公开(公告)号：US20220188899A1

公开(公告)日：2022-06-16

申请号：US17593554

申请日：2020-08-25

Applicant: Tata Consultancy Services Limited

Inventor： PANKAJ MALHOTRA ,  PRIYANKA GUPTA ,  DIKSHA GARG ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G06Q30/06 ,  G06F16/22 ,  G06N3/04

Abstract: This disclosure relates generally to method and system for handling popularity bias in item recommendations. In an embodiment the method includes initializing an item embedding look-up matrix corresponding to items in a sequence of item-clicks with respect to a training data. L2 norm is applied to the item embedding look-up matrix to learn a normalized item embeddings. Using a neural network, a session embeddings corresponding to the sequences of item-clicks is modeled and L2 norm is applied to the session embeddings to obtain a normalized session embeddings. Relevance scores corresponding to each of the plurality of items arc obtained based on similarity between the normalized item embeddings and the normalized session embeddings. A multi-dimensional probability vector corresponding to the relevance scores for the items to be clicked in the sequence is obtained. A list of the items ordered based on the multi-dimensional probability vector is provided as recommendation.

公开(公告)号：US20170140244A1

公开(公告)日：2017-05-18

申请号：US15350962

申请日：2016-11-14

Applicant: Tata Consultancy Services Limited

Inventor： GEETIKA SHARMA ,  GAUTAM SHROFF ,  PUNEET AGARWAL ,  ADITEYA PANDEY ,  GUNJAN SEHGAL ,  KAUSHAL ASHOKBHAI PANERI ,  BRIJENDRA SINGH

IPC: G06K9/62 ,  G06T11/20 ,  G06N99/00

CPC classification number: G06K9/6212 ,  G06F17/30572 ,  G06K9/6221 ,  G06K9/6265 ,  G06N99/005 ,  G06T11/206

Abstract: This disclosure relates generally to multi-sensor visual analytics, and more particularly to method and system for multi-sensor visual analytics using machine-learning models. In one embodiment, a method for multi-sensor visual analytics includes acquiring sensor data associated with a plurality of sensors for a plurality of days of operation. A plurality of multi-dimensional histograms, having operational profiles of the plurality of sensors are computed from the sensor data. The plurality of multi-dimensional histograms are monitored, and a plurality of multi-sensor patterns are obtained from the plurality of multi-dimensional histograms. The plurality of multi-sensor patterns are indicative of one or more properties of a plurality of sensor-clusters of the plurality of sensors. One or more visual analytical tasks are performed by processing the plurality of multi-sensor patterns using at least one machine-learning model. The one or more visual models are rendered based on the processing of the multi-sensor patterns.

公开(公告)号：US20230169569A1

公开(公告)日：2023-06-01

申请号：US17813741

申请日：2022-07-20

Applicant: Tata Consultancy Services Limited

Inventor： PRIYANKA GUPTA ,  PANKAJ MALHOTRA ,  ANKIT SHARMA ,  GAUTAM SHROFF ,  LOVEKESH VIG

IPC: G06Q30/06

CPC classification number: G06Q30/0631

Abstract: Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are recommended. System described herein analyses popularity bias in session-based RS obtained via deep learning (DL) models. DL models trained on historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand source of this bias amplification, potential sources of bias at data-generation stage (user-item interactions captured as session logs) and model training stage are considered by the system for recommendation wherein popularity of item has causal effect on user-item interactions via conformity bias, and item ranking from models via biased training process due to class imbalance. While most existing approaches address only one of these effects, a comprehensive causal inference framework is implemented by present disclosure that identifies and mitigates effects at both stages.

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：US20220342919A1

公开(公告)日：2022-10-27

申请号：US17753514

申请日：2020-03-04

Applicant: Tata Consultancy Services Limited

Inventor： AMIT SANGROYA ,  GAUTAM SHROFF ,  CHANDRASEKHAR ANANTARAM ,  MRINAL RAWAT ,  PRATIK SAINI

IPC: G06F16/33 ,  G06F16/332 ,  G06F40/20

Abstract: For various applications (for example, a Virtual Assistant), mechanisms that are capable of collecting user queries and generating responses are being used. While such systems handle structured queries well, they struggle to or fail to interpret an unstructured Natural Language (NL) query. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for generating responses to unstructured Natural Language (NL) queries. The system collects at least one NL query as input at a time, and generates a sketch, where the sketch is a structured representation of the unstructured NL query. Further by processing the sketch, the system generates one or more database queries. The one or more database queries are then used to search in one or more associated databases and to retrieve matching results, which are then used to generate response to the at least one NL query.

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：US20250131185A1

公开(公告)日：2025-04-24

申请号：US18883765

申请日：2024-09-12

Applicant: Tata Consultancy Services Limited

Inventor： ARUSHI JAIN ,  SHUBHAM SINGH PALIWAL ,  MONIKA SHARMA ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G06F40/174 ,  G06F40/106 ,  G06V30/412

Abstract: Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. Present disclosure provides systems and methods that implement large language models (LLMs) coupled with deep learning based image understanding which adapt to new scenarios, including changes in user interface and variations in input data, without the need for human intervention. System of the present disclosure uses computer vision and natural language processing to perceive visible elements on graphical user interface (GUI) and convert them into a textual representation. This information is then utilized by LLMs to generate one or more navigation workflows that include a sequence of actions that are executed by a scripting engine/code to complete an assigned task from a task-request.

公开(公告)号：US20230109692A1

公开(公告)日：2023-04-13

申请号：US17822722

申请日：2022-08-26

Applicant: Tata Consultancy Services Limited

Inventor： ANUMITA DASGUPTA ,  INDRAJIT BHATTACHARYA ,  GIRISH KESHAV PALSHIKAR ,  PRATIK SAINI ,  SANGAMESHWAR SURYAKANT PATIL ,  SOHAM DATTA ,  PRABIR MALLICK ,  SAMIRAN PAL ,  SUNIL KUMAR KOPPARAPU ,  AISHWARYA CHHABRA ,  AVINASH KUMAR SINGH ,  KAUSTUV MUKHERJI ,  MEGHNA ABHISHEK PANDHARIPANDE ,  ANIKET PRAMANICK ,  ARPITA KUNDU ,  SUBHASISH GHOSH ,  CHANDRASEKHAR ANANTARAM ,  ANAND SIVASUBRAMANIAM ,  GAUTAM SHROFF

IPC: G06Q10/10 ,  G06Q10/06 ,  H04L12/18

Abstract: This disclosure relates generally to method and system for providing assistance to interviewers. Technical interviewing is immensely important for enterprise but requires significant domain expertise and investment of time. The present disclosure aids assists interviewers with a framework via an interview assistant bot. The method initiates an interview session for a job description by selecting a set of qualified candidates resume to be interviewed. Further, the IA bot recommends each interviewer with a set of question and reference answer pairs prior initiating the interview. At each interview step, the IA bot records interview history and recommends interviewer with the revised set of questions. Further, an assessment score is determined for the candidate using the reference answer extracted from a resource corpus. Additionally, statistics about the interview process is generated, such as number and nature of questions asked, and its variation across to identify outliers for corrective actions.

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.