Bit String Consent Channel for JSON Processing Bottlenecks
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Solution Overview
Problem
Existing systems for processing user information incur significant processing penalties due to complex data structures and hard-coded algorithms, particularly when handling consent data in serialized formats like JSON, which are inefficient and require manual modifications for new workflows and entities.
Innovation Solution
The implementation of a lightweight control channel using short bit string encoding for consent data and a regulatory query context object, combined with quick Boolean operators to rapidly evaluate access permissions, thereby reducing processing power and improving consent management efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If JSON-based consent data structures are used, then comprehensive data processing capabilities are achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the consent data into a compact bit string format where each bit represents a specific consent attribute. This segmentation allows the system to maintain comprehensive data processing capabilities while reducing the processing time by eliminating the need to parse and deserialize JSON objects for each consent evaluation.
Solution Approach 2:
The patent changes the data representation parameter from serialized JSON format to a compact bit string format. This parameter change fundamentally alters how consent data is stored and processed, enabling fast bitwise operations while maintaining the full expressiveness needed for regulatory compliance and data processing control.
2Reliability
If hard-coded algorithms are used for consent processing, then existing workflows are supported, but adaptability to new workflows and entities requires manual modifications
Solution Approach 1:
The patent introduces a dynamic evaluation framework where consent decisions are made through programmable logic that can adapt to new workflows and entities without requiring manual algorithm modifications. The system uses a configurable evaluation engine that processes bit string data through flexible logical operations, enabling dynamic response to new regulatory requirements and business needs.
Solution Approach 2:
The patent creates a universal consent evaluation framework that handles multiple workflows, entities, and regulatory requirements through a single bit string processing architecture. The system's multi-functionality allows it to support existing workflows while naturally accommodating new ones through programmable logic rather than hard-coded algorithms.
3Adaptability or versatility
If complex data structures are used for consent management, then comprehensive control is achieved, but device performance and scalability are reduced
Solution Approach 1:
The patent extracts the essential consent information from complex JSON data structures and represents it as a minimal bit string. This extraction process removes unnecessary parsing overhead while preserving the core consent control capabilities, thereby significantly improving device performance and scalability without sacrificing control comprehensiveness.
Solution Approach 2:
The patent uses lightweight bit string representations that are computationally inexpensive to process compared to JSON objects. These compact data structures can be rapidly created, manipulated, and discarded during the consent evaluation process, enabling high-throughput processing that scales efficiently across distributed systems.
Data Source
AI summary
Disclosed are apparatuses, systems, and methods for providing a programmatic control channel for granting or denying access to user data. In one embodiment, a method is disclosed comprising receiving an input stream of data including user data and a first regulatory control channel (RCC) data structure; building a final RCC data structure based on the first RCC data structure and a stored RCC data structure; retrieving a regulatory query context (RQC) from a data requestor; applying one or more Boolean operators to the final RCC data structure and the RQC to obtain an evaluation result; and executing the downstream processing if the evaluation result comprises a passing evaluation result.