Information infrastructure management tools with variable and configurable filters and segmental data stores

What is this patent about?

’073 is related to the field of information management and data security within distributed computing systems. It specifically addresses the challenges of managing unstructured and semi-structured data—such as emails, word processing documents, and web content—which often contain sensitive trade secrets or mission-critical information. The invention provides a framework for identifying, classifying, and isolating this content to prevent unauthorized access, accidental loss, or disclosure through open enterprise ecosystems.

The underlying idea behind ’073 is the granular deconstruction of data streams into atomic elements to separate high-value content from common remainder data. Rather than relying on traditional perimeter defenses or simple file-level encryption, the system uses dynamic categorical filters to identify specific words, images, or data objects. By stripping sensitive elements from a source document and replacing them with placeholders, the invention creates a 'formless' data structure where the most critical information is physically and logically isolated from the rest of the file.

The claims of ’073 focus on a method for creating an information infrastructure that processes data throughput using a plurality of configurable filters. These claims cover the identification of sensitive and select content, the grouping of this data into multiple sensitivity levels, and the use of distributed data stores specifically designated for different classifications. A key aspect of the independent claims is the ability to modify these filters by expanding, contracting, or imposing hierarchical and orthogonal classifications to organize further data throughput.

In practice, the invention works by passing data through a multi-tiered filtering process that includes content-based, contextual, and taxonomic analysis. When a filter detects sensitive content, it extracts those specific elements and disperses them to secure storage locations across a network. This process effectively 'sanitizes' the original document, leaving behind a remainder file that is safe for general distribution. To view the complete information, a user must possess the appropriate security clearance to trigger a reconstruction process that pulls the granular pieces back from their respective data stores.

This approach differs from prior solutions by moving away from static classification labels, which are vulnerable to tampering, and instead focusing on the semantic essence of the data itself. By utilizing adaptive filters that can be expanded or contracted based on operator selection or environmental triggers, the system can respond to real-time threats or changing enterprise policies. This creates a resilient ecosystem where the value of information is protected through fragmentation and dispersal, ensuring that even if a single storage node is compromised, the full context of the sensitive information remains hidden.