Bank of America Corporation

Amit Mishra

Krithika Viswanathan

Nipun Mahajan

Yogesh Raghuvanshi

Pravin Kumar Sankari Bhagavathiappan

Udaya Kumar Raju Ratnakaram

Yaksh Kumar Singh

Subburathinam Krishnan

Vignesh Chandrasekaran

Anupreet K Lamba

Sarvari Tadimalla

Richa Soni

Yugal Malviya

Rajat Singla

Kadekoppa Kiran Rohithakshappa

Silpa Edappilly Santhosh

Shailendra Singh

Mohammed Zubair Mohammed Ajmal

Niharika Marisetti

Balaji Sugumar

S B Pravin Kumar

Sasidhar Purushothaman

Sandeep Verma

Maneesh Kumar Sethia

Sushil Golani

Abhijit Behera

Prabhakaran Balasubramanian

Saurabh Garg

Anantharajan Srinivasarangan

Ngoc Anh Tran

Jitender Singh

Jennifer Corzo

Priyeshkumar Chandrakant Patel

Praveen Kumar Parsa

Vamshi Krishna Jutur

Siva Kumar Peesapati Venkata

Anu Joy

Amaresh Gunasekar

Pavan Kumar Kanamarlapudi

Ayela Chughtai

Sushama Shelke

Stephanie Ann Hammond

Bartholomew Sanjeevinathan

Anushka Pandey

Suvendu Ranjan Pani

John T Blackmon

Saravana Prakash Kumaresan

Suresh K S

Siva Kesava

Murugan Vasudevan

A method for destressing an agent in a contact center in real-time may include receiving an agent's login and routing incoming voice calls to the agent. A monitoring and triggering application ('MTA') may receive data about the agent's performance during calls from a statistics server. A graphics processing unit may run an MTA artificial intelligence machine learning (“MTA AI/ML”) algorithm to determine a stress level of the agent by analyzing video, audio, and other metrics during calls. The MTA AI/ML algorithm may recommend to the agent's manager that the agent be given a destress break. When approved, calls may be routed to other agents while the agent receives a video, audio, or game to destress. The agent may receive the break without any negative impact on metrics relating to the agent's performance. The agent may re-enter the call routing server to receive incoming calls upon completion of the break.

Real time AI/ML based monitoring and triggering app to destress contact center associates

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

Systems and processes are disclosed to leverage conversational AI across voice and digital channels by automating employee interactions with clients to deliver humanized experiences with speed and accuracy. Live phone conversation. Transcriptions between clients and agents are generated. AI assist provides automation based on client interactions. Prompts are provided to speak to customers. Real time updates and summaries are sent to client devices and streamed to customize agent sessions. Auto navigation of agent views and manipulations of the agent screen are optimized by AI. Instant client validations are enabled via audible and visual confirmations on client devices. Various other AI implementations for improved processing our similarly disclosed.

AI automated facilitation of support agent/client interactions in multi-modal communications

A system receives a set of input data streams from different data sources. At least one of the set of input data streams comprises a message that indicates an anomaly with respect to a topic associated with a software application. The system determines the topic, the anomaly, and a set of metadata associated with the topic from the set of input data streams. The set of metadata comprises an occurrence frequency of the anomaly in messages, a number of data sources from which messages are received, or a timeframe window within which the messages are received. The system assigns a set of weight values to the set of metadata and determines an accumulated weight value. If it is determined that the accumulated weight value is more than a threshold weight value, the system communicates an alert indicating to execute a countermeasure action that addresses the anomalous topic.

Anomaly detection from network traffic

Arrangements for machine learning-based dynamic learning are provided. In some examples, audio data associated with a plurality of calls may be received and analyzed to identify a topic and sub-topic of each call and metadata of each call. Feedback data may also be received. A machine learning model may be executed by inputting, to the model, the identified topic and sub-topic and metadata of each call, and the feedback data, to output one or more topics or sub-topics of concern. A plurality of ongoing calls may be monitored to identify an ongoing call related to one of: a topic or sub-topic of concern. A plurality of agents who are not subject matter experts in the identified topic or sub-topic of concern and are available may be identified and joined, via respective computing devices, to the ongoing call in a dynamic learning session.

Communication session interruption and dynamic learning system

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

A method includes receiving a request. The request is encrypted to generate an encrypted request. The encrypted request includes a version information for a first software package deployed in a standalone computing system, and a region information and a hardware information for the standalone computing system. The encrypted request is sent to a respective regional data center of a cloud computing system based on the region information of the first software package. A second software package is identified among software packages stored in the respective regional data center. The second software package corresponds to the hardware information of the first software package. The version information of the first software package is compared to a version information of the second software package. In response to identifying that the second software package is more recent than the first software package, the second software package is deployed to the standalone computing system.

System and method for upgrading non-permitted operating systems on standalone computing systems

A system determines content indicated on a website where a software application is accessible. The system receives a set of rules comprising a first rule that indicates a first generated response is to be published on one or more first platforms. The system generates a first text response according to the first rule and based on the determined content, an anomaly associated with a software application, and a countermeasure action that addresses or resolves the anomaly. The system determines whether the first generated text response corresponds to an expected response. In response to determining that the first generated text response corresponds to the expected response, the system publishes the first generated text response on the one or more first platforms.

Dynamic response generation based on an anomaly associated with a software application

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

Arrangements for intelligent call routing are provided. Audio data from a call between an agent device and a user device may be received and a topic of the call and metadata may be extracted from the audio data. The extracted topic and metadata may be compared to an alert signals database to output whether an alert will be attached to the call. Agent parameters associated with the agent who is part of the call may be identified. A machine learning model may be executed by inputting, to the model, the extracted topic, extracted metadata, the output of whether the alert will be attached to the call, and the agent parameters, to output a routing signal. Based on the routing signal, a second call communication session may be initiated between the agent computing device and a computing device of at least one of another agent or a supervisor.

Intelligent routing signaling system

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 system for generating an expandable pool of virtual agents to provide solutions in a virtual environment comprises a call center server in communication with a metaverse server. The call center server generates the virtual agents with corresponding skills of each real-world agent using a machine learning model based on historical observation operations performed by each particular real-world agent and a historical rule set to address the service issue. The metaverse server identifies the virtual agents in the virtual environment. The call center server routes a service inquiry with a service issue from a user equipment of a user to the metaverse server when determining that a particular real-world agent is not available. The metaverse server identifies a first virtual agent with a first skill and conducts a first interaction between the first virtual agent and a user avatar to generate the customized solution to address the service issue.

System and method for generating an expandable pool of virtual agents to provide solutions in a metaverse

A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies a particular data object that is unique to the original programming language. The system determines a counterpart data object that is used for a particular purpose as the identified data object in a target programming language. The system creates a second piece of code in the target programming language by replacing the particular data object with the counterpart data object. The system determines if the second piece of code is configured to perform a task that the first piece of code is programmed to perform. In response to determining that the second piece of code is configured to perform the particular task, the system executes the second piece of code.

Source code conversion from an original computer programming language to a target programming language

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Chennai, India

Amit Mishra