Automated Resource Request Approval via Chronicle Processing
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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 facilitate resource requests by generating advancement scores and suggested actions, using a chronicle processor to transform metadata into scores and transmit alerts to agent devices for approval or further action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processing methods are used to handle diverse and incomplete data, then flexibility in handling various resource request types is maintained, but processing time and complexity increase significantly
Solution Approach 1:
The system transforms unstructured, diverse data into structured parameters by creating standardized data models and schemas. Resource requests are converted into uniform parameter sets that can be processed automatically, maintaining adaptability while enabling efficient computational handling through parameter standardization and normalization.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers, validation services, and transformation engines that mediate between diverse input data and the core processing system. These intermediaries standardize heterogeneous data formats and complete incomplete information before processing, reducing manual intervention while preserving flexibility.
2Measurement precision
If complex approval rules are implemented to ensure accurate resource allocation, then decision accuracy improves, but system complexity and difficulty of implementation increase
Solution Approach 1:
The approval system is segmented into modular rule engines, each handling specific resource types or approval criteria. Complex approval logic is divided into independent, configurable rule modules that can be assembled and maintained separately, reducing overall system complexity while maintaining comprehensive decision-making capabilities through compositional rule sets.
Solution Approach 2:
The system implements dynamic rule configuration where approval criteria can be adjusted, enabled, or disabled based on resource type, organizational policies, and contextual factors. The rule engine adapts to different scenarios by loading appropriate rule sets, maintaining high decision accuracy without requiring a single monolithic complex system for all cases.
3Productivity
If automated processing is implemented to improve efficiency, then processing speed increases, but handling of incomplete and diverse data becomes more difficult
Solution Approach 1:
The system performs preliminary data validation, completion, and enrichment before automated processing begins. Missing data is identified and flagged, default values are applied where appropriate, and data completeness is verified upfront. This preliminary action enables subsequent automated processing to proceed efficiently without being hindered by incomplete information.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors data quality and completeness throughout the processing pipeline. Validation rules provide feedback on missing or inconsistent data, triggering automated requests for additional information or applying fallback procedures. This continuous feedback loop maintains high processing throughput while ensuring data adequacy through iterative validation and correction.
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.