Contextualizing Engine for Organizational Data Identification
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Solution Overview
Problem
Users in organizations face inefficiencies in finding relevant information about individuals they have not met or worked with closely, as this information is scattered across various systems, making it time-consuming and difficult to discover.
Innovation Solution
An automated system and method that analyzes activity signals across different workloads to establish relationships between people and enterprise data, identifying unfamiliar individuals and providing contextual information through a graph structure and contextualizing engine, which surfaces relevant information to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search for information about individuals across various systems, then they can find relevant information, but it is time-consuming and inefficient
Solution Approach 1:
The patent introduces an automated identification and contextualization system that acts as an intermediary between users and scattered organizational data. The system automatically analyzes activity signals, builds graph structures representing relationships, and retrieves relevant information about individuals without requiring manual user search across multiple systems.
Solution Approach 2:
The system enables self-service by automatically performing the information retrieval task that would otherwise require user effort. The automated analysis of activity signals and generation of contextual information about individuals allows the system to serve itself in finding and presenting relevant data, eliminating the need for users to manually search across various workloads and storage systems.
2Quantity of substance
If information about individuals is scattered across various workloads and storage systems, then comprehensive information is available, but it is difficult to discover and access
Solution Approach 1:
The patent merges data from multiple scattered sources (email accounts, calendars, social feeds, intranet sites, network file systems) into a unified graph structure. This graph structure integrates information about individuals and their relationships across all workloads and storage systems, making comprehensive information accessible through a single interface rather than requiring users to search across disparate systems.
Solution Approach 2:
The automated identification and contextualization system serves as an intermediary that accesses scattered information across various systems and presents it in a consolidated, easily accessible format. The system's ability to query the graph structure and retrieve relevant information about individuals simplifies access to data that would otherwise be distributed across multiple difficult-to-navigate systems.
3Quantity of substance
If users encounter hundreds of documents and information items, then they have access to comprehensive data, but it is time-consuming to find relevant information
Solution Approach 1:
The patent applies local quality by providing customized, context-specific information about individuals based on the user's needs and the specific information item being viewed. Rather than presenting all available data uniformly, the system tailors the information presentation to the local context, showing only the most relevant details about an individual's background, experience, and relationships that pertain to the current task.
Solution Approach 2:
The system uses partial action by retrieving and presenting only the essential information about individuals that is relevant to the user's current context, rather than exhaustively displaying all available data. This selective approach to information retrieval maintains productivity by avoiding information overload while still providing sufficient context for effective decision-making.
Data Source
AI summary
An identification and contextualization system comprising a contextualizing engine is provided. The contextualizing engine comprises various components that are operable to analyze an information item related to a user for identifying one or more individuals associated with the information item, and interrogate a graph structure for determining whether each of the one or more individuals associated with the information item is someone whom the user has met or has worked with based on edges and weights stored in the graph structure. When an individual who is associated with the information item is identified, the contextualizing engine is further operable to discover contextual information associated with the individual, and generate an information element surfacing the contextual information associated with the individual for display to the user.