公开(公告)号：US11977608B2

公开(公告)日：2024-05-07

申请号：US17515834

申请日：2021-11-01

Applicant: Tata Consultancy Services Limited

Inventor： Jayita Dutta ,  Parijat Deshpande ,  Manasi Samarth Patwardhan ,  Shirish Subhash Karande ,  Shankar Kausley ,  Priya Kedia ,  Shrikant Arjunrao Kapse ,  Beena Rai

IPC: G06N3/045 ,  G01N33/02 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N5/02 ,  G06N20/10 ,  G06N20/20 ,  G06T7/00 ,  G06V10/84 ,  G06V20/68

CPC classification number: G06F18/2413 ,  G01N33/02 ,  G06F18/214 ,  G06F18/22 ,  G06T7/001 ,  G06T2207/20081 ,  G06T2207/30128 ,  G06V20/68 ,  G06V2201/10

Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.

公开(公告)号：US20220147755A1

公开(公告)日：2022-05-12

申请号：US17515834

申请日：2021-11-01

Applicant: Tata Consultancy Services Limited

Inventor： Jayita Dutta ,  Parijat Deshpande ,  Manasi Samarth Patwardhan ,  Shirish Subhash Karande ,  Shankar Kausley ,  Priya Kedia ,  Shrikant Arjunrao Kapse ,  Beena Rai

IPC: G06K9/62 ,  G01N33/02 ,  G06T7/00

Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.