Abstract: A computer program product, method and system for transforming data into predictive models. The transformation of data into predictive models comprises a multi stage learning process that uses a plurality of algorithms at each stage to select output for use in the next stage. The final predictive model is a linear or nonlinear predictive model. Analyses of the model and the variables associated with it can be used to produce graphs and other management reports.

Abstract: An automated system, method and media for optimizing the impact of a subset of an organization such as purchasing on the financial performance of said organization.

Abstract: A method of and system for transforming data from a plurality of systems into models of each of the segments of organization value and using said models to support the analysis, measurement and optimization of one or more aspects of organization risk and value.

Abstract: Methods, program storage devices and systems for developing a Personalized Medicine Service for an individual or group of individuals that can support the operation, customization and coordination of computer systems, software, products, services, data, entities and/or devices.

Abstract: An automated system (100) and method for analyzing, modeling and valuing elements of a business enterprise on a specified valuation date. The performance of the elements are analyzed using search algorithms and induction algorithms to determine the value drivers associated with each element. The induction algorithms are also used to create composite variables that relate element performance to enterprise revenue, expenses and changes in capital. Predictive models are then used to determine the correlation between the value drivers and the enterprise revenue, expenses and changes in capital. The correlation percentages for each value driver are then multiplied by capitalized value of future revenue, expenses and changes in capital, the resulting numbers for each value driver associated with each element are then added together to calculate a value for each element.

Abstract: An automated system (100) and method for analyzing, modeling and valuing elements of a business enterprise on a specified valuation date. The performance of the elements are analyzed using search algorithms and induction algorithms to determine the value drivers associated with each element. The induction algorithms are also used to create composite variables that relate element performance to enterprise revenue, expenses and changes in capital. Predictive models are then used to determine the correlation between the value drivers and the enterprise revenue, expenses and changes in capital. The correlation percentages for each value driver are then multiplied by capitalized value of future revenue, expenses and changes in capital, the resulting numbers for each value driver associated with each element are then added together to calculate a value for each element.

Abstract: An automated method and system (100) for supplying risk transfer products to a client company on a continual basis. After the client company provides data to the risk transfer supply chain, swaps and/or insurance products that provide the desired risk transfer are identified and priced. The information regarding the proposed risk transfer swaps and products is optionally reviewed by the user (20) and optionally accepted. If accepted, the transaction is completed in an automated fashion. Prices and contingent capital purchases are established as required to keep the operator of the risk transfer supply chain in compliance with regulatory reserve requirements.

Abstract: A computer based media, method and system for prioritizing search results for an individual, a group, a team, a division, an organization or some combination thereof.

Abstract: A system (100), method and media for an entity centric computer that develops, entity knowledge before analyzing, applying, distributing, maintaining, replicating and/or synchronizing said knowledge as required and/or requested.

Abstract: An automated system (100) and method for developing Complete Context™ Bots (30) for an organization. After extracting data from existing narrowly focused systems, mission measures and organization levels are defined for one or more organizations. The elements, factors and risks that contribute to mission measure performance by organization level and organization are systematically defined and stored in a ContextBase (60) using up to six context layers. ContextBase (60) information is extracted for specified combinations of context layers, organization levels and organizations as required to produce complete context frames that are used to support simulations of bot performance under a variety of scenarios. The program instructions that will maximize bot performance under the forecast scenarios are identified. After this programming is transferred to the Complete Context™ Bot (30), it is activated.