Behavioral Connection Scoring for Bias-Corrected Reputation Measurement
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Solution Overview
Problem
Existing reputation measurement systems for entities are inaccurate and biased, relying on unrealizable data that fails to provide a reliable measure of an entity's ability to fulfill stakeholder expectations, especially in e-commerce where personal interaction is limited.
Innovation Solution
A system and method for determining entity reputation involves selecting a demographically representative sample with predefined familiarity, using supervised machine learning models to weight survey ratings for cultural and demographic biases, and aggregating scores to provide a behavioral connection score, which includes training models with historical data to predict business outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reputation measurement systems are used, then data collection is simple, but measurement precision is poor due to bias and unreliability
Solution Approach 1:
The patent segments the reputation measurement process into distinct components: demographic sampling, behavioral connection assessment, machine learning model training, and score aggregation. This segmentation allows each component to be optimized independently, improving overall measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw survey data and final reputation scores. These models act as mediators that process and weight survey ratings, transforming biased raw data into corrected, reliable reputation measurements through learned patterns from historical data.
2Measurement precision
If demographically representative sampling with predefined familiarity is used, then measurement precision improves, but loss of time increases due to careful sample selection
Solution Approach 1:
The patent performs preliminary actions by pre-defining demographic criteria and familiarity thresholds before conducting surveys. This advance preparation establishes the sampling framework in advance, allowing rapid sample selection while maintaining measurement precision through pre-established demographic representativeness and familiarity requirements.
3Measurement precision
If survey ratings are weighted for cultural and demographic biases, then measurement precision improves, but device complexity increases due to weighting mechanisms
Solution Approach 1:
The patent employs machine learning models as intermediaries that automatically perform weighting adjustments for cultural and demographic biases. These models learn appropriate weighting factors from historical data and apply them transparently, improving survey accuracy while encapsulating the complex weighting logic within the model rather than requiring explicit weighting mechanisms.
Solution Approach 2:
The machine learning models perform self-adjustment by automatically learning and applying weighting factors for bias correction. The system serves itself by using historical data to train models that autonomously determine appropriate weights, eliminating the need for manual weighting configuration and reducing operational complexity.
4Reliability
If behavioral connection scores are used to predict business outcomes, then reliability of reputation measurement improves, but loss of information increases due to data aggregation
Solution Approach 1:
The patent preserves local quality by maintaining detailed survey responses and individual behavioral connection scores alongside aggregated results. This allows the system to provide both comprehensive aggregated reputation scores for high-level prediction and access to granular underlying data when needed, minimizing information loss while achieving reliable predictions.
Data Source
AI summary
A system and method for determining and managing reputation of an entity or industry includes determining a sample size of the population that provides at least a pre-defined percent confidence interval, and which has the predefined level of familiarity (unique group); determining a measure of likelihood that the targeted population will perform a positive action on behalf of an entity (behavioral connection score), wherein determining the measure of the likelihood that the targeted population will perform a positive action on behalf of an entity comprises: receiving survey ratings from behavioral connection survey questions where each survey rating is provided by a party within the unique group; weighting the received survey ratings to accommodate for at least one of the group consisting of cultural bias and missed demographic quotas; and aggregating the ratings within each individual question to provide a single aggregated behavioral connection score for each behavioral connection survey question.