We Help You Sequence
Your PE Funds
Enhance Your Returns While Appealing to a Broader Spectrum of Investors
We help fund managers tailor enhancement strategies for a full spectrum of alternative assets—while attracting a wider investor base. The solutions featured on this site typically improve a GP’s return on AUM by 25% while producing substantially higher returns for LPs.
We Help You Protect the IP in the Products You Develop
We bring deep expertise in patenting advanced computational technologies. With over 50 patents on the essentials used to model complex systems—and a dozen on the computational technologies essential to model SECS for PE funds—we can help you patent the financial products you develop.
Financial technologies are notoriously difficult to patent, unless you can draw upon existing patents that are rooted in essential computational technologies.
Clients Receive Preferential Pricing on Licensing
FinaTech charges an annual open market rate of 25 bps on deal size for its solutions. Clients receive preferential pricing and gain the ability to patent their own financial products. Licensing ensures full protection for everyone involved in a fund—safeguarding GPs, LPs, creditors, credit enhancers, rating agencies, and investment banks.
FinaTech's Patent Portfolio
FinaTech’s IP portfolio protects the computational technologies required to accurately model and simulate the optimization, credit enhancement, issuance of securities, rating of debt, and selection and management of portfolio assets for temporally sequenced equity-based structured securities, including the SECS on this site, and any variations or derivations thereof.
The overview below summarizes six of FinaTech’s patents. The first three patents are directly owned by FinaTech’s intellectual property holding company. The next three are licensed exclusively for application within structured private equity funds and real estate investment trusts (REITs).
Three newly granted patents are not listed below (U.S. Patent Nos. 12,393,410; 12,293,173; and 12,175,224). They specifically protect the SECS described on this site. In addition, two more patents on SECS have been approved and are awaiting publication and issuance. In total, eleven patents currently protect the computation technologies necessary to use SECS in PE funds and REITS, with more in process.
1. US Patent #11,687,328 - Method and System for Software Enhancement and Management
This patent demonstrates that software can be enhanced and/or managed with significant performance gains using the predictive analytics of that software and any interacting software system or inputs. The patent demonstrates that multi-variable attribute input/outputs can be directly converted into algorithms, making this an important patent in general in the computation and software space.
-
This patent shows how to derive predictive analytics from inputs in general and specifically inputs representing companies and assets.
-
How to perform multi-variable parallelization with pinpoint accuracy on data transformation algorithms representing companies and assets.
-
How to construct the time complexity of an algorithm to account for changing data events over time, including changes in inputs like updates in a company's revenue stream, or changing economic conditions.
-
How all input/output pairs such as assets, investment criteria, rating agency criteria, debt criteria, market conditions, changes in technology, economic conditions, and fund structure can be treated as non-software algorithms and deconstructed through the use of functional decomposition to derive analytics for more accurate modeling, predictions, and asset management.
-
How representative data transformation algorithms can be simulated and/or modeled using predictive analytics for selection, rating, merging, management, discretization, and optimized distribution of outputs such as cash flow.
This patent demonstrates how assets and their optimized output values, including cash flow and the proceeds of a liquidation, can be distributed over multiple categories of partners, investors, lenders, and rating agencies. Whenever the projected returns from a complex model of assets, bonds, or prioritized securities accurately predict the actual returns from a fund, they are necessarily using the derived predictive analytics of the vehicle.
-
This patent shows how software, algorithms, and the relationships between input and output data pairs can be transformed with extracted predictive analytics to optimize the accuracy and predictability of a model.
-
How to use and derive multi-dimensional predictive analytics to transform input/output matrices and array pairs.
-
How a simulation of the functional characteristics of an algorithm is equivalent to predictive analytics and can be used to group assets into families, as in portfolios or funds, and into cross-families, as in funds of funds.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to manage assets that have been grouped into families, as in portfolios or funds. and cross-families, as in funds of funds.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to project distributions of cashflow and risk to multiple categories of creditors, LPs, and GPs, each with their own inherent acceptance and rating criteria.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to group, analyze, and manage different types of input data, such as changing economic conditions, rating criteria, investor criteria, and management decisions for the aggregation and discretization of output data for grouped assets such as portfolios, funds, and funds of funds.
-
How to use interacting grouped predictive analytics, functional decomposition, and data transformation algorithms to manage assets and debt in portfolios, funds, or funds of funds.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms with externally defined data such as economic conditions and rating criteria to optimize a fund’s structure in terms of allocating risk and/or returns, and how to optimize risk and/or returns for a given structure.
As with FinaTech’s other patent IP, this patent shows that the more accurately a simulation or model mirrors reality, the closer those simulations and models reflect predictive analytics. It also shows that predictive analytics and data transformation algorithms are agnostic with regard to the use of any standard hardware configuration, including stand-alone servers, centralized computing client servers, decentralized clouds, and decentralized ad hoc networks.
-
This patent shows how to use predictive analytics, functional decomposition, and data transformation algorithms to generate inherent algorithmic and source code outputs that optimize the performance of algorithms and source code, which includes optimizing the return for specific classes of investors, or the security of classes of creditors.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms with input data to select assets, creditors, investors, and fund structure.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to optimize grouped asset performance, such as the performance of a portfolio of companies, based on inputs such as economic conditions, rating criteria, investor criteria, market changes, management decisions, and fund structure.
-
How to use optimization and data transformation algorithms for PE fund data, REIT data, risk and return data, capital call data, prioritized investment unit data, cash flow data, securities data, and interest data.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to discretize and optimize output data from pooled assets for multiple investors and/or creditors with differing investment objectives.
-
How to use predictive analytics, functional decomposition, and data transformation algorithms to model, simulate, and optimize asset data, cashflow data, economic data, market data, rating criteria data, management data, and fund structure data.
As with FinaTech’s other patent IP, this patent shows that the more accurately a simulation or model mirrors reality, the closer those simulations and models necessarily reflect intrinsic analytics and their associated features. It also shows that predictive analytics are agnostic concerning the use of any standard hardware configuration, including stand-alone servers, centralized computing client servers, decentralized clouds, and decentralized ad hoc networks.
4. US Patent #10,496,514 - System and Method for Parallel Processing Prediction
-
This patent shows how curve fitting to generate polynomials that accurately predict reality.
-
How to predict parallelized performance with mathematical precision.
-
How to derive four key prediction polynomials from algorithms: speedup, time complexity, space complexity, and overhead complexity.
This patent discloses the true limits to parallel processing, using a mathematical derivation to:
-
Prove the scaling limits for parallel processing predicted by Amdahl’s Law are incorrect.
-
Prove the scaling limits for parallel processing as predicted by Gustafson’s Law, once proposed as a correction to Amdahl’s Law, only represents a subset of the true scaling potential for parallel processing.
-
Prove a new law for parallel processing, Howard’s Law, that accurately predicts the true scaling limits for any algorithm, showing how to achieve greater performance gains from parallel processing than ever before thought possible.
The patent also demonstrates:
-
How precision parallel processing necessarily relies on intrinsic properties derived from algorithms as prediction polynomials in order to achieve accuracy.
-
How to extract intrinsic analytics from input/output relationships, software code, and algorithms (without limitation to the use or means of extraction).
-
How to optimize non-parallelized software.
-
How to derive prediction polynomials for processing time, memory, and overhead.
-
How the determination of work (such as when projecting changes in asset value or cashflow), algorithmic timings (such as when projecting returns as a function of time), and algorithmic overhead (such as when determining an asset’s overhead) all necessarily rely on prediction polynomials derived from an algorithm’s intrinsic analytics.
This patent unlocks the means to more accurate curve fitting, showing that the ability to fit a curve formed by a set of input and associated output values is necessarily based on the intrinsic properties of algorithms in the environment being modeled. Therefore, the more accurate a model of a complex system is, the more it matches reality and the intrinsic analytics derived from input/output relationships. In other words, the more accurate a model, the closer it is to being indistinguishable from FinaTech’s patent IP.
5. US Patent #11,520,560 - Computer Processing and Outcome Prediction System and Method
This patent defines time-affecting linear pathways (“TALPs”) as a way to analyze and optimize software code and algorithms. TALPs play a key role in FinaTech’s patent portfolio. This patent expands on the concepts of US Patent #10,496,514, showing:
-
How to use input variable attribute values that define non-loop control conditions to decompose an algorithm or software code into TALPs.
-
How to eliminate temporal (workload) ambiguities in algorithms that execute on serial or parallel Turing machines through TALP decomposition and analysis.
-
How to use the input variable attribute values and their associated output values of TALPs to derive the intrinsic predictive analytics of an algorithm or software code.
-
How to use TALPs to ensure that derived intrinsic predictive analytics accurately reflect objective reality.
-
How TALP-derived intrinsic analytics can be applied to the outputs of a software code or algorithm.
-
How TALP-derived analytics can be applied to topological analysis to treat high dimensional algorithms as though they are linearly related.
-
How optimization methods, such as parallel processing (modeling multiple assets to project returns simultaneously for multiple investors), dataset curve fitting (determining when a fund will hit targets), and predicted data intercepts (determining the actions necessary to hit a target), can be derived from input/output relationships, software code, and algorithms using TALP decomposition and analysis.
6. US Patent #11,789,698 B2 - Computer Processing and Outcome Prediction System and Method
-
This patent shows how to derive TALP intrinsic analytics from a list of input/output data pairs using a self-learning search-and-compare curve-fitting model.
-
How to expand from a single input variable to multiple input variables used by a TALP to perform a curve fit, demonstrating a new advanced approach to non-linear multi-variable curve fitting that greatly reduces the computational overhead conventionally associated with partial differential equation curve-fitting solutions.
-
How to automatically select TALPs for analysis from various input/output value pairs, source code, or algorithms.
-
How to use TALPs to determine the original input values of an algorithm by observing the output values, demonstrating that accurate back tracing of values requires TALP-derived intrinsic analytics.
-
How to use TALPs to predict multi-algorithm race conditions like data collisions and other algorithmic interactions.
-
How to use TALPs to automate white-box and black-box testing of algorithms.
-
How to discretize an algorithm’s output values from TALP-derived intrinsic analytics.
-
How to use TALPs to convert standard algorithms, input/output dataset pairs, and software codes into quantum computable form.
TALP-derived intrinsic analytics are shown to be agnostic with regard to any standard hardware configuration, including stand-alone servers, centralized computing client-servers, decentralized clouds, and decentralized ad hoc networks.