Bank of America Corporation

Ashwini Patil

Ramakrishna R Yannam

Ravisha Andar

Priyank R Shah

Emad Noorizadeh

Saahithi Chillara

Ion Gerald McCusker

Sushil Golani

Yogesh Raghuvanshi

Donatus Asumu

Pravin Kumar Sankari Bhagavathiappan

Christopher Keith Restorff

Pavan K Chayanam

Devanshu Mukherjee

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

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

When a caller initiates a conversation with an interactive voice response ('IVR') system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools and artificial intelligence ('AI') into the interaction with the IVR system. The automated tools and AI may track the conversation to decipher when to transfer the caller to the agent. The agent may determine which machine generated responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. The agent may transfer back the caller to the IVR system along with the appropriate machine generated response to maintain efficiency and shorten time of human agent interaction.

Integration of human agent and automated tools for interactive voice response (IVR) systems

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

Methods for leveraging a plurality of machine-learning algorithms to improve a chat interaction are provided. The methods may include monitoring for initiation of a live chat session; alerting and assigning a chat responder to the live chat session; engaging one or more of a plurality of automated chat tools, the tools loaded with artificial intelligence (AI), in order to improve the response of the responder during the session; reviewing and retrieving, using the AI, from a machine learning (ML) library in electronic communication with the AI, historical information; presenting, on a chat responder screen, selected actionable information generated based on the historical information, to the responder; integrating, based on pre-determined conditions, chat responses into the ML library; and integrating into the ML library, based on the same or other pre-determined conditions, chat comments. The chat comments are generated by a chat initiator.

Complex human-computer interactions

When a caller initiates an interaction with an interactive voice response ('IVR') system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools into the interaction after the caller been transferred to the agent. The agent may determine which AI responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. Such human-computer interaction may shorten response time of human agents and improve efficiency of IVR systems.

Growing community of inventors

Richardson, TX, United States of America

Ashwini Patil