US 12,488,362 B2
Hierarchical neural network based implementation for predicting out of stock products
Akash Singh, Gurgaon (IN); and Rajdeep Dua, Hyderabad (IN)
Assigned to Salesforce, Inc.
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on Feb. 18, 2022, as Appl. No. 17/675,817.
Prior Publication US 2023/0267481 A1, Aug. 24, 2023
Int. Cl. G06N 3/04 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0202 (2023.01)
CPC G06Q 30/0202 (2013.01) [G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06Q 10/087 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A system for predicting out of stock (OOS) products, the system, comprising:
one or more processors executing a hierarchical neural network (HNN), the HNN comprising:
one or more data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in one or more stores of a retailer, the disparate datasets comprising:
low-level store data including daily store visits and past occurrences of out of stock products, categorical data including store identifiers and associated city identifiers, store-level product data, and time-series product sales data including monthly or weekly sales per product;
a first level that processes the low-level store data, the categorical data, the store-level product data, and the time-series product sales data from the one or more data sources into respective learned intermediate vector representations, the first level comprising:
a convolutional neural network (CNN) layer that processes the low-level store data;
an embedding layer that processes the categorical data;
a long short-term memory (LSTM) layer that processes the time-series product sales data;
a second level comprising a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation; and
a third level comprising a feed forward network that receives the combined vector representation and outputs to the retailer an OOS probability indicating which store and product combinations are likely to have OOS products over a predetermined timeframe.