Multi-AI Orchestration With Spatial Memory for Heterogeneous Inputs
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Solution Overview
Problem
Existing AI systems struggle with processing heterogeneous data from multiple sources due to limitations in flexibility, compatibility, and lack of orchestration mechanisms, leading to inefficiencies in handling complex tasks and dynamic switching among AI models.
Innovation Solution
A control system that integrates short-term, long-term, and spatial memories to process multi-modal inputs, using AI models to convert data into structured spatial memory, predict events, and autonomously select and execute enterprise APIs while ensuring ethical considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single AI model is used to process all input categories, then system complexity is reduced, but the system becomes insufficient to process heterogeneous data and satisfy varying system objectives
Solution Approach 1:
The system segments the AI processing capability into multiple specialized AI models, each designed to handle specific categories of inputs or tasks. This segmentation allows the system to process heterogeneous data effectively while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements a universal orchestration mechanism that can dynamically select and coordinate multiple AI models based on input characteristics and task requirements. This multi-functional approach enables a single system to handle diverse data types and objectives without requiring separate dedicated systems for each function.
2Adaptability or versatility
If multiple AI models are integrated into the system, then processing capability and versatility improve, but compatibility constraints and lack of standardized interfacing hinder integration
Solution Approach 1:
The patent introduces an intermediary orchestration layer that standardizes interfacing between multiple AI models and the rest of the system. This mediator handles model selection, parameter coordination, and output aggregation, resolving compatibility issues and simplifying integration while preserving the versatility benefits of multiple specialized models.
3Device complexity
If static configurations and predefined model associations are used, then system simplicity is maintained, but flexibility and responsiveness to evolving requirements are limited
Solution Approach 1:
The system implements dynamic configuration capabilities that allow AI model associations and system parameters to be adjusted in response to evolving requirements. The orchestration mechanism can dynamically select appropriate models based on input characteristics, task objectives, and performance metrics, enabling the system to adapt to new requirements without static predefined configurations.
4Productivity
If rapid development cycles of AI technologies are adopted, then enhanced performance and capabilities are achieved, but challenges arise in readily adopting new models
Solution Approach 1:
The orchestration system incorporates self-service capabilities that automatically evaluate, validate, and integrate new AI models as they become available. The system can autonomously assess new models' compatibility, performance characteristics, and suitability for specific tasks, enabling rapid adoption of emerging AI technologies without manual intervention or complex integration processes.
Data Source
AI summary
Methods and systems for generating action-oriented enterprise outputs are disclosed. Multi-modal input data from input data sources are received. Historical data corresponding to enterprise solutions associated with historical input is converted into short-term, long-term, and spatial memory using connectors that interface with artificial intelligence (AI) models. Features are extracted from the multi-modal input data and the historical data. Trends are determined from the extracted features associated with the multi-modal input data and the historical data. A subsequent event is predicted based on the trends using a transformer-based Large Language Model (LLM). At least one action-oriented enterprise output is generated based on the predicted subsequent event.