Abstract: Systems and methods for processing automated message exchanges using artificial intelligence are providing. In some embodiments, a message is generated by populating variable fields within a message template with corresponding data from a knowledge set and/or a lead data set. Lead data is the data known about the intended recipient of the message, whereas the knowledge set is contextual knowledge useful for the artificial intelligence. Once the message has been generated, the system waits for a response from the lead. Once the response is received, the AI algorithms may categorize the response and generate a corresponding confidence value for the categorization. The categorization and confidence level are utilized to determine which subsequent action the system takes. The actions consist of sending a follow-up message, a subsequent message in the series, requesting user input, or discontinuing messaging.

Abstract: Systems and methods for processing automated message exchanges using artificial intelligence are providing. In some embodiments, a message is generated by populating variable fields within a message template with corresponding data from a knowledge set and/or a lead data set. Lead data is the data known about the intended recipient of the message, whereas the knowledge set is contextual knowledge useful for the artificial intelligence. Once the message has been generated, the system waits for a response from the lead. Once the response is received, the AI algorithms may categorize the response and generate a corresponding confidence value for the categorization. The categorization and confidence level are utilized to determine which subsequent action the system takes. The actions consist of sending a follow-up message, a subsequent message in the series, requesting user input, or discontinuing messaging.

Abstract: Systems and methods for management of automated dynamic messages are providing. In some embodiments, a data store is populated with one or more knowledge sets and one or more lead datasets in response to a user's input. A campaign builder may be provided to the user for generating and initiating campaigns. A campaign is a series of messages designed to satisfy one or more objectives. The campaign builder allows the creation of a campaign by allowing the composition of a series of message templates with variable fields. The variable fields correspond to classes of data from the knowledge sets and/or the lead data. Once the campaign has been initiated, the system categorizes the responses using algorithms. These categorizations have corresponding confidence levels. If the confidence level is too low, manual user intervention may be required in order to determine which subsequent action the system should perform.

Abstract: Systems and methods for parsing a message in a conversation series is provided. This involves receiving a message, isolating the current exchange, dividing it up into sentences, and detecting the language being used. The message sentences are normalized, and any ‘speech acts’ are identified. Likewise, any ‘critical intents’ are identified. If there is no critical intent, the classification text is provided to sets of models for parallel prediction of the intent(s) of the message. Models are queried for based upon series of the conversation, the industry involved, the client the model is for, the message campaign, and any speech acts present. Mapping rules and/or prediction machine learning models are used to convert the intents into meanings, which are filtered. It is also possible to apply a decision engine policy for the determination of the meaning. This is followed by entity extraction and response generation by mapping meanings to actions.

Abstract: Systems and methods for configuring AI algorithms and knowledge sets within an automated messaging system are providing. In some embodiments, a message is received. A subsection of text from the training message is selected. Likewise, a knowledge set is selected. The knowledge set includes probabilistic associations between a term and a category. The terms in the selected subsection of text are compared to the knowledge sets to generate insights and contexts. The insights enable the categorization of the training message. This categorization has an associated confidence value based upon how strongly the terms in the text subsection are associated with the category (per the selected knowledge set). A low confidence value causes the message to be a candidate for training (a training message). Once identified as a training message, it may be displayed to an AI developer for approval or rejection of the categorization.

Abstract: Systems and methods for management of automated dynamic messages are providing. In some embodiments, a data store is populated with one or more knowledge sets and one or more lead datasets in response to a user's input. A campaign builder may be provided to the user for generating and initiating campaigns. A campaign is a series of messages designed to satisfy one or more objectives. The campaign builder allows the creation of a campaign by allowing the composition of a series of message templates with variable fields. The variable fields correspond to classes of data from the knowledge sets and/or the lead data. Once the campaign has been initiated, the system categorizes the responses using algorithms. These categorizations have corresponding confidence levels. If the confidence level is too low, manual user intervention may be required in order to determine which subsequent action the system should perform.

Abstract: Systems and methods for configuring AI algorithms and knowledge sets within an automated messaging system are providing. In some embodiments, a message is received. A subsection of text from the training message is selected. Likewise, a knowledge set is selected. The knowledge set includes probabilistic associations between a term and a category. The terms in the selected subsection of text are compared to the knowledge sets to generate insights and contexts. The insights enable the categorization of the training message. This categorization has an associated confidence value based upon how strongly the terms in the text subsection are associated with the category (per the selected knowledge set). A low confidence value causes the message to be a candidate for training (a training message). Once identified as a training message, it may be displayed to an AI developer for approval or rejection of the categorization.

Abstract: Systems and methods for processing automated message exchanges using artificial intelligence are providing. In some embodiments, a message is generated by populating variable fields within a message template with corresponding data from a knowledge set and/or a lead data set. Lead data is the data known about the intended recipient of the message, whereas the knowledge set is contextual knowledge useful for the artificial intelligence. Once the message has been generated, the system waits for a response from the lead. Once the response is received, the AI algorithms may categorize the response and generate a corresponding confidence value for the categorization. The categorization and confidence level are utilized to determine which subsequent action the system takes. The actions consist of sending a follow-up message, a subsequent message in the series, requesting user input, or discontinuing messaging.