US 11,657,456 B2
Systems and methods for allocating resources using information technology infrastructure
Jennifer Irwin, Whitefish Bay, WI (US); John Bull, Nashua, NH (US); and Steven Auerbach, Boston, MA (US)
Assigned to Alegeus Technologies, LLC, Waltham, MA (US)
Filed by Alegeus Technologies, LLC, Waltham, MA (US)
Filed on Nov. 8, 2021, as Appl. No. 17/521,265.
Application 17/521,265 is a continuation of application No. 15/237,354, filed on Aug. 15, 2016, granted, now 11,170,445.
Claims priority of provisional application 62/268,235, filed on Dec. 16, 2015.
Prior Publication US 2022/0172294 A1, Jun. 2, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/06 (2012.01); G06Q 40/12 (2023.01)
CPC G06Q 40/06 (2013.01) [G06Q 40/123 (2013.12)] 20 Claims
OG exemplary drawing
 
1. A data processing system comprising one or more processors, coupled with memory, wherein the data processing system is configured to:
identify, for a participant, a feature vector comprising a first feature indicating demographic information, a second feature indicating healthcare spending amount, a third feature indicating a health savings account contribution, and a fourth feature indicating a healthiness of the participant;
determine a metric that indicates similarity between the feature vector and a plurality of feature vectors of a plurality of healthcare expense prediction models stored in a database and generated from financial data and health data of a plurality of participants via machine learning, the health data comprising diet information, fitness information, or exercise information of the plurality of participants;
select, based on the metric that indicates similarity, a healthcare expense prediction model of the plurality of healthcare expense prediction models configured to predict future healthcare expenses of the participant, the healthcare expense prediction model: i) trained by the data processing system via machine learning based at least in part on the financial data and at least one of the diet information, the fitness information, or the exercise information of the health data of the plurality of participants, and ii) re-trained via machine learning by the data processing system responsive to receipt of an update to the diet information, the fitness information, or the exercise information of the health data of the plurality of participants;
determine, from the healthcare expense prediction model trained and re-trained by the data processing system, a predicted lifetime healthcare expense of the participant;
identify, responsive to determination of the predicted lifetime healthcare expense, a lifetime non-healthcare expense of the participant;
determine, based on the predicted lifetime healthcare expense of the participant, a first amount of funds to allocate per time period to a healthcare tax benefit account of the participant;
determine, based on the lifetime non-healthcare expense of the participant, a second amount of funds to allocate per time period to a non-healthcare tax benefit account of the participant; and
provide, for presentation via an interactive user interface, an indication of the first amount of funds to allocate to the healthcare tax benefit account and the second amount of funds to allocate to the non-healthcare tax benefit account, the interactive user interface including a control object configured to i) receive an input to adjust the first amount of funds, and ii) responsive to receipt of the input to adjust the first amount of funds, update a total amount of funds projected to be allocated to the healthcare tax benefit account.