DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 1 – 2, 6 – 7, 11, 15 and 18 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Misra et al. , Pub. No.: US20180357511A1 , in view of Srivastava , Pub. No.: US20160063387A1 , Chen et al . , Pub. No.: US20200409945A1 and SHAH et al., Pub. No.: US20210349865A1 . Regarding claim 1, Misra teaches: A computer-implemented method for feature engineering in an artificial intelligence/machine learning (AI/ML) computing system, the method comprising: ( Misra , “[0016] … machine learning technique used [ feature engineering in an artificial intelligence/machine learning (AI/ML) computing system ] (e.g., supervised, unsupervised, or a combination of supervised and unsupervised machine learning technique, including support vector machine (SVM), conditional random field (CRF), Naive Bayes, convolutional neural networks, K-nearest neighbors, and/or the like), features used (e.g., key words, n-grams, word relationships, and/or the like), feature relevance scores used (e.g., feature weights assigned to the features, indicating a measure of importance of the feature to the machine learning technique), and/or the like. ”) receiving, via a data acquisition engine of an analytics application provided to a subscriber computing device, an input dataset comprising data regarding operations of a subscriber entity; (Misra, “[0012] Some implementations, described herein, provide an analytics recommendation platform (e.g., cloud computing platform, server computer, and/or the like) designed to identify a recommended machine learning technique, recommended features, and recommended feature relevance scores (e.g., weights) for an analytics application. For example, the analytics recommendation platform may receive [ receiving, via a data acquisition engine of an analytics application provided to a subscriber computing device ], from a user device (e.g., a personal computer, mobile device, server computer, and/or the like), data defining one or more characteristics of an analytics application (e.g., a description of the analytics application, training data with annotations, test data, portions of data to be analyzed, and/or the like) [ an input dataset comprising data regarding operations of a subscriber entity ].”) performing feature engineering operations comprising: performing, by the feature engineering engine, an entity resolution operation on the input dataset, (Misra, “[0040] While the foregoing provides example implementations for determining recommended machine learning techniques, features, and relevance scores, some implementations may include additional or different techniques for determining which machine learning techniques, features, and/or relevance scores to recommend (e.g., random or pseudo-random selection determinations, determining only the top N relevant features should be recommended (where N is a positive integer), using predetermined preferences that might be associated with the entity that provided the first analytics application characteristics, and/or the like) [ performing feature engineering operations comprising: performing, by the feature engineering engine, an entity resolution operation on the input dataset ]. In this way, the analytics recommendation platform may determine one or more recommended machine learning techniques, features, and relevance scores, which could be used to execute the first analytics application.”) based on output of the match operation, generating an instantiated set of features by associatively storing the set of items in the input dataset to the set of features in the feature catalogue; (Misra, “[0027] In some implementations, the analytics recommendation platform may use a similarity model (e.g., a machine learning model trained to determine a measure of similarity between analytics applications based on one or more characteristics of the analytics applications) to determine a measure of similarity between the first analytics application and the second analytics application. In this situation, one or more of the characteristics of the first analytics application may be provided, as input, to the similarity model. The similarity model may provide, as output [ based on output of the match operation ], data indicating a measure of similarity between the first analytics application and the second analytics application (or multiple measures of similarity in a situation where measures of similarity are obtained for multiple second analytics applications) [ generating an instantiated set of features by associatively storing the set of items in the input dataset to the set of features in the feature catalogue ].”) using the instantiated set of features, applying a second trained machine learning model to generate a recommendation, (Misra, “[0047] As shown by reference number 185, the analytics recommendation platform provides the user device with an analytics recommendation. For example, the analytics recommendation platform may provide the user device with a recommended machine learning technique, features, and feature relevance scores. As another example, the analytics recommendation may include a machine learning model (e.g., generated and/or trained using the recommendations provided by the analytics recommendation model). The analytics recommendation platform provides the user device with the analytics recommendation [ using the instantiated set of features, applying a second trained machine learning model to generate a recommendation ] to enable the user device to perform an analytics application associated with the analytics recommendation.”) providing a visual indication of the generated recommendation via the GUI; and (Misra, “[0042] In some implementations, the analytics recommendation platform may provide the recommended machine learning technique(s), features, and/or feature relevance scores to another device. The recommendations may be provided to a device, such as a user device [ providing a visual indication of the generated recommendation via the GUI ], analytics device, and/or the like, in a manner designed to enable the device to review the recommendations and/or cause analytics to be performed based on the recommendations.”) generating or updating a feature definition mark-up file, wherein the feature definition mark-up file comprises at least two of: a feature identifier, a feature configuration parameter, a SQL query, or feature versioning information . (Misra, “[0048] As shown by reference number 190, the analytics recommendation platform receives analytics feedback data from the user device. The analytics feedback data may include a variety of information designed to enable the analytics recommendation platform to determine one or more updated machine learning techniques [ generating or updating a feature definition mark- up file ], features, and/or feature relevance scores for the analytics application associated with the analytics recommendation [ wherein the feature definition mark-up file comprises at least two of: a feature identifier, a feature configuration parameter, a SQL query, or feature versioning information ]. For example, the analytics feedback may include information indicating updated feature relevance scores (e.g., feature weights) that the user device uses in the machine learning model that was recommended by the analytics recommendation platform. The analytics feedback enables the analytics recommendation platform to update the measures of similarity and/or the similarity model used to make the recommendations provided in the analytics recommendation.”) Misra does not teach: generating, by a feature engineering engine of the analytics application, a reduced discovery dataset based on the input dataset and storing at least a portion of the reduced discovery dataset in cache memory associated with the analytics application; while displaying, via a graphical user interface (GUI) associated with the analytics application, at least a portion of the reduced discovery dataset, comprising applying a first machine learning model to a set of items in the input dataset and a set of features retrieved from a feature catalogue to perform a match operation based on fuzzy logic; and based on fuzzy logic; wherein the second trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features; Srivastava teaches: generating, by a feature engineering engine of the analytics application, a reduced discovery dataset based on the input dataset and storing at least a portion of the reduced discovery dataset in cache memory associated with the analytics application; (Srivastava, “[0062] In some implementations, analysis server 220 may preprocess the monitored information utilizing feature selection (e.g., a process of selecting a subset of relevant features for use in model construction); dimensionality reduction (e.g., a process of reducing a number of random variables under consideration); normalization [ generating, by a feature engineering engine of the analytics application, a reduced discovery dataset based on the input dataset ] (e.g., adjusting values measured on different scales to a common scale); data subsetting (e.g., retrieving portions of data that are of interest for a specific purpose) [ storing at least a portion of the reduced discovery dataset in cache memory associated with the analytics application ]; or the like.”) while displaying, via a graphical user interface (GUI) associated with the analytics application, at least a portion of the reduced discovery dataset, (Srivastava, “[0013] In some implementations, the analysis server may enable an entity (e.g., users of the user devices, government agencies, or the like) to access or receive analysis information that is customized for the entity. For example, as shown in FIG. 1, the analysis server may provide, for display, a dashboard that includes analysis information that is customized for the entity [ while displaying, via a graphical user interface (GUI) associated with the analytics application, at least a portion of the reduced discovery dataset ], such as information associated with anomalous readings received by the user devices (e.g., which may be indicative of an adverse environmental event).”) Srivastava and Misra are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Srivastava with teachings of Misra to add analyzing and monitoring environmental event data tied to users and devices to generate and send a notification based on the analysis result. ( Srivastava , Abstract ). Misra in view of Srivastava do not teach: comprising applying a first machine learning model to a set of items in the input dataset and a set of features retrieved from a feature catalogue to perform a match operation based on fuzzy logic; and based on fuzzy logic; wherein the second trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features; Chen teaches: comprising applying a first machine learning model to a set of items in the input dataset and a set of features retrieved from a feature catalogue to perform a match operation based on fuzzy logic; and (Chen, “[0045] Entity resolution service 230 that identifies entity records in one or more source data sources (e.g., TMS 240) [ comprising applying a first machine learning model to a set of items in the input dataset ] and matches one or more of those entity records with one or more entity records in one or more target data sources (e.g., entity database 132) [ and a set of features retrieved from a feature catalogue to perform a match operation based on fuzzy logic; ]. Entity resolution service 230 performs the matching using one or more searches of the target data source and a scoring model 232. The matching involves constructing, based on a source entity record, one or more search queries that will be executed against the target data source.”) Chen, Misra and Srivastava are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Chen with teachings of Misra and Srivastava to add a specific multi-query, score based entity resolution mechanism for matching records across different data sources to provide implementation detail for how cross dataset association or similarity determination is performed . (Chen, Abstract). Misra in view of Srivastava and Chen do not teach: based on fuzzy logic; wherein the second trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features; SHAH teaches: based on fuzzy logic; (SHAH, “[0032] … The data mapper may perform the comparisons using character-based comparison techniques, fuzzy logic techniques [ based on fuzzy logic ], a semantic-based comparison, and/or the like. The data mapper may perform the comparisons above for each object of the one or more objects of the source data and for each field of each object…”) wherein the second trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features; (SHAH, “[0079]… The machine learning system may then train each machine learning model using the entire training set 420 (e.g., without cross-validation), and may test each machine learning model using the test set 425 to generate a corresponding performance score for each machine learning model. The machine learning model may compare the performance scores for each machine learning model, and may select the machine learning model with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) [ wherein the second trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features ] performance score as the trained machine learning model 445.”) SHAH , Misra , Srivastava and Chen are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of SHAH with teachings of Misra , Srivastava and Chen to add a template based model assisted data migration and schema mapping mechanism with user verification to improve data integrity and reduced operational overhead . ( SHAH , Abstract). Claim 19, recites limitations analogous to claim 1, so is rejected under the same rationale. Regarding claim 2, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra further teaches: wherein the analytics application is provided by a provider entity associated with the AI/ML computing system, and wherein the analytics application is on a virtual network associated with the subscriber entity . (Misra, “[0012] Some implementations, described herein, provide an analytics recommendation platform (e.g., cloud computing platform, server computer, and/or the like) [ wherein the analytics application is provided by a provider entity associated with the AI/ML computing system ] designed to identify a recommended machine learning technique, recommended features, and recommended feature relevance scores (e.g., weights) for an analytics application. For example, the analytics recommendation platform may receive, from a user device (e.g., a personal computer, mobile device, server computer, and/or the like) [ wherein the analytics application is on a virtual network associated with the subscriber entity ], data defining one or more characteristics of an analytics application (e.g., a description of the analytics application, training data with annotations, test data, portions of data to be analyzed, and/or the like).”) Claim 18 , recites limitations analogous to claim 2 , so is rejected under the same rationale. Regarding claim 6, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Chen further teaches: wherein performing the entity resolution operations comprises de-duplicating an item in the input dataset . (Chen, “[0072] At block 380, a scoring model is used to generate a score for each result in the first and second sets of results. For example, scoring model 219 generates a score for each target entity record that was identified by at least one of the searches. Block 390 may involve deduplicating the union of the first and second sets of results [ wherein performing the entity resolution operations comprises de-duplicating an item in the input dataset ] since some search results from the second query may be the same as some search results from the first query.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Chen with teachings of Misra , Srivastava and SHAH for the same reasons disclosed for claim 1 . Regarding claim 7, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Srivastava further teaches: wherein performing feature engineering operations further comprises: providing, via the GUI, an analytics engine selection control; and (Srivastava, “[0031] … In some implementations, the user may cause user device 210 to access the analysis application via, for example, a user interface (such as a browser) provided by analysis server 220, or in another manner. The user may then select, using user device 210, information regarding the analysis application from the user interface to cause user device 210 to provide a request for the analysis application to analysis server 220 [ providing, via the GUI, an analytics engine selection control ]. In some implementations, analysis server 220 may offer the analysis application to user device 210 without user device 210 providing the request for the analysis application.”) responsive to detecting a selection using the analytics engine selection control, invoking an executable associated with the selected analytics engine to perform operations comprising: generating a visual summary of an item in the instantiated set of features; and (Srivastava, “[0084] Analysis server 220 may utilize analysis information 730 [ responsive to detecting a selection using the analytics engine selection control ] to generate a first dashboard user interface 765, as shown in FIG. 7C. Analysis server 220 may provide user interface 765, for display, to a user of analysis server 220 so that the user may review analysis information 730 [ invoking an executable associated with the selected analytics engine to perform operations comprising: generating a visual summary ]. As shown in FIG. 7C, user interface 765 may include information associated with user devices 210 (e.g., Your Devices) [ of an item in the instantiated set of features ], such as service plans, connection status, data usage, short message service (SMS) usage, carrier information, state status, or the like associated with user devices 210. User interface 770 may also include a section that displays alerts associated with particular user devices 210 and/or environmental events at particular times. For example, alert section may indicate that, on Jun. 2, 2013, five anomalous user devices 210 were detected, and that, on Jun. 1, 2013, particular user devices 210 detected large vibrations and the earthquake at the location near the earthquake. As further shown in FIG. 7C, user interface 765 may include an “Advanced Analytics” tab 770 that, when selected, may provide additional analysis information 730 for display.”) causing the GUI to display the visual summary along with the instantiated set of features . (Srivastava, “[0073] As further shown in FIG. 6, process 600 may include providing the analysis information for display (block 640) [ causing the GUI to display the visual summary ]. For example, analysis server 220 may provide the analysis information, for display, to a user associated with analysis server 220 and/or to user devices 210 associated with users. In some implementations, analysis server 220 may generate a dashboard of user interfaces that include the analysis information, and may provide the dashboard for display. In some implementations, the dashboard may include information identifying anomalous user devices 210 and/or environmental events; information identifying trends in the network data, the device data, and/or the application data associated with user devices 210 [ along with the instantiated set of features ];”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Srivastava with teachings of Misra , Chen and SHAH for the same reasons disclosed for claim 1 . Regarding claim 11, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra further teaches: wherein the recommendation comprises at least one of: a score, a probability, a discovered cluster, or a data visualization . (Misra, “[0012] Some implementations, described herein, provide an analytics recommendation platform (e.g., cloud computing platform, server computer, and/or the like) designed to identify a recommended machine learning technique, recommended features, and recommended feature relevance scores (e.g., weights) for an analytics application [ wherein the recommendation comprises at least one of: a score, ]. For example, the analytics recommendation platform may receive, from a user device (e.g., a personal computer, mobile device, server computer, and/or the like), data defining one or more characteristics of an analytics application (e.g., a description of the analytics application, training data with annotations, test data, portions of data to be analyzed, and/or the like).”) Regarding claim 15, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra further teaches: wherein the feature definition mark-up file is a first feature definition mark-up file, wherein performing the feature engineering operations further comprises: determining the set of features in the feature catalogue based on a previously generated second feature definition mark-up file . (Misra, “[0004] … determine, for the first analytics application, a first feature associated with the first analytics application, the first feature being based on the measure of similarity and a second feature associated with the second analytics application; [ determining the set of features in the feature catalogue based on a previously generated second feature definition mark-up file ] determine, for the first analytics application, a first machine learning technique associated with the first analytics application, the first machine learning technique being based on the measure of similarity and a second machine learning technique associated with the second analytics application; and perform an action based on the first feature and the first machine learning technique.”) Claim(s) 3 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH and Walenstein et al., Pub. No.: US20200026636A1 . Regarding claim 3, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra , Srivastava , Chen and SHAH do not teach: further comprising generating the reduced discovery dataset using random sampling . Walenstein teaches: further comprising generating the reduced discovery dataset using random sampling . ( Walenstein , “[0033] A number of under-sampling methods can be used, for example, random majority under-sampling, under-sampling with cluster centroids, and extraction of majority-minority Tomek links. For example, the under-sampling with cluster centroids method under-samples the majority class by replacing a cluster of majority data entries with the cluster centroid (middle of a cluster) of a k-means algorithm. The random majority under-sampling method [ further comprising generating the reduced discovery dataset using random sampling ] under-samples the majority class by randomly picking samples with or without replacement.”) Walenstein , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Walenstein with teachings of Misra , Srivastava , Chen and SHAH to add dataset sampling, feature based filtering and classification of new inputs into predefined categories using a trained machine learning model. ( Walenstein , Abstract). Regarding claim 4, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra , Srivastava , Chen and SHAH do not teach: further comprising generating the reduced discovery dataset using stratified sampling . Walenstein teaches: further comprising generating the reduced discovery dataset using stratified sampling . ( Walenstein , “[0040] Various sampling methods can be used to split the modified data set and generate a balanced training data set and a balanced testing data set (i.e., the data set has a similar number of data entries for each class). For example, a stratified sampling method [ further comprising generating the reduced discovery dataset using stratified sampling ] or a stratified-k-fold sampling method can be used for splitting the modified data set. The idea of the stratified sampling method is to use a probabilistic sampling to divide an entire population into different strata which are randomly chosen but balanced and proportional. The stratified-k-fold sampling method is different from the stratified sampling method because the stratified-k-fold sampling method generates more than a single fold of the stratified sampling.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Walenstein with teachings of Misra , Srivastava , Chen and SHAH for the same reasons disclosed for claim 3. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH and Anderson , Pub. No.: US9449057B2 . Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra , Srivastava , Chen and SHAH do not teach: wherein a size of the reduced discovery dataset is optimized by performing at least one of: generating the reduced discovery dataset to be at or under a predetermined size limit, extracting a predetermined number of records from the input dataset, or extracting a predetermined percentage of records from the input dataset . Anderson teaches: wherein a size of the reduced discovery dataset is optimized by performing at least one of: generating the reduced discovery dataset to be at or under a predetermined size limit, extracting a predetermined number of records from the input dataset, or extracting a predetermined percentage of records from the input dataset . (Anderson, (col. 14 Line [51 – 61]), “To address this situation, in some implementations, a data pattern code can be formulated for the production data, i.e., the input data to the application. A dataset of test records may be created based on the data pattern code, and applied to the application. For example, a small number of records (e.g., a single record or another predetermined number of records) [ wherein a size of the reduced discovery dataset is optimized by performing at least one of: generating the reduced discovery dataset to be at or under a predetermined size limit ] can be selected for each distinct data pattern code. Having been taken from the full production data, this dataset may be deemed to represent a comprehensive set of test cases that the application can be configured to handle properly.”) Anderson , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Anderson with teachings of Misra , Srivastava , Chen and SHAH to add structured encoding of dataset records into pattern representative features to improve computational efficiency and pattern extraction from large datasets. ( Anderson , Abstract). Claim(s) 8 – 10 are rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH and Nourian et al., Pub. No.: US20210049503A1 . Regarding claim 8, Misra , Srivastava , Chen and SHAH teach the method of claim 7. Misra , Srivastava , Chen and SHAH do not teach: wherein the item is a derived item, and wherein the visual summary relates to a local explainability statistic for the item Nourian teaches: wherein the item is a derived item, and wherein the visual summary relates to a local explainability statistic for the item . ( Nourian , “[0007] In accordance with some implementations of the disclosed subject matter, computer-implemented machines, systems and methods are disclosed for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. One or more features of the machine learning model may be analyzed [ wherein the item is a derived item ], the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. One or more visual indicators may be displayed based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary [ wherein the visual summary ] of the machine learning model's performance or efficacy. In response to further analyzing the one or more features and the training data, at least one or more of a global explanation about the machine learning model or a local explanation about the machine learning model may be provided [ relates to a local explainability statistic for the item ].”) Nourian , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Nourian with teachings of Misra , Srivastava , Chen and SHAH to add model insight functionality including constraint based feature analysis and visual performance indicators to improve transparency of the machine learning model’s effectiveness . ( Nourian , Abstract). Regarding claim 9, Misra , Srivastava , Chen, SHAH and Nourian teach the method of claim 8. Nourian further teaches: wherein the visual summary relates to a global explainability statistic for at least a subset of the instantiated set of features . ( Nourian , “[0007] In accordance with some implementations of the disclosed subject matter, computer-implemented machines, systems and methods are disclosed for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. One or more features of the machine learning model may be analyzed, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. One or more visual indicators may be displayed based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy. In response to further analyzing the one or more features and the training data, at least one or more of a global explanation about the machine learning model [ wherein the visual summary relates to a global explainability statistic for at least a subset of the instantiated set of features ] or a local explanation about the machine learning model may be provided.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Nourian with teachings of Misra , Srivastava , Chen and SHAH for the same reasons disclosed for claim 8 . Regarding claim 10, Misra , Srivastava , Chen, SHAH and Nourian teach the method of claim 9. Nourian further teaches: further comprising generating and displaying a GUI control structured ( Nourian , “[0008] The global explanation may provide general information about one or more functionalities of the machine learning model and at least one of a visualization that summarizes the machine learning model's global behavior with respect to the one or more features [ comprising generating and displaying a GUI control structured ], or an identification of a first set of features that are influential in generating one or more identifiable outcomes. The further analyzing may be performed based on at least one of a model-dependent approach or a model-independent approach, wherein the model-dependent approach takes into consideration unique properties of the machine learning model, such as the model's structure in calculating a feature's importance to the machine learning's operation for correctly classifying the input data.”) to enable a modification of a threshold relating to the global explainability statistic . ( Nourian , “[0010] In at least one implementation, a first threshold may be determined and the local explanation provides an understanding of how possible changes to the instance's feature values adjust or shift an expected result or projected outcome beyond the first threshold [ to enable a modification of a threshold relating to the global explainability statistic ]. In response to understanding how the machine learning model behaves in the first instance, the machine learning model may be tuned to select outcomes that best suit an expected result in a first set of instances. For example, the machine learning model may be tuned to minimally change one or more of the machine learning model's features in a first set of features having a first characteristic. The machine learning model may be tuned by adjusting instances that demand changes to the fewest number of features or instances with a least amount of change to the most important features of the machine learning model.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Nourian with teachings of Misra , Srivastava , Chen and SHAH for the same reasons disclosed for claim 8 . Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH and Gupta et al., Pub. No.: US20220230181A1 . Misra , Srivastava , Chen and SHAH teach the method of claim 1. Misra , Srivastava , Chen and SHAH do not teach: wherein the input dataset is indicative of one or more activities, and wherein generating the recommendation comprises determining a next best activity for an activity in a set of one or more activities Gupta teaches: wherein the input dataset is indicative of one or more activities, and wherein generating the recommendation comprises determining a next best activity for an activity in a set of one or more activities . (Gupta, “[0040] Accordingly, it is now appreciated that there is a need to provide a narrowly tailored subset of recommended actions [ wherein the input dataset is indicative of one or more activities ] as best recommended actions [ and wherein generating the recommendation comprises determining a next best activity for an activity in a set of one or more activities ], including the most applicable recommended actions, in view of the context. That is, based on certain conditions (e.g., probability of success related to the customer, business interest, etc.), the most applicable recommended actions may be provided to the agent to quickly and efficiently resolve the customer request.”) Gupta , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Gupta with teachings of Misra , Srivastava , Chen and SHAH to add context based recommendation and ranking of actions to support decision through prioritized recommendation . ( Gupta , Abstract). Claim(s) 13 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH , Gupta and Rausch et al., Pub. No.: US20170153914A1 . Regarding claim 13, Misra , Srivastava , Chen and SHAH teach the method of claim 1. Srivastava further teaches: generating and displaying a visual summary of the instantiated set of features, (Srivastava, “[0013] In some implementations, the analysis server may enable an entity (e.g., users of the user devices, government agencies, or the like) to access or receive analysis information that is customized for the entity. For example, as shown in FIG. 1, the analysis server may provide, for display, a dashboard that includes analysis information that is customized for the entity [ generating and displaying a visual summary of the instantiated set of features ], such as information associated with anomalous readings received by the user devices (e.g., which may be indicative of an adverse environmental event).”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Srivastava with teachings of Misra , Chen and SHAH for the same reasons disclosed for claim 1. Misra , Srivastava , Chen and SHAH do not teach: wherein the instantiated set of features is shown as a linking item between a first node in a first set of nodes, the first node indicative of the input dataset, and a second node in a second set of nodes, the second node indicative of the set of features. Gupta teaches: wherein the instantiated set of features is shown as a linking item between a first node in a first set of nodes, the first node indicative of the input dataset, (Gupta, “[0050] Each of the node devices may additionally analyze its corresponding data set [ the first node indicative of the input dataset ] portion to determine one or more features of the data items therein at least partially in parallel with others of the node devices [ wherein the instantiated set of features is shown as a linking item between a first node in a first set of nodes ]. By way of example, each node device may determine the type(s) of data within its corresponding data set portion, including and not limited to, binary values, integer values, floating point values, text characters, bit mapped still images, frames of video data, samples of audio data, etc. Also by way of example, each node device may determine a density and/or sparsity of the data items, and/or may determine a size of the data items within its corresponding data set portion.”) Gupta , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Gupta with teachings of Misra , Srivastava , Chen and SHAH to add context based recommendation and ranking of actions to support decision through prioritized recommendation. ( Gupta , Abstract). Misra , Srivastava , Chen , SHAH and Gupta do not teach: a second node in a second set of nodes, the second node indicative of the set of features Rausch teaches: a second node in a second set of nodes, the second node indicative of the set of features . (Rausch, “[0051] In some embodiments, such analyses and provision of observation data indicative of features of the data items within each of the data set portions [ and a second node in a second set of nodes, the second node indicative of the set of features ] to the coordinating device may be at least partially enabled by the provision of the annotated metadata to each of the node devices. Thus, one or more of the node devices may employ the indications of structural features of the whole of the selected data set provided in the annotated metadata as input to such an analysis of the data items within one or more of the data set portions.”) Rausch, Misra , Srivastava , Chen, SHAH and Gupta are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Rausch with teachings of Misra , Srivastava , Chen, SHAH and Gupta to add automated action selection and execution based on similarities to previous analyzed datasets to leverage past actions to efficiently process new datasets . ( Rausch , Abstract). Regarding claim 14, Misra , Srivastava , Chen, SHAH, Gupta and Rausch teach the method of claim 13. Misra further teaches: wherein the detail record includes at least one of: a project identifier for a project that includes the instantiated feature, an instantiated feature identifier, an instantiated feature configuration parameter, a SQL query associated with the instantiated feature, or feature versioning information . (Misra, “[0018] … The analytics application information may be stored in a variety of data structures that enable access to the analytics application information, such as a database that enables querying the database to obtain analytics application information based on the queries (e.g., queries for analytics applications associated with various analytics application characteristics, machine learning techniques, features, feature relevance scores, and/or the like) [ wherein the detail record includes at least one of: a project identifier for a project that includes the instantiated feature, an instantiated feature identifier, an instantiated feature configuration parameter, a SQL query associated with the instantiated feature, or feature versioning information ]. In this way, the analytics recommendation platform identifies and stores information associated with analytics applications in a manner designed to enable the information to be obtained later (e.g., for comparison with analytics applications for which the analytics recommendation platform is to make a recommendation).”) Srivastava further teaches: further comprising: upon detecting a user interaction with the linking item, generating and displaying, along with the visual summary, a detail record for a particular feature associated with the linking item, (Srivastava, “[0013] In some implementations, the analysis server may enable an entity (e.g., users of the user devices, government agencies, or the like) to access or receive analysis information that is customized for the entity [ upon detecting a user interaction with the linking item, generating and displaying ]. For example, as shown in FIG. 1, the analysis server may provide, for display, a dashboard that includes analysis information that is customized for the entity, such as information associated with anomalous readings received by the user devices (e.g., which may be indicative of an adverse environmental event) [ along with the visual summary, a detail record for a particular feature associated with the linking item ].”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Srivastava with teachings of Misra , Chen and SHAH for the same reasons disclosed for claim 1. Claim(s) 16 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH and Gupta . Regarding claim 16, Misra teaches: based on a feature configuration file, determining a feature catalogue to reference; and (Misra, “[0041] As shown by reference number 170, the analytics recommendation platform performs an action based on the recommended machine learning technique, features, and/or feature relevance scores [ determining a feature catalogue to reference; ]. The action(s) performed by the analytics recommendation platform may vary, and may depend on a configuration of the analytics recommendation platform [ based on a feature configuration file ], data included in a request for a recommendation (e.g., associated with the first analytics application), user settings, and/or the like.”) Misra in view of Srivastava , Chen and SHAH do not teach: using the instantiated set of features, applying a second trained machine learning model to determine a next best activity for an activity in the set of activities; Gupta teaches: using the instantiated set of features, applying a second trained machine learning model to determine a next best activity for an activity in the set of activities; (Gupta, “[0040] Accordingly, it is now appreciated that there is a need to provide a narrowly tailored subset of recommended actions [ using the instantiated set of features, applying a second trained machine learning model ] as best recommended actions [ to determine a next best activity for an activity in the set of activities ], including the most applicable recommended actions, in view of the context. That is, based on certain conditions (e.g., probability of success related to the customer, business interest, etc.), the most applicable recommended actions may be provided to the agent to quickly and efficiently resolve the customer request.”) Gupta , Misra , Srivastava , Chen and SHAH are related to the same field of endeavor (i.e.: recommendation platform) . It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Gupta with teachings of Misra , Srivastava , Chen and SHAH to add context based recommendation and ranking of actions to support decision through prioritized recommendation. ( Gupta , Abstract). The rest of the limitations are analogous to claim 1, so are rejected under similar rationale. Regarding claim 17, Misra , Srivastava , Chen, SHAH and Gupta teach the method of claim 16. Gupta further teaches: further comprising: generating a plurality of customer conversion communication paths; and using the plurality of customer conversion communication paths, (Gupta, “[0070] Generating recommended actions may also involve historical data 508. The historical data 508 may include past data related to the customer. The historical data 508 may include data indicating each customer action (e.g., customer financed home loan through the bank) or customer-client interaction (e.g., customer conversation with the agent of the bank) since association with the client [ generating a plurality of customer conversion communication paths; and using the plurality of customer conversion communication paths ], such as since the date that the customer opened an account with the client.”) determining the next best activity . (Gupta, “[0072] … In some instances, the number of recommended actions to provide to the agent may default to three recommended actions 514, in which the top or highest ranked of the three recommend actions is the best recommended action (e.g., next best action) of best recommended actions [ determining the next best activity ]. Specifically, the machine learning algorithm may learn about the agent, similarly situated agents, the customer, the department, the client interest 509 over time, and/or other business related information, and generate and rank the subset of ranked recommended actions 514 based on a predicted likelihood that the customer will act in accordance with the recommended actions (e.g., apply for the recommended credit card, apply for a new home loan, etc.).”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Gupta with teachings of Misra , Chen, SHAH and Srivastava for the same reasons disclosed for claim 16. Claim(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Misra in view of Srivastava , Chen , SHAH , Gupta and Rausch . Regarding claim 20, Misra , Srivastava , Chen and SHAH teach the method of claim 19. Srivastava further teaches: generating and displaying a visual summary of the instantiated set of features, (Srivastava, “[0013] In