Machine-Learned Calendar Event Suggestions from Historical Interactions
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Solution Overview
Problem
Conventional event scheduling techniques are inefficient and require users to manually analyze large volumes of historical data to determine when and with whom to schedule future events, leading to suboptimal user experiences and excessive time consumption.
Innovation Solution
A communication platform utilizes a machine-learned model to analyze historical data and suggest recommended calendar events, allowing users to quickly identify optimal scheduling opportunities based on previous interactions and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually analyze large volumes of historical data to schedule events, then scheduling accuracy may improve, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical analysis of historical data with an automated machine learning system. The ML model processes historical interaction data to predict optimal scheduling opportunities, eliminating the need for users to manually analyze large volumes of data while maintaining or improving scheduling accuracy through algorithmic pattern recognition.
Solution Approach 2:
The system performs self-service by automatically analyzing historical data and generating event suggestions without requiring user intervention in the data analysis process. The machine learning model autonomously processes past interactions, identifies patterns, and proposes optimal scheduling times, freeing users from manual analysis tasks.
2Ease of operation
If conventional manual scheduling techniques are used, then user control over scheduling remains high, but productivity decreases due to excessive time required
Solution Approach 1:
The machine learning model acts as an intermediary between historical data and scheduling decisions. It processes raw historical interaction data, transforms it into actionable insights, and presents optimized scheduling suggestions to users, thereby maintaining user control while significantly improving productivity by eliminating manual analysis steps.
Solution Approach 2:
The system performs preliminary action by pre-analyzing historical data and pre-computing optimal scheduling recommendations before users need to schedule events. The machine learning model processes past interactions and generates suggested event times in advance, allowing users to quickly accept or modify suggestions rather than performing manual analysis at scheduling time.
3Productivity
If historical data is analyzed to generate event suggestions, then scheduling optimization improves, but computing resource usage increases
Solution Approach 1:
The patent applies parameter changes by transforming raw historical interaction data into processed features and patterns that the machine learning model can efficiently analyze. By pre-processing and structuring data into meaningful parameters (such as interaction frequency, timing patterns, and participant preferences), the system reduces the computational burden during event suggestion generation while maintaining optimization quality.
Data Source
AI summary
Techniques for generating calendar events based on historical data are described herein. A communication platform may evaluate historical data and suggest one or more calendar events to a user profile. In some examples, the communication platform may receive historical data representative of previous activity between a user profile and other user profile(s). The communication platform may analyze the historical data to generate a recommended calendar event. That is, the communication platform may input the historical data into a machine-learned model trained to output one or more recommended calendar events. In such cases, the communication platform may display the recommended calendar event(s) to the user profile. In response to displaying the recommended calendar event, the communication platform may receive user input data representing an intent to generate one or more events corresponding to the recommended calendar event(s). Accordingly, the communication platform may generate the event(s) based on the user input data.