Automated Resource Request Processing via Metadata Scoring
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Solution Overview
Problem
Processing resource requests is complicated by diverse and incomplete data, and is further hindered by complex rules related to specific resource types, making automated approval challenging.
Innovation Solution
A system that processes content objects and bucket metadata to automatically evaluate resource requests, generating advancement scores and suggested actions, and transmitting alerts to facilitate resource request approval or further action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing methods are used to handle diverse and incomplete resource request data, then flexibility in handling complex rules can be maintained, but processing efficiency and productivity are reduced
Solution Approach 1:
The patent segments resource request processing into distinct components: data normalization modules that standardize diverse input formats, validation modules that check completeness against required criteria, and scoring modules that evaluate requests based on predefined rules. This segmentation allows the system to handle complexity through modular processing rather than monolithic manual review, improving productivity while managing system complexity through organized functional blocks.
Solution Approach 2:
The patent transforms unstructured diverse data into standardized parameters through normalization processes. By converting various data formats and types into consistent parameter structures, the system enables automated processing of resource requests. This parameter transformation allows complex diverse data to be handled systematically, improving processing efficiency without requiring proportional increases in system complexity.
2Productivity
If automated processing is implemented to improve productivity, then processing speed increases, but the system becomes more complex due to diverse and incomplete data requirements
Solution Approach 1:
The patent implements preliminary data normalization and validation steps before main processing. By pre-processing diverse and incomplete data to establish standardized formats and identify missing elements early in the workflow, the system reduces the complexity burden during automated processing. This preliminary action allows high-throughput automation while managing complexity through upfront data preparation rather than complex real-time processing.
Solution Approach 2:
The patent introduces intermediary normalization layers and validation buffers between data input and processing engines. These intermediaries standardize diverse data formats and handle incomplete information before data reaches the automated processing core, reducing the complexity burden on the main processing system. This intermediary approach enables high productivity through automation while containing system complexity in dedicated preprocessing components.
3Measurement precision
If comprehensive data validation is performed to ensure complete information, then decision accuracy improves, but processing time and complexity increase
Solution Approach 1:
The patent implements partial validation by prioritizing checks on critical required fields first, then progressively validating additional data elements based on resource type and request context. This staged validation approach ensures accurate decision-making on essential criteria without spending excessive time on optional or secondary data elements, thereby improving measurement precision while minimizing time loss through selective rather than exhaustive validation sequences.
Data Source
AI summary
Techniques described herein relate to automated approval of resource requests. More specifically, resource request data is retrieved, identified, processed and aggregated to automate approval of the request.