Machine Learning List Recommendations for Task Completion
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Solution Overview
Problem
Users face inefficiencies in managing large lists of tasks or content items, leading to intimidation and reduced engagement, as existing systems fail to effectively prioritize or suggest actions for completing items, resulting in unfulfilled interests.
Innovation Solution
A machine learning engine processes user and list item data to create performance and threshold models, identifying indicators and weights to recommend actions such as notifications, graphical effects, and list positioning, which influence users to complete tasks by analyzing historical data and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lists grow to include more items, then users have more content to manage, but management becomes burdensome and inefficient
Solution Approach 1:
The patent segments the list into prioritized sections based on machine learning predictions. Items are divided into high-priority (likely to be completed) and low-priority (less likely to be completed) segments, allowing users to focus on manageable portions rather than facing the entire large list at once.
Solution Approach 2:
The machine learning engine acts as an intermediary between the user and the list items. It processes user data and item characteristics to automatically assign priorities and predictions, mediating the interaction so users don't need to manually evaluate each item's importance.
2Quantity of substance
If lists grow larger, then users have more content of interest, but it becomes intimidating to follow up on initial interest
Solution Approach 1:
The patent applies local quality by customizing the presentation and priority of individual items within the list based on their specific characteristics and user interaction history. Each item receives differentiated treatment (priority level, visibility, interaction cues) appropriate to its likelihood of being completed, reducing the intimidating effect of uniform large lists.
Solution Approach 2:
The system incorporates feedback loops where user interactions with list items (viewing, completing, skipping) feed back into the machine learning model. This continuous learning refines predictions and adjusts item prioritization over time, making the list more responsive to user preferences and reducing feelings of overwhelm as the system adapts to individual usage patterns.
3Adaptability or versatility
If no prioritization is provided, then users see all items equally, but users fail to complete items due to lack of guidance
Solution Approach 1:
The patent implements dynamic prioritization where the system continuously adjusts item ordering and visibility based on real-time data including user behavior patterns, item characteristics, and contextual factors. The list transforms from a static equal-visibility structure to a dynamic arrangement that adapts to maximize completion likelihood while maintaining versatility in item presentation.
Data Source
AI summary
A machine learning engine may correlate user profile data and/or list item data with a service provider action that may lead to a particular user action with respect to a list item (e.g., a task item of a to-do list, a content item of a content item queue, etc.). For example, a task item performance application may process user profile data and/or task item profile data to generate training data. In addition, the machine learning engine may generate a task item performance model using the training data by identifying indicators that correlate with the particular user action. Additionally, the task item performance application may then use the task item performance model to suggest actions that may be performed by a service provider to increase the likelihood of the user performing the particular user action with respect to the list item. Further, the machine learning engine may also determine weights for individual indicators.