Abstract: Tables including cells can be extracted from an input document image, as objects and processed to be output in an XML format. The tables can be filtered based on one or more keywords, to reduce a number of the tables. The filtered tables that include the one or more keywords can be obtained. A query can be executed on the filtered tables, the query including one or more keys composed based on cell level information associated with the filtered tables. One or more cells among the cells can be identified based on the one or more keys. As a response to the query, the target content that corresponds to the one or more cells can be provided.

Abstract: A method includes estimating a number of threads to execute batch job within maximum completion time by executing a thread estimation algorithm. Executing the thread estimation algorithm includes: determining, using an ML model, a collection of one or more combinations, each including an estimated number of threads and an actual completion time for processing the batch job using the estimated number of threads, the actual completion time being less than or equal to the maximum completion time, and saving the collection including the one or more combinations in a list of threads and completion times. The list of threads and completion times is used to select, based on a list of cloud virtual machines available for use, an optimal virtual machine or an optimal virtual machine combination, the optimal virtual machine or the optimal virtual machine combination being capable of executing the batch job within the maximum completion time.

Abstract: A reconciliation engine and data reconciliation techniques are disclosed that partially use machine learning and natural language processing techniques to overcome challenges associated with traditional manual reconciliation engines and data reconciliation techniques without compromising the accuracy and handle the complexity of the features. In one aspect, a computer-implemented method is provided that includes: accessing a source and target table, performing intra-reconciliation on the source table, grouping the source and target table based on system configurations, reconciling data is iterated over each group, where the reconciling data includes: finding, using a machine learning model, a closest match for each group, and outputting the closest match for each group.

Abstract: Techniques are disclosed for understanding the visual structure and patterns of documents and detecting anomalies in data of the documents based on the understanding of the visual structure and patterns of the documents. In one aspect, a computer-implemented method is provided that includes accessing a set of documents, converting the set of documents to a set of images in a binary format, generating a common feature template based on the set of images, comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, and outputting the images with at least one anomalous feature.

Abstract: Systems, methods, and other embodiments associated with decision tree machine learning native to a graph database are described. In one embodiment, a method includes receiving an objective property value of a training graph for a graph-native decision tree to be trained to predict; determining feasible ranges of values from the training graph; automatically generating a graph query language grammar for graph-native decision trees based on the feasible ranges; progressively evolving output decision trees in a graph query language from the graph query language grammar to generate a final decision tree that accurately predicts the objective property value; and storing the final decision tree for subsequent execution against a target graph.

Abstract: Systems, methods, and other embodiments associated with associated with automatic two-way generation and synchronization of notebook and pipeline are described. In one embodiment, a computer-implemented method includes: identifying a difference between pipeline and notebook representations of application logic, wherein the difference is one of (i) a notebook paragraph without a corresponding pipeline node and (ii) a pipeline node without a corresponding notebook paragraph; synchronizing the pipeline representation and notebook representation by, for the notebook paragraph that does not have a corresponding pipeline node, automatically generating a new pipeline node in the pipeline representation, and for the pipeline node that does not have a corresponding notebook paragraph, automatically generating a new notebook paragraph in the notebook representation; and updating either a pipeline user interface to show the new pipeline node or a notebook user interface to show the new notebook paragraph.

Abstract: Systems, methods, and other embodiments associated with associated with unified pipeline flow with common and phase-specific paths are described. In one embodiment, a method includes accepting, through a graphical user interface, a setting of a phase-specific link type for a link between nodes of a pipeline, wherein the phase-specific link type indicates that the link is associated with a particular phase; accepting, through the graphical user interface, a selection to execute the pipeline for the phase; parsing the pipeline to determine an execution set of nodes for execution in the phase based on the nodes being connected with links having either the link type or a default link type common to all phases; and executing the pipeline for the phase by executing the execution set of nodes, and not executing nodes not included in the set of nodes.

Abstract: Method includes obtaining sample records from dataset associated with user and including records associated with identifiers customers of user; executing first clustering using sample records, to obtain first set of clusters for first identifiers associated with sample records, first clustering using features associated with first identifiers; providing visualization of first set of clusters; determining whether user input for optimizing first set of clusters provided in visualization is received; when user input for optimizing first set of clusters is not received, determining first information related to first set of clusters as final result information; when user input for optimizing first set of clusters is received: executing second clustering using sample records, to obtain second set of clusters for first identifiers, second clustering using features associated with first identifiers, and determining second information related to second set of clusters as final result information; and clustering entire

Abstract: Method includes extracting, from document image, tables as objects; outputting tables in an XML format; filtering tables based on keywords and a condition including one from among AND and OR, which includes: for each of tables, determining whether the table includes at least one from among a first keyword and a second keyword, for condition being AND, based on the table containing first keyword and second keyword, outputting the table, as filtered table, and, for condition being OR, based on the table containing first keyword or second keyword, outputting the table, as filtered table, thereby outputting filtered tables that include the first keyword and/or the second keyword based on condition; executing query on filtered tables that includes keys composed based on cell level information associated with filtered tables; identifying cells based on keys; and providing target content that corresponds to cells, as response to query.

Abstract: Systems, methods, and other embodiments associated with a technique for using micro frontends to build a runtime version of a base application are described. In one embodiment, the method allows for selected micro frontends to be incorporated into the base application. Metadata corresponding to the selected micro frontend may be used to identify at least one or more application programming interfaces (APIs) that are defined in the metadata. The identified APIs are incorporated with the micro frontends to create a final version of the micro frontend. A runtime version of the base application is constructed, at least in part, by combining the base application with the retrieved micro frontends including the one or more APIs that were identified.

Abstract: Method includes obtaining historical document images including text that correspond to different document classes; and generating a dictionary using text of the historical document images. The dictionary includes base words occurring with a greatest frequency in each document class. The base words are extracted from the text of the historical document images and arranged in datasets by a document class, where each dataset includes the base words of a same document class that occur with the greatest frequency within that document class. Trie structure is generated using the base words of the datasets that occur with a greatest frequency in each dataset. The trie structure includes internal nodes including root node and leaf nodes in which keys corresponding to the base words occurring with the greatest frequency in each dataset are respectively stored in predefined order. The trie structure is searchable in the predefined order starting with the root node.

Abstract: According to an aspect, a technology system maintains an account data specifying the details of multiple accounts including a specific set of accounts as being delinquent. Upon receiving a feature set to be used, the system clusters the multiple accounts into groups based on the feature set. The system generates a multi-classification model for assignment of agents to the accounts based on the group qualifiers associated with the groups and the feature set. The system then identifies a target group likely to present problems with debt collection, assigns, using the multi-classification model, a corresponding agent for each account of the group and determines a recommended recovery strategy for each assignment. The system provides the details of the group, the corresponding agents assigned and the recommended recovery strategy for each assignment.

Abstract: A computer-implemented method includes: obtaining a record including text narration; inputting the text narration into an NLP model; generating, by the NLP model, encoded narration based on the text narration; determining similarity index between the encoded narration and each of encoded reference topics; comparing the similarity index between the encoded narration and each encoded reference topic to similarity threshold; and determining whether the similarity index between the encoded narration and each encoded reference topic is equal to or greater than the similarity threshold. When the similarity index is equal to or greater than the similarity threshold, a corresponding reference topic is added to a result group, and, when the similarity index is less than the similarity threshold, the corresponding reference topic is left out of the result group.

Abstract: A method includes generating dataset using topics associated with historical records, the dataset including pairs of data that are formed based on the topics, each of the pairs of data including an antecedent topic associated with a historical record corresponding to a preceding event and a consequent topic associated with a historical record corresponding to an event that occurred after the preceding event, the antecedent topic and the consequent topic forming a transitive relation for each of the pairs of data; inputting, into ML model, the pairs of data and input topic associated with a record of a user; generating, by the ML model, a prediction of a next record topic for a next record corresponding to the user, based on the consequent topic included in each of the pairs of data that include the antecedent topic corresponding to the input topic; and outputting the prediction.

Abstract: Aspects of the present disclosure are directed to extracting textual information from image documents. In one embodiment, a system, upon receiving a request to extract textual information from an image document, a digital processing system performs character recognition based on content of the image document using multiple approaches to generate corresponding texts. The texts are then combined to determine a result text representing the textual information contained in the image document. The result is then provided as a response to the request.

Abstract: Disclosed herein is a declarative modeling paradigm for a graph model, graph-physicalization, delta load/offload, automatic generation of sub-graphs based on user entitlements, a graph-pipeline for low-code graph file formats, and machine-learning pipeline for analysis of the graph model. In a particular aspect, a computer-implemented process includes generating a graphical user interface comprising one or more tools configured to allow a user to build and visualize a graph model based on data, obtaining the data from one or more sources, receiving input from the user via the graphical user interface, where the input comprises a request to create at least two nodes representing logical entities within the data and at least one edge representing one or more relationships between the logical entities, generating the graph model based on the input from the user, analyzing the data using the graph model, and output results based on the analyzing.

Abstract: Machine learning techniques are disclosed for predicting a task event such as a service completion event based on a predefined workflow. In one aspect a method includes obtaining initial data for a service request (e.g., an account application), enriching the initial data with data from one or more repositories of an enterprise executing the service request, generating a data structure comprising independent variables extracted from the enriched data, receiving a request for a prediction of a completion time for the service request (e.g., an account opening event) at a first time during processing of the service request in accordance with each workflow, in response to receiving the request for the prediction, inputting the data structure into a machine-learning regression model, predicting, using the machine-learning regression model, a completion time for the service request, and providing the completion time for the service request.

Abstract: Method includes generating a base model by training a pretrained model using a base training dataset including first training datapoints identifying tables in historical document images that include the tables and text, where the generated base model is configured to extract the tables as objects; and generating a table extraction model by training the base model using an enhanced training dataset including second training datapoints that are different from the first training datapoints and identify a plurality of cells disposed in each of the tables in a row direction and a column direction. The table extraction model is trained to output content of the tables and table information in an XML format, the table information including cell level information of the plurality of cells that is searchable via a query configured to provide target content that corresponds to one or more cells.

Abstract: Machine learning techniques are disclosed for rebuilding transactions to predict cash position. In one aspect a method includes obtaining data for an original transaction, classifying the original transaction into a class of multiple classes based on the data, predicting first tranche delay days for the original transaction based on the class and the data, predicting a tranche count for the original transaction based on the class and the data, predicting a tranche interval for the original transaction based on the class and the data; and rebuilding the original transaction as one or more future transactions based on the class, the first tranche delay days, the tranche count, and tranche interval. Each of the one or more future transactions comprise an updated amount of the original transaction, an updated date upon which the original transaction is anticipated, or both.

Abstract: Techniques are disclosed as an optimization data system for eliminating correlated independent variables programmatically from data with ranked exclusion scores. The system can obtain an initial dataset comprising variables, determine a set of correlation values by analyzing linear correlation between the variables, generate a correlation matrix using at least in part the set of correlation values and corresponding variables from the initial data, calculate exclusion scores for the variables in the correlation matrix that exhibit multicollinearity, and update the initial dataset by removing at least one variable with the highest exclusion score from the variables to generate an updated dataset comprising optimized variables. The steps for correlation and elimination of variables are iterated until an updated dataset without any correlation is obtained and then a machine learning model may be trained using the updated dataset.