Thought Object Distribution via Custom Filtering and Diversification
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Solution Overview
Problem
Existing systems face challenges in ensuring a diverse and unbiased distribution of qualitative responses from user devices, leading to incomplete views of a subject, as they often rely on previous user interactions and fail to provide equal coverage of thought objects.
Innovation Solution
A network-connected system and method that computes a distribution strategy for thought objects, using filtering and selection algorithms to ensure equal coverage and diversity, involving custom selection, random selection, and topic-based filtering to redistribute thought objects to user devices based on priority values received from participant devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If thought objects are distributed based on previous user interactions and engagements, then user engagement and relevance are improved, but diversity and equal coverage of thought objects deteriorate
Solution Approach 1:
The system dynamically changes the distribution parameters by introducing diversity scores and equal coverage metrics that modify how thought objects are allocated. Instead of relying solely on historical engagement data, the system adjusts distribution parameters to include measures of underrepresentation and topic diversity, ensuring that thought objects are not only shown based on past interactions but also to maximize overall coverage and representativeness across the user base.
2Adaptability or versatility
If a themed fashion display based on previous selections is used, then personalization and user interest are improved, but completeness and unbiased view of the subject deteriorate
Solution Approach 1:
The system segments the thought object distribution into multiple independent dimensions: personalization based on user history, diversity based on topic coverage, and equal coverage based on overall representation. Each dimension is calculated and applied separately, allowing the system to maintain personalized themed displays while simultaneously ensuring that no single theme or perspective dominates to the exclusion of others, thus preserving completeness and unbiased views.
3Ease of operation
If qualitative responses are solicited without a structured distribution strategy, then response freedom and detail are improved, but distribution efficiency and coverage completeness deteriorate
Solution Approach 1:
The system implements a feedback loop where the distribution strategy continuously monitors and adjusts based on the cumulative state of thought object exposure across all users. As qualitative responses are collected and processed, the system updates its understanding of which thought objects have been adequately represented and which remain underrepresented, using this feedback to dynamically adjust future distribution decisions. This maintains response freedom while improving distribution efficiency through data-driven optimization.
Data Source
AI summary
Systems and methods for processing qualitative responses from a plurality of user devices whereby a selection of a next thought object, to deliver to a first user device, may be based on a plurality of qualitative responses received from a plurality of user devices. In a preferred embodiment, a thought object selection computer may compute the selection by determining a filtered set of thought objects by custom selection. In some embodiments, if the quantity of the filtered set of thought objects is greater than a pre-configured amount the selection may be computed by randomly selecting a subset of the filtered set of thought objects. Further filtering the filtered set of thought objects by determining one or more least seen thought objects, and selecting a most diverse thought object, updating the filtered set of thought objects and sending the filtered set of thought objects to the first user device.