Abstract: An autonomous automated intent mining system extracts intents from stored conversations in a text-based chat corpus for use in an AI-driven dialog system. The intent mining system includes an intelligent pre-processor for extracting the conversation data from a computer memory and operating on the conversation data to identify relevant utterances. The system additionally includes a representation processor for operating on the identified relevant utterances to create a numerical representation. The system also includes a clustering processor for generating clusters of utterances and classifying each of the identified relevant utterances into a cluster based on the numerical representation of each utterance. Each generated cluster represents a unique intent for use in the AI-driven dialog system.

Abstract: Provided herein is a process for preparing solid lipid nanoparticles of curcumin. Also provided herein are solid lipid nanoparticles of curcumin having a particle size in the range of 20-800 nm. The solid lipid nanoparticles of curcumin show a very high entrapment efficiency of curcumin in the range of 50-100% in terms of actual curcumin content of the formulation. The solid lipid nanoparticles of curcumin show increased efficacy of the curcumin.

Abstract: A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Abstract: System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.

Abstract: An autonomous automated intent mining system extracts intents from stored conversations in a text-based chat corpus for use in an AI-driven dialog system. The intent mining system includes an intelligent pre-processor for extracting the conversation data from a computer memory and operating on the conversation data to identify relevant utterances. The system additionally includes a representation processor for operating on the identified relevant utterances to create a numerical representation. The system also includes a clustering processor for generating clusters of utterances and classifying each of the identified relevant utterances into a cluster based on the numerical representation of each utterance. Each generated cluster represents a unique intent for use in the AI-driven dialog system.

Abstract: Methods and systems for personalized trust recommendation based on a dynamic trust model. In an example embodiment, a trust framework can be derived from a set of use-case specific factors. Invasive data and non-invasive data can be collected from a user (or a group of users) based on activity data and profile data associated with the user. A dynamic trust profile can be created (or learned) based on the invasive data and the non-invasive data collected from the user. A recommended level of trustworthiness can be then provided to the user respect to a particular situation and/or entity (e.g. other users) within the trust framework based on the dynamic trust profile of the user and which is personalized for the user.

Abstract: A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Abstract: Methods and systems for personalized trust recommendation based on a dynamic trust model. In an example embodiment, a trust framework can be derived from a set of use-case specific factors. Invasive data and non-invasive data can be collected from a user (or a group of users) based on activity data and profile data associated with the user. A dynamic trust profile can be created (or learned) based on the invasive data and the non-invasive data collected from the user. A recommended level of trustworthiness can be then provided to the user respect to a particular situation and/or entity (e.g. other users) within the trust framework based on the dynamic trust profile of the user and which is personalized for the user.

Abstract: System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.