US 11,972,469 B2
System and machine-readable media for selection of regulated products
Nickolas Jikomes, Seattle, WA (US); Marc Brandon Hensley, Seattle, WA (US); Jason Makuch, Seattle, WA (US); Andrew MacRae, Seattle, WA (US); Nathan Lauer, Seattle, WA (US); Matthew Bollen, Seattle, WA (US); Stephanie Smith, Seattle, WA (US); Camille Lim, Seattle, WA (US); Michael Wityk, Seattle, WA (US); Adam Hilborn, New York, NY (US); Sam Starr, Sao Paulo (BR); Christian Ramsey, Seattle, WA (US); Renata Le Duartes, III, Seattle, WA (US); Santiago Seira, San Francisco, CA (US); and Anna Zeng, Seattle, WA (US)
Assigned to Leafly Holdings, Inc., Seattle, WA (US)
Filed by Leafly Holdings, Inc., Seattle, WA (US)
Filed on Nov. 15, 2021, as Appl. No. 17/527,120.
Application 17/527,120 is a continuation of application No. 16/228,197, filed on Dec. 20, 2018, granted, now 11,205,210.
Prior Publication US 2022/0148067 A1, May 12, 2022
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0601 (2023.01); G16H 40/63 (2018.01)
CPC G06Q 30/0631 (2013.01) [G06Q 30/0607 (2013.01); G16H 40/63 (2018.01)] 14 Claims
OG exemplary drawing
 
1. A recommendation system for normalizing subjective components across different users in recommending regulated products for consumption, the system comprising:
a user interface for receiving user input from one or more user devices, the user input including (1) physiological data representative of health-monitor data from a wearable user device that represents real-time physiological condition of a user and (2) preference data for the user; and
a decision engine configured to make a recommendation of a matching product for the user based on applying a machine learning model to the user input, wherein:
the machine learning model corresponds to a combination of experience data corresponding to and normalized across multiple clinical subjects and multiple different batches of available products, wherein the experience data is weighted and correlated according to physiological information associated with the clinical subjects,
applying the machine learning model includes:
providing the user preference as an input,
predicting a user experience for the user based on applying one or more weights according to the physiological data of the user, the user preference, and the machine learning model, and
identifying in real-time the matching product that corresponds to a combination of the one or more weights and the user preference;
wherein the user interface is for communicating the recommendation for the matching product to the user.