Event-Based Collaboration Score Updates

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Solution Overview

Problem

Legacy approaches in distributed computing and storage platforms are inefficient in identifying timely and relevant collaboration candidates, leading to delays and wasteful consumption of resources, as they fail to adapt to dynamic user and content environments.

Innovation Solution

The implementation of event-based recommendations that incrementally update user-to-user collaboration scores in response to current user-to-object interaction events, allowing for real-time collaboration suggestions by processing streams of event objects and using time-decayed interaction weights to quantify user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If legacy techniques are used to identify collaboration candidates based on historical user-to-object interactions, then collaboration candidates can be identified, but the identification is slow and consumes significant computing, storage, and networking resources

Engineering Contradiction:

Improvecollaboration candidate identification speedVSAvoidcomputing and storage resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system segments the collaboration identification process into incremental updates triggered by specific user-to-object events. Instead of periodically reprocessing all historical data, the system divides the workload into small, event-driven updates that only recalculate scores for affected user pairs, dramatically reducing computational overhead while maintaining real-time relevance

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system pre-calculates and maintains collaboration scores incrementally as user interactions occur. By continuously updating scores in the background as events are generated, the system prepares collaboration recommendations in advance so that when a user needs suggestions, they are immediately available without requiring intensive on-demand computation

Inventive Principle:

Principle #10Preliminary action

2Reliability

If legacy techniques identify collaboration candidates based on historical interactions, then collaboration suggestions can be generated, but the suggestions may no longer be relevant due to delays

Engineering Contradiction:

Improverelevance of collaboration suggestionsVSAvoiddelay between user-to-object event and collaboration suggestion

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements continuous feedback loops where user-to-object events immediately trigger score recalculations. Each interaction event feeds back into the collaboration score computation, ensuring that the system constantly adapts to current user behavior patterns and maintains up-to-date collaboration recommendations that reflect the latest interactions

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system maintains continuous collaboration score updates as user interactions occur, rather than performing batch processing at intervals. This continuous action ensures that collaboration suggestions are always based on the most recent user behavior, eliminating delays and maintaining relevance without requiring periodic reprocessing

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If all past user-to-object interaction events are stored and processed, then complete collaboration scores can be calculated, but storage and processing demands increase significantly

Engineering Contradiction:

Improvecollaboration score accuracyVSAvoidstorage and processing resources

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system extracts only the essential interaction data needed for collaboration scoring, storing minimal event information (user IDs, object IDs, and interaction types) rather than complete event logs. By extracting only the critical elements required for score calculation, the system maintains accurate collaboration measurements while dramatically reducing storage requirements and processing overhead

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20230401537A1Forming event-based recommendations

Publication Date: 2023.12.14 BOX INC

AI summary

Systems and methods for forming collaboration recommendations. Techniques for forming event-based recommendations use time-decayed event values. A shared content management system supports a plurality of users that generate events by interacting with content objects of the shared content management system. Events over the content objects are captured as event objects. Method steps are invoked upon receiving event objects that describes user-to-object interaction events that arise from interactions by users over content objects. Different types of interactions carry different importance values. The importance values can be applied as weights when scoring user-to-object interaction activities. The importance can decay over time. As time progresses and as the importance of older interactions decay, score components of a user-to-object interaction can be updated based at least in part on a time decay function. The system emits collaboration recommendations based on the decayed user-to-user collaboration scores.