公开(公告)号：US20240120085A1

公开(公告)日：2024-04-11

申请号：US18480109

申请日：2023-10-03

Applicant: Tata Consultancy Services Limited

Inventor： VIVEK CHANDEL ,  AVIK GHOSE ,  MAYURI DUGGIRALA ,  ARNAB CHATTERJEE ,  SAKYAJIT BHATTACHARYA

IPC: G16H40/63

CPC classification number: G16H40/63

Abstract: Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to pre-empt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.

公开(公告)号：US20220093249A1

公开(公告)日：2022-03-24

申请号：US17374033

申请日：2021-07-13

Applicant: Tata Consultancy Services Limited

Inventor： ANKIT SHARMA ,  GARIMA GUPTA ,  RANJITHA PRASAD ,  ARNAB CHATTERJEE ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G16H50/20 ,  G16H50/70 ,  G16H70/40 ,  G06N3/08

Abstract: In presence of high-cardinality treatment variables, number of counterfactual outcomes to be estimated is much larger than number of factual observations, rendering the problem to be ill-posed. Furthermore, lack of information regarding the confounders among large number of covariates pose challenges in handling confounding bias. Essential is to find lower-dimensional manifold where an equivalent problem of causal inference can be posed, and counterfactual outcomes can be computed. Embodiments herein provide a method and system for CI in presence of high-dimensional covariates and high-cardinality treatments using Hi-CI DNN architecture comprising Hi-CI DNN model built by concatenating a decorrelation network and a modified regression network for jointly generating low-dimensional decorrelated covariates from the high-dimensional covariates, and predicting a set of outcomes for the input data set having the high-cardinality treatments comprising of the plurality of dosage levels by generating per-dosage level embedding to learn representation of the high-cardinality treatments.