact location data matches the object location data according to predetermined match criteria. 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 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. 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, Ms. Jennifer Mehmood can be reached on 571-272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov . Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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: 29 May 2026 Application/Control Number: 18/778,610 Page 2 Art Unit: 2664 Application/Control Number: 18/778,610 Page 3 Art Unit: 2664
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