Cognitive System Communication Model Context Interpretation
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Solution Overview
Problem
Current communication models, such as the Shannon-Weaver and Emmert-Donaghy models, fail to effectively address the interpretation of context and learning from communication failures or successes, particularly in human-cognitive system interactions, and do not account for the storage of messages and their impact on future interactions.
Innovation Solution
A cognitive system interacts with users by receiving sensor data, extracting features from environmental and observational data, analyzing contexts using user and cognitive system profiles, identifying trigger events, and determining proposed actions to initiate appropriate responses, which are stored for future interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional communication models (Shannon-Weaver, Emmert-Donaghy) are used, then communication between sender and receiver is established, but context interpretation and learning from communication failures or successes are not effectively addressed
Solution Approach 1:
The patent implements feedback mechanisms where the cognitive system analyzes communication outcomes (successes and failures) and uses this information to learn and improve future interactions. The system stores communication history and patterns, allowing it to adapt its communication strategy based on past experiences, thereby simultaneously improving reliability through better context interpretation and adaptability through continuous learning.
Solution Approach 2:
The patent applies preliminary action by pre-processing and storing communication patterns, context information, and interaction histories before actual communication events occur. This allows the cognitive system to have pre-established knowledge bases and pattern recognition capabilities that enhance real-time context interpretation and enable adaptive responses based on previously learned communication dynamics.
2Adaptability or versatility
If cognitive systems store and analyze extensive user data for personalized interaction, then individualized responses improve, but system complexity increases
Solution Approach 1:
The patent segments user data and communication patterns into distinct categories and modules (e.g., user profiles, interaction histories, context databases, pattern recognition modules). This segmentation allows the system to manage complexity by organizing extensive data into structured, manageable components while still providing comprehensive individualized responses through the integrated analysis of these segments.
Solution Approach 2:
The patent implements universal data structures and processing mechanisms that can handle multiple types of user data and interaction patterns through a single framework. This multi-functional approach allows the cognitive system to process diverse information (user preferences, communication styles, contextual data) using unified algorithms and storage schemas, reducing overall system complexity while maintaining high adaptability for individualized responses.
Data Source
AI summary
Systems and methods for a cognitive system to interact with a user are provide. Aspects include receiving a cognitive system profile and observational data associated with the user. Environmental data associated with the user is received and features are extracted from the observations data and the environmental data. The features are stored in the user profile and analyzed to determine a situational context for each of the features based on the cognitive system profile and the user profile. Trigger events are identified based on the situational context for each of the features. One or more proposed actions are determined based at least in part on the one or more trigger events. At least one action is initiated from the one or more proposed actions and are stored in the user profile along with the one or more trigger events and the one or more features.