Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: There is a need for more effective and efficient predictive data analysis solutions. This need can be addressed by, for example, obtaining prediction input objects each associated with a predictive entity; performing iterations of an iterative cohort generation routine until a qualified predictive model is identified, wherein each iteration comprises determining one or more predictive cohorts for predictive entities based on the prediction input objects, generating a predictive model based on the predictive cohorts, performing a predictive inference based on the predictive model to generate a current iteration prediction, generating a predictive score based on the current iteration prediction, and determining whether the predictive model is the qualified predictive model based on whether the predictive score exceeds a predictive score threshold; and performing cohort-based predictive data analysis based on the qualified predictive model to generate a respective final prediction for each predictive entity.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: A natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. The operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: There is a need for more effective and efficient predictive data analysis solutions. This need can be addressed by, for example, obtaining prediction input objects each associated with a predictive entity; performing iterations of an iterative cohort generation routine until a qualified predictive model is identified, wherein each iteration comprises determining one or more predictive cohorts for predictive entities based on the prediction input objects, generating a predictive model based on the predictive cohorts, performing a predictive inference based on the predictive model to generate a current iteration prediction, generating a predictive score based on the current iteration prediction, and determining whether the predictive model is the qualified predictive model based on whether the predictive score exceeds a predictive score threshold; and performing cohort-based predictive data analysis based on the qualified predictive model to generate a respective final prediction for each predictive entity.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: A solid phase microextraction substrate is disclosed. The solid phase microextraction substrate has a sorbent coating on at least part of a surface thereof. The coating is adapted for extracting at least one analyte component from a fluid matrix. The coating includes sorbent particles in a polymeric adhesive matrix. A majority of pores in each sorbent particle in the coating do not contain substantially any of the polymeric adhesive matrices.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. As described, the NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.