Bank of America Corporation

Yogesh Raghuvanshi

Nipun Mahajan

Amit Mishra

Sushil Golani

Pravin Kumar Sankari Bhagavathiappan

Ramakrishna R Yannam

Ravisha Andar

Ashwini Patil

Emad Noorizadeh

Saahithi Chillara

Ion Gerald McCusker

Yaksh Kumar Singh

Priyank R Shah

Christopher Keith Restorff

Sivakumar Nagarajan

S B Pravin Kumar

Balaji Sugumar

Dhiraj Jain

Pavan K Chayanam

Pinak Chakraborty

Gaurav Bansal

Dinesh Kumar Agrawal

Saurabh Mehta

Juraj Partila

Sergey Alexandrov

Sushama Shelke

Stephanie Ann Hammond

Saravana Prakash Kumaresan

Chung Han

Shali Liu

Scott S Randrup

Lining Mo

ChiaHsun Lin

John T Blackmon

Ruma Balse

Robert E Lutzkow

Anand Daniel

Amiya R Sahoo

Ayela Chughtai

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

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.

Source code validation based on converting the source code to a non-programming language

An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

System and method for voice controlled automatic information access and retrieval

An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. This determination indicates that the text is associated with a probability that the automatically generated reply includes the requested information that is greater than a threshold. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

System and method for dynamically identifying and retrieving information responsive to voice requests

Apparatus and methods for enhancing security of an authentication process of a caller that initiated a call in an Interactive Voice Response ('IVR') system are provided. The methods may include routing the call through the IVR system to a headset. After the routing, the methods may include retrieving data associated with the caller. In response to the retrieved data meeting one or more predetermined conditions, a graphical user interface of an agent computer paired to the headset may display a plurality of validation information for validating the caller. Selection by the agent of validation information may trigger methods for transmitting a request to the caller, via a caller mobile phone, to input the validation information selected by the agent. The methods may additionally include authenticating the caller based at least in part on the information input into the mobile phone.

Secure remote data submission for IVR systems

An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Selective use of tools for automatically identifying, accessing, and retrieving information responsive to voice requests

When a caller initiates an interaction with an interactive voice response ('IVR') system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools into the interaction after the caller been transferred to the agent. The agent may determine which AI responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. Such human-computer interaction may shorten response time of human agents and improve efficiency of IVR systems.

Artificial intelligence ('AI') integration with live chat

Aspects of the disclosure relate to computing systems for receiving user requests through channels of a webpage and performing computational functions corresponding to the user requests. In one embodiment, a computing platform may detect a change corresponding to input parameters for performing a user request through a first representation of a webpage. The computing platform may update, based on the detected change corresponding to the input parameters, a natural language processing application associated with a second representation of the webpage. The computing platform may generate, based on the updated natural language processing application associated with the second representation of the webpage, a logical table including one or more questions to ask a user through the second representation of the webpage in response to one or more user inputs.

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Princeton, NJ, United States of America

Yogesh Raghuvanshi