Abstract: Systems and methods of software enhancement and management can comprise inputting one or more data transformation algorithms representing asset data; decomposing the one or more data transformation algorithms into a plurality of Time-Affecting Linear Pathways (TALPs), executing the plurality of TALPs to generate at least one or more value complexity prediction polynomials, executing a TALP execution engine using predictive analytics and external unoptimized context data to create temporally sequenced TALP output data from the plurality of TALPs, modeling predictive outcomes using at least TALP optimization criteria data and the temporally sequenced TALP output data and merging additional external unoptimized context data via a feedback loop over time, and outputting optimized and discretized temporally sequenced output data based on the modeled predictive outcomes.

Abstract: The present invention relates generally to generating and processing spatiotemporal data transformation objects of an algorithm, and a Time-Affecting Linear Pathway (TALP)-based enhancement and management system that can use blockchain networks to generate and pool TALP predictive analytics.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (I/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: A software enhancement and management system (E&M System) can include two ways to decompose existing software such that new functionality can be added: functional decomposition and time-affecting linear pathway (TALP) decomposition. Functional decomposition captures the inputs and outputs of the existing software's functions and attaches the new algorithmic constructs presented as other functions that receive the outputs of the existing software's functions. TALP decomposition allows for the generation of time-prediction polynomials that approximate time-complexity functions, speedup, and automatic dynamic loop-unrolling-based parallelization for each TALP.

Abstract: Software systems and methods convert algorithms and software codes into time affecting linear pathways (TALPs) via decomposition and convert paired Input/Output (l/O) datasets into TALPs via Value Complexity polynomials. Generated TALPs can be enhanced through merging with other TALPs. TALPs can be grouped by matching the outputs of the TALP-associated prediction polynomials with some set of given criteria into families and cross-families that are useful in a new type of software optimization that allows for output values of grouped TALPs to be modeled, pooled, discretized and optimized to enhance goals or meet user goals.

Abstract: A software enhancement and management system (E&M System) can include two ways to decompose existing software such that new functionality can be added: functional decomposition and time-affecting linear pathway (TALP) decomposition. Functional decomposition captures the inputs and outputs of the existing software's functions and attaches the new algorithmic constructs presented as other functions that receive the outputs of the existing software's functions. TALP decomposition allows for the generation of time-prediction polynomials that approximate time-complexity functions, speedup, and automatic dynamic loop-unrolling-based parallelization for each TALP.

Abstract: A method, apparatus, and system for processing data to create investment structures can include a simulation module configured to operatively receive a plurality of external economic condition parameters from a system database and a plurality of investment parameters from an investment criteria database. In addition, an investment selection module configured to process the simulation output in conjunction with the plurality of investment parameters is included. Further, an allocation module configured to process flagged one or more investment vehicles from the system database, a finance structure module configured to process the simulation output from the simulation module, and a unit evaluation module configured to process at least the optimal financing unit structure can also be included.

Abstract: A method, apparatus or system is provided to receive, over a network connection, investment data associated with one or more investment vehicles. Investment data associated with one or more investment vehicles is received. Data analysis of the investment data is performed according to a plurality of investment parameters. Selection, based on the analysis of the investment data, of the one or more investment vehicles is made. The selected one or more investment vehicles is designated as part of a portfolio of a private equity fund. A yield value representing future cash flow of the selected portfolio is computed, wherein the prioritized units are associated correspondingly with one of or a combination of a plurality of rates of return, a plurality of risk tolerances, and a plurality of maturities, each of the rates of return being different from one another, each of the risk tolerances being different from one another, each of the maturities being different from one another.