IoT Propensity Ranking via Predictive Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for inferring consumer propensities are computationally expensive due to the use of regression models, require human expertise, and rely on proprietary and personally identifiable data, making them costly and limited in scalability and privacy compliance.
Innovation Solution
A machine-based predictive segmentation method that calculates indexes from relationships between various categories of input data, eliminating the need for individual regression models and human intervention, using publicly available or non-PII data to infer consumer propensities, suitable for the IoT environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If regression models are used to infer consumer propensities, then measurement precision is improved, but computational cost increases
Solution Approach 1:
The patent segments the population into distinct clusters based on observable characteristics, then applies separate propensity models to each segment. This allows using simpler models within segments while maintaining overall accuracy, reducing computational burden compared to applying a single complex regression model to the entire population.
Solution Approach 2:
The patent creates propensity scores by copying and adapting models from segments with complete data to segments with partial data. Instead of building complex regression models for every segment, it copies proven models and adjusts them, significantly reducing computational cost while maintaining measurement precision.
2Productivity
If regression analysis is performed for millions of consumers, then productivity is improved, but device complexity increases
Solution Approach 1:
By segmenting consumers into clusters and applying different modeling strategies to each segment, the system manages complexity through organization. This allows parallel processing of segments, improving productivity while keeping individual segment models simple enough to handle.
Solution Approach 2:
The patent develops a universal framework that handles multiple data completeness scenarios (complete data, partial data, minimal data) through a single integrated approach. This multi-functional system manages diverse consumer data types without requiring separate complex systems for each data scenario, reducing overall device complexity.
3Measurement precision
If proprietary and personally identifiable data are used, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent extracts and removes personally identifiable information from the data processing pipeline, working only with anonymized or aggregated data where possible. This extraction of PII allows the system to maintain measurement precision through rich data analysis while preventing privacy loss by eliminating identifying information from the analysis.
Solution Approach 2:
The patent introduces segmentation clusters as an intermediary layer between raw consumer data and propensity predictions. This intermediary aggregates individual data into group-level patterns, enabling accurate predictions while protecting individual privacy by never directly linking predictions to identifiable individuals.
4Measurement precision
If human expertise is required to build models, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent implements self-service through automated model selection and segment assignment algorithms that operate without human intervention. The system automatically identifies appropriate models for each segment and assigns consumers to segments based on their characteristics, eliminating the need for manual model building while maintaining high accuracy through algorithmic optimization.
Solution Approach 2:
The patent incorporates feedback loops where model performance is continuously evaluated and used to automatically adjust segment definitions and model parameters. This feedback mechanism allows the system to self-optimize and maintain high measurement precision without requiring ongoing human expertise, improving ease of operation while preserving accuracy.
Data Source
AI summary
A specially programmed machine and method generates propensity information based on inputs received from machines connected as part of an Internet of Things (IoT) environment, where the machine appends a predictive segmentation attribute to each data input received and generates a matrix based on the counts of each attribute-input combination. The attribute-input counts for each combination are converted to a statistical metric that represents propensity information for consumers fitting into the segment associated with the particular attribute. The propensity information can be relayed to a client in a variety of ways, including a direct display of the propensity information, appendage of the propensity information to the clients database, or using the information to provide an audience report to the client.