Bank of America Corporation

Nipun Mahajan

Amit Mishra

Yogesh Raghuvanshi

Yaksh Kumar Singh

Pravin Kumar Sankari Bhagavathiappan

S B Pravin Kumar

Balaji Sugumar

Sushil Golani

Sergey Alexandrov

Jitender Singh

Jennifer Corzo

Dhiraj Jain

Scott S Randrup

Chung Han

Saravana Prakash Kumaresan

John T Blackmon

Bartholomew Sanjeevinathan

Stephanie Ann Hammond

Sushama Shelke

Ayela Chughtai

Amiya R Sahoo

Anand Daniel

Robert E Lutzkow

Ruma Balse

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

An apparatus may comprise a memory communicatively coupled to a processor. The processor may be configured to receive a request to perform multiple data processing operations and determine multiple sub-dialogues corresponding to the request. The sub-dialogues are part of a plurality of IVR operations. The processor may be further configured to route the request to a microservice of the microservices based at least in part upon the sub-dialogues. The microservice may be configured to fulfill the request. The processor may generate a response to the request associated with the microservice, update publishing commands in accordance with one or more rules and policies in response to generating the response, and publish the first microservice. The publishing commands associated with the microservice may be updated in isolation from the rest of the microservices.

System and method to integrate microservices in interactive voice response (IVR) operations

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 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

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 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

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 an expression syntax in the first piece of code and determines an interpretation of the expression syntax. The system creates a sentence that indicates an interpretation of a respective code line. The system determines a counterpart data object in a target programming language for each word in the created sentence. In response, the system creates a second piece of code in the target programming language, such that the second price of code satisfies syntax rules in the target programming language. In response to determining that the second piece of code is configured to perform a particular task that the first piece of code is configured to, the system executes the second piece of code.

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Lawrenceville, NJ, United States of America

Nipun Mahajan