Graph-Based User Data Discovery Across Productivity Applications
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Solution Overview
Problem
Conventional methods for locating pertinent data require multiple applications and extensive manual input, leading to computational burden and frustrating user experiences.
Innovation Solution
A computing system utilizes graph intelligence to cluster user data into productivity areas, identifying and presenting relevant people, documents, and derived information through a single graphical user interface, reducing the need for manual searches across multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple applications are employed to locate data, then data completeness is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent merges multiple applications (email, calendar, messaging, document storage) into a unified graph-based interface. The graph data model integrates entities and relationships from all applications into a single structure, allowing users to access consolidated information through one interface rather than switching between multiple applications, thus reducing device complexity while maintaining data completeness.
Solution Approach 2:
The graph-based interface provides universal access to data from multiple applications through a single unified structure. The system performs multi-functionality by querying the graph for information across different application domains (emails, calendar events, messages, documents) using a consistent interface, eliminating the need for users to navigate separate application-specific search mechanisms.
2Loss of information
If multiple applications are used to locate data, then data comprehensiveness is improved, but ease of operation deteriorates
Solution Approach 1:
The graph-based system performs self-service by automatically traversing the graph structure to identify and present relevant information based on user queries. The system autonomously navigates relationships between entities (people, documents, meetings, messages) without requiring users to manually search through multiple applications or specify detailed search criteria, thus improving ease of operation while maintaining data comprehensiveness.
Solution Approach 2:
The graph data model acts as an intermediary between the user and the underlying data stored in multiple applications. Instead of users directly querying each application separately, the graph interface mediates by translating user queries into graph traversals that automatically aggregate relevant information from all applications, simplifying the user interaction while preserving comprehensive data access.
3Ease of operation
If conventional search methods are used, then user control over search results is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-building and maintaining the graph data model that indexes all entities and relationships across applications. This pre-processed graph structure enables rapid queries without requiring time-consuming searches through raw data in multiple applications. The graph is updated in real-time as data changes, ensuring search results remain current while reducing search time.
Solution Approach 2:
The graph-based interface provides feedback by dynamically adjusting search results based on user interactions and graph traversal depth. The system can refine results based on user selections and preferences, automatically navigating the graph to present increasingly relevant information. This feedback mechanism maintains user control over results while significantly reducing the time required to find pertinent information compared to conventional search methods.
Data Source
AI summary
A computing system identifies a heterogenous multi-entity graph user graph for a user based upon an identifier for the user. The user graph includes nodes and edges connecting the nodes. The nodes include topic nodes representing topics and entity nodes. The entity nodes represent people associated with the user, documents of the user, or derived information that is derived from the documents. The computing system identifies a cluster of the topic nodes corresponding to a productivity area of the user and performs a walk of the user graph based upon the subset of the topic nodes to identify a subset of the people, the documents, and the derived information. The computing system causes a graphical user interface (GUI) to be presented on a display, where the GUI includes identifiers for the subset of the people, the documents, and the derived information.