Hybrid AI Decision Engine for Natural Language Processing
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Solution Overview
Problem
Existing methods for interacting with electronic devices through natural language processing often face challenges with resource utilization and latency, as they require either local processing that consumes device resources or remote processing that incurs network latency, affecting user satisfaction.
Innovation Solution
A hybrid decision engine that dynamically chooses between local, remote, or combined execution engines for natural language processing tasks based on user and device context, optimizing resource usage and latency by personalizing the processing approach through feedback data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If local processing is used for natural language processing tasks, then processing speed is improved, but device resources are consumed
Solution Approach 1:
The patent segments natural language processing tasks into different types (e.g., simple vs. complex, frequent vs. infrequent) and processes them differently. Simple and frequent tasks are handled locally for speed, while complex or infrequent tasks are sent to remote servers, optimizing the balance between processing speed and device resource consumption.
Solution Approach 2:
The system dynamically adjusts the processing location based on real-time factors including device resource availability, network conditions, task complexity, and user preferences. This dynamic decision-making allows the system to optimize between local processing speed and remote processing resource efficiency for each specific task context.
2Use of energy by moving object
If remote processing is used for natural language processing tasks, then device resources are saved, but network latency is introduced
Solution Approach 1:
The patent segments processing tasks based on their characteristics and processes them at different locations. Tasks that can be handled remotely are sent to servers to save device resources, while tasks requiring immediate response are processed locally, minimizing the impact of network latency on user experience.
Solution Approach 2:
The system changes parameters such as processing location, processing mode, and task prioritization based on conditions including network quality, device state, and task urgency. This allows the system to optimize the trade-off between saving device resources and minimizing latency for each specific situation.
3Device complexity
If a fixed processing approach is used, then system complexity is reduced, but adaptability to different conditions deteriorates
Solution Approach 1:
The patent implements a dynamic decision-making framework that adapts to changing conditions including device resource availability, network quality, task characteristics, and user preferences. The system automatically adjusts processing location and mode based on real-time conditions, providing high adaptability without requiring complex manual configuration.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor performance, resource usage, and user interactions to continuously improve processing decisions. This feedback-driven optimization allows the system to learn from past experiences and make better adaptive decisions, balancing complexity and adaptability.
Data Source
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AI summary
A method, an electronic device, and non-transitory machine-readable medium are provided. The method includes receiving, on an electronic device, a request to perform an action. The method also includes deriving an aggregated predicted confidence level using one or more confidence levels. The one or more confidence levels are based on usage information and context of the electronic device. The method further includes determining an execution engine to process the request based on the aggregated predicted confidence level. The method additionally includes providing at least a portion of the request to the execution engine for processing.