Bank of America Corporation

Priyank R Shah

Ramakrishna R Yannam

Emad Noorizadeh

Ravisha Andar

Sushil Golani

Saahithi Chillara

Ashwini Patil

Prejish Thomas

Ion Gerald McCusker

Castigliana Cimpian

Rajan Jigish Jhaveri

Alicia C Jones-McFadden

Matthew Hsieh

Caitlin Chrisman Bullock

Maruthi Z Shanmugam

Yogesh Raghuvanshi

Viju Kothuvatiparambil

Hari Gopalkrishnan

Kevin Cole

Dariane Katinka Hunt

Kurt R Schultz

Christian S Kitchell

Shahram Khosraviani

Ryan Strug

Steven Zhao

Donatus Asumu

Christopher Keith Restorff

Pavan K Chayanam

Devanshu Mukherjee

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

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

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 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

Systems, methods, and apparatus are provided for integrating data from multiple computer-based communication platforms within a single user session at a mobile device application. The mobile device application may interact with a first platform in a first format. The first platform may include an interactive response system such as a chatbot. The mobile device application may receive a request to interact with a live agent and may interact with a second platform in a second format. The second platform may include an agent interface. In response to the transfer request, the first interface may capture the interactions with the first platform, convert the interactions from the first format to the second format, and transmit them to the second platform. The second platform may display the session history to the agent on the same screen as real-time, ongoing agent interactions with the user.

Transfer of AI-powered conversational messaging within a mobile application

A system for responding to external requests received at an internal bot network is provided. The system may include an internal bot network including a plurality of internal bots. The plurality of internal bots may interact with a plurality of external bots. The system may include a translation layer/barrier. Each bot included in the plurality of internal bots and the plurality of external bots may be resident on one or more hardware processors. Each external bot may communicate using its own unique set of external specifications. Each internal bot may communicate using a universal set of internal specifications and is prevented from communicating using a set of external specifications. The translation layer/barrier may intercept requests and responses between the plurality of internal bots and the plurality of external bots. The translation layer/barrier reformats intercepted requests and responses to correspond to the set of specifications specific to the receiving bot.

Internal chatbot universe with a universal communication protocol

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.

Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

Growing community of inventors

Plano, TX, United States of America

Priyank R Shah