公开(公告)号：US20210390033A1

公开(公告)日：2021-12-16

申请号：US17345166

申请日：2021-06-11

Applicant: Tata Consultancy Services Limited

Inventor： Rekha SINGHAL ,  Gautam SHROFF ,  Dheeraj CHAHAL ,  Mayank MISHRA ,  Shruti KUNDE ,  Manoj NAMBIAR

IPC: G06F11/36 ,  G06F9/48 ,  G06N3/02 ,  G06K9/62

Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

公开(公告)号：US20210357443A1

公开(公告)日：2021-11-18

申请号：US17319635

申请日：2021-05-13

Applicant: Tata Consultancy Services Limited

Inventor： Mrinal RAWAT ,  Lovekesh VIG ,  Amit SANGROYA ,  Gautam SHROFF

IPC: G06F16/34 ,  G06F40/177 ,  G06N20/00

Abstract: This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.

公开(公告)号：US20150356752A1

公开(公告)日：2015-12-10

申请号：US14731756

申请日：2015-06-05

Applicant: Tata Consultancy Services Limited

Inventor： Geetika SHARMA ,  Gautam SHROFF ,  Aditeya PANDEY ,  Puneet AGARWAL

IPC: G06T11/00 ,  G06F17/30

CPC classification number: G06T11/001 ,  G06F16/26 ,  G06F2203/04806 ,  G06N5/025 ,  G06T11/206

Abstract: The present disclosure discloses system and method for providing perceptually efficient visualization of rules and exceptions mined from dataset. Further, parsing is performed on data-attributes associated with the rules. The data-attributes may include antecedents, consequents, ranges of the antecedents, syntax and statistics of the rules and exceptions. The visualization scheme of present disclosure present an overview first, allows semantic zooming, and then shows details on demand. Further, data attributes of the rules are mapped with visual attributes of graphical elements such as shape, color, opacity to create the perceptually efficient visualization of the rules and exceptions. Initially, the visualization shows main rule highlighting the exceptions associated and properties of the exceptions. Further, a semantic zoom slider is provided for allowing a user to navigate through different exception levels of the exception. Further, an interface is provided for obtaining additional information associated with the rules and the exceptions.

Abstract translation: 本公开公开了用于提供从数据集开采的规则和例外的感知有效的可视化的系统和方法。 此外，对与规则相关联的数据属性执行解析。 数据属性可能包括前提，后果，前提的范围，规则和异常的语法和统计。 本公开的可视化方案首先呈现概述，允许语义缩放，然后根据需要显示细节。 此外，规则的数据属性与图形元素的视觉属性（如形状，颜色，不透明度）进行映射，以创建感知上有效的可视化规则和异常。 最初，可视化显示了突出异常关联的主要规则和异常的属性。 此外，提供语义缩放滑块以允许用户在异常的不同异常级别中导航。 此外，提供了用于获得与规则和异常相关联的附加信息的接口。

公开(公告)号：US20150269242A1

公开(公告)日：2015-09-24

申请号：US14519637

申请日：2014-10-21

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Lipika DEY ,  Gautam SHROFF ,  Somya SINGH

IPC: G06F17/30 ,  H04L29/08 ,  H04L12/58

CPC classification number: G06F17/30598 ,  G06Q10/107 ,  H04L51/00 ,  H04L51/16 ,  H04L51/34

Abstract: A method for performing email analytics is described. The method includes extracting emails from the configured email repository. The emails are then grouped into mail groups based on identification of content similarity of the emails. A network graph is then constructed for each of the mail group to identify an association of emails in the mail group based on header-level analysis of emails. Thereafter, email analytics is performed on the mail groups by clustering the mail groups into mail clusters based on temporal progression of emails in the mail groups. Key phrases are then determined based on a content analysis of emails in the mail groups in the mail clusters. The key phrases are then associated with the network graphs of the mail groups.

Abstract translation: 描述了执行邮件分析的方法。 该方法包括从配置的电子邮件库中提取电子邮件。 然后根据电子邮件的内容相似性的识别将电子邮件分组成邮件组。 然后为每个邮件组构建网络图，以基于电子邮件的头级分析来识别邮件组中的电子邮件的关联。 此后，基于邮件组中的电子邮件的时间进度，通过将邮件群集群化成邮件群集来对邮件组执行电子邮件分析。 然后基于邮件群集中的邮件组中的电子邮件的内容分析来确定关键短语。 关键短语然后与邮件组的网络图相关联。

公开(公告)号：US20210326727A1

公开(公告)日：2021-10-21

申请号：US17249454

申请日：2021-03-02

Applicant: Tata Consultancy Services Limited

Inventor： Garima GUPTA ,  Ankit SHARMA ,  Ranjitha PRASAD ,  Arnab CHATTERJEE ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N5/04 ,  G06N3/04 ,  G06F16/2458

Abstract: Causality is a crucial paradigm in several domains where observational data is available. Primary goal of Causal Inference (CI) is to uncover cause-effect relationship between entities. Conventional methods face challenges in providing an accurate CI framework due to cofounding and selection bias in multiple treatment scenario. The present disclosure computes a Propensity Score (PS) from a received CI data for the plurality of subjects under test for a treatment. A Generalized Propensity Score (GPS) is computed for a plurality of treatments corresponding to the plurality of subjects by using the PS. Further, a plurality of task batches are created using the GPS and given as input to the DNN for training. Errors in factual data and in balancing representation of the DNN are rectified using a novel loss function. The trained DNN is further used for predicting the counter factual treatment response corresponding to the factual treatment data.

公开(公告)号：US20200012941A1

公开(公告)日：2020-01-09

申请号：US16506828

申请日：2019-07-09

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Pankaj MALHOTRA ,  Arpan PAL ,  Lovekesh VIG ,  Gautam SHROFF ,  Tulika BOSE ,  Ishan SAHU ,  Ayan MUKHERJEE

IPC: G06N3/08 ,  G06N3/04

Abstract: The disclosure herein describes a method and a system for generating hybrid learning techniques. The hybrid learning technique refers to learning techniques that are a combination a plurality of techniques that include of deep learning, machine learning and signal processing to enable a rich feature space representation and classifier construction. The generation of the hybrid learning techniques also considers influence/impact of domain constraints that include business requirements and computational constraints, while generating hybrid learning techniques. Further from the plurality hybrid learning techniques a single hybrid learning technique is chosen based on performance matrix based on optimization techniques.

公开(公告)号：US20200012918A1

公开(公告)日：2020-01-09

申请号：US16353375

申请日：2019-03-14

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Narendhar GUGULOTHU ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N3/04 ,  G06N3/08

Abstract: Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain an anomaly score.

公开(公告)号：US20160180229A1

公开(公告)日：2016-06-23

申请号：US14970726

申请日：2015-12-16

Applicant: Tata Consultancy Services Limited

Inventor： Puneet AGARWAL ,  Gautam SHROFF ,  Sarmimala SAIKIA ,  Ashwin SRINIVASAN

IPC: G06N5/04 ,  G06F17/16

CPC classification number: G06N5/046 ,  G06F17/16 ,  G06Q30/0201

Abstract: A method and a system for interpreting a dataset comprising a plurality of items is described herein. The method may include computing a rule set pertaining to the dataset, generating a rule cover, calculating a plurality of distances between the plurality of rule pairs in the rule cover and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs, storing the calculated plurality of distances between the plurality of rule pairs, clustering the overlapping rules within the rule cover using the distance matrix; selecting a representative rule from each cluster, determining at least one exception for each representative rule in the rule cover selected from each cluster and interpreting the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set.

Abstract translation: 本文描述了用于解释包括多个项目的数据集的方法和系统。 该方法可以包括计算属于数据集的规则集，生成规则封面，计算规则封面中的多个规则对之间的多个距离，并基于计算出的多个规则之间的多个距离生成距离矩阵 存储所计算的多个规则对之间的多个距离，使用所述距离矩阵将所述重叠规则聚类在所述规则盖内; 从每个聚类中选择代表性规则，确定从每个聚类中选择的规则覆盖中的每个代表规则的至少一个异常，并使用代表规则解释数据集，以及为规则集中的每个代表规则确定的至少一个异常。

公开(公告)号：US20240013006A1

公开(公告)日：2024-01-11

申请号：US18215939

申请日：2023-06-29

Applicant: Tata Consultancy Services Limited

Inventor： Rajat KUMAR ,  Gautam SHROFF ,  Mayur PATIDAR ,  Lovekesh VIG ,  Vaibhav VARSHNEY

IPC: G06F40/35 ,  G06N3/0895

CPC classification number: G06F40/35 ,  G06N3/0895

Abstract: Existing semi-supervised and unsupervised approaches for intent discovery require an estimate of the number of new intents present in the user logs. The present disclosure receives labeled utterances from known intents and update parameters of a pre-trained language model (PLM). Representation learning and clustering is performed iteratively using labeled and unlabeled utterances from known intents and unlabeled utterances from unknown intents to fine-tune PLM and a plurality of clusters is generated. Cluster merger algorithm is executed iteratively on generated plurality of clusters. A query cluster is obtained by randomly selecting one cluster from the plurality of clusters and by obtaining a corresponding plurality of nearest neighbors based on a cosine-similarity. A response for merging the query cluster and corresponding plurality of nearest neighbors is obtained, and a new cluster is created. The corresponding cluster representation is recalculated and each of the new cluster is interpreted as an intent.

公开(公告)号：US20200019610A1

公开(公告)日：2020-01-16

申请号：US16506521

申请日：2019-07-09

Applicant: Tata Consultancy Services Limited

Inventor： Puneet AGARWAL ,  Prerna KHURANA ,  Gautam SHROFF ,  Lovekesh VIG

IPC: G06F17/27 ,  H04L12/58 ,  G06N3/08

Abstract: Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems. Further, a response for an incoming sequence of utterances is retrieved based on classification of incoming sequence of utterances into one or more pre-created responses. The proposed model takes lesser time to retrain.