Bank of America Corporation

Krithika Viswanathan

Amit Mishra

Udaya Kumar Raju Ratnakaram

Subburathinam Krishnan

Sarvari Tadimalla

Richa Soni

Rajat Singla

Kadekoppa Kiran Rohithakshappa

Silpa Edappilly Santhosh

Shailendra Singh

Pravin Kumar Sankari Bhagavathiappan

Mohammed Zubair Mohammed Ajmal

Niharika Marisetti

Anu Joy

Amaresh Gunasekar

Anushka Pandey

Suvendu Ranjan Pani

Suresh K S

Siva Kesava

Murugan Vasudevan

Praveen Kumar Parsa

A system receives an input from a user, where the input indicates a request to perform one or more tasks. The system generates a plurality of utterances from the input. Each utterance indicates a different operation. The system determines a sequence of the operations to be performed in order to perform the one or more tasks. The system identifies a conversational software algorithm that is configured to process a respective utterance based on the topic of the utterance. The system communicates each utterance to the respective conversational software algorithm. The system receives responses from the conversational software algorithms. The system determines whether each response is valid. The system generates a final response based on the valid responses. The system outputs the final response.

Response generation from multi-intent input

A method includes rendering, on displays of an extended reality (XR) device, an XR environment, and detecting, based on sensor data obtained from sensors of the XR device, a device capability associated with the XR device and one or more environmental conditions associated with the XR environment. The method further includes receiving a request corresponding to one or more user interactions with an XR application. In response to receiving the request, the method includes generating, based the device capability and the environmental conditions, a dynamic digital profile to be associated with the user, in which the dynamic digital profile is dynamically generated to be responsive to one or more input modalities associated with the request, and render, on the displays, the dynamic digital profile. The rendered dynamic digital profile includes a set of elements responsive to the one or more input modalities of the request.

System and method for generating and securing a user-specific application in an extended reality environment

Utterance building to convey user input to conversational agents

A system includes a memory and a processor coupled to the memory. The processor receives a first user credential associated with a first user and authorizes an avatar of the first user to enter a virtual environment. The processor receives a first selection of an experience theme and a second selection of at least one product type from the selected experience theme. The processor determines a first data file that provides a first benefit count to the first user when a first amount of data objects is transferred from a first data file of the first user to a second data file. The processor indicates to the first user that the first data file provides the first benefit count to the first user, receives a command to obtain the first data file, and initiates a registration process to register the first user to obtain the first data file.

System and method for generating a virtual experience in a virtual environment

Systems, computer program products, and methods are described herein for developers to initiate development operations from within a development environment of a user application. This can also be extendable dynamically for different user groups (e.g., testers, support, internal associates or employees, or the like), and environments (e.g., lower-level environments, production environments, or the like). As such, the invention offers a dynamic debug mode feature for virtual assistants based on different sections of users and environments.

System and method for dynamic debugging of data in a triage environment integration with use of a virtual assistant graphical user interface

A method includes monitoring an interaction of a user with a metaverse session. A notification is sent to a first developer that the user is facing an issue while interacting with the metaverse session. The first developer is allowed access to the metaverse session at a location of the issue using a first developer avatar. One or more error flags are generated within a field of view of the first developer avatar in the metaverse session. Each error flag includes a respective error code. In response to determining that a first error code of a first error flag matches a first stored error code, a first solution is determined. The first solution includes a first stored solution corresponding to the first stored error code. In response to determining that the first solution solves the issue of the user, the first solution is deployed to the metaverse session.

System and method for metaverse debugging

A solution implementation system includes a solution registry with a corrective action for each of a plurality of predefined root causes of exception errors. The system receives communication data transmitted through a distributed network comprising a plurality of servers. An exception error is detected corresponding to a failed transmission of the communication data between communicatively coupled servers of the distributed network. A first root cause of the exception error is determined. Properties of the distributed network at the time of the detected exception error are determined. Using the solution registry, a first corrective action is determined for the first root cause that is associated with the determined properties of the distributed network at the time of the detected exception error. The first corrective action is automatically implemented to resolve the exception error.

Self-resolution of exception errors in a distributed network

Embodiments of the invention are directed to machine learning-based anomaly detection in program code. The system provides a machine learning (ML) anomaly detection model component structured to detect architectural flaws in program code based on processing application logs associated with technology program code and determining flow sequences between a plurality of layers of code. In particular, the system trains the machine learning (ML) anomaly detection model that is structured to (i) construct a first application layer map based on mapping each of the plurality of first classes associated with the first technology program code to one or more application layers, (ii) determine a first architecture pattern associated with the first technology program code, and (iii) determine whether the first technology program code is associated with an anti-pattern.

Electronic system for machine learning based anomaly detection in program code

Apparatus and methods are provided for providing zero-(permanent) byte real-time customized software to a user's client station. A user may request real-time customized software from one channel. The request may be transmitted to a server and analyzed to determine an intent for the customized software. The intent may be matched to one or more features in a feature catalog and an appropriate target output channel may be determined. A feature bundler may extract source code corresponding to the feature(s) and output channel, compile the code and transmit the customized software to the target output channel. The target output channel may execute the customized software within a container and delete the software when a deletion condition is satisfied, such as after the user finishes using the software.

Apparatus and methods for real-time rendering of zero-byte customized software on a client station

An exception resolution system determines that an exception error corresponding to a failed transmission of communication data between servers of a distributed network. Based in part on a shortest data transfer route associated with the exception error, a containerized test environment is generated with the minimum replica components needed to replicate the exception error. The containerized test environment can be used to test possible changes for resolving the exception error.

Developer test environment with containerization of tightly coupled systems

Apparatus and methods are provided for providing zero- (permanent) byte real-time customized software to a user's client station. A user may request real-time customized software from one channel. The request may be transmitted to a server and analyzed to determine an intent for the customized software. The intent may be matched to one or more features in a feature catalog and an appropriate target output channel may be determined. A feature bundler may extract source code corresponding to the feature(s) and output channel, compile the code and transmit the customized software to the target output channel. The target output channel may execute the customized software within a container and delete the software when a deletion condition is satisfied, such as after the user finishes using the software.

Apparatus and methods for remotely viewing and authenticating/validating the contents of a safe deposit box (or safe) are provided. The apparatus and methods may include a safe deposit box including a locking internal cavity, a camera, a weight sensor, a light, a control circuit, and a communication circuit. After receiving a request to view the contents and authenticating a user, the safe deposit box may take still or motion pictures of the contents and weigh the contents and send the results to the user or a server. If the contents of the safe deposit box have changed an alert may be sent to the user or a server.

Apparatus and methods for remote view of safe deposit box contents

Embodiments of the invention are directed to static program code analysis and detection of architectural flaws. The system provides a rule-based anomaly detection engine structured to capture application logs during construction of technology program code and dynamically detect anti-pattern conflicts to remediate defects in the technology program code. In particular, the system receives a request to perform defect analysis of a first technology program code. In response, the system constructs a first layer transition map based on mapping each of a plurality of first classes associated with the first technology program code to one or more application layers. The system may then determine, via an anomaly detection engine component, one or more anomalies associated with the first technology program code.

Electronic system for static program code analysis and detection of architectural flaws

Embodiments of the invention are directed to active detection and mitigation of anomalies in program code construction interfaces. The system provides a proactive plug-in with a dynamic machine learning (ML) anomaly detection model cloud component structured to dynamically detect architectural flaws in program code in real-time in a user coding interface. In particular, the system activates a machine learning (ML) anomaly detection plug-in for dynamically analyzing the first technology program code being constructed in the user coding interface. Moreover, the system modifies, via the ML anomaly detection plug-in, the user coding interface to embed interface elements associated with the one or more flaws in the first technology program code detected by the ML anomaly detection model cloud component.

Dynamic system for active detection and mitigation of anomalies in program code construction interfaces

An exception-resolution monitoring system determines an exception error is detected corresponding to a failed transmission of a message between servers of a distributed network. Changes are monitored that are tested and implemented to correct the detected exception errors. An executable corrective action is determined based on the monitored changes. The executable action is stored for future use and linked to a root cause of the exception error and properties of the distributed network at the time of the exception error.

Growing community of inventors

Chennai, India

Krithika Viswanathan