Cognitive Model for Predicting Activity Consequences
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Solution Overview
Problem
Current cognitive modeling techniques are limited in predicting the probable consequences of activities and generating effective action steps to mitigate these consequences, as they often focus on single phenomena or specific events, failing to provide comprehensive analysis across multiple activities and their impacts.
Innovation Solution
A computer-implemented method that collects data from various sources to identify activity patterns linked to events, generates a cognitive model using text mining and natural language processing, and recommends action steps to reduce the impact of probable consequences by analyzing activity context information, including involved parties, location, and timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cognitive modeling focuses on single cognitive phenomenon or specific event, then the model can be simplified and easier to implement, but the predictive capability is limited and cannot provide comprehensive analysis across multiple activities
Solution Approach 1:
The patent creates a universal cognitive modeling system that can handle multiple cognitive phenomena and event types through a common framework. The system uses standardized data structures and processing mechanisms that work across diverse activities, making the model multi-functional while maintaining manageable complexity through reusability of core components
Solution Approach 2:
The patent segments the cognitive modeling process into distinct modular components: data collection module, pattern detection module, context extraction module, and prediction module. Each module handles specific aspects of the analysis independently, allowing the system to scale to multiple activities without proportionally increasing overall complexity
2Measurement precision
If comprehensive data from multiple sources is collected and analyzed, then the prediction of activity consequences becomes more accurate, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing data from multiple sources, pre-detecting patterns in historical data, and pre-extracting context information before actual prediction is needed. This preparation work is stored and reused when making predictions, reducing real-time processing requirements while maintaining high accuracy
Solution Approach 2:
The patent introduces intermediary structures such as pattern databases and context indexes that mediate between raw multi-source data and the prediction engine. These intermediaries pre-process and structure the comprehensive data, making it more efficient to query and analyze without requiring direct processing of all raw data sources for each prediction
Data Source
AI summary
Predicting probable activity consequences is provided. Information is collected from data sources to identify various activities. Patterns of how any identified activity is linked with a corresponding event are detected based on analyzing the information. The patterns are indexed with data having a relationship to a particular event. Activity context information associated with a set of identified activities corresponding to the particular event is extracted from the information. A cognitive model of how the set of identified activities corresponding to the particular event are related to a set of activity consequences is generated. Probable activity consequences with degree of severity corresponding to the activity context information is predicted based on the cognitive model. A recommendation to perform a set of action steps to reduce impact of the probable activity consequences on different aspects of the activity context information associated with the set of identified activities is generated.