Probabilistic Prediction Sets for Social Media Demographics

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems face challenges in accurately predicting demographic information for audiences on social media due to privacy concerns and the vast amount of data, often relying on simplistic assumptions that lead to inaccurate results.

Innovation Solution

A system that filters social media data, uses probabilistic classifiers like Naive-Bayes, and Monte Carlo methods to generate prediction sets by identifying individual features and determining probability distributions, allowing for accurate demographic predictions and targeted advertisements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If simplistic assumptions are used to generate predictions, then the process is simple and fast, but the prediction accuracy deteriorates

Engineering Contradiction:

Improveprediction generation speedVSAvoiddemographic prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system changes the parameters of the prediction model from simple deterministic assumptions to probabilistic distributions. By representing demographic features as probability distributions rather than fixed values, the system maintains computational efficiency while significantly improving prediction accuracy and capturing the uncertainty inherent in social media data analysis.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces an intermediary layer of probability distributions between the input social media data and the final demographic predictions. This intermediary representation allows the system to process complex relationships and uncertainties without requiring computationally expensive direct modeling, thus maintaining productivity while improving accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If probabilistic classifiers and Monte Carlo methods are used, then prediction accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvedemographic prediction accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the complex prediction task into distinct modular components: feature extraction from social media data, probabilistic classification for each demographic feature, Monte Carlo sampling for prediction generation, and result aggregation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the advanced methods used.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The probabilistic classifier and Monte Carlo method work together in a self-service manner where the classifier generates probability distributions that are directly consumed by the Monte Carlo sampling process. This self-contained pipeline reduces the need for complex external processing and simplifies system integration.

Inventive Principle:

Principle #25Self-service

3Loss of information

If demographic information is collected from social media, then valuable insights are obtained, but privacy concerns and data volume challenges arise

Engineering Contradiction:

Improvedemographic information qualityVSAvoidprivacy concerns

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The system uses disposable probability distributions that are generated on-demand from social media data and discarded after use. Instead of storing sensitive demographic information, the system creates temporary probabilistic representations that capture the essential insights without retaining personal data, thus addressing privacy concerns while maintaining information quality.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Solution Approach 2:

Probability distributions serve as an intermediary between raw social media data and demographic conclusions. This intermediary layer allows the system to extract valuable demographic insights without directly handling or storing sensitive personal information, thereby reducing privacy risks while maintaining data quality.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11636367B2Systems, apparatus, and methods for generating prediction sets based on a known set of features

Publication Date: 2023.04.25 ZOOMPH INC

AI summary

One example method of operation may include identifying a number of features associated with information of one or more entities, accessing a probability distribution store comprising defined numerical ranges as potential possibilities for being paired with the features of the one or more entities, determining first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features, determining second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features, determining a merged probability distribution based on the first probability distributions and the second probability distributions, determining and storing one or more prediction sets based on the merged probability distribution, selecting one or more content items to display on a device interface based on the one or more prediction sets, and displaying the one or more content items the device interface.