Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: Systems and methods for scheduling appointments are provided. This scheduling process includes generating an introductory message proposing an appointment with the target with a request for timing. The target responds, and this response is processed for a positive interest and the presence of a proposed time. If there is an absence of positive interest then the messaging may be discontinued. However, in the presence of a positive interest, and a proposed time from the target, the system may access an external scheduling system when a proposed time is present. This includes determining availability of at least one resource at the proposed time. The system then iteratively provides suggested times close to the proposed time when the resource is not available for the proposed time. The system then confirms the appointment when the resource is available for either the proposed time or any of the suggested times.

Abstract: Systems and methods for natural language processing and classification are provided. In some embodiments, the systems and methods include a communication editor dashboard which receives the message, performs natural language processing to divide the message into component parts. The system displays the message in a first pane with each of the component parts overlaid with a different color, and displaying in a second pane the insights, the confidence scores associated with each insight, the sentiment and the actions. In another embodiment, the systems and methods include combining outputs from multiple machine learned AI models into a unified output. In another embodiment, the systems and methods include responding to simple question using natural language processing.

Abstract: Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.

Abstract: Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.

Abstract: Systems and methods for natural language processing and classification are provided. In some embodiments, the systems and methods include a communication editor dashboard which receives the message, performs natural language processing to divide the message into component parts. The system displays the message in a first pane with each of the component parts overlaid with a different color, and displaying in a second pane the insights, the confidence scores associated with each insight, the sentiment and the actions. In another embodiment, the systems and methods include combining outputs from multiple machine learned AI models into a unified output. In another embodiment, the systems and methods include responding to simple question using natural language processing.

Abstract: Systems and methods for setting service appointments in an automated conversation system, ensuring data fidelity, return on investment (ROI) analysis, accelerating response times, and scraping third party system to populate the conversation system are all provided. These systems and methods automatically schedules appointments for services, and performs all necessary follow-up activity, ensures target duplication isn't present in conversations, ensures that representatives are incorporated into the system, enhances speeds by altering processing, response generation and sending queues if timing won't meet goals, presents suitably noteworthy ROI metrics and scrapes a third party databases using phantom scripts. All this activity improves the automated conversation experience, and its ability to effectuate business objectives.

Abstract: Systems and methods for scheduling appointments are provided. This scheduling process includes generating an introductory message proposing an appointment with the target with a request for timing. The target responds, and this response is processed for a positive interest and the presence of a proposed time. If there is an absence of positive interest then the messaging may be discontinued. However, in the presence of a positive interest, and a proposed time from the target, the system may access an external scheduling system when a proposed time is present. This includes determining availability of at least one resource at the proposed time. The system then iteratively provides suggested times close to the proposed time when the resource is not available for the proposed time. The system then confirms the appointment when the resource is available for either the proposed time or any of the suggested times.

Abstract: Systems and methods for variable field replacement are provided. Message templates include variable fields that can be populated with industry and client specific information through entity replacement, lexical replacement and phrase package selection. In addition to the generation of messages, the system may also be able to perform other actions that leverage external third-party systems. The templates may be drawn from a conversation library with hierarchical inheritance. Likewise, actions may leverage an action response library that links triggers in the response to required actions. Packet selection is based upon how closely the phrase fits a personality for the AI identity, and how well historically the phrase has performed. Lastly, while the AI systems disclosed herein have the ability to understand and respond to conversations in natural language format, this is computationally expensive. These AI systems may use an objective and intent based communication protocol when communicating with one another.

Abstract: Systems and methods for variable field replacement are provided. Message templates include variable fields that can be populated with industry and client specific information through entity replacement, lexical replacement and phrase package selection. In addition to the generation of messages, the system may also be able to perform other actions that leverage external third-party systems. The templates may be drawn from a conversation library with hierarchical inheritance. Likewise, actions may leverage an action response library that links triggers in the response to required actions. Packet selection is based upon how closely the phrase fits a personality for the AI identity, and how well historically the phrase has performed. Lastly, while the AI systems disclosed herein have the ability to understand and respond to conversations in natural language format, this is computationally expensive. These AI systems may use an objective and intent based communication protocol when communicating with one another.

Abstract: Systems and methods for variable field replacement are provided. Message templates include variable fields that can be populated with industry and client specific information through entity replacement, lexical replacement and phrase package selection. In addition to the generation of messages, the system may also be able to perform other actions that leverage external third-party systems. The templates may be drawn from a conversation library with hierarchical inheritance. Likewise, actions may leverage an action response library that links triggers in the response to required actions. Packet selection is based upon how closely the phrase fits a personality for the AI identity, and how well historically the phrase has performed. Lastly, while the AI systems disclosed herein have the ability to understand and respond to conversations in natural language format, this is computationally expensive. These AI systems may use an objective and intent based communication protocol when communicating with one another.

Abstract: Systems and methods for more effective AI operations, improvements to the experience of a conversation target, and increased productivity through AI assistance are provided. In some embodiments, the systems use machine learning models to classify a number of message responses with a confidence. If these classifications are below a threshold the messages are sent to a user for analysis, after prioritization, along with guidance data. Feedback from the user modified the models. In another embodiment, a system and method for an AI assistant is also provided which receives messages and determines instructions using keywords and/or classifications. The AI assistant then executes upon these instructions. In another embodiment, a conversation editor interface is provided. The conversation editor includes one or more displays that illustrate an overview flow diagram for the conversation, specific node analysis, libraries of conversations and potentially metrics that can help inform conversation flow.

Abstract: Systems and methods for more effective AI operations, improvements to the experience of a conversation target, and increased productivity through AI assistance are provided. In some embodiments, the systems use machine learning models to classify a number of message responses with a confidence. If these classifications are below a threshold the messages are sent to a user for analysis, after prioritization, along with guidance data. Feedback from the user modified the models. In another embodiment, a system and method for an AI assistant is also provided which receives messages and determines instructions using keywords and/or classifications. The AI assistant then executes upon these instructions. In another embodiment, a conversation editor interface is provided. The conversation editor includes one or more displays that illustrate an overview flow diagram for the conversation, specific node analysis, libraries of conversations and potentially metrics that can help inform conversation flow.

Abstract: Systems and methods for more effective AI operations, improvements to the experience of a conversation target, and increased productivity through AI assistance are provided. In some embodiments, the systems use machine learning models to classify a number of message responses with a confidence. If these classifications are below a threshold the messages are sent to a user for analysis, after prioritization, along with guidance data. Feedback from the user modified the models. In another embodiment, a system and method for an AI assistant is also provided which receives messages and determines instructions using keywords and/or classifications. The AI assistant then executes upon these instructions. In another embodiment, a conversation editor interface is provided. The conversation editor includes one or more displays that illustrate an overview flow diagram for the conversation, specific node analysis, libraries of conversations and potentially metrics that can help inform conversation flow.

Abstract: Systems and methods for more effective AI operations, improvements to the experience of a conversation target, and increased productivity through AI assistance are provided. In some embodiments, the systems use machine learning models to classify a number of message responses with a confidence. If these classifications are below a threshold the messages are sent to a user for analysis, after prioritization, along with guidance data. Feedback from the user modified the models. In another embodiment, a system and method for an AI assistant is also provided which receives messages and determines instructions using keywords and/or classifications. The AI assistant then executes upon these instructions. In another embodiment, a conversation editor interface is provided. The conversation editor includes one or more displays that illustrate an overview flow diagram for the conversation, specific node analysis, libraries of conversations and potentially metrics that can help inform conversation flow.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.

Abstract: Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.