US 11,790,383 B2
System and method for selecting promotional products for retail
Kari Saarenvirta, Toronto (CA)
Assigned to Daisy Intelligence Corporation, Toronto (CA)
Filed by Daisy Intelligence Corporation, Toronto (CA)
Filed on Mar. 30, 2020, as Appl. No. 16/834,628.
Application 16/834,628 is a division of application No. 15/795,821, filed on Oct. 27, 2017, granted, now 11,562,386.
Claims priority of application No. CA 2982930 (CA), filed on Oct. 18, 2017.
Prior Publication US 2020/0372529 A1, Nov. 26, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/0637 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0204 (2023.01); G06Q 30/0241 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0207 (2023.01)
CPC G06Q 30/0206 (2013.01) [G06Q 10/0637 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0207 (2013.01); G06Q 30/0247 (2013.01); G06Q 30/0252 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving retail data from at least one retailer computer system;
transforming the retail data, the retail data comprising at least one selected from the group of customer data, product data, transaction data, price data, promotion data, store data and time data, the retail data corresponding to a time series having one or more time periods;
determining an at least one feature of the transformed retail data;
generating a retail promotional model for operating an intelligent agent, the retail promotional model comprising:
the at least one feature of the transformed retail data; and
a control policy for operating the intelligent agent for selecting two or more promotional products,
providing an intelligent agent module for providing the intelligent agent, the intelligent agent module comprising:
a sensor input component for receiving sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of an environment, the sensor input component correcting a simulated state of the environment based on the sensor data comprising the measured reward;
a memory component comprising a long-term memory representative of a long-term frequency response, the long-term memory storing a plurality of long-term sensor data and a plurality of long-term prior actions, and a short-term memory representative of a short-term frequency response, the short-term memory storing a plurality of short-term sensor data and a plurality of short-term prior actions, the memory component storing the sensor data in the plurality of short-term sensor data of the short-term memory, the memory component storing the sensor data in the plurality of long-term sensor data of the long-term memory;
a simulation component for executing a reinforcement learning algorithm to simulate based on the short-term memory of the memory component, the long-term memory of the memory component, the simulated state of the environment, and the retail promotional model, an expected reward based on the two or more promotional products, the expected reward simulating a sales metric of a plurality of product records in the retail data based on the two or more promotional products; and
the retail promotional model.