Multiple TALP Family Optimization for Modular Software Extension
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Solution Overview
Problem
Existing software systems lack efficient methods to automatically extend functionality without sacrificing processing performance, necessitating the use of non-software personnel for software creation, update, and repair, which is inefficient and costly.
Innovation Solution
The Multiple Time-Affecting Linear Pathway (TALP) family enhancement and management system optimizes pooled TALP output data using feedback and feedforward loops, converting algorithms and I/O datasets into TALPs with prediction polynomials, and grouping them into families for enhanced optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software functionality is extended using traditional methods with non-software personnel, then software creation and update needs can be met, but processing performance is sacrificed and development cost increases
Solution Approach 1:
The system segments software functionality into modular TALP (Time-Affecting Linear Pathway) components that can be independently developed, tested, and optimized. Each TALP represents a discrete functional unit that can be automatically generated from algorithms and I/O datasets, allowing non-software personnel to contribute to specific modules without compromising overall system performance.
Solution Approach 2:
The system implements feedback loops that automatically evaluate TALP performance metrics and guide optimization processes. By continuously monitoring processing performance and automatically adjusting TALP configurations, the system maintains high productivity while enabling extensive functionality extension through automated optimization rather than manual intervention.
2Adaptability or versatility
If software functionality is extended using traditional methods with non-software personnel, then software creation and update needs can be met, but development cost increases
Solution Approach 1:
The system enables self-service software development by automatically generating TALP code from algorithms and I/O datasets without requiring manual programming. Non-software personnel can define functional requirements and data flows, and the system automatically produces optimized software components, eliminating the need to hire expensive software engineers while maintaining high-quality code generation.
Solution Approach 2:
The system changes the parameters of software development by transitioning from manual code writing to automated algorithmic transformation. By adjusting development parameters such as algorithm selection, data flow configuration, and optimization criteria, the system enables cost-effective software creation with non-software personnel while maintaining professional-grade output quality.
3Adaptability or versatility
If TALPs are grouped into families without optimization, then system complexity increases, but processing efficiency remains unoptimized
Solution Approach 1:
The system performs preliminary optimization actions by automatically analyzing TALP families and identifying optimization opportunities before execution. By pre-processing TALP groups to detect patterns, redundancies, and optimization potentials, the system reduces the complexity burden of grouping while preparing optimized configurations that will execute efficiently, turning potential complexity into structured organization.
Data Source
AI summary
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.