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FinaTech's Patent IP

Overview on TALPs

Projecting the future performance of Alternative Asset-Back Securities (AABS) hinges upon multiple factors. To make accurate projections, one must assess how each company within a portfolio will react to a variety of inputs, which serves to define the algorithms used in a model. Actuarial averages as used in mortgage-backed securities are insufficient for such modeling because each company possesses its own unique set of intrinsic analytics. Not all companies respond uniformly to the same input variables. Consequently, top-quality fund management not only necessitates accurate modeling for the identification and acquisition of promising companies but also for the ongoing management of each company, as well as the management of the overall portfolio to efficiently capitalize on changing market conditions. Moreover, this must be done in a manner that caters to the varied objectives of multiple investors with very different goals.

 

FinaTech’s patent IP plays a critical role in being able to address this complexity by enabling the extraction and utilization of the intrinsic analytics that predict how each asset will respond to a variety of inputs. With AABS, this is not a static, one-time undertaking. As projections are updated, the analytics pertaining to each company or asset’s response to inputs need recalibration since an asset’s responsive characteristics often change over time. Additionally, with structured AABS, the recalculation of pooled asset outputs occurs concurrently with distributions of information and cashflow to diverse classes of investors. FinaTech’s patent IP covers the computation and software technologies required for the continuous reassessment of intrinsic analytics and the optimization of returns from grouped inputs for the benefit of multiple classes of investors.

 

FinaTech’s team demonstrated that for complex systems like structured AABS, accurate software calculations require the utilization of Time-Affecting Linear Pathways (“TALPs”) and their associated analytics, as defined and protected by FinaTech’s patent IP. TALP-extracted intrinsic predictive analytics were patented as an indispensable tool for accurately comprehending, analyzing, enhancing, and managing the intricate time-based interplay of data, algorithms, and code in complex models. 

 

In the context of structured AABS, these TALP-extracted predictive analytics are used to accurately convert assets and groups of assets with multiple inputs and associated outputs into algorithms and code to precisely model parallel grouped outcomes. These predictive analytics are also used to convert the activities associated with creditors, LPs, GPs, shifting economic landscapes, new technologies, tax considerations, market forces, rating agencies, and fund structure into algorithms and code. 

 

TALP-extracted intrinsic predictive analytics are used in modeling information for marketing, issuing, rating, managing, monitoring, reporting, and trading AABS. As defined in FinaTech’s patent IP, the use of these analytics for such purposes is not limited to any specific means of extraction or application and is thus applicable to a broad range of computation and software technology employed in the AABS industry today. It also suggests new structures that enable GPs to access lower-cost capital upon the formation of funds, simultaneously boosting returns for LPs, which FinaTech gladly licenses for a nominal fee.

FinaTech's TALP Patents

One can look up FinaTech's patents by using the United States Patent and Trade Offices' (USPTO) public search engine at https://ppubs.uspto.gov/pubwebapp/static/pages/ppubsbasic.html. After entering the patent number, the USPTO’s archived PDF for each patent is accessed by clicking search and then scrolling. The first, second, and third patents listed below have been issued, or are approved to be issued, directly to FinaTech’s IP holding company. The fourth, fifth, and sixth patents are exclusively licensed by FinaTech for use in AABS.

 

 

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 TALP-extracted intrinsic predictive analytics of that software and any interacting software system. The patent demonstrates that multi-variable attribute input/outputs can be directly converted into algorithms, making this an important patent in the computation and software space. The patent uses a more general form of TALP-extracted intrinsic analytics than shown in the patents that FinaTech licenses, which broadens the analytics possible.

 

Among other things, this patent shows:

 

  • How to use TALPs to generate intrinsic analytics directly from data transformation algorithms.

  • How to use TALPs to perform complex multi-variable parallelization with pinpoint accuracy on data transformation algorithms representing companies and assets.

  • How to use TALPs to construct the time complexity of an algorithm for arbitrary data events.

  • 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 into TALPs through the use of inherent analytics for precision modeling, pin-point predictions, and asset management.

  • TALPs of representative algorithms are simulated and/or modeled using TALP-extracted analytics for selection, merging, management, discretization, and optimized distribution.

 

This patent demonstrates how assets and their optimized output values 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 necessarily mirror the TALP-extracted analytics of the vehicle. 

 

 

2. Notice of Allowance for US Patent Application #18/102,638 - Software Systems and Methods for Multiple TALP Family Enhancement and Management

 

 

This patent shows:

 

  • How software, algorithms, and the relationships between input and output data pairs can be transformed into TALPs with extracted intrinsic predictive analytics to optimize the accuracy of a model.

  • How to use intrinsic, multi-dimensional analytics to transform input/output matrices and array pairs into TALPs (other patents focus on the transformation of scalar values).

  • How a simulation of the functional characteristics of an algorithm is equivalent to TALP-extracted intrinsic analytics and can be used to group assets into families, as in a portfolio or fund, and into cross-families, as in funds of funds.

  • How to use TALP-extracted intrinsic analytics to manage assets that have been grouped into families (funds) and cross-families (funds of funds).

  • How to use TALP-extracted intrinsic analytics to project distributions of cashflow and risk to multiple categories of creditors, LPs, and GPs, each with their own inherent criteria.

  • How to use TALP-extracted intrinsic analytics 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 TALP-extracted intrinsic analytics to manage assets and debt in portfolios, funds, or funds of funds.

  • How to use TALP-extracted intrinsic analytics with externally defined data such as economic conditions and rating criteria to optimize a fund’s structure in terms of 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 the TALP-extracted intrinsic predictive analytics. It also shows that TALP-extracted intrinsic analytics 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.

 

 

3. Notice of Allowance for US Patent Application #18/242,943, Software Systems and Methods for Multiple TALP Family Enhancement and Management - Continuation

 

  • Continuation of US Patent Application #18/102,638

  • Issued Date: Allowed October 25, 2023, to be issued soon

  • Link: Not yet issued by the USPTO

  • Will issue directly to FinaTech’s IP holding company

 

This patent shows:

 

  • How to use TALP-extracted intrinsic analytics to generate inherent algorithmic and source code outputs that optimize the performance of algorithms and source code.

  • How to use TALP-extracted intrinsic analytics with input data to select assets, creditors, investors, and fund structure.

  • How to use TALP-extracted intrinsic analytics to optimize grouped asset performance based on inputs such as economic conditions, rating criteria, investor criteria, market changes, management decisions, and fund structure.

  • How to use TALP-extracted optimization 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 TALP-extracted intrinsic analytics to discretize and optimize output data from pooled assets for multiple investors with different goals.

  • How to use TALP-extracted intrinsic analytics 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 reflect the TALP-extracted intrinsic analytics. It also shows that TALP-extracted intrinsic 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

 

 

Among its many disclosures, this patent shows how to:

 

  • Use curve fitting to generate polynomials that accurately predict reality.

  • Predict parallelized performance with mathematical precision.

  • 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 extract the intrinsic predictive analytics of an algorithm or software code.

  • How to use TALPs to ensure that extracted intrinsic predictive analytics accurately reflect objective reality.

  • How TALP intrinsic analytics can be applied to the outputs of a software code or algorithm.

  • How TALP 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 extracted 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 extract 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-extracted 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-extracted intrinsic analytics.

  • How to use TALPs to convert standard algorithms, input/output dataset pairs, and software codes into quantum computable form.

 

TALP-extracted 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.

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