Abstract: A supervised learning processing (SLP) system and method provide cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, provide prediction driven responses to input objects, and provide operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data. The SLP system can dynamically sequence SLP subsystem operations to improve resource utilization, training quality, and/or processing speed. A system monitor-controller can dynamically determine if process environmental data indicates initiation of dynamic subsystem processing sequencing. Concurrently training SLP's provides accurate predictions of input objects and responses thereto and enhances the network by providing high quality value predictions and responses and avoiding potential training and operational delays.

Abstract: A supervised learning processing (SLP) system and method provide cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, provide prediction driven responses to input objects, and provide operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data. The SLP system can dynamically sequence SLP subsystem operations to improve resource utilization, training quality, and/or processing speed. A system monitor-controller can dynamically determine if process environmental data indicates initiation of dynamic subsystem processing sequencing. Concurrently training SLP's provides accurate predictions of input objects and responses thereto and enhances the network by providing high quality value predictions and responses and avoiding potential training and operational delays.

Abstract: A supervised learning processing (SLP) system and method provide cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, provide prediction driven responses to input objects, and provide operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data. The SLP system can dynamically sequence SLP subsystem operations to improve resource utilization, training quality, and/or processing speed. A system monitor-controller can dynamically determine if process environmental data indicates initiation of dynamic subsystem processing sequencing. Concurrently training SLP's provides accurate predictions of input objects and responses thereto and enhances the network by providing high quality value predictions and responses and avoiding potential training and operational delays.

Abstract: A supervised learning processing (SLP) system and non-transitory, computer program product provides cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, and provide prediction driven responses to input objects, such as NL statements. The SLP system includes SLP stages that are distributed across multiple SLP subsystems. Concurrently training SLP's provides accurate predictions of input objects and responses thereto, the SLP system and non-transitory, computer program product enhance the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train at least a proper subset of the SLP models while concurrently using the SLP system and non-transitory, computer program product in commercial operation.

Abstract: A machine learning of response selection to structured data input enables a machine to flexibly and responsively actively engage with a response recipient through a device, such as any electronic device connected to a data network. A system of one or more computers can be configured to respond to structured language messages, such as a textual messages, received from user devices with one or more templates that have been ranked using predetermined criteria including a machine learning classification of a momentum of a structured language message using a machine learning model. The momentum classification is correlated with templates. A ranking engine ranks the selection of candidate response templates in accordance with ranking criteria, including the momentum classifications for the candidate response templates. A highest ranked candidate response template is selected to provide a response to the textual messages received from the user devices.

Abstract: A machine learning of insight communication selection in response to a triggering event in which event data specific to a user is provided to a machine to flexibly and proactively engage with the user through a device, such as any electronic device connected to a data network. In at least one embodiment, an insight selection engine improves insight communication selection to the triggering event by initially filtering a library of insight templates to identify candidate templates that best respond to the event data. In at least one embodiment, the insight selection engine includes an insight ranking module that ranks the identified candidate insight templates to provide an insight communication to the device two proactively engage the user with the system. The insight ranking module learns by receiving feedback, such as a linked recipient action result signal.

Abstract: A machine learning of response selection to structured data input enables a machine to flexibly and responsively actively engage with a response recipient through a device, such as any electronic device connected to a data network. In at least one embodiment, the response selection module improves response selection to the structure data input by initially filtering a library of templates to identify candidate templates that best respond to the input. In at least one embodiment, the response selection module ranks the identified candidate templates to provide the response to the device. The response selection module learns by receiving feedback, such as a linked recipient action result signal.

Abstract: A machine learning of response selection to structured data input enables a machine to flexibly and responsively actively engage with a response recipient through a device, such as any electronic device connected to a data network. In at least one embodiment, the response selection module improves response selection to the structure data input by initially filtering a library of templates to identify candidate templates that best respond to the input. In at least one embodiment, the response selection module ranks the identified candidate templates to provide the response to the device. The response selection module learns by receiving feedback, such as a linked recipient action result signal.

Abstract: A supervised learning processing (SLP) system and non-transitory, computer program product provides cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, and provide prediction driven responses to input objects, such as NL statements. The SLP system includes SLP stages that are distributed across multiple SLP subsystems. Concurrently training SLP's provides accurate predictions of input objects and responses thereto, the SLP system and non-transitory, computer program product enhance the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train at least a proper subset of the SLP models while concurrently using the SLP system and non-transitory, computer program product in commercial operation.