Action Strategy Generation Using Composition Data Classification
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Solution Overview
Problem
Existing solutions for generating strategy are inefficient and do not adequately support the classification and generation of action strategies based on user composition data.
Innovation Solution
A system and method that utilizes a processor to receive, classify, and generate an action strategy from user composition data, including personal and financial information, using machine learning techniques to determine action items and strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing solutions are used for generating strategy, then the process is simple, but the efficiency and effectiveness are insufficient
Solution Approach 1:
The patent segments the strategy generation process into distinct functional modules: data reception module, classification module, action item determination module, and strategy generation module. Each module handles a specific aspect of the process, improving overall efficiency while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary classification system that categorizes composition data into composition groups before generating action items. This intermediary step acts as a mediator between raw data and final strategy generation, enabling more efficient and targeted strategy development while organizing the complexity into structured categories.
2Measurement precision
If manual strategy formulation is used, then understanding of strengths and weaknesses is achieved, but time consumption and inefficiency increase
Solution Approach 1:
The system enables self-service strategy generation by automatically processing user composition data through classification and analysis algorithms. The system performs the analytical work that would traditionally require manual human effort, maintaining high measurement precision in identifying strengths and weaknesses while dramatically reducing time consumption through automated computation.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. The processor-based system substitutes human manual formulation with algorithmic processing, maintaining analytical accuracy while eliminating the time loss associated with manual strategy development.
3Adaptability or versatility
If comprehensive composition data is collected, then personalized strategy accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing comprehensive composition data into distinct composition groups through classification. This segmentation approach enables personalized strategy generation by organizing diverse data elements into manageable categories, improving adaptability while reducing processing complexity through structured organization of the comprehensive data set.
Solution Approach 2:
The system performs preliminary classification of composition data into composition groups before generating action items and strategies. This preliminary action organizes the comprehensive data in advance, enabling personalized adaptation while simplifying subsequent processing steps by pre-structuring the data according to meaningful categories.
Data Source
AI summary
A system for generating an action strategy is disclosed. The system includes at least a processor. The system includes a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive composition data from a user, classify the composition data to one or more composition groups, provide a composition course as a function of the one or more composition groups, determine an action item as a function of the one or more composition groups, and generate an action strategy as a function of the action item.