Bank of America Corporation

Sushil Golani

Ramakrishna R Yannam

Emad Noorizadeh

Priyank R Shah

Hari Gopalkrishnan

Yogesh Raghuvanshi

Ravisha Andar

Maruthi Z Shanmugam

Ashwini Patil

Castigliana Cimpian

Saahithi Chillara

Ion Gerald McCusker

Isaac Persing

Dana Patrice Morrow Branch

Alicia C Jones-McFadden

Matthew Hsieh

Caitlin Chrisman Bullock

Kevin Cole

Dariane Katinka Hunt

Christian S Kitchell

Shahram Khosraviani

Nishant H Shah

Pravin Kumar Sankari Bhagavathiappan

Christopher Keith Restorff

Kent Steger

Sangeetha Mohan

Raja Gottumukkala

Satish Dandamudi

Amit Mishra

Pavan K Chayanam

Viju Kothuvatiparambil

Nipun Mahajan

Jennifer Russell

Yaksh Kumar Singh

Chris Welles

David H Witting

Bharathiraja Krishnamoorthy

Mahalakshmi Chandra Sekaran

Balaji Sugumar

S B Pravin Kumar

Emmanuel Dibia

Stephanie Ann Hammond

Johnson Athial

Johnson Althial

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

An interactive voice response system for use with a real-time complaint identification system and dashboard may be provided. The interactive voice response system may receive and respond to human callers in real-time. Simultaneously, the interactive voice response system may use a model to transcribe the audio call and identify complaints within the audio calls. Identified complaints may be resolved by one of a plurality of resolution executables. One of the resolution executables may include registering the identified complaint with a complaint register.

Call center voice system for use with a real-time complaint identification system

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

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

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

Apparatus and methods for leveraging machine learning and artificial intelligence to generate a response to an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score. The methods may further include, based on the utterance intent, the label and the score, to output a response.

Methods and apparatus for leveraging machine learning for generating responses in an interactive response system

Aspects of the disclosure relate to receiving a stateless application programming interface ('API') request. The API request may store an utterance, previous utterance data and a sequence of labels, each label in the sequence of labels being associated with a previous utterance expressed by a user during an interaction. The previous utterance data may, in certain embodiments, be limited to a pre-determined number of utterances occurring prior to the utterance. Embodiments process the utterance, using a natural language processor in electronic communication with the first processor, to output an utterance intent, a semantic meaning of the utterance and an utterance parameter. The utterance parameter may include words in the utterance and be associated with the intent. The natural language processor may append the utterance intent, the semantic meaning of the utterance and the utterance parameter to the API request. A signal extractor processor may append the plurality of utterance signals to the API request.

Methods and apparatus for leveraging an application programming interface ('API') request for storing a list of sentiment values in real time interactive response systems

Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.

Multi-tier rule and AI processing for high-speed conversation scoring and selecting of optimal responses

Multi-tier rule and AI processing for high-speed conversation scoring

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.

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

Growing community of inventors

Charlotte, NC, United States of America

Sushil Golani