US 12,217,099 B1
Resource allocation optimization using artificial intelligence
Joshua R. Seeman, Castle Rock, CO (US); and Daniel G. Fox-Gliessman, Highlands Ranch, CO (US)
Assigned to Source Technologies, Inc, Castle Rock, CO (US)
Filed by Source Technologies, Inc, Castle Rock, CO (US)
Filed on Jun. 20, 2024, as Appl. No. 18/749,419.
Claims priority of provisional application 63/509,272, filed on Jun. 20, 2023.
Claims priority of provisional application 63/509,279, filed on Jun. 20, 2023.
Int. Cl. G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06Q 10/0631 (2023.01)
CPC G06F 9/5077 (2013.01) [G06F 9/5038 (2013.01); G06N 3/08 (2013.01); G06Q 10/0631 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for resource allocation using artificial intelligence, the method comprising:
receiving input information related to one or more queries from a plurality of sources, wherein the one or more queries are received via query initiators;
ingesting and processing the input information, including query parameters, relational data, performance metrics, and past records to generate structured data;
analyzing the structured data using one or more machine learning models to generate predictions about which entities are likely to qualify for projects;
generating a list of qualified entities based on the predictions;
transmitting the list of qualified entities and recommendations for resource distribution to a query initiator;
managing ongoing transactions with service entities based on present resource distribution, adherence information, and transaction benchmarks to predict prospective distribution, recommend cost-efficient service entities, and distribute resources based on the recommendations;
receiving compliance data of the service entities and determining digital record event markers for ongoing transactions, wherein the digital record event markers enables determination of future resource allocation needs;
predicting future resource allocation needs based on the present resource distribution and the digital record event markers;
comparing the predicted future resource allocation with a predefined project allocation threshold;
issuing a warning based on a determination that the predicted future resource allocation surpasses the predefined project allocation threshold, wherein the determination is based on comparison of the predicted future resource allocation with the predefined project allocation threshold;
based on the determination that the predicted future resource allocation surpasses the predefined project allocation threshold, presenting an interface to the query initiators to receive input for adjusting resource distribution strategy, wherein the interface includes visual analytics dashboards for monitoring resource allocation and project progress;
analyzing feedback data from the service entities, wherein a first score is assigned to a project, which determines relevance of past digital records and performance of the service entities;
quantifying the feedback data to facilitate comparison with a threshold value;
analyzing social graph data to determine if a quantified value is less than the threshold value; and
assigning a second score to select a service entity for the project if the quantified value is greater than the threshold value and a difference between the first score and the second score is higher than a second threshold value.