Aptima, Inc.

Manzama, Inc.

Andrew P Duchon

Paul David Allopenna

Bruce Skarin

Richard Alexis DeJordy

Robert McCormack

Kari Kelton

Pacey Foster

Kara Orvis

John Colonna-Romano

William J Salter

Shawn Weil

Peter J Ozolin

Phil H Duong

David Kramer

Rich Dejordy

Embodiments of the subject invention comprise a computer based system and methods to collect and compare the attributes of a group of entities using data representing topic data of the entity and interaction data between entities. Embodiments of the invention comprise using minimally invasive means to automatically collect and model both an entity's attributes such as their knowledge/work/interest as well as model the social interactions of the entity together with a means to identify opportunities to influence changes in the entity attributes. Minimally invasive means to collect and model attributes include semantic analysis and topic modeling techniques. Means to model social interactions include social network analysis techniques that can incorporate location data of the entity. Embodiments of the invention further provide a sharable index of the attributes of the entities and the group of entities.

Systems and methods for analyzing entity profiles

Techniques related to a system for news classification comprising one or more non-transitory memory devices and one or more hardware processors configured to execute instructions from the one or more non-transitory memory devices to cause the system to receive an article, the article including text, extract text from the received article, store the extracted text in a database, determine a set of potential target entities based on the extracted text, determine a classification of the article for each potential target entity of the set of potential target entities for a category, valence, presence of litigation, rumor, or opinion based on the extracted text, associate the classification of the article, along with a probability of the determined classification of the article for each potential target entity, assign the classification of the article if the probability of the classification is greater than a threshold probability, and store the classification of the article and the probability.

System and method for organizational health analysis

The present invention relate to a method and system to predict the likelihood of data topics that may occur from data sources. The likelihood of the data topics may be predicted over other dimensions of time or over other dimensions. In the present invention, a topic means a defining characteristic, usually represented as a data element, of a single feature, activity, subject, behavior, event or an aggregation of such defining characteristics.

Method and system to predict the likelihood of topics

Embodiments of the present invention include methods and systems for predicting the likelihood of topics appearing in a set of data such as text. A number of latent variable methods are used to convert the data into a set of topics, topic values and topic profiles. A number of time-course methods are used to model how topic values change given previous topic profiles, or to find historical times with similar topic values and then projecting the topic profile forward from that historical time to predict the likelihood of the topics appearing. Embodiments include utilizing focus topics, such as valence topics, and data representing financial measures to predict the likelihood of topics. Methods and systems for modeling data and predicting the likelihood of topics over other dimensions are also contemplated.

Method and system to predict a data value

The present invention relates in general to methods and systems for comparing and maximizing the optimal selection of a first set of one or more data objects to a set of second data objects. In one embodiment, the first set of data objects represent one or more tasks to be fulfilled by a set of capabilities represented by the second data objects. In one embodiment, methods and systems are provided that apply topic modeling and similarity metrics to determine the optimal selection. In one embodiment, methods and systems are provided to determine the appropriateness of a set of second data objects to satisfy the requirements of a first data object given interaction attributes. Embodiments may be used to compare mission requirements with potential team members to determine the appropriateness of team members and teams for a given mission based on interaction attributes of the team members and teams.

Method and system to compare data objects

The present invention relates in general to methods and systems for comparing and maximizing the appropriateness of a first set of one or more data objects to a set of second data objects. In one embodiment, the first set of data objects represent one or more tasks to be fulfilled by a set of capabilities represented by the second data objects. In one embodiment, this invention provides an effective and accurate method and system to compare and maximize the appropriateness between the requirements of a task and the second set's capabilities, while these capabilities and requirements are contained, even if only latently, in data objects such as written documents, electronic databases or other sources of data and information. In one embodiment, topic modeling techniques are utilized to compare the data objects.

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Somerville, MA, United States of America

Andrew P Duchon