Cognitive System Auditory Relevance Ranking
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Solution Overview
Problem
Cognitive systems face difficulties in processing multiple auditory inputs from diverse sources, leading to conflicting or irrelevant comments, which can hinder efficient decision-making in activities involving multiple individuals or systems.
Innovation Solution
A computer-implemented method that receives and analyzes auditory communications to determine intended actions, creates simulations to assess outcomes, ranks these outcomes based on relevance, and physically implements the highest-ranked actions, utilizing techniques like natural language understanding and machine learning to improve contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple auditory inputs are processed simultaneously, then the system can consider more perspectives and information, but the decision-making efficiency deteriorates due to conflicting and diverse comments
Solution Approach 1:
The patent segments the complex task of processing multiple auditory inputs by dividing it into distinct stages: receiving auditory inputs, analyzing them to determine intended actions, creating simulations of those actions, examining simulation results, and finally determining contextual relevance. This segmentation allows the system to handle multiple inputs systematically without overwhelming the decision-making process, thereby maintaining efficiency while processing diverse comments from multiple sources.
2Measurement precision
If all auditory communications are analyzed in detail, then the accuracy of identifying relevant actions improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by creating simulations of intended actions before actual implementation. The system analyzes auditory communications to determine intended actions, then creates simulated versions of these actions and examines their results in advance. This preliminary simulation allows the system to pre-evaluate the relevance and effectiveness of potential actions, so that when actual decisions need to be made, the system can quickly identify the most relevant actions without having to perform exhaustive analysis at the moment of decision-making, thus reducing processing time while maintaining accuracy.
3Adaptability or versatility
If the system implements all suggested actions from multiple sources, then the comprehensiveness of the activity improvement increases, but the complexity of coordinating and managing multiple actions increases
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of simulation examination. Between receiving auditory communications and implementing actions, the system creates simulations of intended actions and examines their results. This intermediary simulation layer acts as a mediator that evaluates and prioritizes multiple suggested actions, determining their contextual relevance and potential effectiveness before implementation. This allows the system to comprehensively consider improvements from multiple sources while the simulation examination process manages the complexity by filtering and ranking actions, so that not all suggestions are implemented but only those deemed most relevant and effective.
Data Source
AI summary
A computer-implemented method for determining contextual relevance in multi-auditory source scenarios is disclosed, the method including receiving, by a cognitive system, auditory communications regarding a current activity, analyzing, by the cognitive system, each auditory communication to determine an intended action. For each intended action, the cognitive system creates a simulation to identify a resulting outcome of each intended action. The method further includes ranking, by the cognitive system, the resulting outcome, the ranking based on a comparison of each simulated result and the corresponding intended action regarding the current activity, and physically implementing the highest rated resulting outcome(s) for the current activity. The analyzing, in one example, includes assigning a weight to each of the relevant auditory communications based on one or more criterion, and ranking the relevant auditory communications by weight.