Abstract: The present invention relates to model aggregation tools utilizing model encapsulation of user-directed iterative (UDI) machine learning, and the related methods that offer a typical user, without programming expertise, the ability to create and modify machine learning models. In particular, the present invention further provides methods and tools that not only afford machine learning models that are easily created and configured without the necessity of hard coding by the user, but also to afford the user with the ability to share their “know-how” derived from these models to collectively improve the models while maintaining privacy by obscuring the original training data, so that no confidential or proprietary information is shared between users of this collective model.

Abstract: Systems and methods for searching text and textual content, and retrieving and summarizing and interpreting search results, are provided. The inventive systems and methods obtain content or are configured to be applied to content. The systems and methods review all of the content, creating one or more data collections of Related-Concept and Themes, and statistical relations between Related-Concepts and Themes. When a user enters a search query, the systems and methods present the user with Related-Concepts and Themes that match the search query, and the user can iteratively select Related-Concepts and Themes, and in response the present invention will narrow or broaden the search and display an altered documents results set and different Related-Concepts and Themes. The systems and methods then provide the user with interpretation about the content in the form of summaries or conclusions based on the Related Concepts and Themes.