Collaboration Event Data Filtering via Proximity and Calendar Detection
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Solution Overview
Problem
Users face difficulties in locating relevant information during collaboration events due to the vast amount of data generated, especially in virtual and in-person contexts, as existing methods require determining actual collaborations and related data, which can be complex and prone to missing relevant information.
Innovation Solution
A method and system that determine a collaboration event by presuming its occurrence and identifying data common to a predetermined percentage of users, providing data related to the event without requiring actual collaboration, using techniques such as NFC, GPS, and calendar entries to detect user proximity and communication sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search through vast amounts of data to locate relevant information during collaboration events, then information completeness may be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by proactively monitoring collaboration events, identifying participants, and pre-fetching potential relevant data before users need it. The server automatically detects collaboration events through various triggers (calendar events, communication sessions, proximity detection) and prepares candidate data sets in advance, so when users need information, it is already available rather than requiring manual search through vast data stores.
Solution Approach 2:
The patent introduces an intermediary system (server 104) that acts as a mediator between users and the vast data stores. This intermediary automatically processes the complex task of filtering and selecting relevant data from multiple sources (emails, documents, calendars, communications) based on collaboration event context, relieving users of the burden of manual information searching while ensuring comprehensive information retrieval.
2Measurement precision
If the system determines actual collaborations and related data using complex methods, then data accuracy may be improved, but system complexity increases
Solution Approach 1:
Instead of starting with complex collaboration detection algorithms to identify actual collaborations and then finding related data, the patent inverts the approach: it first identifies collaboration events through simpler, more direct methods (calendar events, active communication sessions, proximity detection via NFC/GPS), then automatically determines relevant data based on these established events. This inversion simplifies the system architecture while maintaining data accuracy.
Solution Approach 2:
The patent segments the complex task of collaboration detection into multiple independent, simpler components: (1) detecting collaboration events through various independent channels (calendar, communication, proximity), (2) identifying participants in each event, (3) retrieving data related to each participant and event type, and (4) presenting synthesized results. This segmentation reduces system complexity by breaking down the monolithic complex detection problem into manageable modular components.
Data Source
AI summary
A computing device determines a collaboration event in which users are participating. For instance, the users' computing devices may be located within a same location, the users' communication devices may be currently engaging in a common communication session, or calendar entries of the users' calendars may indicate that they are currently participating in a common session. The computing device determines data related to the collaboration event, and may filter this data to yield the data most related to the collaboration event. The computing device provides the data related to the collaboration event to at least one of the users participating in the collaboration event.