Bank of America Corporation

Prejish Thomas

Ramakrishna R Yannam

Priyank R Shah

Ravisha Andar

Saahithi Chillara

Emad Noorizadeh

Rajan Jigish Jhaveri

Kurt R Schultz

Ryan Strug

Steven Zhao

Maruthi Z Shanmugam

Ion Gerald McCusker

Bhargav Aditya Ayyagari

Amrita Dasgupta

Lindsay Payne

Jimmy Autin Carroll

Jimmy Austin Carroll

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 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

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

Chatbots may be integrated into a customer service workflow and assist a user before, during and after a user-agent interaction. The chatbot may assist an agent during a user-agent interaction. The chatbot may provide customized responses for a target agent or user. Customized responses may be formulated based on conversation context, account information, sentiment and diagnostic tools. Chabot responses may be customized to meet habits and patterns of a target agent or user. The chatbot may crowdsource questions to other agents or users. The chatbot may employ search engines, entity and slot extraction and heat maps and clustering analysis to generate relevant responses for the agent or user.

Chatbot co-pilot

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.

Hierarchical multi-tier language processing platform

Artificial intelligence (AI)-powered conversational messaging within a mobile application

A system for performing data trend analysis is disclosed. The disclosed system categorizes a number of transaction records based on their associated merchant IDs. For each of the merchant ID, the system determines whether the determined number of transaction records exceeds a pre-determined threshold. In response to identifying a merchant ID with the determined number of transaction records that exceeds the pre-determined threshold, the system identifies a location associated with the merchant ID and generates a trending notification indicating a transaction burst. The system then identifies a set of users located in the location that have a transaction history with the merchant ID and sends the trending notification to the set of users.

Data trend analysis based on real-time data aggregation

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 distinct rule sets in a database. Each of the rule sets may be associated with a different pipeline from a set of pipelines. The method may include receiving the utterance. The method may include tokenizing and/or annotating the utterance, determining a pipeline for the utterance, and comparing the utterance to the rule set that is associated with the pipeline. When a match is achieved between the utterance and the rule set, the method may include resolving the intent of the utterance based on the match. The method may include transmitting a request corresponding to the intent to a central server, receiving a response, and transmitting the response to the system user.

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Plano, TX, United States of America

Prejish Thomas