Bank of America Corporation

Saahithi Chillara

Ramakrishna R Yannam

Priyank R Shah

Ravisha Andar

Emad Noorizadeh

Prejish Thomas

Ion Gerald McCusker

Ashwini Patil

Sushil Golani

Yogesh Raghuvanshi

Rajan Jigish Jhaveri

Kurt R Schultz

Ryan Strug

Steven Zhao

Donatus Asumu

Pravin Kumar Sankari Bhagavathiappan

Christopher Keith Restorff

Devanshu Mukherjee

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

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

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 device that is configured to establish a network connection between a user and an agent. The device is further configured to identify a first issue type for the user and to identify a first resolution type provided by the agent based on a conversation between the user and the agent. The device is further configured to identify a performance score from a resolution mapping based on a combination of the first issue type and the first resolution type. The device is further configured to identify a first knowledge area that is associated with the first issue type and to update a first knowledge score that is associated with the first knowledge area in a performance record for the agent based on the performance score. The device is further configured to send a recommendation to the agent based at least in part on the performance score.

Performance monitoring for communication 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

A device that is configured to assign users to an issue cluster based on issue types for the users. The device is further configured to identify available agents and to assign each available agent to one or more knowledge area clusters based on knowledge scores. A knowledge score indicates an expertise level for an agent in a knowledge area. The device is further configured to identify an issue cluster that is associated with an issue type and to identify a user from the issue cluster. The device is further configured to identify a knowledge area cluster that is associated with the issue type and to identify an agent from the knowledge area cluster. The device is further configured to establish a network connection between a user device associated with the user and a user device associated with the agent.

Dynamic routing for communication systems

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.

Multi-pipeline language processing platform

When a customer initiates an interaction with an interactive voice response ('IVR') system, the customer may need to be transferred to a live agent. Apparatus and methods may formulate timing information for integrating a live agent into an interaction controlled by an artificial intelligence ('AI') engine. The system may integrate machine generated responses into a customer interaction controlled by a live agent. The system may formulate timing information for intercepting the live agent with responses generated by the AI engine. The system may formulate the timing information using interactional analytics and preferences of a specific customer.

Growing community of inventors

Allen, TX, United States of America

Saahithi Chillara