Task-agnostic Human-Machine Intelligence Integration

The patent introduces a machine learning model as an intermediary between crowdsourcing human intelligence and existing computer science research. The model learns from both human responses and established algorithms, synthesizing their strengths. This intermediary structure allows the system to leverage decades of computer science research while still benefiting from human intelligence, resolving the contradiction between adapting to multiple sources and managing system complexity.

The patent creates a universal machine learning model that can perform multiple functions: learning from human crowdsourcing data, incorporating existing algorithmic knowledge, and automatically determining when to use each approach. This multi-functional model eliminates the need for separate systems for human processing and machine processing, reducing overall system complexity while maintaining versatility.

The patent develops a universal machine learning model that can generalize across multiple tasks without requiring task-specific customization. The model learns fundamental patterns from diverse crowdsourcing tasks and applies them broadly, achieving both task-specific performance optimization and high scalability. This eliminates the need to create separate customized systems for each task, thereby improving productivity while maintaining reliability.

The patent employs parameter changes in the machine learning model to adapt to different tasks dynamically. Rather than restructuring the entire system for each task, the model adjusts its parameters and learning focus based on the specific task requirements, maintaining optimal performance across diverse tasks while preserving the same underlying system architecture, thus enabling scalability.

The patent applies partial action by using human processing only when necessary and supplementing it with machine processing for routine tasks. The machine learning model learns from human responses and gradually takes over simpler tasks, reducing the proportion of human involvement while maintaining overall accuracy. This partial substitution approach lowers costs and improves efficiency while preserving the high accuracy benefits of human processing where needed.

The patent implements feedback mechanisms where the machine learning model continuously learns from human processing results and improves its own performance. As the model's accuracy increases through feedback from human corrections and validations, it can handle more tasks autonomously, reducing the need for expensive human intervention while maintaining or improving accuracy over time.

The patent applies preliminary action by pre-training the machine learning model on extensive datasets and incorporating existing computer science research before deployment. This preliminary preparation enables the model to reach human-comparable performance levels more quickly without requiring extensive task-specific customization later, thus achieving both efficiency gains and high performance from the outset.

The patent uses feedback loops where the machine learning model's performance is continuously evaluated against human performance benchmarks. The model learns from discrepancies between machine and human responses, gradually improving its accuracy to reach human-comparable levels. This feedback-driven improvement allows the system to maintain high efficiency while achieving the necessary performance parity with human processors.