Bank of America Corporation

Bank of America Corporaiton

Ramakrishna R Yannam

Emad Noorizadeh

Priyank R Shah

Ravisha Andar

Sushil Golani

Saahithi Chillara

Ion Gerald McCusker

Ashwini Patil

Prejish Thomas

Viju Kothuvatiparambil

Hari Gopalkrishnan

Rajan Jigish Jhaveri

Yogesh Raghuvanshi

Donatus Asumu

Isaac Persing

Castigliana Cimpian

Christopher Keith Restorff

Dana Patrice Morrow Branch

Alicia C Jones-McFadden

Matthew Hsieh

Caitlin Chrisman Bullock

Maruthi Z Shanmugam

Kevin Cole

Tao Huang

Dariane Katinka Hunt

Kurt R Schultz

Bharathiraja Krishnamoorthy

Ryan Strug

Steven Zhao

Christian S Kitchell

Shahram Khosraviani

Xianhong Zhang

Pravin Kumar Sankari Bhagavathiappan

Jennifer Russell

Stephen Yeager

Kent Steger

David H Witting

Jie Zhang Nissel

Joshua Maki

Pavan K Chayanam

Marco Ramirez

Brad Sadler

Devanshu Mukherjee

Anuradha Girish Sethuram

Mahalakshmi Chandra Sekaran

Martin Goettmann

Jorge Kara

Aaron Michael Hosford

Manoj Shankar

Ali Salim

Christine Co

Sarah M Fatemi

Sridevi Rachakulla

Michael Bostian

Systems and methods for increasing accuracy in an online chat interface are provided. Methods may be executed via a machine-learning (ML)-based chat monitoring engine. Methods may include intercepting a request utterance. Methods may include computing, via a trained ML model, an intent of the request utterance; generating a target response to the request utterance; intercepting a response utterance; and calculating a difference between the response utterance and the target response. In response to the difference being less than a threshold difference, methods may include releasing the response utterance to be transmitted as a response message. In response to the difference being more than a threshold difference, methods may include preventing the response utterance from being transmitted as a response message, generating a revised response utterance that is less than a threshold difference apart from the target response, and transmitting the revised response as a response message to a remote computing device.

Machine learning (ML)-based dual layer conversational assist system

A telephonic routing system to enable rerouting a call between a live agent and an automated agent. The system may include a receiver configured to receive a telephonic call from a caller. The system may also include a router configured to route the telephonic call to a first communication pathway where the first communication pathway may connect the telephonic call to a live agent headset device. The system may also include a processor configured to retrieve a customer profile associated with a phone number of the telephonic call and display authentication data, on a graphical user interface ('GUI') of a computing device associated with the live agent headset device. The router may be further configured to reroute the telephonic call to a second communication pathway for verifying an authenticity of the caller. The second communication pathway may establish a connection between the telephonic call and the automated agent.

Enhanced interactive voice response system providing bi-communication pathways for call support and routing

Methods, apparatus, and systems for detection and remediation of an identified attempt of impersonation of a digital entity. The methods may include authenticating a call received by a user device. The methods may include instantiating an entity-operated application on the user device. The methods may include detecting one or more keywords indicating that the call is associated with a predefined entity. The methods may include generating and transmitting a one-time token to the predefined entity. The methods may include identifying, during the call, a first voice and a second voice, the second voice being different from the first voice. The methods may include processing words identified as being uttered by the second voice into groups of text utterances using a natural language processing ('NLP') model. The methods may include detecting, during a predetermined time period, if any text utterances from the groups of text utterances correspond to the one-time token.

User initiated entity verification

Systems, methods, and apparatus are provided for integrating AI-powered bot-generated responses with an agent interface during a live session with a customer. In response to a customer request, a live chat session may be initiated with an agent at first platform that includes an agent interface. A parallel session may be initiated at a second platform that includes an interactive response system and AI engine. An input from a customer may be displayed at the first platform and may also be received at the second platform. The second platform may derive intent from the input and generate an AI-based response. The response may be displayed in a window at the first platform. The agent may approve, reject, or modify the generated response. Following agent approval, the response may be inserted into the live customer session.

Integration of AI-powered conversational messaging with a live agent interaction

Apparatus, methods and systems for contextual prediction processing is provided. Methods may include receiving a conversation from an entity. The conversation may include current utterance, previous utterances and details. Methods may include using an action-topic ontology to build, using data retrieved from the current utterance, a conversation frame that corresponds to the current utterance. Methods may include merging the conversation frame with data, retrieved from the previous utterances and the details, to generate a target conversation frame. Methods may include validating the target conversation frame to prevent looping over historic data in the event that the current utterance fails to add relevant information. Methods may include generating an enhanced contextual utterance based on algorithms and the target conversation frame. The enhanced contextual utterance may be used to understand the current utterance in a context of the conversation. Methods may include returning the enhanced contextual utterance to the entity.

Selection system for contextual prediction processing versus classical prediction processing

Systems are provided for building semantic frames. Systems may include building a semantic frame using a machine learning algorithm. The algorithm may identify: an index number of a token, a semantic role classifier assigned to the token, a corresponding correlation value and an index number of one or more related tokens. The algorithm may also create a semantic frame using the identified information. Systems may include building semantic frames for multiple tokens within an utterance. Systems may include building semantic frames for a plurality of tokens within a plurality of utterances. The plurality of utterances may be components of a conversation. Systems may also include summarizing the conversation using the semantic frames.

Semantic frame builder

Embodiments of the invention are directed to systems, methods, and computer program products for providing a multi-channel cognitive resource platform configured for intelligent, proactive and responsive communication with a user, via a user device. The system is further configured to perform one or more user activities, in an integrated manner, within a single interface of the user device, without requiring the user to operate disparate applications. Furthermore, the system is configured to receive user input through multiple communication channels such as a textual communication channel and an audio communication channel. The system is further configured to switch between the various communication channels seamlessly, and in real-time.

Multi-channel cognitive resource platform

A one-way data migration system may shift authentication data from a legacy database to a current database. The system may include one or more databases, a mobile device, a mobile device processor, a backend server, a backend receiver, a backend processor, and a backend transmitter. The backend transmitter may transmit requests to the mobile device. The backend receiver may receive the usernames and passwords. The backend processor may authenticate the input usernames and passwords using a hash previously stored within a first database. The backend processor may create a current or updated hash for the password. The system may change the underlying pathway from a pathway associated with the hash previously stored within the first database to a current pathway associated with the current hash. A second database may store the current hash. Following storage of the current hash, the processor may flag the username and/or password as migrated.

One way data migration system

Methods and apparatus for parallel intent processing at a network of chatbots may be provided. A query may be received at a user interface. The query may be transmitted to multiple chatbots at the same time. Each chatbot may identify an intent for the query. Each chatbot may also identify a percentage of accuracy that the chatbot considers that the intent corresponds to the query. The chatbots may present the intents to the user interface. The user interface may rank the intents. The user interface may display the ranked intents to the user. The user interface may receive an intent selection from the user. The user interface may direct the user to the chatbot that identified the selected intent.

Executing a network of chatbots using a parallel bot approach

A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence ('AI') engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.

Machine learning model training system

A method for filtering a plurality of agent-customer interactions to determine whether one or more of a plurality of agent-customer interactions should be stored in a library of Artificial Intelligence (AI) files related to an interactive voice response system (IVR) is provided. The method may include receiving an identification of a plurality of IVR flashpoints, monitoring and/or reviewing the plurality of agent-customer interactions, and determining whether one of the plurality of agent-customer interactions meets a threshold number of the IVR flashpoints. For each of the plurality of agent-customer interactions that meets a threshold number of the IVR flashpoints, the method may further direct the IVR to convert the interaction into an IVR workflow and store the IVR workflow in the library of AI related to IVR.

Agent assist design—autoplay

Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Building and training a network of chatbots

A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Artificial intelligence (AI)-powered legacy scenarios for use within a mobile application

Artificial intelligence (AI)-powered conversational messaging and completion engine for use within a mobile application

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

Growing community of inventors

The Colony, TX, United States of America

Ramakrishna R Yannam