| CPC G06Q 30/0201 (2013.01) [G06Q 20/209 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/0222 (2013.01); G06Q 30/0236 (2013.01); G06Q 30/0246 (2013.01); G06Q 30/0251 (2013.01); G06Q 30/0254 (2013.01); G06Q 30/0255 (2013.01); G06Q 30/0261 (2013.01); G06Q 30/0267 (2013.01); G06Q 30/0269 (2013.01); G06Q 30/0282 (2013.01); G06Q 30/0631 (2013.01); G06F 21/6218 (2013.01); G06Q 50/01 (2013.01)] | 12 Claims |
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1. A method comprising:
identifying, by a computing device, a plurality of records of charges (ROCs) associated with a set of merchants, wherein the ROCs represent an amount of spend by a consumer on a meal type with the set of merchants, and wherein each ROC of the plurality of ROCs comprises a data set within a data entry of a data structure, and wherein each data set comprises information at least indicating a time of transaction with point-of-sale (POS) devices of the set of merchants occurring within a first time period;
filtering, by the computing device, the plurality of ROCs based on a first merchant category, wherein the filtering comprises identifying a first subset of ROCs that are corresponding to the first merchant category, and wherein the first merchant category is the meal type, and wherein the ROCs associated with the first merchant category are based on a dining period;
filtering, by the computing device, the first subset of ROCs based on the first time period, the filtering comprising a second subset of ROCs that have a transaction that occurred during the first time period;
analyzing, by the computing device, the data set of each ROC in the first subset of ROCs and the second subset of ROCs, and based on the analysis, determining a type of transaction of the ROCs in the first subset of ROCs and the second subset of ROCs within the first time period, wherein the type of transaction represents a percentage of the meal type based on a first dining period or a second dining period within the first time period;
determining, by the computing device, the second subset of ROCs associated with the type of transaction based on the first dining period or the second dining period and determining a maximum percentage of the ROCs in the second subset of ROCs associated with the type of transaction based on the first dining period or the second dining period, a total number of ROCs in the second subset of ROCs associated with the type of transaction based on the first dining period or the second dining period, and a range of ROC amounts based on the data set of each ROC in the second subset of ROCs associated with the type of transaction based on the first dining period or the second dining period;
determining, by the computing device, an estimated price per transaction spent on the meal type based on the first dining period or the second dining period;
generating, by the computing device, a recommendation for a merchant from the set of merchants to a consumer based on the estimated price per transaction;
for each of the set of merchants,
(i) comparing a number of days since a last ROC for the merchant to a maximum number of consecutive days plus a buffer value during which the merchant was not associated with a ROC, and
(ii) based on the comparing in (i), determining a merchant status indicating an active status or an inactive status for the POS device of the merchant; and
communicating, by the computing device over a network, the recommendation to the consumer based on the merchant status.
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