US 11,990,244 B2
System and method providing risk relationship transaction automation in accordance with medical condition code
Gary S. Tomchik, Houston, TX (US); Oksana Kamyshin, Spring, TX (US); Keren Shemesh, West Hartford, CT (US); Lori Katherine Walters, West Simsbury, CT (US); Chris C. Kuo, Grendale, NY (US); Seda V. Remus, Issaquah, WA (US); Dean M. Mazzotta, North Granby, CT (US); and Shashank Aditya Adepu, Charlotte, NC (US)
Assigned to HARTFORD FIRE INSURANCE COMPANY, Hartford, CT (US)
Filed by HARTFORD FIRE INSURANCE COMPANY, Hartford, CT (US)
Filed on Jan. 18, 2023, as Appl. No. 18/156,000.
Application 18/156,000 is a continuation of application No. 16/849,437, filed on Apr. 15, 2020, granted, now 11,594,334.
Prior Publication US 2023/0154622 A1, May 18, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/30 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01)
CPC G16H 50/30 (2018.01) [G16H 50/20 (2018.01); G16H 70/60 (2018.01)] 18 Claims
OG exemplary drawing
 
1. A system to provide a risk relationship resource allocation tool via a back-end application computer server of an enterprise, comprising:
(a) a risk relationship data store containing electronic records that represent a plurality of transactions associated with requested resource allocations between the enterprise and a plurality of entities, wherein each electronic record includes an electronic record identifier and at least one code representing a medical condition;
(b) the back-end application computer server, coupled to the risk relationship data store, programmed to:
(i) receive an indication of a selected requested resource allocation transaction between the enterprise and an entity;
(ii) retrieve, from the risk relationship data store, the electronic record associated with the selected requested resource allocation transaction, including the at least one code representing a medical condition;
(iii) execute a predictive model trained with a machine learning process and business rules based on the at least one code to generate a likelihood of acceptance score for the selected requested resource allocation transaction, wherein the likelihood of acceptance score predicts a likelihood the at least one code is approved;
(iv) assign the selected requested resource allocation transaction to a recommendation category based on the likelihood of acceptance score, wherein the assigned recommendation category represents automatic approval of the code thereby reducing a number of electronic messages being transmitted via a distributed communication network;
(v) automatically route the selected requested resource allocation transaction to a claim handler device based on the assigned recommendation category;
(vi) generate a “next best action” for the selected requested resource allocation transaction based on output of multiple machine learning assets;
(vii) transmit the “next best action”, wherein a summary associated with the code and the “next best action” is displayed on a user interface of the claim handler device;
(viii) generate a pop-up window on the user interface in response to selection of the code, wherein the pop-up window provides additional information that is not available without the selection to reduce a number of electronic messages transmitted via the distributed communication network; and
(c) a communication port coupled to the back-end application computer server to facilitate a transmission of data with a remote device to support a graphical interactive user interface display and the distributed communication network, the interactive user interface display providing resource allocation data including the likelihood of acceptance score and the recommendation category.