User Information Collection System for Relevance Filtering

The patent combines multiple information sources (calendars, search applications, social networks, weather, travel, traffic, dining, entertainment) into a unified information collection system. The system merges these diverse sources and presents them through a single interface, eliminating the need for users to manually access and organize information from multiple separate applications and websites.

The patent introduces an intermediary system that acts as a mediator between multiple information sources and the user. This intermediary automatically collects, processes, and organizes information from various sources, then presents it in a unified manner. The system serves as the intermediary that handles the complexity of information integration, freeing users from direct engagement with multiple information channels.

The patent implements a feedback mechanism where users can review inferences made about them and provide input on the relevance of displayed information. The system uses this feedback to continuously refine its inference accuracy and adjust the relevance threshold. This feedback loop allows users to control what information is displayed while maintaining high relevance, and users can adjust confidence thresholds to balance relevance against privacy concerns.

The patent allows dynamic adjustment of the confidence threshold parameter that determines whether inferred information is displayed. Users can modify this parameter to control the level of certainty required before information is presented. By changing this parameter, users can balance between receiving highly relevant information (lower threshold) and maintaining stricter privacy controls (higher threshold), thus controlling unwanted data exposure.

The patent makes the confidence threshold dynamic rather than fixed. The system automatically adjusts the confidence threshold based on contextual factors, user preferences, and the type of information being evaluated. For high-stakes inferences (e.g., health-related), the system applies higher thresholds to ensure quality, while for lower-stakes information, it uses lower thresholds to maintain quantity. This dynamic adjustment allows the system to optimize the balance between quality and quantity across different information categories.

The patent applies different confidence thresholds to different types of information and inference categories rather than using a single global threshold. High-confidence thresholds are applied to critical information categories where accuracy is paramount, while lower thresholds are used for less critical categories where quantity is more important. This localized quality control allows the system to maintain high information quality where needed while preserving information quantity in other areas.

The patent implements optional and incremental feedback mechanisms rather than requiring comprehensive user input. Users can choose to provide feedback on specific inferences rather than all information presented. The system accepts partial feedback and uses it to progressively improve accuracy over time. This partial action approach maintains accuracy improvement while minimizing the time burden on users, as they only need to interact when they have meaningful feedback to provide.

The patent implements self-correcting inference mechanisms that automatically refine accuracy without requiring continuous user intervention. The system uses collected feedback to automatically adjust its inference algorithms and improve future accuracy. Once the system learns from initial user feedback, it becomes increasingly autonomous in providing accurate information, reducing the need for ongoing user interaction time while maintaining or improving accuracy.