Graph Embedding Inference Layer for API Object Resolution
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Solution Overview
Problem
Managing and optimizing complex data objects and computing resources in large-scale application frameworks is challenging due to the sheer volume and complexity of data, making it difficult to derive meaningful insights and inefficiently utilize computing resources.
Innovation Solution
A graphical representation layer integrated with an application framework processes multi-domain data using deep learning to generate graph embeddings, enabling efficient utilization of computing resources and providing predictive inferences through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If graph data structures are used to represent relationships between data objects, then relationships and connections can be visualized and traced, but the complexity of managing and processing the data increases
Solution Approach 1:
The patent introduces graph embedding structures as an intermediary representation layer between the raw graph data and machine learning models. These embeddings convert complex graph relationships into simplified vector representations that can be efficiently processed by ML algorithms, thus maintaining the ability to trace relationships while reducing processing complexity
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with machine learning-based inference systems. Instead of manually processing complex graph data through traditional algorithms, the system uses trained ML models to automatically extract insights and make predictions from the graph embeddings, significantly reducing processing complexity
2Productivity
If traditional data processing methods are used, then computing resources are consumed, but meaningful insights from graphical data structures cannot be obtained efficiently
Solution Approach 1:
The patent performs preliminary processing by pre-computing graph embeddings for graph data structures. These pre-computed embeddings are stored and can be quickly queried by machine learning models without reprocessing the entire graph data, enabling efficient insight generation while reducing computing resource consumption
Solution Approach 2:
The patent creates simplified copies of the complex graph data in the form of embedding vectors. These vector representations capture the essential relationships and patterns from the original graph data in a compressed format that is much easier and more efficient for machine learning models to process, thereby improving productivity while reducing energy consumption
Data Source
AI summary
Methods, apparatuses, or computer program products disclosed herein provide for receiving an API object via a communication channel, applying a machine learning model to (i) the API object and (ii) one or more graph embedding structures for a portion of graph data to generate one or more predictive inferences related to the API object, and initiating a resolution action related to the API object based at least in part on the one or more predictive inferences.