Abstract: There is provided a computer implemented data driven classification and troubleshooting system and method. The system has an interface application enabled to receive data. The system has an associative memory software in communication with the interface application via an API. The associative memory software has an associative memory and a machine learning algorithm. The system has one or more individual areas, within the associative memory, requiring one or more troubleshooting actions to improve accuracy of the individual areas. The system has at least one troubleshooting tool enabled by the interface application. The at least one troubleshooting tool enables or performs the troubleshooting actions. The system has a quality rating metric (QRM) that measures a strength and an assurance that one or more predictions of the associative memory are correct. The one or more troubleshooting actions results in improving the accuracy and the performance of the associative memory.

Abstract: There is provided a computer implemented data driven classification and troubleshooting system and method. The system has an interface application enabled to receive data. The system has an associative memory software in communication with the interface application via an API. The associative memory software has an associative memory and a machine learning algorithm. The system has one or more individual areas, within the associative memory, requiring one or more troubleshooting actions to improve accuracy of the individual areas. The system has at least one troubleshooting tool enabled by the interface application. The at least one troubleshooting tool enables or performs the troubleshooting actions. The system has a quality rating metric (QRM) that measures a strength and an assurance that one or more predictions of the associative memory are correct. The one or more troubleshooting actions results in improving the accuracy and the performance of the associative memory.

Abstract: A method for processing at least partially unstructured data is provided. The method includes receiving, at a data processing tool, at least partially unstructured data from at least one data source, and processing the at least partially unstructured data to generate at least partially structured data that includes tagged data, wherein processing the at least partially unstructured data includes at least one of processing the at least partially unstructured data using an associative memory application, and processing the at least partially unstructured data using a regular expression processing program. The method further includes transmitting the at least partially structured data to a main application, and incorporating the at least partially structured data into the main application based at least in part on the tagged data, wherein incorporating the at least partially structured data includes at least one of including and excluding data based on the existence, content and/or type of a tag.

Abstract: A method for processing at least partially unstructured data is provided. The method includes receiving, at a data processing tool, at least partially unstructured data from at least one data source, and processing the at least partially unstructured data to generate at least partially structured data that includes tagged data, wherein processing the at least partially unstructured data includes at least one of processing the at least partially unstructured data using an associative memory application, and processing the at least partially unstructured data using a regular expression processing program. The method further includes transmitting the at least partially structured data to a main application, and incorporating the at least partially structured data into the main application based at least in part on the tagged data, wherein incorporating the at least partially structured data includes at least one of including and excluding data based on the existence, content and/or type of a tag.

Abstract: Systems, methods and computer program products for determining a learning curve value and modeling an associated profitability of a good are provided. According to one method of determining a learning curve value, recurring costs of producing each unit of the good are modeled as a function of potential learning curve values. Nonrecurring costs of producing each unit of the good are then modeled as a function of potential learning curve values. Next, the learning curve value is determined based upon the recurring costs model and the nonrecurring costs value such that the sum of the recurring costs and nonrecurring costs at the determined learning curve value is minimized over the potential learning curve values.

Abstract: Systems, methods and computer program products for determining a learning curve value and modeling an associated profitability of a good are provided. According to one method of determining a learning curve value, recurring costs of producing each unit of the good are modeled as a function of potential learning curve values. Nonrecurring costs of producing each unit of the good are then modeled as a function of potential learning curve values. Next, the learning curve value is determined based upon the recurring costs model and the nonrecurring costs value such that the sum of the recurring costs and nonrecurring costs at the determined learning curve value is minimized over the potential learning curve values.

Abstract: A computer implemented method, apparatus, and computer usable program code for generating a route for a vessel to travel from a start point to an end point. A start point and an end point for the vessel are identified. A forecast of wave conditions during a period of time is obtained. A model of the vessel is obtained, wherein the model includes parameters used to calculate a response of the vessel to the wave conditions. A route is generated from the start point to the end point for the vessel to travel on using the model of the vessel and the forecast of the wave conditions, wherein the route avoids conditions that may cause a treacherous environment and meets a set of goals.

Abstract: A computer implemented method, apparatus, and computer usable program code for generating a route for a vessel to travel from a start point to an end point. A start point and an end point for the vessel are identified. A forecast of wave conditions during a period of time is obtained. A model of the vessel is obtained, wherein the model includes parameters used to calculate a response of the vessel to the wave conditions. A route is generated from the start point to the end point for the vessel to travel on using the model of the vessel and the forecast of the wave conditions, wherein the route avoids conditions that may cause a treacherous environment and meets a set of goals.

Abstract: A computer implemented method, apparatus, and computer usable program code for generating a route for a vessel to travel from a start point to an end point. A start point and an end point for the vessel are identified. A forecast of wave conditions during a period of time is obtained. A model of the vessel is obtained, wherein the model includes parameters used to calculate a response of the vessel to the wave conditions. A route is generated from the start point to the end point for the vessel to travel on using the model of the vessel and the forecast of the wave conditions, wherein the route avoids conditions that may cause a treacherous environment and meets a set of goals.

Abstract: A method is provided that includes modeling future demand and/or future supply for a good at one or more segments of a period of time. In accordance with the method, future demand/supply at a respective time segment is modeled based upon a price sensitivity distribution of a unit purchase of the good, as well as a market potential distribution of a number of units of the good in a market associated with the good, during the respective time segment. The price sensitivity distribution and/or the market potential distribution for a respective time segment is based upon a growth rate and an uncertainty for the respective time segment. The method can further include modeling cost and/or profitability of the good at a respective time segment. Profitability can be modeled based upon the demand model and the cost model for the respective time segment.

Abstract: A system, method and computer program product are provided for performing a contingent claim valuation of a combination option including one or more multi-stage contingent claims, and an early-launch contingent claim. The method may include determining a first value representing payoffs attributable to exercise of the early-launch contingent claim at a selected decision point; and a second value representing payoffs the attributable to exercise of the multi-stage contingent claim(s) at respective decision point(s), and a contingent claim at an expiration exercise point. The first and/or second values may be determined based upon a respective present value distribution of contingent future value and a respective present value of an exercise price, the present values including a respective distribution and exercise price discounted according to first and second discount rates, respectively. A value of the contingent claim may then be determined based upon the first value and/or the second value.

Abstract: A system, method and computer program product are provided for performing a contingent claim valuation of a multi-stage option including a plurality of contingent claims exercisable at a plurality of respective exercise points including one or more exercise points before an expiration exercise point. The method may include determining a present value distribution of contingent future benefits attributable to the exercise of the contingent claim at the expiration exercise point, including discounting a distribution of contingent future benefits according to a first discount rate; and determining present values of exercise prices required to exercise the contingent claim at respective exercise points, including discounting exercise prices at respective exercise points according to a second discount rate that need not equal the first discount rate.

Abstract: A system, method and computer program product are provided for determining a minimum future benefits value for exercising a contingent claim of an option. The method may include determining present value distribution(s) of contingent future value and present value(s) of respective exercise price(s) at an expiration exercise point and/or one or more decision points before that point. Determining these present value distribution(s) and present value(s) may include discounting respective distribution(s) and value(s) according to first and second discount rates, respectively. The method may also include repeatedly determining, for a plurality of candidate minimum asset values at a selected decision point, respective values based upon one or more of the present value distribution(s) and one or more of the present value(s), where the respective values may be conditioned on the candidate minimum asset values. A candidate minimum asset value that maximizes the value may then be selected.

Abstract: A system, method and computer program product are provided for performing a contingent claim valuation of an early-launch option including a contingent claim exercisable at one of a plurality of decision points including one or more decision points before an expiration exercise point. The method may include determining first and second values representing payoffs attributable to exercise of a contingent claim at a selected decision point before the expiration exercise point, and at the expiration exercise point, respectively. The first and/or second values may be determined based upon respective present value distribution of contingent future value and a respective present value of an exercise price, the present values including a respective distribution and exercise price discounted according to first and second discount rates, respectively. A value of the contingent claim may then be determined based upon the first value and/or the second value.

Abstract: A method is provided that includes modeling demand and/or future supply for a good in a differentiated market. In accordance with the method, demand/supply in a differentiated market is modeled by first modeling demand and/or supply for a good in a non-differentiated market based upon a price sensitivity distribution of a unit purchase of the good, as well as a market potential distribution of a number of units of the good in a market associated with the good. Thereafter, the model of demand and/or supply in the non-differentiated market is integrated to thereby model demand and/or supply for a good in a differentiated market. The method can further include modeling cost and/or profitability of the good in a differentiated market. Profitability can be modeled based upon the demand model and the cost model for the differentiated market.

Abstract: A method is provided that includes defining a plurality of independent component markets for a good. In accordance with the method, each component market can be defined a respective price sensitivity distribution of a unit purchase of the good, as well as a market potential distribution of a number of units of the good. The demand and/or supply in the aggregate market can thus then be modeled based upon the price sensitivity distributions and market potential distributions of the component markets. The method can further include modeling cost and/or profitability of the good in an aggregate market. Profitability can be modeled based upon the demand model and the cost model for the aggregate market.

Abstract: A system, method and computer program product are provided for determining a minimum future benefits value for exercising a contingent claim of an option. The method may include determining a present value distribution of contingent future benefits at an expiration exercise point, and present values of respective exercise price(s) at the expiration exercise point and one or more decision points before that point, including discounting a respective distribution and values according to first and second discount rates, respectively. The method may also include defining a value as a function of a variable asset value at a selected decision point, the value being determinable based upon the present value distribution and present values, where the present value distribution may be correlated with the asset value. The function may then be solved for a root thereof, the root being selected as a minimum asset value.

Abstract: A system, method and computer program product are provided for determining a minimum asset value for exercising a contingent claim of an option. The method may include determining a present value conditional distribution of contingent future benefits attributable to the exercise of a contingent claim, including conditioning a distribution of contingent future benefits on an estimated minimum asset value, and discounting the distribution according to a first discount rate. Similarly, the method may include determining present values of respective exercise prices required to exercise one or more contingent claims, including discounting respective exercise prices according to a second discount rate.

Abstract: A system, method and computer program product are provided for determining a minimum future benefits value for exercising a contingent claim of an option. The method may include determining a present value distribution of contingent future benefits at an expiration exercise point, and present values of respective exercise prices at the expiration exercise point and one or more decision points before that point. Determining these present value distribution and present values may include discounting a distribution and respective values according to first and second discount rates, respectively. The method may also include repeatedly determining, for a plurality of forecasted asset values at a selected decision point, respective values based upon the present value distribution and the present values, where the respective values may be conditioned on the forecasted asset values. A forecasted asset value that maximizes the value may then be selected.

Abstract: Systems, methods and computer program products for determining a learning curve value and modeling an associated profitability of a good are provided. According to one method of determining a learning curve value, recurring costs of producing each unit of the good are modeled as a function of potential learning curve values. Nonrecurring costs of producing each unit of the good are then modeled as a function of potential learning curve values. Next, the learning curve value is determined based upon the recurring costs model and the nonrecurring costs value such that the sum of the recurring costs and nonrecurring costs at the determined learning curve value is minimized over the potential learning curve values.