Chat Response Ranking Using Overton Windows and Polarization Scores

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Solution Overview

Problem

Conventional chatbot systems struggle to balance user preferences with societal perspectives, leading to a lack of accuracy and credibility in recommending relevant and modulated responses.

Innovation Solution

A chat recommendation server uses machine learning models to determine user-specific and general Overton windows, generating candidate responses that minimize the distance between user preferences and societal acceptability, thereby selecting a recommended response that reflects both perspectives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the platforms recommend polarizing content to cater to user interests, then user engagement is improved, but societal credibility deteriorates

Engineering Contradiction:

Improveuser engagementVSAvoidsocietal credibility

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent introduces an intermediary mechanism that embeds both user preferences and societal perspectives into the recommendation system. This intermediary layer processes user inputs through multiple lenses (user profile, societal norms, topic sensitivity) to generate recommendations that balance engagement with credibility, rather than directly choosing between polarizing or vanilla content

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically adjusts recommendation parameters based on the intersection of user-specific Overton windows and societal Overton windows. By changing the parameters of content selection (polarization level, topic sensitivity, personalization degree) based on real-time analysis of both user and societal contexts, the system optimizes for both engagement and credibility simultaneously

Inventive Principle:

Principle #35Parameter changes

2Reliability

If the platforms remove polarizing content and replace it with vanilla content, then societal credibility is improved, but user engagement deteriorates

Engineering Contradiction:

Improvesocietal credibilityVSAvoiduser engagement

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The recommendation system transitions from a static approach (fixed vanilla or polarizing content) to a dynamic system that continuously adapts recommendations based on the user's Overton window and societal Overton window. The system dynamically adjusts content polarization levels while maintaining credibility, rather than permanently removing polarizing content

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes recommendation parameters based on the analysis of user preferences and societal norms. By adjusting parameters such as content polarization, personalization degree, and topic sensitivity dynamically, the system maintains societal credibility while preserving user engagement through context-appropriate content

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the system provides highly personalized responses, then user preference alignment is improved, but distance from societal norms increases

Engineering Contradiction:

Improveuser preference alignmentVSAvoidsocietal norm adherence

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent adds another dimension to personalization by considering not just user preferences but also societal perspectives. Instead of personalizing along a single dimension (user interest), the system personalizes in a multi-dimensional space that includes user Overton window, societal Overton window, and their intersection, achieving both personalization and societal alignment

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

4Reliability

If the system provides vanilla responses, then societal norm adherence is improved, but user preference alignment deteriorates

Engineering Contradiction:

Improvesocietal norm adherenceVSAvoiduser preference alignment

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts the degree of personalization and vanilla-ness based on real-time analysis of user and societal contexts. Rather than providing fixed vanilla responses, the system adapts its output to match user preferences within societal boundaries, making the recommendation style dynamic rather than static

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12596886B2Personalized responses to chatbot prompt based on embedding spaces between user and society

Publication Date: 2026.04.07 CAPITAL ONE SERVICES LLC

AI summary

Systems described herein may provide responses to chatbot prompts that correspond to both a user's preferences and accepted views of society. A chat recommendation server may receive a prompt from a user device. The chat recommendation server may determine a general Overton window and a user-specific Overton window associated with the prompt. The chat recommendation server may generate a plurality of candidate response using the first machine learning model, input the prompt and the plurality of candidate responses to a second machine learning model, and receive, as output from the second machine learning model, a polarization score for each of the plurality of candidate responses. Based on the polarization scores, a recommended response may be selected which minimizes a distance between the user-specific Overton window and the general Overton window. Accordingly, the recommended response may be displayed on the user device.