Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to generate a response to an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score. The methods may further include, based on the utterance intent, the label and the score, to output a response.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.

Abstract: Aspects of the disclosure relate to receiving a stateless application programming interface (“API”) request. The API request may store an utterance, previous utterance data and a sequence of labels, each label in the sequence of labels being associated with a previous utterance expressed by a user during an interaction. The previous utterance data may, in certain embodiments, be limited to a pre-determined number of utterances occurring prior to the utterance. Embodiments process the utterance, using a natural language processor in electronic communication with the first processor, to output an utterance intent, a semantic meaning of the utterance and an utterance parameter. The utterance parameter may include words in the utterance and be associated with the intent. The natural language processor may append the utterance intent, the semantic meaning of the utterance and the utterance parameter to the API request. A signal extractor processor may append the plurality of utterance signals to the API request.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.

Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: A network of chatbots is provided. The network may include a user-facing router for receiving queries and a plurality of chatbots. Each chatbot included in the plurality of chatbots may identify a single logical grouping of a domain, identify a limited number of intents from each other chatbot included in the plurality of chatbots and communicate with each other chatbot included in the plurality of chatbots. When the router receives a query, the router may receive the query with an associated domain. The router may select a chatbot based on the received domain. The router may direct the query to the selected chatbot. The selected chatbot may determine that the domain associated with the query is incorrect. The selected chatbot may identify a second chatbot based on a hook included in the query and identified within the selected chatbot. The selected chatbot may transfer the query to the second chatbot.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to generate a response to an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score. The methods may further include, based on the utterance intent, the label and the score, to output a response.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.

Abstract: Aspects of the disclosure relate to receiving a stateless application programming interface (“API”) request. The API request may store an utterance, previous utterance data and a sequence of labels, each label in the sequence of labels being associated with a previous utterance expressed by a user during an interaction. The previous utterance data may, in certain embodiments, be limited to a pre-determined number of utterances occurring prior to the utterance. Embodiments process the utterance, using a natural language processor in electronic communication with the first processor, to output an utterance intent, a semantic meaning of the utterance and an utterance parameter. The utterance parameter may include words in the utterance and be associated with the intent. The natural language processor may append the utterance intent, the semantic meaning of the utterance and the utterance parameter to the API request. A signal extractor processor may append the plurality of utterance signals to the API request.

Abstract: Systems, computer program products, and methods are described herein for information transfer between communication channels. The present invention is configured to electronically receive, via a first communication channel associated with an entity, information associated with a transfer of resources from a user at a first time; initiate one or more machine learning algorithms at on the information associated with the transfer of resources at the first time; generate, using the one or more machine learning algorithms, a summary of the information associated with the transfer of resources; electronically receive an indication that the user wishes to transfer to a second communication channel; and initiate a communication channel transfer protocol to transmit the summary of the information associated with the transfer of resources to the second communication channel.

Abstract: A network of chatbots is provided. The network may include a user-facing router for receiving queries and a plurality of chatbots. Each chatbot included in the plurality of chatbots may identify a single logical grouping of a domain, identify a limited number of intents from each other chatbot included in the plurality of chatbots and communicate with each other chatbot included in the plurality of chatbots. When the router receives a query, the router may receive the query with an associated domain. The router may select a chatbot based on the received domain. The router may direct the query to the selected chatbot. The selected chatbot may determine that the domain associated with the query is incorrect. The selected chatbot may identify a second chatbot based on a hook included in the query and identified within the selected chatbot. The selected chatbot may transfer the query to the second chatbot.

Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: Systems, computer program products, and methods are described herein for information transfer between communication channels. The present invention is configured to electronically receive, via a first communication channel associated with an entity, information associated with a transfer of resources from a user at a first time; initiate one or more machine learning algorithms at on the information associated with the transfer of resources at the first time; generate, using the one or more machine learning algorithms, a summary of the information associated with the transfer of resources; electronically receive an indication that the user wishes to transfer to a second communication channel; and initiate a communication channel transfer protocol to transmit the summary of the information associated with the transfer of resources to the second communication channel.

Abstract: Systems, computer program products, and methods are described herein for information transfer between communication channels. The present invention is configured to electronically receive, via a first communication channel associated with an entity, information associated with a transfer of resources from a user at a first time; initiate one or more machine learning algorithms at on the information associated with the transfer of resources at the first time; generate, using the one or more machine learning algorithms, a summary of the information associated with the transfer of resources; electronically receive an indication that the user wishes to transfer to a second communication channel; and initiate a communication channel transfer protocol to transmit the summary of the information associated with the transfer of resources to the second communication channel.

Abstract: Contactless dispensing of objects from object-dispensing machines. A digital token is wirelessly communicated to the machine via short-range wireless communication, such as Near Field Communication (NFC) or the like. In response, a push notification is communicated to the user's wireless device, which includes an activatable link for launching a mobile application. In response to launching the mobile application and authenticating the user, the mobile application provides object-dispensing machine-like user interfaces that are configured to allow the user to input object requesting parameters and submit a request for objects to be dispensed from the object-dispensing machine. The request is communicated to the object-dispensing machines (or corresponding backend processing systems) and, once the request is verified, the object-dispensing machine dispenses the requested objects.

Abstract: Systems, computer program products, and methods are described herein for information transfer between communication channels. The present invention is configured to electronically receive, via a first communication channel associated with an entity, information associated with a transfer of resources from a user at a first time; initiate one or more machine learning algorithms at on the information associated with the transfer of resources at the first time; generate, using the one or more machine learning algorithms, a summary of the information associated with the transfer of resources; electronically receive an indication that the user wishes to transfer to a second communication channel; and initiate a communication channel transfer protocol to transmit the summary of the information associated with the transfer of resources to the second communication channel.

Abstract: An apparatus for electronic transmission via a virtual persona is provided. The apparatus may include a receiver. The receiver may be located at a first entity's mobile device. The receiver may be configured to receive a request to transmit an electronic transmission. The apparatus may include a processor. The processor may be located at the first entity's mobile device. The processor may be configured to identify a plurality of visible virtual personas. The plurality of visible virtual personas may be associated with a plurality of other mobile devices. Each of the plurality of virtual personas may be associated with a displayable image and/or characteristic set. The identification may be based on information received from a third party fraud detection network. The information may include location information associated with the plurality of other devices. The location information may be currently exploited by the third party fraud detection network.