# US 2023/0409303 A1 · Software systems and methods for multiple talp family enhancement and management

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- Number: US 2023/0409303 A1
- Document ID: US20230409303A1
- Status: Published application
- Kind: A1
- Application number: 18/241,943
- Filed: 2023-09-04
- Published: 2023-12-21
- Earliest claimed: 2021-08-12
- Assignees: C Squared IP Holdings LLC
- Applicants: C Squared IP Holdings LLC
- Inventors: Scott Andrew SMITH; Christopher Graham SMITH; Kevin David HOWARD
- Web page: https://patentgrep.com/patent/US20230409303A1

## CPC

- G06F 8/447: {Target code generation}
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06F 11/3698: Environments for analysis, debugging or testing of software
- G06N 20/00: Machine learning
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## 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.

## Claims

### Claim 1

A method of software enhancement and management, comprising:
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 of one or more value complexity prediction polynomials, one or more advanced time complexity prediction polynomials, and one or more advanced space complexity prediction polynomials;
simulating TALP behavior by executing at least one of the one or more value complexity prediction polynomials, the one or more advanced time complexity prediction polynomials, and the one or more advanced space complexity prediction polynomials;
selecting one or more of the plurality of TALPs based at least on acceptance criteria, wherein the acceptance criteria includes one or more expected input and output value ranges and one or more expected TALP execution timings;
modeling one or more outcomes using at least the one or more value complexity prediction polynomials for each of the selected one or more of the plurality of TALPs; and
outputting optimum TALP-derived investment data from the modeled outcomes.

### Claim 2 (depends on 1)

The method of claim 1, further comprising inputting TALP Family Selection criteria.

### Claim 3 (depends on 1)

The method of claim 1, further comprising parallelizing the selected one or more of the plurality of TALPS.

### Claim 4 (depends on 1)

The method of claim 1, wherein modeling one or more outcomes occurs at a Data Discretization Optimization (DDO) engine.

### Claim 5 (depends on 1)

The method of claim 1, wherein the acceptance criteria includes asset acceptance criteria.

### Claim 6 (depends on 1)

The method of claim 1, wherein the outputted optimum TALP-derived data comprises at least one of Private Equity (PE) fund data, venture fund data, Real Estate Investment Trust (REIT) data, risk and return data, capital call data, prioritized investment units data, cash flow data, securities data, and interest data.

### Claim 7 (depends on 1)

The method of claim 1, wherein the acceptance criteria comprises at least one of investment return data, investment risk data, and economic conditions data.

### Claim 8 (depends on 1)

The method of claim 1, wherein the acceptance criteria is based at least on input values received from a general partner or limited partner.

### Claim 9 (depends on 1)

The method of claim 1, further including merging the plurality of TALPS with one or more enhancement TALPS.

### Claim 10 (depends on 1)

The method of claim 1, wherein at least simulating TALP behavior occurs at one of a stand-alone server system, a client-server system, and a cloud-based server system.

### Claim 11 (depends on 1)

The method of claim 1, wherein at least the outcome modeling is processed via a Risk/Return Allocation Vehicle (rRAV) software engine.

### Claim 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 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 of one or more value complexity prediction polynomials, one or more advanced time complexity prediction polynomials, and one or more advanced space complexity prediction polynomials;
simulate TALP behavior by executing at least one of the one or more value complexity prediction polynomials, the one or more advanced time complexity prediction polynomials, and the one or more advanced space complexity prediction polynomials;
select one or more of the plurality of TALPs based at least on acceptance criteria, wherein the acceptance criteria includes one or more expected input and output value ranges and one or more expected TALP execution timings;
model one or more outcomes using at least the one or more value complexity prediction polynomials for each of the selected one or more of the plurality of TALPs; and
output optimum TALP-derived investment data from the modeled outcomes.

### Claim 13 (depends on 12)

The system of claim 12, wherein the processor is further configured to execute the program code to input TALP Family Selection criteria.

### Claim 14 (depends on 12)

The system of claim 12, wherein the processor is further configured to execute the program code to parallelize the selected one or more of the plurality of TALPS.

### Claim 15 (depends on 12)

The system of claim 12, wherein modeling one or more outcomes occurs at a Data Discretization Optimization (DDO) engine.

### Claim 16 (depends on 12)

The system of claim 12, wherein the acceptance criteria includes asset acceptance criteria.

### Claim 17 (depends on 12)

The system of claim 12, wherein the outputted optimum TALP-derived data comprises at least one of Private Equity (PE) fund data, venture fund data, Real Estate Investment Trust (REIT) data, risk and return data, capital call data, prioritized investment units data, cash flow data, securities data, and interest data.

### Claim 18 (depends on 12)

The system of claim 12, wherein the acceptance criteria comprises at least one of investment return data, investment risk data, and economic conditions data.

### Claim 19 (depends on 12)

The system of claim 12, wherein the acceptance criteria is based at least on input values received from a general partner or limited partner.

### Claim 20 (depends on 12)

The system of claim 12, wherein at least the outcome modeling is processed via a Risk/Return Allocation Vehicle (rRAV) software engine.