Object Relationship Expression Modeling for Dynamic Prediction
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Solution Overview
Problem
Simple prediction models often oversimplify relationships and ignore nuances, leading to inaccurate predictions by relying on static assumptions.
Innovation Solution
A system and method for determining an expression of an object using a computing device with a processor and memory, which includes dynamic modeling techniques to capture intricate and evolving relationships between objects, utilizing machine-learning models and natural language processing to generate expressions representing relationships between objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple prediction models are used, then device complexity is reduced, but measurement precision deteriorates due to oversimplified relationships and static assumptions
Solution Approach 1:
The patent implements dynamic modeling that adapts to changing relationships between objects over time, moving from static assumptions to dynamic expressions that capture evolving interactions. This resolves the contradiction by introducing time-varying parameters and adaptive modeling mechanisms that improve prediction accuracy without requiring excessively complex model structures.
Solution Approach 2:
The system changes parameters dynamically based on observed relationships, adjusting model parameters according to the specific context and data patterns. This allows the model to maintain simplicity while adapting its parameters to capture nuanced relationships, thereby improving prediction accuracy without proportionally increasing model complexity.
2Measurement precision
If dynamic modeling techniques are used to capture intricate relationships, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the modeling process into distinct components: expression generation, relationship identification, and parameter adjustment. This modular approach allows complex dynamic modeling to be broken down into manageable segments, improving prediction accuracy through comprehensive relationship capture while controlling overall system complexity through structured organization.
Solution Approach 2:
The system introduces intermediary elements such as expression models and relationship representations that mediate between raw data and final predictions. These intermediaries simplify the complex task of modeling intricate relationships by providing structured representations that capture essential patterns without requiring the full complexity of underlying relationships.
3Adaptability or versatility
If natural language processing and machine-learning models are used to generate expressions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the machine-learning models automatically generate and refine expressions without extensive manual configuration. The models adapt to new data patterns and generate appropriate expressions autonomously, improving system adaptability while reducing the operational complexity of managing and maintaining the system.
Solution Approach 2:
The patent implements universal expression models that can handle multiple types of relationships and data formats through a unified framework. This multi-functional approach allows the system to adapt to diverse scenarios using the same core machinery, thereby improving versatility without proportionally increasing system complexity through specialized components for each case.
Data Source
AI summary
A system for determining an expression of an object. The system includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to determine an expression of an object. The instructions may include receive a first object, receive a second object, determine an expression of the first object in relation to the second object. Further, determining an expression of the first object in relation to the second object may include inputting the first object and the second object into an expression model, generating an expression, wherein the expression represents a relationship between the first object and the second object, outputting the expression representing the first object and the second object, and returning a value associated with the relationship between the first object and the second object.