Bank of America Corporation

Srinivasa Jitendra Dhanwada

Siva Kumar Paini

Sudhakar Balu

Sakshi Bakshi

Donna Maria Welch

Sathya Thamilarasan

Nagalaxmi Sama

Shyamala Manoharan

Sri Lakshmi Priya Doraiswamy

Sri Doraiswamy

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

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, 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.

System and method of automated processing for dynamic API generation

A system includes a memory, a display, and a processor. The processor determines that the display is displaying an element associated with an application. The element includes at least one of a graphical user interface and a web browser. In response to determining that the display is displaying the element, the processor records a video of the display, which depicts a process performed using the element, to obtain a first set of information from the application. The processor extracts, from the video, metadata associated with the process depicted in the video. The processor generates, based at least in part on the extracted metadata, a set of instructions for interfacing with the application. When executed by the processor, the set of instructions are configured to cause the processor to interface with the application to obtain the first set of information. The processor further stores the set of instructions in the memory.

System and method for intelligently generating code for use in integrating automated response generating systems with non-API applications

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 create system nodes and virtual nodes. The system nodes and virtual nodes may be dynamically linked in order to determine errors caused by the application upgrade within the simulated version of the enterprise system. 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.

Graphical neural network for error identification

A system includes a memory and a processor. The memory stores a queue that includes a sequence of slots, each of which is configured to store a request. The processor receives, from a user device, a request for information. In response, the processor stores the request in an end slot of the queue. The processor collects values for parameters associated with the system. The processor determines, based on the request and the collected values for the parameters, an estimate of a time to generate a response to the request. Generating the response includes identifying an application for use in generating the response, generating, based on a set of videos of processes previously performed using the application, instructions for interfacing with the application to generate the response, and executing the instructions to interface with the application. The processor further transmits, to the user device, the estimate of the time.

Smart queue for distributing user requests to automated response generating systems

A system for classifying tasks based on workflow patterns detected on workflows through a real time video feed that shows steps being performed to accomplish a plurality of tasks. Each task is associated with a different set of steps. The system accesses a first set of steps known to be performed to accomplish a first task on the webpages. The first set of steps is represented by a first set of metadata. The system extracts a second set of metadata from the video feed. The second set of metadata represents a second set of steps to perform a second task. The system determines whether the second set of metadata corresponds to the first set of metadata. If it is determined that the second set of metadata corresponds to the first set of metadata, the system classifies the second task in a class to which the first task belongs.

System and method for splitting a video stream using breakpoints based on recognizing workflow patterns

A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

Growing community of inventors

Hyderabad Telangana, India

Srinivasa Jitendra Dhanwada