Semantic Sensing Smart Device for Drift-Aware Circumstance Detection
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Solution Overview
Problem
Existing smart device systems struggle to effectively determine affirmative and non-affirmative circumstances and perform semantic augmentation based on inputs from sensing elements, leading to inefficiencies in user interactions.
Innovation Solution
A smart device system comprising a processor configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances, inferring and applying measures to reduce drift or entropy, thereby enhancing semantic augmentation towards users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system applies semantic drift or entropy to determine affirmative and non-affirmative circumstances, then the system can perform semantic augmentation towards users, but the system complexity increases
Solution Approach 1:
The system segments the complex semantic processing into distinct functional modules: sensing elements for data collection, processor for applying semantic drift/entropy calculations, and semantic augmentation engine for user-specific adaptations. This modular segmentation manages system complexity while enabling advanced semantic capabilities.
Solution Approach 2:
The patent introduces semantic drift and entropy as intermediary computational concepts that bridge raw sensing data and user-specific semantic augmentation. These intermediaries transform complex sensory inputs into structured affirmative/non-affirmative circumstances that can be systematically processed and adapted to individual users.
2Reliability
If the system infers and applies measures to reduce drift or entropy, then the reliability of semantic determination improves, but the processing time increases
Solution Approach 1:
The system performs preliminary semantic drift and entropy calculations on sensing inputs before full semantic augmentation processing. By pre-determining affirmative and non-affirmative circumstances and applying reduction measures in advance, the system improves reliability of subsequent user-specific adaptations while reducing overall processing time through staged computation.
Data Source
AI summary
A semantic sensing system includes a processor, a memory and at least one sensing element, wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the affirmative and non-affirmative determinations.