US 12,354,748 B2
Health condition evaluation based on temporal analysis of dietary habits
Lijun Mei, Beijing (CN); Jie Ma, Nanjing (CN); Xin Zhou, Beijing (CN); Qi Cheng Li, Beijing (CN); and Hao Chen, Chaoyang District (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Aug. 12, 2020, as Appl. No. 16/991,020.
Prior Publication US 2022/0051793 A1, Feb. 17, 2022
Int. Cl. G16H 50/20 (2018.01); G16H 10/60 (2018.01); G16H 20/60 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)
CPC G16H 50/20 (2018.01) [G16H 10/60 (2018.01); G16H 20/60 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)] 17 Claims
 
1. A computer-implemented method comprising:
using a collection of historical profiles associated with multiple reference users, generating, by one or more processors, correlations between dietary habits and health issues of the multiple reference users, each of the correlations being determined based on a respective temporal relation sequence of a respective reference pattern associated with a respective reference user of the multiple reference users, each of the reference patterns indicating temporal characteristics of multiple dietary habits and at least one health issue of the respective reference user of the multiple reference users, the temporal relation sequence indicating a dietary habit of the multiple dietary habits and the at least one health issue of the respective reference user were aligned in time by occurring at least partially simultaneously;
determining, by the one or more processors, a target pattern of a target user at least in part based on a profile of the target user, the target pattern at least indicating temporal characteristics of multiple dietary habits of the target user;
generating, by the one or more processors, an evaluation of a health condition of the target user based on the target pattern and the correlations and based on the target pattern having a similarity to a reference pattern of at least one of the reference users that exceeds a predetermined threshold value, the similarity being determined based on the multiple dietary habits being weighted based on a ratio of the overlap of their respective time period of occurrence compared to corresponding time period of occurrence for corresponding dietary habits of the reference users, the overlap being determined for a relative time scale, wherein the evaluation is further based on a first correlation from the at least one of the reference users; and
sending, by the one or more processors, a dietary habit suggestion and a health issue notification to a target user device based on the evaluation of the health condition of the target user, wherein the health issue notification indicates a potential health issue for the target user and a recommended physical examination that relates to the potential health issue.