Bank of America Corporation

Utkarsh Raj

Maharaj Mukherjee

Elvis Nyamwange

Carl M Benda

Suman Roy Choudhury

Colin Murphy

Vidya Srikanth

Veerandra S Srivastava

Mehul Jayant Pandya

Vijay Kumar Yarabolu

Vijaya Rudraraju

Paul Jacob Abernathy

William Cruise

Embodiments of the present invention provide a system for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies. The system is configured for extracting one or more historical images associated with resiliency status of electronic and software components associated with an entity application, analyzing the one or more historical images to generate a pixel wise average of the one or more historical images, generating similarity scores between the one or more historical images, determining a distribution of the similarity scores, receiving a real-time image associated with a current resiliency status of the electronic and software components associated with the entity application, generating a real-time image similarity score for the real-time image, and comparing the real-time image similarity score with the distribution to detect presence of an anomaly.

System and method for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies

A computing platform may train, using historical telemetry state images, an image comparison model to identify matches between telemetry state images. The computing platform may generate a plurality of system alerts corresponding to a period of time. The computing platform may access telemetry data corresponding to the period of time. The computing platform may generate, based on the telemetry data and for a time corresponding to each of the plurality of system alerts, a telemetry state image. The computing platform may input, into the image comparison model, the telemetry state images to identify whether or not any of the plurality of telemetry state images match. Based on detecting a match, the computing platform may consolidate system alerts corresponding to the matching telemetry state images, which may produce a single system alert and may send, to a user device, the single system alert.

System and method for consolidation of alerts and events using image matching of heatmap descriptions of infrastructure status

Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

Hybrid neural network for preventing system failure

Aspects of the disclosure relate to transliteration of machine interpretable languages. The computing platform may receive a query, formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential parameters from the query to create a query key; 2) storing the non-essential parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key; 4) based on identifying that the query library includes portions of the query key rather than the query key, recursively identify the translated query by nesting the portions of the query key; and 5) input the non-essential parameters into the translated query to create an output query. The computing platform may execute the output query on the second database.

System and method for recursive transliteration of machine interpretable languages

Aspects of the disclosure relate to evaluating sources of training data for model generation. A computing platform may receive, from one or more data sources, a labelled data set. The computing platform may apply, to the labelled data set, an unsupervised learning algorithm, resulting in a clustered data set. The computing platform may compare, for each data point in the labelled data set, corresponding clustering information and labelling information to identify discrepancies. The computing platform may flag, for data points with identified discrepancies between the clustering information and labelling information, a labelling error. The computing platform may grade, based on the flagged labelling errors, each of the one or more data sources. Using remaining data of the labelled data set, not flagged with labelling errors, the computing platform may train a supervised learning model by weighting the remaining data based on: a corresponding data source and its grade.

Data source evaluation platform for improved generation of supervised learning models

Aspects of the disclosure relate to machine learning models and knowledge graphs. A computing platform may receive event processing data. Using a machine learning mode, the computing platform may identify k nearest data points corresponding to the event processing data. Using a knowledge graph, the computing platform may identify k nearest data nodes corresponding to the event processing data. The computing platform may generate first weighted relative distances between the event processing data and the k nearest data points, and second weighted relative distances between the event processing data and the k nearest data nodes. Based on the weighted relative distances, the computing platform may identify a data cluster for the event processing data. The computing platform may send, based on the identified data cluster, event processing information and one or more commands directing an enterprise computing device to display the event processing information.

Decision making using integrated machine learning models and knowledge graphs

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a request to perform a data migration from a first database configured in a first format to a second database configured in a second format. The computing platform may receive, from the client application and at an abstraction layer, a query. Based on identifying that the query is formatted for execution at the second database, the computing platform may route the query to the second database for execution. Based on identifying that the query is not formatted for execution at the second database, the computing platform may: 1) translate the query from the first format to the second format by using pre-verified query keys to convert the query from the first format into the second format, and 2) route the translated query to the second database for execution.

Abstraction layer for efficient transliteration of machine interpretable languages

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform receive a source query formatted in a first format for execution on a source database. The computing platform may execute the source query on the source database to produce a first data result. The computing platform may input the first data result into a reversal logic engine to produce a target query formatted in a second format corresponding to a target database. The computing platform may execute the target query on the target database to produce a second data result. Based on identifying that the second data result matches the first data result, the computing platform may validate the target query. Based on identifying that the second data result does not match the first data result, the computing platform may adjust the reversal logic engine based on the discrepancy.

Recursive logic engine for efficient transliteration of machine interpretable languages

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may train a machine learning model using source syntax trees and target dialect syntax trees, which may configure the model to output source dialect keys and their corresponding target dialect queries. The computing platform may execute the corresponding target dialect queries to identify whether they are valid. For a valid target dialect query, the computing platform may store the valid target dialect query and first source dialect keys corresponding to the valid target dialect query in a lookup table. For an invalid target dialect query resulting in error, the computing platform may: 1) identify a cause of the error; 2) generate a transliteration rule to correct the error; and 3) store, in the lookup table, the invalid target dialect query, second source dialect keys corresponding to the invalid target dialect query, and the transliteration rule.

System and method for transpilation of machine interpretable languages

Various aspects of the disclosure relate to bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system may include bi-directional hopping to support multi language transpilation, automatic conversion of a mapping into a transformation specification, a hybrid feedback mechanism to update the transformation mappings, automatic scaling and/or creation of enterprise wide mapping and token (e.g., grammar) vocabulary, and/or a self-healing and/or corrective translation capability to perform automatic correction of any partial transpilations over time from a learned mapping.

Hybrid-feedback driven transpiler system

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may configure a client application to use a custom driver when communicating with an enterprise database. The computing platform may receive a database query formatted in a first database format corresponding to a first database. The computing platform may translate, using a query translation library, the database query from the first database format into a second database format corresponding to a second database, which may cause the custom driver to execute a transliteration process using pre-verified query keys stored in the query translation library to convert the database query from the first database format into the second database format. The computing platform may execute the translated database query on the second database to obtain a query result, and may send the query result to the client application.

System and method for efficient transliteration of machine interpretable languages

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Aspects of the disclosure relate to annotated video content. A computing platform may train a model using video content, which may configure the model to classify topics and identify speakers of future video content using STT and NLP. The computing platform may input new video content into the model, which may output an annotated version of the new video content. The model may be configured to generate the annotated version of the new video content by: 1) using one or more STT techniques to produce STT results, 2) classifying topics based on the STT results, 3) identifying speakers based on analysis of voice information, and 4) generating an index indicating a speaker and/or a topic corresponding to time segments. The computing platform may store the annotated version of the new video content, which may be navigable by a user using the visual index.

Speech to text (STT) and natural language processing (NLP) based video bookmarking and classification system

Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.

Machine learning platform for real time occupancy forecasting and resource planning

A model correction tool automatically detects and corrects model drift in a model for a machine learning application. To detect drift, the tool continuously monitors input data, outputs, and/or technical resources (e.g., processor, memory, network, and input/output resources) used to generate outputs. The tool analyzes changes to input data, outputs, and/or resource usage to determine when drift has occurred. When drift is determined to be occurring, the tool retrains a model for a machine learning application.

Growing community of inventors

Charlotte, NC, United States of America

Utkarsh Raj