公开(公告)号：US20230072173A1

公开(公告)日：2023-03-09

申请号：US17812411

申请日：2022-07-13

Applicant: Tata Consultancy Services Limited

Inventor： GARIMA GUPTA ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G16H50/20 ,  G06N5/04

Abstract: Existing techniques assume that all time varying covariates are confounding and thus attempts to balance a full state representation of a plurality of historical observants. The present disclosure processes a plurality of historical observants and treatment at a timestep t specific to each patient using an encoder network to a obtain a state representation st. A first set of disentangled representations comprising an outcome, a confounding and a treatment representation is learnt to predict an outcome t+1. The first set of disentangled representations are concatenated to obtain a unified representation and the decoder network is initialized using the unified representation to obtain a state representation st+1. A second set of disentangled representations is learnt and concatenated to predict outcome t+m+1 m+1 timesteps ahead of the timestep t and proceeding iteratively until m=τ−1.

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.

公开(公告)号：US20230072777A1

公开(公告)日：2023-03-09

申请号：US17812396

申请日：2022-07-13

Applicant: Tata Consultancy Services Limited

Inventor： GARIMA GUPTA ,  LOVEKESH VIG ,  GAUTAM SHROFF ,  MANASI MALIK

IPC: G06Q30/02

Abstract: In the world of digital advertising, optimally allocating an advertisement campaign within a fixed pre-defined budget for an advertising duration aimed at maximizing number of conversions is very important for an advertiser. Embodiments of present disclosure provides a robust and easily generalizable method of optimal allocation of advertisement campaign by formulating it as a constrained Markov Decision Process (MDP) defined by agent state comprising user state and advertiser state, action space comprising a plurality of ad campaigns, state transition routine and a cumulative reward model which rewards maximum total conversions in an advertising duration. The cumulative reward model is trained in conjunction with a deep Q-network for solving the MDP to optimally allocate advertisement campaign for an advertising duration within a constrained budget.