Streaming Prediction Platform Using NLP and Dynamic Odds Adjustment

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Solution Overview

Problem

Existing technologies lack an efficient platform for users to predict occurrences in streaming media and receive corresponding betting opportunities, with existing systems often biased towards the operator rather than providing fair odds.

Innovation Solution

A platform that utilizes neural networks and various modules to detect streaming media, process user predictions, calculate likelihoods, and offer betting opportunities with dynamically adjusted proposal ratios based on user interactions and acceptance rates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a platform processes user predictions using natural language processing and machine learning to determine occurrence likelihood, then the fairness and transparency of betting opportunities are improved, but the device complexity and computational requirements increase

Engineering Contradiction:

Improvefairness of betting opportunitiesVSAvoidplatform complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The platform is divided into distinct functional modules: natural language processing module for interpreting user predictions, machine learning module for determining occurrence likelihood, module for formulating prediction value offers, and module for tracking acceptance rates. Each module performs a specific function, reducing overall system complexity while maintaining fairness through systematic processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback loops where acceptance rates and rejection rates of prediction value offers are continuously monitored and fed back into the machine learning model. This feedback mechanism allows the system to dynamically adjust occurrence likelihood determinations, improving fairness while using established feedback control patterns to manage complexity

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If the platform dynamically adjusts proposal ratios based on user acceptance and rejection rates, then the adaptability and user experience are improved, but the computational processing requirements increase

Engineering Contradiction:

Improveadaptability of betting opportunitiesVSAvoidcomputational processing energy

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The proposal ratio is made dynamic rather than static, automatically adjusting based on real-time acceptance and rejection rate data. The system adapts to user preferences and market conditions without requiring manual intervention, improving versatility while using established dynamic adjustment mechanisms to control processing demands

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes key parameters (proposal ratios, occurrence likelihood values) based on observed user behavior patterns. By monitoring acceptance rates and adjusting parameters accordingly, the platform achieves adaptability while using parameter optimization techniques to balance computational energy consumption

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If the platform reduces user predictions into standardized formulae and determines occurrence parameters, then the measurement precision of predictions is improved, but the difficulty of processing and standardization increases

Engineering Contradiction:

Improveprecision of prediction evaluationVSAvoiddifficulty of prediction standardization

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system performs preliminary standardization by establishing predetermined formulae and parameter structures before receiving user predictions. Natural language predictions are mapped to pre-defined categories and standardized formats, improving measurement precision while reducing the complexity of real-time processing through advance preparation of evaluation frameworks

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12289500B2Platform for adaptably engaging with live streaming applications, providing users access via an interface framework, receiving and processing user predictions using natural language processing and machine learning, reducing the predictions into standardized formulae, determining occurrence and value parameters pertaining to the predictions, formulating prediction value offers based on the occurrence and value parameters, and proposing prediction value offers via the interface framework

Publication Date: 2025.04.29 MURCIN DAVID C

AI summary

A system, method, and platform for enabling users to engage in predictions pertaining to streamed media by receiving natural language predictions from users, standardizing the natural language predictions into formulated predictions, performing occurrence searches of the formulated predictions using literal, significant, and associated terms, determining likelihoods of occurrences based on the results, calculating proposal ratios based on occurrence percentiles, and communicating proposals to users