Event-Driven Resource Allocation for Rule-Based Asset Distribution
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Solution Overview
Problem
Resource allocation processes are complex, time-consuming, and often require significant manual input due to the large quantity of documents and myriad rules involved, with existing software solutions being burdensome and not fully suited to specialized requirements.
Innovation Solution
A computing system generates prompts based on resource documents, receives responses, and uses machine-learning models to generate resource data and visualizations for efficient resource allocation, incorporating rules and personalized analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple software applications are used to manage resources, then resource information can be collected and disseminated, but the system becomes burdensome and demands significant manual input
Solution Approach 1:
The patent combines multiple software applications into a single integrated resource allocation system that performs document collection, storage, dissemination, and allocation functions through unified machine-learning models and event-driven architecture, eliminating the need for multiple separate applications and reducing manual input requirements
Solution Approach 2:
The resource allocation system is designed to perform multiple functions including collecting resource information from various documents, storing data, disseminating information to recipients, and automatically allocating resources based on rules and events, making a single system capable of replacing multiple specialized applications
2Ease of manufacture
If generalized software applications are used for resource allocation, then basic resource management can be performed, but they are not fully suited to meeting specialized requirements
Solution Approach 1:
The system applies local quality by using specialized machine-learning models trained on specific resource allocation domains and rules, allowing different parts of the system to handle different types of resources and allocation criteria with appropriate expertise, rather than using a single generalized approach for all resources
Solution Approach 2:
The system performs preliminary action by pre-training machine-learning models with domain-specific knowledge and rules before actual resource allocation occurs, and by pre-establishing event criteria and recipient profiles, enabling the system to quickly adapt to specialized requirements without extensive customization during operation
3Reliability
If manual processes are used for resource allocation, then complex rules and policies can be reviewed, but significant time and effort are expended
Solution Approach 1:
The patent replaces manual mechanical review processes with automated machine-learning models that can process and evaluate complex rules, policies, and resource allocation criteria at high speed, maintaining rule compliance through programmed logic while dramatically increasing allocation speed and reducing human effort
Solution Approach 2:
The system implements self-service by enabling automated resource allocation where the machine-learning models independently evaluate resource documents, apply allocation rules, determine eligible recipients, and execute distribution without requiring manual intervention at each step, thereby maintaining reliability while significantly improving productivity
4Adaptability or versatility
If instructions to disburse resources undergo numerous revisions, then resource allocation can be adjusted to meet changing requirements, but the process becomes expensive and time-consuming
Solution Approach 1:
The system implements dynamics by using event-driven architecture where resource allocation instructions can be dynamically adjusted in response to detected events such as changes in recipient eligibility, resource availability, or policy updates, allowing the system to adapt to changing requirements automatically without requiring numerous manual revisions
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine-learning models continuously monitor resource allocation outcomes, recipient responses, and changing conditions, using this feedback to automatically adjust allocation instructions and optimize resource distribution, reducing the need for external revisions while maintaining flexibility
Data Source
AI summary
Provided are methods, systems, devices, apparatuses, and tangible non-transitory computer readable media for processing and allocating resources. Prompts that comprise requests for information associated with resource allocation instructions can be generated. Generation of the prompts can be based on resource documents that are associated with resources and comprise resource document fields. The resource allocation instructions can be associated with the distribution of assets of the resources to entities that comprise resource recipients. Based on rule data and received responses to the prompts, resource data can be generated. The resource data can comprise resource data fields that are based on the resource document fields and resource data field values that are based on the responses. Based on the resource data, indications associated with the resource allocation instructions can be generated. Furthermore, the indications can comprise visualizations of the distribution of the assets to the resource recipients.