Live Streaming Prediction Interface With NLP Offer Formulation

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Solution Overview

Problem

Existing systems lack an efficient and interactive platform for users to make predictions about occurrences in streaming media, such as sporting events or other content, and receive betting opportunities based on these predictions, while integrating real-time training and feedback mechanisms for improved accuracy.

Innovation Solution

A platform utilizing neural networks and modules for media detection, natural language processing, and user interaction to predict and formulate betting opportunities, incorporating real-time training and feedback for enhanced prediction accuracy and user engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a platform integrates real-time neural network processing and multiple functional modules for prediction analysis, then prediction accuracy and user engagement are improved, but system complexity and computational resource requirements increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system is divided into distinct functional modules including streaming media detection module, recent transpiration recordation module, context module, natural language module, search abstraction module, occurrence module, evaluation and proposal formulation module, proposal communication module, acceptance determination module, proposal fulfillment module, outcome determination module, transient desirability module, proposal formulation examination module, betting options module, neutral advisory module, and human motion recognition module. Each module handles specific aspects of prediction processing independently, allowing for modular development, maintenance, and optimization while maintaining high prediction accuracy through specialized processing in each segment.

Inventive Principle:

Principle #1Segmentation

2Productivity

If the platform processes user predictions in real-time with multiple analysis modules, then user engagement and prediction quality improve, but processing time and computational energy consumption increase

Engineering Contradiction:

Improveprediction processing efficiencyVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary processing by detecting streaming media content and recording recent transpiration (recent events) before prediction analysis occurs. The context module pre-processes information about the streaming content, and the natural language module prepares for prediction interpretation in advance. This preliminary action allows the system to have prediction analysis ready when users submit predictions, improving response time while distributing computational load across different time periods.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses the streaming media content and recent events as self-generated training data for the neural networks. The occurrence module and outcome determination module continuously learn from actual streaming content and user predictions without requiring external manual annotation or intervention. This self-service learning mechanism improves prediction accuracy over time while reducing the energy cost associated with external data collection and manual processing.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If the system incorporates multiple specialized modules for media detection, natural language processing, and prediction analysis, then functionality and user experience are enhanced, but ease of operation and system maintenance become more difficult

Engineering Contradiction:

Improveplatform functionalityVSAvoidsystem operability

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The platform is designed as a universal system that can handle multiple types of streaming media (video, audio, live streams), various prediction types (sports, entertainment, news), and different user interaction modes (text, voice, gestures). The modular architecture allows the same core framework to serve multiple functions through different module combinations, making the system highly adaptable while maintaining a consistent user interface and operational paradigm across different application scenarios.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260025553A1Platform 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: 2026.01.22 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