Multi-Channel Interaction Analysis System for Collaboration
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Solution Overview
Problem
Current systems lack an efficient method to analyze electronic data for identifying relevant collaboration opportunities and contacts across multiple communication channels, limiting their ability to provide proactive and responsive communication for users.
Innovation Solution
A system that integrates multi-channel inputs, including textual and audio communication, to analyze interaction data and metadata, generating weighted scores for parties and subjects, and storing these for future reference, allowing seamless communication and authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple communication channels are integrated for data analysis, then the system's ability to identify collaboration opportunities is improved, but the system complexity increases
Solution Approach 1:
The patent combines multiple communication channels (email, calendar, contacts, messaging) into a single unified system that analyzes interaction data across all these channels simultaneously. This merging approach enables comprehensive collaboration opportunity identification while managing complexity through integrated architecture.
Solution Approach 2:
The system is designed to perform multiple functions: analyzing interaction data, generating weighted scores, identifying collaboration opportunities, and providing recommendations across diverse communication channels. This multi-functional design allows a single system to handle various communication types without requiring separate specialized systems.
2Measurement precision
If interaction data from multiple sources is analyzed, then the accuracy of identifying relevant parties is improved, but the data processing time increases
Solution Approach 1:
The system pre-calculates and stores weighted scores for interaction parties based on historical data from multiple communication channels. These pre-computed scores are readily available when collaboration opportunities need to be identified, eliminating the need for real-time analysis of all historical data and thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with machine learning algorithms that can efficiently analyze large volumes of interaction data. The system uses automated scoring mechanisms and pattern recognition to quickly identify relevant parties without manual intervention, significantly reducing processing time while improving identification accuracy.
3Reliability
If comprehensive interaction metadata is stored, then the ability to generate accurate weighted scores is improved, but the storage requirements increase
Solution Approach 1:
The system extracts and stores only the most relevant features from comprehensive interaction metadata, such as frequency of interaction, recency, and types of communication. By selecting only the critical data elements needed for accurate weighted score calculation, the system maintains reliability while reducing storage requirements compared to storing all raw interaction data.
Solution Approach 2:
The patent transforms comprehensive interaction metadata into condensed parameters and metrics that capture the essential information needed for scoring. Instead of storing raw communication logs, the system stores aggregated parameters like interaction frequency, diversity of communication channels, and engagement patterns, which maintain scoring accuracy while occupying less storage space.
Data Source
AI summary
Embodiments of the present invention provide systems and methods for automated and intelligent analysis of information. The system receives interaction data, interaction metadata, and external information in order to identify parties of interactions, subjects of interactions, and infer relationships between parties and subjects based on the content, context, frequency, and amount of available interaction data. Weighted score scores are generated and used to rank the inferred relationships and determined relevance between parties and subjects. This data may be stored in a graphical database and later used to response to user data queries to facilitate collaboration.