Communication Event Prediction System Using Behavioral Pattern Analysis
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Solution Overview
Problem
The complexity of scheduling and managing communication events, such as meetings and conferences, becomes cumbersome due to the numerous factors involved, making it difficult to quickly and efficiently find relevant information and resources during or before the event.
Innovation Solution
A system that analyzes past behavior patterns to predict and suggest subjects, logistics, and resources associated with communication events, allowing users to confirm predictions via input, and iteratively updates suggestions based on user modifications, providing ranked listings of likely subjects, logistics, and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search through previous communication events and resources to find relevant information, then they can locate needed details, but the time and effort required increases significantly as the number of past events grows
Solution Approach 1:
The system performs preliminary analysis of past communication events, user behavior patterns, and resource usage before the user needs the information. By pre-processing and storing structured data about previous events, participants, attachments, and outcomes, the system prepares prediction-ready data structures in advance, enabling rapid retrieval and prediction without manual searching when the user needs to create or join a new communication event.
Solution Approach 2:
The system incorporates feedback loops where user interactions with predicted subjects, logistics, and resources are monitored and used to refine future predictions. When users confirm or modify predicted information, this feedback is fed back into the machine learning models to improve accuracy over time, creating a self-improving system that becomes more precise as it accumulates usage data.
2Adaptability or versatility
If the system provides comprehensive suggestions for all possible subjects, logistics, and resources, then users have more options, but the complexity of the interface and information overload increases
Solution Approach 1:
The system applies local quality by providing different levels and types of suggestions in different interface areas based on user needs and context. Rather than uniformly presenting all possible suggestions everywhere, the system tailors the density and specificity of suggestions to local contexts - such as providing more detailed logistics suggestions when creating events versus simpler reminders during active events - thereby maintaining versatility while managing interface complexity.
Solution Approach 2:
The system implements partial action by presenting a curated subset of the most relevant predictions rather than all possible suggestions. The machine learning models rank predictions by relevance probability, and the interface displays only the top-ranked suggestions that exceed a certain confidence threshold, allowing users to access comprehensive functionality through a simplified interface that shows only the most likely useful options.
3Productivity
If the system automatically populates all fields based on predictions, then the creation process is faster, but users lose control and ability to customize their events
Solution Approach 1:
The system implements self-service by providing intelligent defaults that automatically populate event fields based on predictions from past behavior patterns, while simultaneously enabling users to easily modify any prediction. The system serves itself by learning from historical data and making informed suggestions, but the user retains full control to adjust or reject any suggestion, combining automated efficiency with human oversight.
Solution Approach 2:
The system applies dynamics by making the prediction confidence levels and suggestion firmness adjustable and adaptive. As users interact with the system and confirm or modify predictions, the system dynamically adjusts its behavior - becoming more or less aggressive in auto-population based on user preferences and the reliability of predictions in specific contexts, thereby balancing speed and control based on real-time conditions.
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for predicting the subject, logistics, and resources of associated with a communication event. Predictions and suggestions can occur prior to, during, or in response to communication events. The user can confirm the prediction or suggestion via user input such as a click or a voice command. The system can analyze past behavior patterns with respect to the subject, logistics and resources of communication events, followed by preparing ranked listings of which subjects, logistics, and resources are most likely to be used in a given situation. The predicted logistics may then include people to invite, time and date of the meeting, its duration, location, and anything else useful in helping potential participants gather together. The resources may include files attached, files used, communication event minutes, recordings made, Internet browsers and other programs which may be utilized by the user.