ML-Based Access Configuration via Timing Data
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Solution Overview
Problem
Current technologies fail to provide personalized program access settings to users, leading to user frustration and dissatisfaction due to generic options that do not align with individual user access levels.
Innovation Solution
The system utilizes interaction and timing data to generate interaction datasets and category timing datasets, which are then used to train a machine learning model. This model predicts the length of time a user will be in an inactive state, allowing for personalized access settings configuration based on predicted user activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic access options are provided to all users, then the system is simple to operate and maintain, but user satisfaction decreases because options do not align with individual access levels
Solution Approach 1:
The system automatically configures access settings by analyzing user interaction data and timing patterns, eliminating the need for manual user configuration. The machine learning model autonomously determines appropriate access levels based on observed usage patterns, making the system both simple to operate and highly personalized.
Solution Approach 2:
The system dynamically adjusts access parameters based on user behavior data. By monitoring interaction frequency, timing patterns, and usage intensity, the system modifies access settings in real-time to match actual user needs, achieving both simplicity and personalization.
2Adaptability or versatility
If personalized access settings are implemented based on user data analysis, then user satisfaction improves, but system complexity increases due to data collection and machine learning model requirements
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes interaction data, predicts user needs, determines access levels, and configures settings automatically. This multi-functionality consolidates what would otherwise require separate systems into a single unified component, managing complexity while enabling personalization.
Solution Approach 2:
The system continuously monitors user interactions and uses this feedback to refine access settings. The machine learning model learns from observed patterns and adjusts configurations dynamically, creating a self-improving system that becomes more accurate over time without requiring manual intervention.
3Adaptability or versatility
If access settings are manually configured for each user, then personalized access is achieved, but time consumption increases due to manual configuration requirements
Solution Approach 1:
The system performs preliminary analysis of user interaction patterns and pre-configures access settings before users need them. By proactively analyzing behavior data and predicting requirements, the system has access settings ready in advance, eliminating configuration delays.
Solution Approach 2:
The machine learning model automatically configures access settings without human intervention, making the system self-serve the personalization need. This eliminates the time-consuming manual configuration process while maintaining high levels of personalization.
Data Source
AI summary
In certain embodiments, access to a plurality of sites associated with a plurality of users may be obtained. The plurality of sites may comprise categories of the plurality of users and interactions among the plurality of users. Based on the plurality of sites, a plurality of interaction datasets and a plurality of timing datasets may be generated. The plurality of interaction datasets and the plurality of timing datasets may be provided as inputs to a machine learning model and the machine learning model may be configured based on the inputs. Subsequent to the configuration, an interaction dataset and a timing dataset associated with a user may be provided to the machine learning model. A predicted length of time for the user may be obtained via the machine learning model and one or more settings of a program may be configured to the user based on the predicted length of time.