US 12,273,784 B2
Machine-learning model for detecting a device within a venue
Carrie Isaacson, San Francisco, CA (US); Kapil Mohan, Sunnyvale, CA (US); and Kai Umezawa, San Francisco, CA (US)
Assigned to ADENTRO, INC., San Francisco, CA (US)
Filed by Zenreach, Inc., San Francisco, CA (US)
Filed on Feb. 1, 2021, as Appl. No. 17/164,246.
Prior Publication US 2022/0248166 A1, Aug. 4, 2022
Int. Cl. H04W 4/021 (2018.01); G06N 20/00 (2019.01); G06Q 20/20 (2012.01); G06Q 30/0251 (2023.01); H04B 17/318 (2015.01); H04L 43/10 (2022.01)
CPC H04W 4/021 (2013.01) [G06N 20/00 (2019.01); G06Q 20/202 (2013.01); G06Q 30/0267 (2013.01); H04B 17/318 (2015.01); H04L 43/10 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A non-transitory computer readable storage medium comprising stored instructions, the instructions when executed cause at least one processor to:
generate a set of training data representative of devices previously located within boundaries associated with a physical structure and devices previously located outside the boundaries associated with the physical structure, the set of training data including characteristics of pings received from the devices previously located within the boundaries and outside the boundaries and including labels indicative of whether a device each ping was received from was located within the boundaries or outside the boundaries, wherein the pings received at times outside of hours of operation of the physical structure are labeled as located outside the boundaries;
train a neural network specific to the physical structure using the generated set of training data, wherein the neural network is configured to determine, based on weighted scores assigned to device parameters of a mobile device, whether the mobile device is physically located within the boundaries associated with the physical structure;
detect, via a plurality of wireless access points of the physical structure, a plurality of pings from a device;
measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point; and
determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.