Bank of America Corporation

Siva Kumar Paini

Sudhakar Balu

Sakshi Bakshi

Srinivasa Jitendra Dhanwada

Sathya Thamilarasan

Donna Maria Welch

Nagalaxmi Sama

Shyamala Manoharan

Sri Lakshmi Priya Doraiswamy

Amod Jha

Sri Doraiswamy

Anup Kumar Kedia

Amit Kumar Sati

Ashlesha Mithra

A system for updating a software code executes a current version of the software code on a web application. The system receives a request message to update the current version of the software code to a new version of the software code. The system determines whether the software code is still being executed on the web application. In response to determining that the software code is still being executed on the web application, the system identifies a subset of portions of the software code that have not yet been executed, executes an update instruction to cause the subset of portions of the software code to be updated, and moves the updated subset of portions of the software code from a first memory block to a second memory block.

System and method for dynamic switching between an old version and a new version of a software code

A system for classifying software code determines a first function, a first set of operations, and a first criticality level of a first software code. The system determines a second function, a second set of operations, and a second criticality level of the second software code. The system determines that the first function of the first software code corresponds to the second function of the second software code. The system compares the operations of the first software code with the operations of the second software code and determines that the first and second software code have at least one operation in common with each other. The system determines that the first criticality level of the first software code corresponds to the second criticality level of the second software code. In response, the system classifies the first software code and the second software code in a first group.

System and method for classifying software code based on functionality and criticality

A device configured to identify automated process instructions for a network device. The automated process instructions include instructions for performing a series of actions on the network device. The device is further configured to identify a first time instance for executing the automated process instructions that does not occur within a time interval associated with a first system update for the network device and to execute the machine-executable code at the first time instance to perform the series of actions on the network device.

Scheduling a pausable automated process in a computer network

Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may determine errors caused by the application upgrade within the simulated version of the enterprise system based on differences between the system parameters and the virtual parameters. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.

Artificial intelligence (AI) supported graph enabled method to manage upgrades for applications

Methods and systems described herein for addressing issues associated with varying graph analytics tools that require different tool-specific coding languages. An artificial intelligence (AI) sub-system of various modules extracts metadata from a dataset and identifies nodes and relationships in the dataset using the metadata. The dataset is matched with a corresponding graph-analytics template in a data store, and a dynamic template modifier modifies the corresponding graph-analytics template. In some examples, the AI system generates smart guided videos with logical breakpoints that are embedded along with templates for quick learning and to build faster graphical analytics. The AI system includes a dynamic template modifier and a cognitive smart AI engine that includes a graph.

Method for dynamic AI supported graph-analytics self learning templates

A system for protecting non-public information from a malicious user determines a set of input data entered into a web application. The system determines whether each input data is associated with non-public information. In this process, the system compares each input data with each of a set of keywords that are known to be associated with non-public information. In response to determining that input data corresponds to a keyword, the system determines that the input data comprises non-public information. In response, the system masks the input data and tags an authentication instruction to the masked input data. The system may receive a request to unmask the masked input data. In response to receiving the request, the system executes the authentication instruction.

System and method for protecting non-public information from malicious users

A system includes a memory and a processor. The memory stores a set of videos each of which is associated with an application of a set of applications and depicts a process performed using the application. For each video, the processor generates metadata associated with the depicted process, and stores the metadata within a collection of metadata in the memory. The processor receives a request for information from a user. In response, the processor identifies an application of the set of applications for use in generating a response to the request. The processor generates, based on the collection of metadata, instructions for interfacing with the first application to generate the response. The processor executes the instructions to interface with the first application to generate the response, and transmits the response to the user.

System and method for integration of automatic response generating systems with non-API applications

Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.

System for implementing parametric optimization analysis for resource selection

Aspects of the disclosure relate to monitoring, evaluating, and repairing bots in a hashchain-based distributed bot hub that process a workflow. In some embodiments, a computing platform may receive workflow information associated with performing a first workflow that includes executing one or more tasks using a plurality of virtual bots, instantiate a first subset of the plurality of bots to process the one or more tasks of the first workflow, and instantiate a first subset of the plurality of bots to process the one or more tasks of the first workflow. identifying a potential anomalous activity may include causing the monitor bot hub to remove the identified bot to a quarantine hub, and execute a repair process on the identified bot in the quarantine hub.

Dynamic workflow engine in an enterprise bot hub system

A system for detecting and countering malicious code executed on a web application detects a set of configuration parameter values from the web application. The system compares each configuration parameter value with a counterpart from among a set of historical configuration parameter values. The system determines the difference between a configuration parameter with a counterpart historical configuration parameter values. If the system determines that the difference between the configuration parameter and the counterpart historical configuration parameter is more than a threshold rate, the system determines that a malicious code is executed on the web application. In response, the system determines a function associated with the malicious code. The system determines one or more actions to counter the malicious code based on the function of the malicious code. The system executes at least one of the one or more actions.

System and method for detecting and countering malicious code

Aspects of the disclosure relate to an intelligent resource evaluation engine. A computing platform may monitor the plurality of RPA machines to detect parameter information. The computing platform may store the parameter information along with corresponding RPA machines as a key value pairs in a database. The computing platform may identify first current parameter information for a first RPA machine using the key value pairs. The computing platform may input the first current parameter information into an intelligent resource evaluation model, which may output first machine selection information for the first RPA machine. Based on identifying that the first RPA machine is sufficient to execute the first robotic automation process, the computing platform may send direct the first RPA machine to execute the first robotic automation process.

Intelligent resource evaluator system for robotic process automations

A smart change evaluation engine analyzes an existing and updated Robotic Process Automation (RPA) system and process flow and evaluates identified changes in the process, components, elements, and controls. The smart change evaluation engine outputs an appropriate change estimate value corresponding to an amount of change and/or effort to modify the RPA process flow. The smart change evaluation engine analyzes the recorded changes and assigns a rule-based value for every corresponding component in the process flow. A scoring algorithm is used to generate a change estimate value based on the analysis of the pre-change recordings, the post-change recordings, and from the data values captured from the actual robotic automated system, by doing dual checks resulting in best calculated estimates. The scoring value derived is used as an input for evaluating changes for (RPA) that may be required to support further development of services and/or applications provided by an enterprise organization.

Smart change evaluator for robotics automation

Systems, methods, and apparatus are provided for modification of RPA controls during a workflow without impacting bot performance. A background scan initiated in parallel to an RPA application workflow may identify values for an application control parameter and a corresponding bot control parameter. Hashes of the values may be validated against a key value pair stored as a block in a distributed ledger. If changes have been made to the application control values, the bot control value will not match the application control value and validation will fail. If validation fails, an override may be generated for the bot value. An updated bot value may be stored in a temporary cache until the workflow is complete. Following completion of the workflow, a new block may be added to the blockchain storing a new key value pair including the application control value and the updated bot control value.

Dynamic modification of robotic process automation control parameters using blockchain technology for correcting workflow failure

Various systems, methods, and computer program products are provided for dynamically generating an API. The method includes extracting metadata from a command recording of a system API. The command recording of the system includes a starting breakpoint and an ending breakpoint for one or more repetitive processes. The method also includes determining one or more API parameters for an API to be generated based on the extracted metadata. The method further includes dynamically generating the API based on the API parameters. The method still further includes causing an authentication of the credentials relating to the API via robotic process automation.

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Telangana, India

Siva Kumar Paini