Rule-Based Data Traversal for Accurate Resource Allocation
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Solution Overview
Problem
Existing data item traversal rules face challenges related to rule subjectivity, complexity, dynamic interdependencies, and resource allocation failures, leading to incomplete or incorrect resource allocation actions, especially in complex datasets.
Innovation Solution
Implementing rule-based resource allocation actions that are automatically triggered by identifying filtered data items, using prediction models to optimize and correct sequences of actions, including remediation workflows and automated system redirection for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data item traversal rules are used to process sequences of actions, then basic validation can be performed, but rule subjectivity and complexity lead to incorrect resource allocation actions
Solution Approach 1:
The patent introduces an intermediary system comprising a prediction model and remediation workflow that mediates between traditional traversal rules and resource allocation actions. This intermediary analyzes the output of traversal rules, predicts potential failures, and generates corrected action sequences, thereby improving reliability without requiring the traversal rules themselves to become more complex
Solution Approach 2:
The patent replaces the mechanical rule-based validation system with an AI-driven prediction model that uses machine learning to identify and correct resource allocation failures. This substitution transitions from rigid rule matching to intelligent predictive analysis, improving accuracy while reducing the complexity of rule maintenance
2Reliability
If comprehensive data validation rules are applied to ensure accuracy, then resource allocation correctness improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing predicted failure scenarios and their corrections in a remediation workflow database. During actual resource allocation, the system quickly matches current traversal results against pre-analyzed patterns rather than performing comprehensive real-time analysis, significantly reducing processing time while maintaining high correctness
Solution Approach 2:
The patent dynamically adjusts validation parameters based on the complexity and risk level of the dataset being processed. For low-risk scenarios, minimal validation is applied; for high-risk scenarios, more comprehensive checks are performed. This parameter adaptation balances correctness requirements with processing efficiency
3Reliability
If manual correction of resource allocation failures is performed, then accuracy can be improved, but productivity and speed of action completion decrease
Solution Approach 1:
The patent implements self-service through an automated remediation workflow that detects resource allocation failures and generates corrected action sequences without human intervention. The system autonomously analyzes failure patterns, applies appropriate corrections from the remediation workflow, and executes corrected actions, thereby maintaining high accuracy while maximizing productivity through elimination of manual correction steps
4Productivity
If incomplete information about action failure is used for correction, then processing speed is maintained, but the quality and accuracy of corrected resource allocation actions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the prediction model continuously learns from both complete and incomplete failure information. When partial information is available, the system generates probabilistic corrections and seeks additional data if confidence thresholds are not met. This feedback loop enables the system to maintain high correction quality by adaptively utilizing available information while knowing when to request more data
Data Source
AI summary
Methods, systems, and computer-readable storage media for resource allocation actions. An inquiry to render a user interface with first visual representations of upcoming events is received, from a user device. The inquiry includes an identifier used to retrieve first data items specifying first user qualifications. Second data items specifying user attributes and labeled with an indicator representing a location, are looked-up, in the memory. The second data items are filtered to identify filtered data items by matching attributes with the qualifications relative to the location. The filtered data items are transmitted, to the user interface module, to generate the user interface comprising first visual representations of upcoming events within the location. An indication of a selected visual representation is received, through the user interface from the user device. Second data items stored in the memory are automatically traversed to update the user interface.