ML Event Outcome Validation for Non-Conventional Wagering

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional event outcome prediction and validation systems struggle with non-conventional and lesser-known events due to limited data sources, leading to inefficient markets, manual processes, and regulatory compliance issues, which hinder accurate prediction and confirmation of outcomes, especially in wagering systems.

Innovation Solution

A system utilizing unique data sources and machine learning to automatically predict and validate event outcomes, leveraging user and external data sources for classification and rating, adjusting odds based on prediction and confirmation data, and processing payments while ensuring regulatory compliance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional data sources are used for event outcome prediction, then reliability is improved for well-known events, but adaptability deteriorates for non-conventional and lesser-known events

Engineering Contradiction:

Improveprediction reliabilityVSAvoidevent type coverage

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system employs multiple data sources including conventional sources (broadcasters, broadcasting networks) and non-conventional sources (social media, user-generated content, alternative data providers) to serve both well-known events and non-conventional events, making the prediction system universally applicable across diverse event types

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Ease of operation

If manual processes are employed for odds setting and market management, then ease of operation is improved for operators, but productivity deteriorates due to time-consuming operations

Engineering Contradiction:

Improveoperator controlVSAvoidmarket efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system implements automated odds setting and market management algorithms that independently analyze data sources, predict event outcomes, and adjust market parameters without requiring continuous manual intervention, allowing the system to serve itself while maintaining operator oversight capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes for odds calculation and market management are replaced with automated computational algorithms and machine learning models that process data and generate predictions at scale, dramatically increasing productivity while preserving operational control

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If multiple data sources are integrated for non-conventional events, then adaptability is improved, but device complexity increases due to data integration challenges

Engineering Contradiction:

Improveevent coverageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system introduces intermediary components including data integration layers, standardized data interfaces, and processing intermediaries that mediate between diverse data sources and the core prediction engine, reducing system complexity while enabling multi-source data integration for non-conventional events

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If conventional wagering systems are used, then reliability is maintained for traditional events, but adaptability deteriorates for time-bound events with mutually exclusive outcomes

Engineering Contradiction:

Improvemarket stabilityVSAvoidmarket maturity

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements dynamic market mechanisms that adapt to different event types and time constraints, allowing market parameters, odds structures, and validation rules to dynamically adjust based on event characteristics, thereby maintaining reliability for traditional events while enabling adaptability for time-bound events with mutually exclusive outcomes

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260105329A1Systems and methods for automatic event outcome prediction, confirmation, and validation using machine learning

Publication Date: 2026.04.16 BASCH AARON

AI summary

Systems and methods for event outcome validation are provided. The system receives a user input indicative of an event and at least one anticipated outcome of the event to be wagered on by the user. The system receives confirmation data associated with an outcome of the event from at least one confirmation data source confirming the outcome of the event and classifies the confirmation data utilizing at least one machine learning algorithm. The system determines a threshold of confirmation data sources to validate the outcome of the event and utilizes the at least one machine learning algorithm to determine a reduced threshold of confirmation data sources to validate the outcome of the event based on at least one of the classified confirmation data and a confirmation rating of the at least one confirmation data source. The system validates the outcome of the event based on the reduced threshold.