Enriching Calendar Events with Predicted User Location
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Solution Overview
Problem
Conventional computerized meeting and calendaring solutions lack enriched data on user location and event type, failing to leverage advances in computing technology and user device data to provide accurate and relevant information.
Innovation Solution
A system that detects user location patterns and calendar event patterns by monitoring user activity and sensors, predicting user location and event type for future events based on past data, and providing this information to virtual assistants or APIs for enhanced calendar management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional meeting and calendaring solutions are used, then basic calendar management is provided, but enriched calendar event data including user location and event type information is not available
Solution Approach 1:
The system performs preliminary analysis of past calendar events and user location patterns before future events occur. By pre-processing historical data to identify patterns and characteristics, the system prepares prediction models in advance, enabling enriched event data to be generated when needed without excessive real-time computational complexity
Solution Approach 2:
The system creates simplified representations (models) of past calendar events and user behavior patterns. These copied patterns serve as templates that can be efficiently applied to predict characteristics of future events, providing enriched data without requiring complete re-analysis of all historical information
2Loss of information
If enriched calendar event data is provided to all downstream consumers, then comprehensive information availability is achieved, but computing resources and processor load increase
Solution Approach 1:
The system provides enriched calendar event data selectively based on the specific needs and characteristics of different downstream consumers. Rather than uniformly providing all possible enriched data to all consumers, the system tailors the data delivery to match local requirements, reducing overall computational resource consumption while ensuring each consumer receives the information they need
Solution Approach 2:
The system pre-computes and stores enriched event data characteristics in advance, so that downstream consumers can access pre-processed information without requiring intensive real-time computation. This preliminary processing reduces the energy and processor load required at the point of data consumption
Data Source
AI summary
Computerized systems for providing a personalized computing experience are provided through enriched calendar event data. The enriched calendar event data provides an event type, additional location data for the calendar event, and the likely user attendance. To determine the enriched calendar event data, a user location pattern, and a calendar event pattern are determined. As future calendar events are detected, a set of features for the future calendar events is determined. Past calendar events having features similar to the detected future calendar event can then be determined. A user location for the similar past calendar events can be determined to form a user behavior pattern model indicating a user location for the similar past calendar events. A predicted user location for the future calendar event can be determined based upon the similar past calendar events and the user behavior pattern model.