Methods, computer-readable media and systems are disclosed for building, deploying, operating, and maintaining an intelligent dynamic form in which a trained machine learning (ML) model is embedded. A universe of questions is associated with a plurality of output classifiers, which could represent eligibilities for respective benefits. The questions are partitioned into blocks. Each block can be associated with one or more of the classifiers, and each classifier can have a dependency on one or more blocks. An ML model is trained to make inferences from varied combinations of responses to questions and pre-existing data, and determine probabilities or predictions of values of the output classifiers. Based on outputs of the trained model, blocks of questions can be selectively rendered. The trained model is packaged with the question blocks and other components suitably for offline deployment. Uploading collected responses and maintenance of the dynamic form are also disclosed.

A method and system for capturing research decision flow in a Research and Development (R&D) activity are provided. The method includes defining an objective of the R&D activity and determining one or more requirements for fulfilling the objective of the R&D activity. Further, the method includes identifying a set of risks associated with failure to satisfy the one or more requirements. Furthermore, the method also includes defining a set of tests to verify that the requirements are satisfied or the risks have been mitigated. Furthermore, the method also includes developing a task plan to satisfy the one or more requirements and mitigate one or more risks of the set of risks. Furthermore, the method also includes linking the objective, the one or more requirements, the set of risks and the task plan, to generate a linked information record.

A method and system for capturing research decision flow in a Research and Development (R&D) activity are provided. The method includes defining an objective of the R&D activity and determining one or more requirements for fulfilling the objective of the R&D activity. Further, the method includes identifying a set of risks associated with failure to satisfy the one or more requirements. Furthermore, the method also includes defining a set of tests to verify that the requirements are satisfied or the risks have been mitigated. Furthermore, the method also includes developing a task plan to satisfy the one or more requirements and mitigate one or more risks of the set of risks. Furthermore, the method also includes linking the objective, the one or more requirements, the set of risks and the task plan, to generate a linked information record.

A system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events.

A system performs a loss factor determination which refers to, for each of two or more different Ms from among 4 Ms (Man, Machine, Material, and Method), a time series data group representing a time series of a state of an element belonging to the M, and which determines, for each period between time points, whether or not a state combination in the same period between time points corresponds to one or a plurality of loss state combinations. In each period between time points, the state combination is a combination of two or more states belonging to the period between time points and corresponding to the two or more Ms. The loss state combination is a state combination defined as an opportunity loss. With each loss state combination, a loss factor, which is a factor of an opportunity loss corresponding to the loss state combination, is associated.

A system performs a loss factor determination which refers to, for each of two or more different Ms from among 4 Ms (Man, Machine, Material, and Method), a time series data group representing a time series of a state of an element belonging to the M, and which determines, for each period between time points, whether or not a state combination in the same period between time points corresponds to one or a plurality of loss state combinations. In each period between time points, the state combination is a combination of two or more states belonging to the period between time points and corresponding to the two or more Ms. The loss state combination is a state combination defined as an opportunity loss. With each loss state combination, a loss factor, which is a factor of an opportunity loss corresponding to the loss state combination, is associated.

Disclosed are various examples for determining an IT topology for an enterprise IT organization based on operational goals of the enterprise organization. Visual models that are designed to facilitate responses associated with the current IT topology and operational goals of an enterprise IT organization are displayed to an entity of the enterprise. The entity can provide enterprise profile data to an IT solutions system by interacting with user interfaces. The IT solutions system can generate the IT topology prescription and roadmap for implementing the IT topology prescription based on the operational goals identified in the responses. Statements of work can be dynamically generated that outline details and terms of implementing the IT topology prescription for the enterprise IT organization.

Disclosed are various examples for determining an IT topology for an enterprise IT organization based on operational goals of the enterprise organization. Visual models that are designed to facilitate responses associated with the current IT topology and operational goals of an enterprise IT organization are displayed to an entity of the enterprise. The entity can provide enterprise profile data to an IT solutions system by interacting with user interfaces. The IT solutions system can generate the IT topology prescription and roadmap for implementing the IT topology prescription based on the operational goals identified in the responses. Statements of work can be dynamically generated that outline details and terms of implementing the IT topology prescription for the enterprise IT organization.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.