Software systems and methods for multiple TALP family enhancement and management
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.
1. A method of software enhancement and management, comprising:
inputting one or more data transformation algorithms representing asset investment and management 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;
modeling a chained unit system of one or more temporally sequenced TALP investment vehicle options based at least on the one or more value complexity prediction polynomials;
simulating the one or more temporally sequenced TALP investment vehicle options to optimize one or more predictive algorithmic output values based at least on acceptance criteria, wherein the acceptance criteria include one or more acceptable input values and one or more acceptable output values; and
outputting optimized temporally sequenced TALP investment vehicle options from the one or more predictive algorithmic output values.
2. The method of claim 1 , further comprising generating one or more advanced time complexity prediction polynomials to determine optimized timing for the temporally sequenced TALP investment vehicle options.
3. The method of claim 1 , further comprising generating one or more advanced space complexity prediction polynomials to predict memory allocation.
4. The method of claim 1 , wherein the modeling of the temporally sequenced TALP investment vehicle options use at least one of one or more advanced time complexity prediction polynomials and one or more advanced space complexity prediction polynomials.
5. The method of claim 1 , wherein the modeling of the temporally sequenced TALP investment vehicle options are processed via at least one of a Data Discretization Optimization (DDO) software engine or a Risk/Return Allocation Vehicle (rRAV) software engine.
6. The method of claim 1 , wherein the optimized temporally sequenced TALP investment vehicle options comprise at least one of: Private Equity (PE) fund data, venture fund data, asset structure data, Real Estate Investment Trust (REIT) data, capital call data, venture capital requirements data, prioritized investment units data, principle data, cash flow data, payment data, payment timing data, and interest data.
7. The method of claim 1 , wherein the optimized temporally sequenced TALP investment vehicle options comprise at least one of: investment return data, investment risk data, bond rater data, rating agency data, derivative data, securities data, multiple assets data, and multiple securities data.
8. The method of claim 1 , wherein the optimized temporally sequenced TALP investment vehicle options comprise data for multiple investment portfolios or multiple investment families.
9. The method of claim 1 , wherein the acceptance criteria further comprise at least one of: asset acceptance criteria, venture capital requirements criteria, investment acceptance criteria, investment return criteria, investment risk criteria, bond rater criteria, rating agency criteria, and economic conditions data.
10. The method of claim 1 , wherein the acceptance criteria are based at least on input values received from a general partner, a limited partner, a bond investor, or a rating agency.
11. The method of claim 1 , wherein at least the modeling of the temporally sequenced TALP investment vehicle options occur at one of a stand-alone server system, a client-server system, or a cloud-based server system.
12. A software enhancement and management system, comprising:
a memory; and
a processor operatively coupled to the memory, wherein the processor is configured to execute a program code to:
input one or more data transformation algorithms representing asset investment and management data;
decompose the one or more data transformation algorithms into a plurality of time-affecting linear pathways (TALPs);
execute the plurality of TALPs to generate at least one or more value complexity prediction polynomials;
model a chained unit system of one or more temporally sequenced TALP investment vehicle options based at least on the one or more value complexity prediction polynomials;
simulate the one or more temporally sequenced TALP investment vehicle options to optimize one or more predictive algorithmic output values based at least on acceptance criteria, wherein the acceptance criteria include one or more acceptable input values and one or more acceptable output values; and
output optimized temporally sequenced TALP investment vehicle options from the one or more predictive algorithmic output values.
13. The system of claim 12 , wherein the processor is further configured to execute the program code to generate one or more advanced time complexity prediction polynomials to determine optimized timing for the temporally sequenced TALP investment vehicle options.
14. The system of claim 12 , wherein the processor is further configured to execute the program code to generate one or more advanced space complexity prediction polynomials to predict memory allocation.
15. The system of claim 12 , wherein the modeling of the temporally sequenced TALP investment vehicle options use at least one of one or more advanced time complexity prediction polynomials and one or more advanced space complexity prediction polynomials.
16. The system of claim 12 , wherein the modeling of the temporally sequenced TALP investment vehicle options are processed via at least one of a Data Discretization Optimization (DDO) software engine and a Risk/Return Allocation Vehicle (rRAV) software engine.
17. The system of claim 12 , wherein the optimized temporally sequenced TALP investment vehicle options comprise at least one of: Private Equity (PE) fund data, venture fund data, asset structure data, Real Estate Investment Trust (REIT) data, capital call data, venture capital requirements data, prioritized investment units data, principle data, cash flow data, payment data, payment timing data, and interest data.
18. The system of claim 12 , wherein the optimized temporally sequenced TALP investment vehicle options comprise at least one of: investment return data, investment risk data, bond rater data, rating agency data, derivative data, securities data, multiple assets data, and multiple securities data.
19. The system of claim 12 , wherein the optimized temporally sequenced TALP investment vehicle options comprise data for multiple investment portfolios or multiple investment families.
20. The system of claim 12 , wherein the acceptance criteria further comprise at least one of: asset acceptance criteria, venture capital requirements criteria, investment acceptance criteria, investment return criteria, investment risk criteria, bond rater criteria, rating agency criteria, and economic conditions data.
21. The system of claim 12 , wherein the acceptance criteria are based at least on input values received from a general partner, a limited partner, a bond investor, or a rating agency.
22. The system of claim 12 , wherein at least the modeling of the temporally sequenced TALP investment vehicle options occur at one of a stand-alone server system, a client-server system, or a cloud-based server system.