CTFR 18/166,081 CTFR 92068 DETAILED ACTION This office action is responsive to the response filed 3/11/2026. The application contains claims 1-24, all examined and rejected. 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. 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 7-8, 12, and 19, 21, 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 7-8, 12, and 19, 21, 24 are each directed to a statutory category, it recites a series of steps pertaining to analyze received data to detect issues and recommend remediation, which appears to be directed to an abstract idea (mental process). Claims 7-8, 12, and 19, 21, 24 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include manufacture, process, and machine as in independent Claim 1, 14, and 20, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, The invention is directed analyze received data to detect issues and recommend remediation, which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: Claim 7 “detecting a merger of values among two or more data objects; and detecting the first issue with the target set of one or more data objects based on the merger of the values among the two or more data objects” (Mental process, observation, evaluation and judgment). Claim 12 identifying a plurality of issues in the target set of one or more data objects (Mental process, observation, evaluation and judgment); generating, (a) priority values associated with the respective plurality of issues, and (b) a respective plurality of suggestions for remediating the plurality of issues; and determining a first priority value associated with the first issue exceeds a second priority value associated with a second issue (Mental process, observation, evaluation and judgment). Claim 21 “detecting, without intervening user input, a modification to one or more values in the target set of one or more data objects” (Mental process, observation, evaluation and judgment) Claim 24 “generating an embedding of the target set of one or more data objects“ (Mental process, observation, evaluation and judgment, Mathematical concept). The claim recites additional elements as Claim 7 “The non-transitory computer readable medium of claim 1” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); applying the machine learning model to the target set of one or more data objects is responsive to detecting the first issue (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 8 “The non-transitory computer readable medium of claim 7” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “wherein detecting the merger of the values of the two or more data objects includes detecting the merger of a first set of values from a spreadsheet and a second set of values from a database record, wherein detecting the first issue with the target set of one or more data objects comprises detecting an anomaly in one of the spreadsheet and the database record resulting from the merger.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). Claim 12 “The non-transitory computer readable medium of claim 9” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)) “training the machine learning model includes training the machine learning model to identify a priority level associated with the first issue, wherein the operations” (training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)), machine learning model (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), responsive to determining a first priority value associated with the first issue exceeds a second priority value associated with a second issue: presenting, by the graphical user interface (GUI), the interface element representing the suggestion; and refraining from presenting, by the GUI, any interface element representing a suggestion corresponding to the second issue (insignificant extra-solution, MPEP 2106.05(g)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 21 “responsive to detecting the modification, automatically applying the machine learning model to the target set of one or more data objects to generate the suggestion for remediating the first issue” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) Claim 24 the embedding comprising one or more vectors representing (a) values stored in fields of the target set of one or more data objects and at least one of (b) structural information about the target set of one or more data objects and (c) information about a database in which the target set of one or more data objects is stored (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “applying the machine learning model to the embedding to generate the suggestion (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claims additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: Claim 7 “The non-transitory computer readable medium of claim 1” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “applying the machine learning model to the target set of one or more data objects is responsive to detecting the first issue” (training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). Claim 8 “The non-transitory computer readable medium of claim 7” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “wherein detecting the merger of the values of the two or more data objects includes detecting the merger of a first set of values from a spreadsheet and a second set of values from a database record, wherein detecting the first issue with the target set of one or more data objects comprises detecting an anomaly in one of the spreadsheet and the database record resulting from the merger.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). Claim 12 “The non-transitory computer readable medium of claim 9” (“Using a computer as a tool to perform a mental process” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)), training the machine learning model includes training the machine learning model to identify a priority level associated with the first issue, wherein the operations (training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)), machine learning model (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), responsive to determining a first priority value associated with the first issue exceeds a second priority value associated with a second issue: presenting, by the graphical user interface (GUI), the interface element representing the suggestion; and refraining from presenting, by the GUI, any interface element representing a suggestion corresponding to the second issue (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)) Claim 21 “responsive to detecting the modification, automatically applying the machine learning model to the target set of one or more data objects to generate the suggestion for remediating the first issue” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), Claim 24 the embedding comprising one or more vectors representing (a) values stored in fields of the target set of one or more data objects and at least one of (b) structural information about the target set of one or more data objects and (c) information about a database in which the target set of one or more data objects is stored (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “applying the machine learning model to the embedding to generate the suggestion (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). In the instant case, Claims 7-8, 12, and 19, 21, 24 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. Dependent claim 19 is the same analogy and rejected using similar analysis as claim 12. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims as claim 8 which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 7-8, 12, and 19, 21, 24 are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101. 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. 07-21-aia AIA Claims 1-3, 5-7, 13-16, 20-21, and 24 are rejec ted under 35 U.S.C. 103 as being unpatentable over Boost Clean: Automated Error Detection and Repair for Machine Learning Published 2017 [hereinafter D1] in view of Triplet et al. [US 2022/0345356 A1, hereinafter Triplet]. With r egard to Claim 1, D1 teach a non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors (P.8, Col. 2, “we used standard classification models and featurization techniques from Python sklearn. The classifiers were trained in Python 2.7 and timing experiments were run on an Amazon EC2 m4.16xlarge instance2”, P.1, 1. INTRODUCTION, “The availability of data and vast cloud-based computational resources has ushered in an era of more sophisticated machine learning (ML) models in prediction, recommendation, and automation”, P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”) , causes performance of operations comprising: training a machine learning model (P.1, Col. 2, ¶3, “To reduce this burden, we present a new system, called Boost-Clean, that automates the process of detecting and repairing a common class of data errors called domain value violations that occur when an attribute value is outside of its value domain”, Abstract, “We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting”, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected, by selecting the operation that performs best with updated weights. The result is a new classifier Cclean that is derived from the ensemble.”) , the training comprising: obtaining training data sets (P.3, Col. 2, 3.1 problem setup, “BoostClean takes as input a dirty training dataset (Xtrain; Ytrain) …”) , each training data set comprising: a particular set of one or more data objects associated with a particular issue (Abstract, “An important class of such inconsistencies are domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations”, P.1, ¶3, “this class of errors, which include missing data, incorrect data, or inconsistent representations of the same logical data value”, P.7, 5.1.2, “Missing Values … Text error Using Word Embedding …”) ; and a label associated with the particular set of one or more data objects (P.3, Col. 2, 3.1 problem setup, “BoostClean takes as input a dirty training dataset (Xtrain; Ytrain) where both the features Xtrain and labels Ytrain may have errors, as well as a test dataset (Xtest; Ytest) where the features may contain errors however the labels Ytest are correct”), that specifies indicating a remediation action for the particular issue for the particular set of one or more data objects (P.2, Col. 1, ¶3, “BoostClean takes as input a relational table, a library of detector functions D that generate (possibly incorrect) predicates that match candidate dirty records, a library of repair functions F that transform or delete a record”, P.4, “3.2.3 Conditional Repairs BoostClean applies repair functions to specific sets of records through the use of conditional repairs. A conditional repair lk = (pk; fk) …”) ; applying the training data sets to the machine learning model, wherein suggestions for remediation actions for remediating issues associated with a set of data objects of the training data sets are output by the machine learning model ( P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”, P.4, Col. 2, ¶3, “During the training phase, we apply the data repairs in sequence over the training dataset prior to training the classifier …”) ; based on the suggestions for remediations output by the machine learning model and labels associated with the set of data objects of the training data sets, iteratively retraining the machine learning model to improve an accuracy of the machine learning model (P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions … we recalculate the wights. Repeat until B cleaning operations are selected …”, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected, by selecting the operation that performs best with updated weights. The result is a new classifier Cclean that is derived from the ensemble”) ; receiving a target set of one or more data objects (P.3, Col. 2, 3.1 problem setup, “BoostClean takes as input a dirty training dataset (Xtrain; Ytrain) where both the features Xtrain and labels Ytrain may have errors, as well as a test dataset (Xtest; Ytest) where the features may contain errors”) ; applying the machine learning model to the target set of one or more data objects (P.5, Col. 1, ¶3, “Figure 2 summarizes the training and prediction workflows given the optimal sequence of conditional repairs L_ … The blue lines depict how BoostClean generates a prediction for a test record: the classifier C makes a prediction using the record cleaned by the conditional data repairs”) , wherein a suggestion for remediating a first issue with the target set of one or more data objects is generated by the machine learning model based on applying the machine learning model to the target set of one or more data objects (P. 6, “Finally, the conditional repairs L_ are sent to the Deployer, which compiles the sequence into a classifier that can detect and repair errors in the test records”, P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”, P.4, Col. 2, ¶3, “During the training phase, we apply the data repairs in sequence over the training dataset prior to training the classifier …”, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected …”) . D1 does not explicitly teach presenting, by a graphical user interface, an interface element representing the suggestion. Triplet teach a non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors (¶6, “non-transitory computer-readable media“, “ analysis system also includes a processing device and a memory device configured to store a computer program having instructions. When executed”) , causes performance of operations comprising: training a machine learning model (¶62, “configured to utilize ML techniques for training a ML model. In this sense, the ML techniques can be used to analyze the collected data to learn correlations within the data”) to the training comprising: obtaining training data sets (¶211, “obtained test data was divided into training and testing sets. The training set was used for learning and testing set was used to test the system on new data”) , each training data set comprising: a particular set of one or more data objects associated with a particular issue (¶61, “collected data may include information regarding events in the Wi-Fi system 40, alarms, syslog data, performance metrics, and/or other suitable types of data”, ¶10, “one or more root-causes may include errors related to the client device and/or server device”) ; and a label associated with the particular set of one or more data objects (Fig. 13, 200, Fig. 7A, true label, predicted label, ¶215, “processing and labeling the heterogeneous data; and training the ML models with the heterogeneous data”), that specifies indicating a remediation action for the particular issue for the particular set of one or more data objects (¶224, “Each point (or node) in the hierarchical model may be mapped to a resolution workflow for close-loop automation”, “root-cause and associated resolution workflow may be triggered …”) ; applying the training data sets to the machine learning model, wherein suggestions for remediation actions for remediating issues associated with a set of data objects of the training data sets are output by the machine learning model (¶208, “vector-transformed data may be fed to an ML classifier for model training and probabilistic analysis”, ¶164, “perform root-cause analysis and come up with a list of ranked probable root-causes for a given set of symptoms. The problem management component 140 may then use interfaces with a ticketing system 148 and a network orchestrator 150 to push remediation actions to the devices of the Wi-Fi system”) ; based on the suggestions for remediations output by the machine learning model and labels associated with the set of data objects of the training data sets, iteratively retraining the machine learning model to improve an accuracy of the machine learning model (“The user is allowed to select one of the root-causes that he or she thinks is a reasonable root-cause remediation and submit it to a workflow management tool. User feedback is implicitly collected when the user selects a particular root-cause and is stored in the knowledge error database (e.g., database 30, KeDB 105 144, etc.), which can be used offline to retrain the ML models and/or tune the distance metric”, ¶223, “user feedback may be used to retrain and improve the accuracy of the ML sub-models”) ; receiving a target set of one or more data objects, applying the machine learning model to the target set of one or more data objects (¶228, “new incoming data that are similar to the ones that are used for training data. The transformations can be fed to pre-trained models to perform root-cause analysis in near real time”) , wherein a suggestion for remediating a first issue with the target set of one or more data objects is generated by the machine learning model based on applying the machine learning model to the target set of one or more data objects (¶9, “analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes”) ; and presenting, by a graphical user interface, an interface element representing the suggestion (¶163, “User Interface (UI) 146 implemented in a presentation layer that gives a user access to the information via any suitable display device (e.g., a Graphical User Interface (GUI))”, ¶160, “ recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”) . D1 and Triplet are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by Triplet with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to provide to the user with interactive user interface, thereby improving usability and allowing users to review, select, and initiate corrective actions for detected data issues which save time and effort. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 2, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the first issue comprises a non-mathematical relationship between different data objects or between different components of a same data object (D1, Abstract, “An important class of such inconsistencies are domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations”, P. 2, Col. 1, ¶3, “neural network can be individually tailored to each dataset and learn to predict the co-occurrence of attributes in a record”, P. 4, 3.2.1, “predicate expressions may reference combinations of attributes”, P. 3, Col. 2, ¶1, “identifies attribute values not likely to co-occur in the same record”, P.1, ¶3, “this class of errors, which include missing data, incorrect data, or inconsistent representations of the same logical data value”, P.7, 5.1.2, “Missing Values … Text error Using Word Embedding …”, P. 4, ¶2, “if we knew that there are no oil and natural gas companies in the northwest, the predicate … would return fregion; industry”) , wherein the non-mathematical relationship was associated with a plurality of training data sets among the training data sets (D1, P. 3, Col. 2, ¶2, “We surveyed 8 ML datasets used in Kaggle competitions and benchmarks in the UCI ML repository, and found that a majority of the non-statistical errors could be detected as domain integrity constraints, i.e., disallowed values in single columns. We apply a combination of heuristic checks for missing values and data type errors, and a neural network based error detector that identifies attribute values not likely to co-occur in the same record”, P.8, 6, ¶1, “We execute BoostClean on 12 datasets based on three sets of real-world cases—machine learning competitions, data analysis pipelines, and Company X—and report accuracy measures and end-to-end runtime”, Abstract, “Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real world data analyses, and production datasets that show that Boost-Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives”) , and wherein a particular remediation action corresponding to the suggestion was indicated in the plurality of training data sets (P.1, Abstract, “We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy”, P.2, “BoostClean takes as input a relational table, a library of detector functions D that generate (possibly incorrect) predicates that match candidate dirty records, a library of repair functions F that transform or delete a record, and a user-specified classifier training procedure train()”, P.2, Col. 1, ¶3, “The second component then uses boosting to generate a sequence of conditional cleaning scripts (pi; ri) to be applied to the training and test datasets, where ri is the repair function to be applied to records matching predicate pi”, P.7, 5.2, Repairs, “In addition to detector generators, BoostClean is pre-populated with a set of simple repair functions. A function is applied to all records identified by a detector’s predicate … Default Prediction (prediction)”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 3, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the first issue comprises a mathematical relationship between different data objects or between different components of a same data object (D1, P.4, 3.2.1, ¶2, “predicate performs Quantitative Error Detection [23] by checking whether the record’s n_emp value is further than 5 standard deviations of the mean”, P.7, 5.1.2, “Numerical Attributes: This featurizer projects the numerical attributes into a feature vector. We find that this is effective at identifying numerical outliers that are statistically different from the rest of the data. For example, the sensor dataset has sensor readings on the order of 300C when typical readings are 17C”) , wherein the mathematical relationship was associated with a plurality of training data sets of the training data sets (D1, P. 4, 3.3, “we focus on domain integrity constraints, i.e., a set of allowed values in each attribute’s domain–an error being defined as an attribute value not in this set. Given a violation, we assume that each of the repair actions sets the attribute to an allowed value”, Abstract, “Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real world data analyses, and production datasets) , and wherein a particular remediation action corresponding to the suggestion was indicated in the plurality of training data sets (P.1, Abstract, “We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy”, “BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting”, P.2, “BoostClean takes as input a relational table, a library of detector functions D that generate (possibly incorrect) predicates that match candidate dirty records, a library of repair functions F that transform or delete a record, and a user-specified classifier training procedure train()”, P.2, Col. 1, ¶3, “The second component then uses boosting to generate a sequence of conditional cleaning scripts (pi; ri) to be applied to the training and test datasets, where ri is the repair function to be applied to records matching predicate pi”, P.7, 5.2, Repairs, “In addition to detector generators, BoostClean is pre-populated with a set of simple repair functions. A function is applied to all records identified by a detector’s predicate … Default Prediction (prediction)”, Abstract, “Our evaluation on a collection of 12 datasets … show that Boost-Clean can increase absolute prediction accuracy by up to 9%”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 5, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein each data object of the target set of one or more data objects includes a plurality of fields storing a respective plurality of values (D1, P. 2, 2, “Past clients are stored in a relational database: R(id; name; num_emp; industry; region; successful) where name is the company name, num_emp is the number of employees in the company, industry is a categorical attribute that describes the industry segment, region is a code indicating the region of the country the business is headquartered, and is_successful is a Boolean …”, P. 3, 3.1, “Let a record ri = (xi; yi) 2 (Xtrain; Ytrain) denote the features along with its corresponding (possibly null) label”, P.2, Col. 1, ¶3, “BoostClean takes as input a relational table, a library of detector functions D that generate (possibly incorrect) predicates that match candidate dirty records, a library of repair functions F that transform or delete a record”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 6, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the target set of one or more data objects includes at least one of a spreadsheet and a database record (D1, P. 2, 2, “Past clients are stored in a relational database: R(id; name; num_emp; industry; region; successful) where name is the company name, num_emp is the number of employees in the company, industry is a categorical attribute that describes the industry segment, region is a code indicating the region of the country the business is headquartered, and is_successful is a Boolean …”, P. 3, 3.1, “Let a record ri = (xi; yi) 2 (Xtrain; Ytrain) denote the features along with its corresponding (possibly null) label”, P.2, Col. 1, ¶3, “BoostClean takes as input a relational table, a library of detector functions D that generate (possibly incorrect) predicates that match candidate dirty records, a library of repair functions F that transform or delete a record”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 7, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the operations further comprise: detecting a merger of values among two or more data objects (D1, Abstract, “Training data are often combined from a variety of different sources, each susceptible to different types of inconsistencies, and as new data stream in during prediction time, the model may encounter previously unseen inconsistencies …”, P.1, Col. 2, ¶2, “modern prediction models rely on data integrated from a wide variety of sources (e.g., Company X combines on average 5-10 sources to train a model)”, P. 4, 3.2.1, “We define a predicate pi … we say that r is a candidate dirty record if pi(r) ≠ϕ”, P. 3, 5.1.2, “neural network based error detector that identifies attribute values not likely to co-occur in the same record”, P.7, Col. 2, ¶2, “We treat each record as a document, where each attribute value is a “word”. The model learns to embed attributes that co-occur in the same records closer in the vector-space”, system detect errors and inconsistence in merged data caused by the merger ) ; and detecting the first issue with the target set of one or more data objects based on the merger of the values among the two or more data objects (D1, P.4, 3.2.2, ¶2, “Data repairs modify the values of a training record in response to a detected error (due to a predicate)”, P. 3, 5.1.2, “neural network based error detector that identifies attribute values not likely to co-occur in the same record”, P.4, “EXAMPLE 4 (VALUE CANONICALIZATION). The following script canonicalizes different representations for Western United States”) wherein applying the machine learning model to the target set of one or more data objects is responsive to detecting the first issue (D1, P.6 , “the conditional repairs L_ …, which compiles the sequence into a classifier that can detect and repair errors in the test records”, P. 5, Col. 1, ¶1, “identify the optimal sequence L_ of B conditional repairs such that the resulting classifier CL_ maximizes prediction accuracy ”, classifier runs after issue detection ) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 13, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the first issue is an anomaly in the target set of one or more data objects (D1, P.2, “Automatic Model Improvements: We evaluated BoostClean on 12 datasets collected from Kaggle, the UCI repository, real-world data analyses, and Company X, and improved absolute prediction accuracy by up to 9% in comparison to baseline (non-ensembled integrity constraint+quantitative outlier detector) approaches on completely unseen test data”, P.6, Col. 2, 5.1.1, ¶4, “We instead use a variant of Random Forest classification, called Isolation Forests [35]. The Isolation Forest is inspired by the observation that outliers are more easily separable from the rest of the dataset than non-outliers. It grows a forest of isolation trees, where each tree is randomly grown—it selects a random attribute and a random threshold value—until a leaf node contains a single record. The length of the path to the leaf node is a measure for the outlierness of the record—a shorter path more strongly suggests that the record is an outlier”, D2, ¶61, “Errors are easily identified by noting numeric outliers, identifying duplicate records (including perfect and imperfect matches), and providing comprehensive data quality and data expansion suggestions”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 14, Claim 14 is similar in scope to claim 1; therefore it is rejected under similar rationale. With regard to Claim 15, Claim 15 is similar in scope to claim 2; therefore it is rejected under similar rationale. With regard to Claim 16, Claim 16 is similar in scope to claim 3; therefore it is rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 1; therefore it is rejected under similar rationale. D1 further teach a system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform claim 1 (P.8, Col. 2, “we used standard classification models and featurization techniques from Python sklearn. The classifiers were trained in Python 2.7 and timing experiments were run on an Amazon EC2 m4.16xlarge instance2”, P.1, 1. INTRODUCTION, “The availability of data and vast cloud-based computational resources has ushered in an era of more sophisticated machine learning (ML) models in prediction, recommendation, and automation”, P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”). The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 21, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein the operations further comprise: detecting, without intervening user input, a modification to one or more values in the target set of one or more data objects (Triplet, ¶9, “In response to detecting that a client device experiences a network access failure that prevents communication with a server device, the process may include the step of analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes”, ¶228, “new incoming data that are similar to the ones that are used for training data. The transformations can be fed to pre-trained models to perform root-cause analysis in near real time”, ¶61, “collected data may include information regarding events in the Wi-Fi system 40, alarms, syslog data, performance metrics, and/or other suitable types of data”, ¶228, “new incoming data that are similar to the ones that are used for training data. The transformations can be fed to pre-trained models to perform root-cause analysis in near real time”) ; and responsive to detecting the modification, automatically applying the machine learning model to the target set of one or more data objects to generate the suggestion for remediating the first issue (Triplet, ¶9, “In response to detecting that a client device experiences a network access failure that prevents communication with a server device, the process may include the step of analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes”, ¶228, “new incoming data that are similar to the ones that are used for training data. The transformations can be fed to pre-trained models to perform root-cause analysis in near real time”, ¶164, “perform root-cause analysis and come up with a list of ranked probable root-causes”, ¶224, “Each point (or node) in the hierarchical model may be mapped to a resolution workflow for close-loop automation. The most likely root-cause prediction may be automatically triggered if it is above a threshold. If one or more root-causes is above the threshold, a root-cause and associated resolution workflow may be triggered”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 24, D1-Triplet teach the non-transitory computer readable medium of claim 1, further comprising: generating an embedding of the target set of one or more data objects (P.7, Col. 2, ¶2, “We treat each record as a document, where each attribute value is a “word”. The model learns to embed attributes that co-occur in the same records closer in the vector-space”) , the embedding comprising one or more vectors representing (a) values stored in fields of the target set of one or more data objects (D1, P.7, 5.1.2, “Numerical Attributes: This featurizer projects the numerical attributes into a feature vector”) and at least one of (b) structural information about the target set of one or more data objects (P.7, Col. 2, ¶2, “We treat each record as a document, where each attribute value is a “word”) and (c) information about a database in which the target set of one or more data objects is stored; and applying the machine learning model to the embedding to generate the suggestion (D1, P. 3, 5.1.2, “neural network based error detector that identifies attribute values not likely to co-occur in the same record ”, D1, P. 6, “Finally, the conditional repairs L_ are sent to the Deployer, which compiles the sequence into a classifier that can detect and repair errors in the test records”, P.12, 8, “ detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”) . The same motivation to combine for claim 1 equally applies for current claim . 07-21-aia AIA Claim s 4, and 8-11, 17-18, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over BoostClean: Automated Error Detection and Repair for Machine Learning Published 2017 [hereinafter D1] in view of Triplet et al. [US 2022/0345356 A1, hereinafter Triplet] in view of Sankaran et al. [US 2015/0324346 A1, hereinafter D2] . With regard to Claim 4, D1- Triplet teach the non-transitory computer readable medium of claim 1. The same motivation to combine for claim 1 equally applies for current claim. D1-Triplet does not explicitly teach presenting the interface element includes visually identifying cells in a spreadsheet representing a subset of the target set of one or more data objects. D2 teach presenting the interface element includes visually identifying cells in a spreadsheet representing a subset of the target set of one or more data objects (D2, Fig. 4-10, ¶6, “Typically a spreadsheet has data records arranged in rows, with columns containing different attributes or fields of data”, ¶70, “The user interface 400 is populated with data extracted from one or more data sources, e.g., 102. As shown, the user interface 400 includes one or more spreadsheet-formatted data sets 405 in a data section 410 “, ¶72, “The UI provides a visual distinction between rows that matched and rows that did not match”, ¶81, “the email blast list.csv spreadsheet is shown in the data section 910 and the email column 909 is highlighted or otherwise visually distinguished , ¶94, “the user has clicked on the 40-60% rate bar (1695) on the graphical representation of the column data 1630, which caused the rows in column 1607 in the data section 1610 to be visually distinguished for easy identification”) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet use spreadsheets formatted data sources as described above to provide D1-Triplet with the ability to process, display, remediate inconsistencies in spreadsheets formatted datasets and other merged data sources. This would improve usability and expand the types of structured data that could be analyzed and repaired within D1-Triplet remediation framework. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 8, D1-Triplet teach the non-transitory computer readable medium of claim 7, wherein detecting the merger of the values of the two or more data objects includes detecting a merger of a first set of values from a [first set of values] and a second set of values from a database record (D1, Abstract, “Training data are often combined from a variety of different sources, each susceptible to different types of inconsistencies, and as new data stream in during prediction time, the model may encounter previously unseen inconsistencies …”, P.1, Col. 2, ¶2, “modern prediction models rely on data integrated from a wide variety of sources (e.g., Company X combines on average 5-10 sources to train a model)”), wherein detecting the first issue with the target set of one or more data objects comprises detecting an anomaly in one of the [first set of values] and the database record resulting from the merger (D1, P. 5, Col. 1, ¶1, “identify the optimal sequence L_ of B conditional repairs such that the resulting classifier CL_ maximizes prediction accuracy ”, Abstract, “Training data are often combined from a variety of different sources, each susceptible to different types of inconsistencies, and as new data stream in during prediction time, the model may encounter previously unseen inconsistencies …”, P.1, Col. 2, ¶2, “modern prediction models rely on data integrated from a wide variety of sources (e.g., Company X combines on average 5-10 sources to train a model)”, P.4, 3.2.2, ¶2, “Data repairs modify the values of a training record in response to a detected error (due to a predicate)”, P. 3, 5.1.2, “neural network based error detector that identifies attribute values not likely to co-occur in the same record ”) . The same motivation to combine for claim 1 equally applies for current claim. D1-Triplet does not explicitly teach that one of the multiple sources of data include spreadsheet. D2 disclose detecting the merger of the values of the two or more data objects includes detecting a merger of a first set of values from a spreadsheet and a second set of values from a database record D2, ¶6, ¶40, ¶42, ¶46, ” The data profiling module 110 also identifies at least one matching column among the spreadsheets selected. The data profiling module 110 calculates a match metric for the at least one matching column, and unifies the spreadsheets into a single composite spreadsheet using the at least one identified matching column”, ¶67, “ “spreadsheet,” as used herein, is any data file in which data is arranged in a table of rows and columns, and may be in various formats”, ¶64, “The data sources/targets 102 (also individually referred to herein as data source/target 102 and) include one or more systems for managing data … Examples of data sources 102 include databases, applications, and local files”) ; wherein detecting the first issue with the target set of one or more data objects comprises detecting an anomaly in one of the spreadsheet and the database record resulting from the merger (D2, “For joining disparate data, the system provides simplicity to the user by providing more intelligence at each step of the process. The system recognizes likely sources, provides visualization of most reasonable keys or overlapping columns, and improved summarization”, ¶46, “The data profiling module 110 receives user selection of spreadsheets, and the data from the selected spreadsheets is profiled by the data profiling module 110. The data profiling module 110 also identifies at least one matching column among the spreadsheets selected. The data profiling module 110 calculates a match metric for the at least one matching column, and unifies the spreadsheets into a single composite spreadsheet”, ¶66, “Using the profile information, the data sets can be unified in various ways … combine or join function … merge or perform a union function … a lookup function …“, ¶67, “As a next step for unifying the two data sets, at least one matching column is identified 330 … the system calculates 340 a match metric for the at least one matching column”, ¶68, “The method then unifies 350 the data sets into a single composite spreadsheet using the at least one identified matching column. … The system also generates 360 a preview view of the composite spreadsheet, visually indicating the at least one matching column, any non-matching columns between the data sets, and the match metric for the matching columns”, ¶79, “For the merge, the system automatically profiles all of the information in every single column between the two sheets and identifies matching and non-matching columns, as shown in information section 815”, ¶80, “ As a result of the merge, the information section 815, now shows an overview 821 with the merged names of the sources, the total number of rows (990=498+550) and the total number of columns (15)”) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet use spreadsheets formatted data sources as described above to provide D1-Triplet with the ability to process, display, remediate inconsistencies in spreadsheets formatted datasets and other merged data sources. This would improve usability and expand the types of structured data that could be analyzed and repaired within D1-Triplet remediation framework. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 9, D1-Triplet teach the non-transitory computer readable medium of claim 1, wherein presenting the interface element representing the suggestion (¶163, “User Interface (UI) 146 implemented in a presentation layer that gives a user access to the information via any suitable display device (e.g., a Graphical User Interface (GUI))”, ¶160, “ recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”) . The same motivation to combine for claim 1 equally applies for current claim. D1-Triplet does not explicitly teach generating a selectable task card icon in a first region of a graphical user interface (GUI); while displaying one or more fields associated with the first issue in a second region of the GUI different from the first region. D2 teach presenting the interface element representing the suggestion (D2, ¶51, “The UI module 122 receives data for display in the UI, generates a user interface corresponding to received data, populates the interface with the data received, displays data refinement suggestions”, ¶¶44-45, ¶70, “A suggestions card 435 provides the user with suggestions”) comprises: generating a selectable task card icon in a first region of a graphical user interface (GUI) (D2, ¶70, “ various cards 420-435 in an information section 415”, “A suggestions card 435 provides the user with suggestions”, ¶57, “The suggestion module 230 … Once the system has a list of suggestions, it returns them to the user using basic ranking logic”) while displaying one or more fields associated with the first issue in a second region of the GUI different from the first region (D2, ¶53, “The data section of the UI is for displaying the spreadsheets for analysis. The information section of the UI is for displaying profiled information about the spreadsheets”, ¶70, “ user interface 400 includes one or more spreadsheet-formatted data sets 405 in a data section 410 and various cards 420-435 in an information section 415”) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet to improve usability by visually separating issues and corresponding remediation suggestions separately from the underlying data fields, thereby allowing users to more efficiently identify affected records and reviews or apply corrected actions. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 10, D1-Triplet-D2 teach the non-transitory computer readable medium of claim 9, wherein the operations further comprise: detecting a selection of the selectable task card icon (D2, “Fig. 13A-13C, ¶88, ¶89, “FIG. 13B, the user hovers over the Validate as email suggestion option in suggestions card 1335, and a preview view is shown in data section 1310 that adds to the data an email validation column 1313. If the user clicks the preview commits and the suggestion is applied, whereas if the user stops hovering over the suggestion, the preview view disappears and the data reverts to the prior state”) ; and responsive to detecting the selection of the selectable task card icon: changing data displayed in the second region of the GUI from a first set of data excluding any value associated with the first issue to a second set of data including at least one value associated with the first issue (D2, ¶89, “a preview view is shown … If the user clicks the preview commits and the suggestion is applied … “, ¶90, “ after the user clicks on the Extract as domain name from email suggestion and the suggestion is applied, showing new column email domain 1314. In this example, the user has selected the email domain column 1314, and the information section 1315 thus now shows information corresponding to that column”) . The same motivation to combine for claim 9 equally applies for current claim. With regard to Claim 11, D1-Triplet-D2 teach the non-transitory computer readable medium of claim 9, wherein training the machine learning model includes training the machine learning model to identify a category associated with the first issue (D1, D1, Abstract, “An important class of such inconsistencies are domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations”, P.1, ¶3, “this class of errors, which include missing data, incorrect data, or inconsistent representations of the same logical data value”, P.7, 5.1.2, “Missing Values … Text error Using Word Embedding …”, P.7, 5.1.2, “effective at identifying numerical outliers” D2, ¶55, “An inference module 210 discovers characteristics of columns and tables. It leverages profiling functionality, data type information, and domains as well as content from data quality sources. The inference module 210 provides enhanced data profiling for business users (e.g., by recognizing data such as state, phone number, etc.)”, ¶56, “The system stores rules that may vary for each data set. A first set of rules pertains to data type … A second set of rules are based on data domains … A third set of rules are based on enterprise data … the recommendation engine also may learn from usage” , ¶57, “The suggestions module 230 uses the characteristics of the selected data to filter the operations that are appropriate to suggest to the user. … The system can learn from user actions and capture the data for a future data set, learn from suggestions to increase the relevance of suggestions. The system can capture a data experts actions and suggest to a novice via sharing when a similar characteristics shows up. Once the system has a list of suggestions, it returns them to the user”) , wherein the operations further comprise: presenting the category together with the selectable task card icon (D2, Fig. 4, ¶70, “The user interface 400 is populated with data extracted from one or more data sources, e.g., 102. As shown, the user interface 400 includes one or more spreadsheet-formatted data sets 405 in a data section 410 and various cards 420-435 in an information section 415”, “A suggestions card 435 provides the user with suggestions for correcting, enhancing, or otherwise augmenting the data, if any. In the example shown, the suggestion is verify as first name”, Fig. 13A-13C, ¶88, “FIGS. 13A-13C show an example …In FIG. 13A, the user wants to look more closely at email addresses, and thus has selected email column 1307 in data section 1310. As before, the information section 1315 displayed the profiled data related to the column 1307. … The information section 1315 includes … a suggestions card 1335 listing two suggestions: validate as email and extract domain name form email”, ¶95, “FIG. 9B, the value frequencies card 930 shows four values for the Opt column 911: N, FALSE, Y, and TRUE”) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet so that detected issue categories and corresponding remediation suggestions could be presented together in an organized and interactive user interface, thereby improving user understanding and ability to review detected issues and action. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 17, Claim 17 is similar in scope to claim 9; therefore it is rejected under similar rationale. With regard to Claim 18, Claim 18 is similar in scope to claim 10; therefore it is rejected under similar rationale. With regard to Claim 22, D1-Triplet teach non-transitory computer readable medium of claim 1, wherein generating the suggestion for remediating the first issue (D1, P. 6, “Finally, the conditional repairs L_ are sent to the Deployer, which compiles the sequence into a classifier that can detect and repair errors in the test records”, P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”, P.4, Col. 2, ¶3, “During the training phase, we apply the data repairs in sequence over the training dataset prior to training the classifier …”, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected …”, Triplet ¶9, “analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes”) . The same motivation to combine for claim 1 equally applies for current claim. D1-Triplet does not teach generating a recommendation to add a new column to a data object of the target set of one or more data objects, the new column corresponding to an attribute absent from the data object, wherein the recommendation is based on a co-occurrence pattern between values of the attribute and values of one or more other attributes learned by the machine learning model during training. D2 teach generating the suggestion for remediating the first issue comprises generating a recommendation to add a new column to a data object of the target set of one or more data objects, the new column corresponding to an attribute absent from the data object, wherein the recommendation is based on a co-occurrence pattern between values of the attribute and values of one or more other attributes learned by the machine learning model during training (Fig. 10, ¶66, “a lookup function can be used to add column of data from one sheet, the lookup sheet, to another sheet that doesn't include that column”, ¶83, “ Because the system has profiled other data related to product information, it provides a list of possible additional data to enrich with … The user has selected MSRP, and as a result an additional column 1011 has been added in the data section 1010 for the MSRP corresponding to the various products“) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet so that attributes identified as related through learned attributes relationships and correlations could be recommended and added to the data set, thereby improving data completeness and providing additional information useful for the user to facilitate detecting issues, remediation, and predictive analysis which save user’s time and effort. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 23, D1-Triplet teach non-transitory computer readable medium of claim 1, wherein , wherein generating the suggestion for remediating the first issue comprises (D1, P. 6, “Finally, the conditional repairs L_ are sent to the Deployer, which compiles the sequence into a classifier that can detect and repair errors in the test records”, P.12, 8, “We have prototyped this idea in BoostClean, a new data cleaning system that detects errors in ML data and uses knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy”, P.4, Col. 2, ¶3, “During the training phase, we apply the data repairs in sequence over the training dataset prior to training the classifier …”, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected …”, Triplet ¶9, “analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes”, Triplet, ¶62, “configured to utilize ML techniques for training a ML model. In this sense, the ML techniques can be used to analyze the collected data to learn correlations within the data”) , the recommendation is based on a co-occurrence pattern between values of the attribute and values of one or more other attributes learned by the machine learning model during training (D1, P.7, Col. 2, ¶2, “We treat each record as a document, where each attribute value is a “word”. The model learns to embed attributes that co-occur in the same records closer in the vector-space”) . The same motivation to combine for claim 1 equally applies for current claim. D1-Triplet does not teach generating a recommendation to add a new column to a data object of the target set of one or more data objects, the new column corresponding to an attribute absent from the data object. D2 teach generating a recommendation to add a new column to a data object of the target set of one or more data objects, the new column corresponding to an attribute absent from the data object (Fig. 10, ¶66, “a lookup function can be used to add column of data from one sheet, the lookup sheet, to another sheet that doesn't include that column”, ¶83, “ Because the system has profiled other data related to product information, it provides a list of possible additional data to enrich with … The user has selected MSRP, and as a result an additional column 1011 has been added in the data section 1010 for the MSRP corresponding to the various products“) . D1-Triplet and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of detecting issues and providing and displaying remediation recommendations for users. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet so that attributes identified as related through learned attributes relationships and correlations could be recommended and added to the data set, thereby improving data completeness and providing additional information useful for the user to facilitate detecting issues, remediation, and predictive analysis which save user’s time and effort. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143) . 07-21-aia AIA Claim s 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over BoostClean: Automated Error Detection and Repair for Machine Learning Published 2017 [hereinafter D1] in view of Triplet et al. [US 2022/0345356 A1, hereinafter Triplet] in view of Sankaran et al. [US 2015/0324346 A1, hereinafter D2] further in view of Wilson et al. [US 2022/0207575 A1, hereinafter Wilson] . With regard to Claim 12, D1-Triplet-D2 teach the non-transitory computer readable medium of claim 9, wherein training the machine learning model includes training the machine learning model to identify a priority level associated with the first issue (D1, P.5 , 4.3, Boost-and-Clean Algorithm, “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected, by selecting the operation that performs best with updated weights. The result is a new classifier Cclean that is derived from the ensemble.”, P .5, 4.3, “The algorithm has a few intuitive properties: (1) it prioritizes cleaning operations that improve performance, (2) if no such operations exist it does no worse than the base classifier, and (3) it is agnostic to the implementation of the classifiers”, P.2, Col. 1, “Cleaning as Boosting: We present a new automated data cleaning system based on statistical boosting that finds the best ensemble of operations from a library of operations to maximize the predictive performance of a downstream model.”, P.4-5, “Given (Xtrain; Ytrain), (Xtest; Ytest), a library of detector generators D and of repair functions F, and a training procedure train, identify the optimal sequence L_ of B conditional repairs such that the resulting classifier CL_ maximizes prediction accuracy”, Triplet, ¶160, “ recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”, D2, ¶57, ¶58, “the suggestions module 230 captures the users' feedback and reactions to the suggestions, as well as alternative operations for improvement of the ranking model and to leverage collaborative filtering”, “ for improvement of the ranking model … The system may use various forms of machine learning or other learning models to use information about changes applied by one user and provide those changes as suggestions to subsequent users”) wherein the operations further comprise: identifying a plurality of issues in the target set of one or more data objects (Triplet ¶164, “list of ranked probable root-causes for a given set of symptoms”, D2, ¶61, “Errors are easily identified by noting numeric outliers, identifying duplicate records (including perfect and imperfect matches), and providing comprehensive data quality and data expansion suggestions“, ¶69, “further analysis of the unified data is done, e.g., via data profiling module 110, to identify … inconsistencies in data, value frequencies“, ¶45, “the data profiling module 110 identifies data patterns in the data”, “If a data field in a column stores data that does not conform to a pattern associated with the column, then the system may make a suggestion to correct the data. … system stores patterns and rules that may vary for each data set and that correspond to the various data types, as well as formatting, enterprise specific data, profiling information learned from previous users, and any other profiling information that may be applicable to the various data sets”) ; generating, by the machine learning model, (a) priority values associated with the respective plurality of issues, and (b) a respective plurality of suggestions for remediating the plurality of issues (Triplet, ¶160, “ recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”, ¶164, “list of ranked probable root-causes for a given set of symptoms”, D2, ¶57, “suggestions module 230 uses the inference data to search for suggestions”, “ Once the system has a list of suggestions, it returns them to the user using basic ranking logic”, ¶58, “the suggestions module 230 captures the users' feedback and reactions to the suggestions, as well as alternative operations for improvement of the ranking model …”, ¶88, “The information section 1315 also includes a domain card 1325 showing a single domain for the column, a value frequency card 1330 showing a frequency of one for each email address, and a suggestions card 1335 listing two suggestions: validate as email and extract domain name form email”, ¶91, “In the suggestion card 1435 of the information section 1415 the suggestion is Split by punctuation”) ; and determining a first priority value associated with the first issue exceeds a second priority value associated with a second issue (Triplet, ¶160, “ recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”, ¶164, “list of ranked probable root-causes for a given set of symptoms”, D2, ¶57, “ Once the system has a list of suggestions, it returns them to the user using basic ranking logic”, ¶58, “the suggestions module 230 captures the users' feedback and reactions to the suggestions, as well as alternative operations for improvement of the ranking model …”) ; responsive to determining the first priority value associated with the first issue exceeds the second priority value associated with the second issue: presenting, by the GUI, the interface element representing the suggestion (Triplet, ¶160, “Once an inference engine recommends ranked potential root-causes for the generated syslog and alarm events in the system, they will be presented to a user”, ¶164, (D2, Fig. 13B, ¶57, “ suggestions module 230 uses the inference data to search for suggestions”, “ Once the system has a list of suggestions, it returns them to the user using basic ranking logic”, ¶58, “the suggestions module 230 captures the users' feedback and reactions to the suggestions, as well as alternative operations for improvement of the ranking model …”, ¶70, “A suggestions card 435 provides the user with suggestions for correcting, enhancing, or otherwise augmenting the data, if any”) . The same motivation to combine for claim 9 equally applies for current claim. D1-Triplet-D2 does not explicitly teach refraining from presenting, by the GUI, any interface element representing a particular suggestion corresponding to the second issue. Wilson teach responsive to determining a first priority value associated with the first [element] exceeds a second priority value associated with a second [element], presenting, by the graphical user interface (GUI), the interface element representing the suggestion, and refraining from presenting, by the GUI, any interface element representing a suggestion corresponding to the second [element] (¶¶314-316, “determined which venues best match the encoded values of each venue within the area, filtered the set of venues to a final filter set and determined overall scores for each venue in the filter set. The recommendation engine 112 may then provide at step S3812 a variety of outputs to the user such as supplying only Restaurant 1 to the user via user interface 110 as it has the highest overall score … In selected embodiments, the recommendation engine 112 may set a predetermined recommendation threshold, such as 0.24, and only provide restaurants meeting or exceeding this value. In this case, only Restaurant 1 would be supplied to User 1 via the user interface 110. Assuming none of the recommendations are above threshold value, geometric contextualization could be implemented as described herein to resolve this issue”) . D1-Triplet-D2 and Wilson are analogous art to the claimed invention because they are from a similar field of endeavor of providing and displaying recommendations using a graphical user interface. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-Triplet-D2 resulting in resolutions as disclosed by Wilson with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-Triplet-D2 as described above to provide to the user the data with highest priority without overwhelming the user with a large number of results which could distract the user from the important issues. This is simply Combining prior art elements according to known methods to yield predictable results and a usage of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 19, Claim 19 is similar in scope to claim 12; therefore it is rejected under similar rationale. Response to Arguments Applicant argue that the current amended independent claims do not disclose an abstract idea. Examiner agrees that the current independent claims does not recite steps that could be categorized in the abstract groups; therefore the rejection is withdrawn. However, dependent claims 4, 8-9 are still rejected under 35 USC 101 as they disclose abstract ideas. Applicant argue that BoostClean does not apply training datasets to a model, obtain remediation suggestions as output, and iteratively retrain that model based on a comparison of those suggestions against labels specifying remediation actions. BoostClean assembles a sequence of independently trained weak learners, none of which outputs suggestions for remediating issues in data objects, none of which is associated with labels specifying remediation actions for issues in data objects, and none of which is retrained based on the accuracy of remediation suggestions against such labels. Examiner respectfully disagrees, First, the claims does not require “the output suggestion for remediation actions” be stored as separate explicit remediation labels, nor does the claims require a specific “boosting” or “retraining” architecture. Rather the claims broadly require “training machine learning model”, “applying the training data sets to the machine learning model”, outputting “suggestions for remediation actions”, and “iteratively retraining the machine learning model to improve an accuracy of the model” based on labels and outputs associated with datasets. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a specific “boosting” or “retraining” architecture) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns , 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Second, D1 meets the claims by expressly teaching “automatically selects an ensemble of error detection and repair combinations using statistical boosting” (Abstract), and “knowledge of the labels to adaptively select from a set of repair actions to maximize prediction accuracy” (P. 12, 8). D1 further teaches that the “The boosting algorithm weights the dataset depending on mispredictions, focusing future effort on the ensembles current mispredictions. In each round, we find the L 2 L that generates the classifier with highest test accuracy on the weighted data. After selection, we recalculate the wights. Repeat until B cleaning operations are selected, by selecting the operation that performs best with updated weights. The result is a new classifier Cclean that is derived from the ensemble.”” (P.5 , 4.3). Thus D1 teaches applying training datasets to machine learning process that output remediation selections and iteratively improve the accuracy of those selections using labels, misprediction and repeated retraining operations. Third, Triplet further teaches “processing and labeling the heterogeneous data; and training the ML models with the heterogeneous data” (¶215) where the “obtained data was divided into training data sets and testing data sets” (¶137). Triplet also teach using the trained model to “ predict one or more root-causes”, “configured to begin a remediation procedure for remediating the one or more root-causes” (¶180). Additionally, the usage of user feedback and selected remediation actions to retrain models and improve accuracy (¶204). Applicant argue that the Examiner cites page 12 of BoostClean, describing "using knowledge of the labels to adaptively select form a set of repair actions to maximize prediction accuracy" as corresponding to generating a suggestion for remediating an issue with a data object, as recited in Applicant's claims. However, this portion of BoostClean does not correspond to training, and retraining, a machine learning model on pairings of data object issues and corresponding remediation actions to generate suggestions for remediating issues in data objects. Applicant's claims, a machine learning model is trained with a dataset including a label that is a training signal that teaches the model what remediation action corresponds to what issue. In contrast, BoostClean describes a label that is class labels of a downstream predictive classifier, used to measure whether a candidate cleaning operation improves the classifier's prediction accuracy on held-out test data. Examiner respectfully disagrees, the arguments highlight a single citation in isolation. The mapping provided also relies on D1 deployment and prediction workflow, where the selected conditional repairs are compiled into a classifier that “can detect and repair errors in the test records” and the classifier “makes a prediction using the records cleaned by the conditional data repairs”. Thus the repair/remediation is part of the model application to the target record, not just unrelated classifier evaluation. In addition Triplet disclose the same by expressly teaching labeled heterogeneous training data, ML model training, retraining to improve accuracy, root-cause prediction, and associated remediation workflow See at least ¶9, “analyzing a network access failure to predict one or more root-causes. The process also includes the step of beginning a remediation procedure for remediating the one or more root-causes” ¶215, “processing and labeling the heterogeneous data; and training the ML models with the heterogeneous data”, ¶224, “Each point (or node) in the hierarchical model may be mapped to a resolution workflow for close-loop automation”, “root-cause and associated resolution workflow may be triggered …”. Applicant argue that D1 is not equivalent to the claims as it performs different function than the function specified in the claims, in a different way and produces a different result. Examiner respectfully disagrees, the arguments narrow the claims to require a single model architecture and explicit remediation action labels, neither of which is part of the claims. D1 expressly disclose a ML process that applies repair/remediation operations to detect dirty records, evaluate results against labels, recalculate weights based on misprediction, and iteratively repeats the process to improve accuracy. The deployed classifier “can detect and repair errors and make predictions using the records cleaned by the conditional data repairs See at least P.5, Col. 1, ¶3, “Figure 2 summarizes the training and prediction workflows given the optimal sequence of conditional repairs L_ … The blue lines depict how BoostClean generates a prediction for a test record: the classifier C makes a prediction using the record cleaned by the conditional data repairs”, P. 6, “Finally, the conditional repairs L_ are sent to the Deployer, which compiles the sequence into a classifier that can detect and repair errors in the test records”. Thus, D1 teaches iterative ML based remediation selection and improvement using labeled data, even though implemented through an ensemble boosting architecture and not a single model implementation. Further, Triplet disclose the argued limitation of retraining and remediation through labeled training data, retraining to improve accuracy. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a specific “boosting” or “retraining” architecture) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns , 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant argue BoostClean still does not support a rejection of Claims 1, 14, and 20. "In order to rely on equivalence as a rationale supporting an obviousness rejection, the equivalency must be recognized in the prior art, and cannot be based on applicant's disclosure or the mere fact that the components at issue are functional or mechanical equivalents." (See MPEP 2144.06). A rejection that relies on a reference describing a boosting model of detector-repair pairs as an equivalent of training, and retraining, a machine learning model on pairings of data object issues and corresponding remediation actions to generate suggestions for remediating issues in data objects clearly violates MPEP 2144.06. Examiner respectfully disagrees, it is unclear how a single D1 reference is related to an obvious rejection and how it could be based on the applicant disclosure. As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this "Response to Arguments" section in this office action. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. US Patent Application Publication No. 20200274894 filed by ARGOETI et al. that disclose the ability to use machine learning for anomaly detection See at least ¶30 and to provide content that may help explain grounds for a recommendation score or a risk assessment See at least ¶¶159-164, ¶¶204-214 Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. 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, Michelle Bechtold can be reached at (571) 431-0762. 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. 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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. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/166,081 Page 2 Art Unit: 2148 Application/Control Number: 18/166,081 Page 3 Art Unit: 2148 Application/Control Number: 18/166,081 Page 4 Art Unit: 2148 Application/Control Number: 18/166,081 Page 5 Art Unit: 2148 Application/Control Number: 18/166,081 Page 6 Art Unit: 2148 Application/Control Number: 18/166,081 Page 7 Art Unit: 2148 Application/Control Number: 18/166,081 Page 8 Art Unit: 2148 Application/Control Number: 18/166,081 Page 9 Art Unit: 2148 Application/Control Number: 18/166,081 Page 10 Art Unit: 2148 Application/Control Number: 18/166,081 Page 11 Art Unit: 2148 Application/Control Number: 18/166,081 Page 12 Art Unit: 2148 Application/Control Number: 18/166,081 Page 13 Art Unit: 2148 Application/Control Number: 18/166,081 Page 14 Art Unit: 2148 Application/Control Number: 18/166,081 Page 15 Art Unit: 2148 Application/Control Number: 18/166,081 Page 16 Art Unit: 2148 Application/Control Number: 18/166,081 Page 17 Art Unit: 2148 Application/Control Number: 18/166,081 Page 18 Art Unit: 2148 Application/Control Number: 18/166,081 Page 19 Art Unit: 2148 Application/Control Number: 18/166,081 Page 20 Art Unit: 2148 Application/Control Number: 18/166,081 Page 21 Art Unit: 2148 Application/Control Number: 18/166,081 Page 22 Art Unit: 2148 Application/Control Number: 18/166,081 Page 23 Art Unit: 2148 Application/Control Number: 18/166,081 Page 24 Art Unit: 2148 Application/Control Number: 18/166,081 Page 25 Art Unit: 2148 Application/Control Number: 18/166,081 Page 26 Art Unit: 2148 Application/Control Number: 18/166,081 Page 27 Art Unit: 2148 Application/Control Number: 18/166,081 Page 28 Art Unit: 2148 Application/Control Number: 18/166,081 Page 29 Art Unit: 2148 Application/Control Number: 18/166,081 Page 30 Art Unit: 2148 Application/Control Number: 18/166,081 Page 31 Art Unit: 2148 Application/Control Number: 18/166,081 Page 32 Art Unit: 2148 Application/Control Number: 18/166,081 Page 33 Art Unit: 2148 Application/Control Number: 18/166,081 Page 34 Art Unit: 2148 Application/Control Number: 18/166,081 Page 35 Art Unit: 2148 Application/Control Number: 18/166,081 Page 36 Art Unit: 2148 Application/Control Number: 18/166,081 Page 37 Art Unit: 2148 Application/Control Number: 18/166,081 Page 38 Art Unit: 2148 Application/Control Number: 18/166,081 Page 39 Art Unit: 2148 Application/Control Number: 18/166,081 Page 40 Art Unit: 2148 Application/Control Number: 18/166,081 Page 41 Art Unit: 2148 Application/Control Number: 18/166,081 Page 42 Art Unit: 2148 Application/Control Number: 18/166,081 Page 43 Art Unit: 2148 Application/Control Number: 18/166,081 Page 44 Art Unit: 2148 Application/Control Number: 18/166,081 Page 45 Art Unit: 2148 Application/Control Number: 18/166,081 Page 46 Art Unit: 2148 Application/Control Number: 18/166,081 Page 47 Art Unit: 2148 Application/Control Number: 18/166,081 Page 48 Art Unit: 2148 Application/Control Number: 18/166,081 Page 49 Art Unit: 2148 Application/Control Number: 18/166,081 Page 50 Art Unit: 2148 Application/Control Number: 18/166,081 Page 51 Art Unit: 2148 Application/Control Number: 18/166,081 Page 52 Art Unit: 2148 Application/Control Number: 18/166,081 Page 53 Art Unit: 2148 Application/Control Number: 18/166,081 Page 54 Art Unit: 2148 Application/Control Number: 18/166,081 Page 55 Art Unit: 2148 Application/Control Number: 18/166,081 Page 56 Art Unit: 2148 Application/Control Number: 18/166,081 Page 57 Art Unit: 2148 Application/Control Number: 18/166,081 Page 58 Art Unit: 2148
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