A method of automatically generating content summaries for topics includes receiving a taxonomy for a concept and a text corpus. The method further includes generating an annotated dataset having term annotations corresponding to the concept from the text corpus based on the taxonomy, parsing the annotated dataset into a custom generated document object having a structured layout, determining features for the term annotations, and extracting snippets from the custom generated document object, where each of the snippets corresponds to a section of the custom generated document object. The method further includes scoring the snippets based on the features such that each of the snippets corresponds to a score, filtering one or more snippets from the snippets when one or more snippet filtering conditions is met, ranking the snippets into an ordered list for the concept based on the score, and providing, to a user computing device, the ordered list.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

A device configured to identify a first digital document in a digital document repository, to identify a first graphical code that represents the first digital document, and to send the first graphical code to an approved user device. The device is further configured to obtain a second graphical code that represents a public encryption key for the organization and to extract the public encryption key for an organization from the second graphical code. The device is further configured to obtain a third graphical code from the approved user device. The third graphical code represents a second digital document comprising data and a digital signature that was signed using a private encryption key for the organization. The device is further configured to determine the third graphical code passes validation using the public encryption key for the organization and to store the second digital document in a digital document repository.

A device configured to identify a first digital document in a digital document repository, to identify a first graphical code that represents the first digital document, and to send the first graphical code to an approved user device. The device is further configured to obtain a second graphical code that represents a public encryption key for the organization and to extract the public encryption key for an organization from the second graphical code. The device is further configured to obtain a third graphical code from the approved user device. The third graphical code represents a second digital document comprising data and a digital signature that was signed using a private encryption key for the organization. The device is further configured to determine the third graphical code passes validation using the public encryption key for the organization and to store the second digital document in a digital document repository.

A device configured to obtain a first graphical code that represents a public encryption key for an organization and to extract the public encryption key for the organization from the first graphical code. The device is further configured to obtain a second graphical code that represents a digital document comprising data and a digital signature that was signed using a private encryption key for the organization. The device is further configured to extract the digital document from the second graphical code and to validate the second graphical code using the public encryption key for the organization. The device is further configured to determine the second graphical code passes validation using the public encryption key for the organization and to store the digital document in a digital document repository.

A device configured to obtain a first graphical code that represents a public encryption key for an organization and to extract the public encryption key for the organization from the first graphical code. The device is further configured to obtain a second graphical code that represents a digital document comprising data and a digital signature that was signed using a private encryption key for the organization. The device is further configured to extract the digital document from the second graphical code and to validate the second graphical code using the public encryption key for the organization. The device is further configured to determine the second graphical code passes validation using the public encryption key for the organization and to store the digital document in a digital document repository.

In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

Prioritizing crawled data in a document store for reprocess operations. Reprocessing occurs upon a triggering change to configurations. Prioritization is based on the status of the crawled data with respect to an ACL. During reprocessing, the crawled data is reprocessed in an order defined by assigned priority levels.