Cognitive Profile Matching for Intent-Based Collaboration
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Solution Overview
Problem
Conventional techniques fail to connect users within organizations on a deeper level based on their intent and activities, as they primarily focus on ingesting data without continuous monitoring and interaction, limiting the discovery and collaboration of connections and opportunities.
Innovation Solution
A computer-implemented cognitive assisting method that determines a user's intent from identifiable attributes of their content, builds a cognitive profile, and detects similarities with other users' profiles to suggest connections, enabling collaboration and knowledge sharing within an organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional techniques ingest data to connect users with information, then information accessibility is improved, but user interaction depth and intent-based connections deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously monitoring user activities and pre-building cognitive profiles that capture user intents, expertise, and interests before connection needs arise. This allows the system to have user intent information ready when connection opportunities appear, enabling deep intent-based matching rather than reactive information retrieval.
Solution Approach 2:
The system implements feedback loops by continuously monitoring user responses to connection suggestions and using this information to refine cognitive profiles. User interactions, acceptance patterns, and engagement metrics feed back into the profile building process, improving the accuracy of intent detection and connection recommendations over time.
2Measurement precision
If the system continuously monitors and builds cognitive profiles, then connection accuracy based on intent is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of cognitive profile building into distinct functional modules: activity monitoring components, intent detection components, profile building components, and similarity matching components. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while achieving high precision in intent detection.
Solution Approach 2:
The patent introduces cognitive profiles as intermediary data structures that mediate between raw user activity data and connection recommendations. These profiles serve as a simplified representation layer that captures essential user characteristics without requiring the full complexity of raw activity data to be processed for every connection decision.
3Productivity
If conventional techniques facilitate discovery between individuals, then collaboration opportunities are improved, but depth of understanding user intent deteriorates
Solution Approach 1:
The system adds a new dimension to user representation by incorporating intent, context, and cognitive characteristics beyond basic demographic or role information. This multi-dimensional cognitive profiling enables the system to discover collaboration opportunities based on deeper understanding of what users are actually working on and what they need, rather than just matching superficial attributes.
Data Source
AI summary
A cognitive assisting method, system, and computer program product, includes building a first cognitive profile of a first user by determining content with identifiable attributes that may be incorporated into the cognitive profile and detecting a current activity of the first user and continually finding a match to a second cognitive profile of a second user in a repository of cognitive profiles belonging to others.