Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: A computer system processes a message from a user mobile device by receiving the message from the user mobile device, wherein the message comprises a purchase request, and then determining a matching purchase offer in a database containing a plurality of purchase offers, wherein the matching purchase offer corresponds to an offer identified in the purchase request. The system then transmits a purchase request to a purchase agent at a third party provider that is associated with the matching purchase offer, and processing a purchase transaction received from the purchase agent at the third party provider.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: A recommendation system communicates with end user devices over a computer communications network and includes a recommendations engine that communicates with the network from behind a firewall that inhibits unauthorized communications from the network, a data file system that receives disposition update data in response to purchase actions initiated by an end user device and receives ingested user activity data in response to activity at a network storefront initiated by an end user device, and a database system that updates a recommendation model for the end user device in response to receiving the disposition update data and ingested user activity data. The recommendations engine generates a recommendation for the end user device in response to the update of the recommendation model.

Abstract: Source data of an event stream is parsed and supplemented with additional data from reference data sources, producing an enriched event stream from the parsed event stream data. The data records of the enriched event stream are partitioned into data fields designated as a dimension partition and a metric partition, which are partitioned into sub-dimension projections mapped to a plurality of storage keys, such that each of the storage keys includes one or more placeholder wildcard values and each of the storage keys is stored into a database of the computer system by the computer processor.

Abstract: Source data of an event stream is parsed and supplemented with additional data from reference data sources, producing an enriched event stream from the parsed event stream data. The data records of the enriched event stream are partitioned into data fields designated as a dimension partition and a metric partition, which are partitioned into sub-dimension projections mapped to a plurality of storage keys, such that each of the storage keys includes one or more placeholder wildcard values and each of the storage keys is stored into a database of the computer system by the computer processor.

Abstract: A computer system processes a message from a user mobile device by receiving the message from the user mobile device, wherein the message comprises a purchase request, and then determining a matching purchase offer in a database containing a plurality of purchase offers, wherein the matching purchase offer corresponds to an offer identified in the purchase request. The system then transmits a purchase request to a purchase agent at a third party provider that is associated with the matching purchase offer, and processing a purchase transaction received from the purchase agent at the third party provider.

Abstract: A recommendation system communicates with end user devices over a computer communications network and includes a recommendations engine that communicates with the network from behind a firewall that inhibits unauthorized communications from the network, a data file system that receives disposition update data in response to purchase actions initiated by an end user device and receives ingested user activity data in response to activity at a network storefront initiated by an end user device, and a database system that updates a recommendation model for the end user device in response to receiving the disposition update data and ingested user activity data. The recommendations engine generates a recommendation for the end user device in response to the update of the recommendation model.

Abstract: Content is streamed from a providing device to a receiving device, where the content is successively buffered in a receiving buffer, and then successively removed for processing. Periodically, the likelihood of the receiving buffer becoming empty or reaching an unacceptable low threshold is assessed, and the streaming rate is adapted based on the result of the determination. In one embodiment, a probability is computed, modeling the packet arrival into the receiving buffer and removal as Markovian input and output processes respectively, and the receiving buffer as a single channel queue. In one embodiment, the likelihood is determined by the content providing device, based at least in part on feedback information from the content receiving device, and the streaming rate is stochastically adapted accordingly.

Abstract: Content is streamed from a providing device to a receiving device, where the content is successively buffered in a receiving buffer, and then successively removed for processing. Periodically, the likelihood of the receiving buffer becoming empty or reaching an unacceptable low threshold is assessed, and the streaming rate is adapted based on the result of the determination. In one embodiment, a probability is computed, modeling the packet arrival into the receiving buffer and removal as Markovian input and output processes respectively, and the receiving buffer as a single channel queue. In one embodiment, the likelihood is determined by the content providing device, based at least in part on feedback information from the content receiving device, and the streaming rate is stochastically adapted accordingly.