Communication Analytics Server for Remote Team Pattern Identification
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Solution Overview
Problem
Organizations face challenges in identifying and promoting effective communication patterns among remote teams, as existing methods lack practical means to efficiently review interactions across various enterprise communication tools, hindering the formation of successful project teams and knowledge sharing.
Innovation Solution
A server system that extracts communication records from multiple tools, converts them into a normalized format, and uses graph data structures and clustering techniques to identify and characterize communication patterns, forming teams and inter-project communication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If communication records are collected from multiple enterprise communication tools, then the quantity and diversity of communication data increases, but the complexity of data processing and analysis increases
Solution Approach 1:
The system segments communication data from different enterprise tools (email, instant messaging, collaboration platforms) into separate processing streams, normalizing each data type independently before integration. This segmentation allows the system to handle diverse communication formats without overwhelming complexity, processing each tool's data structure separately while maintaining unified analysis capabilities.
Solution Approach 2:
The patent introduces an intermediary normalization layer that translates various communication tool data formats into a unified structure. This intermediary component sits between the diverse communication sources and the analysis engine, converting different data schemas into a common format that enables seamless cross-tool communication analysis without requiring complex point-to-point integration logic.
2Measurement precision
If manual review of communication interactions is performed, then analysis accuracy can be maintained, but the time required for review increases significantly
Solution Approach 1:
The system replaces manual mechanical review processes with automated computational analysis. Machine learning models and natural language processing algorithms automatically examine communication patterns, extracting insights from large volumes of data without human intervention. This substitution maintains high analysis accuracy through sophisticated algorithms while eliminating the time-consuming nature of manual review.
Solution Approach 2:
The patent creates simplified copies or representations of complex communication data through structured extraction and normalization. Instead of analyzing raw, unstructured communication logs manually, the system generates standardized data models that capture essential communication patterns, making automated analysis both accurate and efficient while reducing the cognitive load that would require manual review.
3Adaptability or versatility
If communication data from all enterprise tools is integrated, then comprehensive communication patterns can be identified, but data normalization and compatibility challenges increase
Solution Approach 1:
The system implements a universal data normalization framework that handles multiple communication tool formats through a single integrated approach. This universal layer provides common data structures, validation rules, and transformation logic that work across all communication sources, enabling comprehensive pattern identification without requiring separate integration pathways for each tool type.
Solution Approach 2:
The patent applies parameter transformation techniques to convert diverse communication data into a standardized format. By changing the parameters of different data structures (field names, data types, hierarchical relationships) into a common parameter set, the system achieves compatibility across tools while preserving the essential characteristics needed for communication pattern analysis.
4Measurement precision
If clustering techniques are applied to identify communication teams, then team formation insights are improved, but computational processing requirements increase
Solution Approach 1:
The system performs preliminary data preprocessing and feature extraction before applying clustering algorithms. By pre-processing communication data to extract relevant features (communication frequency, interaction patterns, network centrality) and organize data into optimized structures, the system reduces the computational burden of subsequent clustering operations while maintaining high team identification accuracy.
Solution Approach 2:
The patent extracts only the essential features and metadata needed for team identification from complete communication datasets. By taking out and focusing on critical parameters (communication patterns, interaction frequencies, network relationships) rather than processing all raw data, the system achieves accurate clustering results with reduced computational requirements.
Data Source
AI summary
Embodiments of this disclosure relate to systems and methods for determining a set of one or more identified characteristics correlated with high performing projects. Methods include receiving communication data from a plurality of servers, the communication data associated with a plurality of conversations involving one or more users. The communication data is converted into a common format and used to generate a graph, the graph based upon characteristics identified in the communication data and users involved with the plurality of conversations. The communication data can be clustered according to the characteristics and the users, thereby generating one or more clusters around at least one of a characteristic and a user. User data and project data can be generated based on the one or more clusters and be used to determine the set of one or more identified characteristics correlated with high performing projects.