公开(公告)号：US20160299938A1

公开(公告)日：2016-10-13

申请号：US15019681

申请日：2016-02-09

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Gautam Shroff ,  Puneet Agarwal ,  Lovekesh Vig

IPC: G06F17/30

CPC classification number: G06F17/30371 ,  G06F17/18 ,  G06F17/30324 ,  G06K9/0055 ,  G06K9/6284 ,  G06N3/0445

Abstract: An anomaly detection system and method is provided. The system comprising: a hardware processor; and a memory storing instructions to configure the hardware processor, wherein the hardware processor receives a first time-series data comprising a first set of points and a second time-series data comprising a second set of points, computes a first set of error vectors for each point of the first set, and a second set of error vectors for each point of the second set, each set of error vectors comprising one or more prediction errors; estimates parameters based on the first set of error vectors comprising; applies (or uses) the parameters on the second set of error vectors; and detects an anomaly in the second time-series data when the parameters are applied on the second set of error vectors.

Abstract translation: 提供了一种异常检测系统和方法。 该系统包括：硬件处理器; 以及存储器，其存储用于配置所述硬件处理器的指令，其中所述硬件处理器接收包括第一组点的第一时间序列数据和包括第二组点的第二时间序列数据，计算第一组误差向量， 第一组的每个点，以及第二组的每个点的第二组误差向量，每组错误矢量包括一个或多个预测误差; 基于所述第一组误差向量来估计参数，所述第一组误差向量包括： 在第二组误差向量上应用（或使用）参数; 并且当将参数应用于第二组误差向量时，检测第二时间序列数据中的异常。

公开(公告)号：US20220036166A1

公开(公告)日：2022-02-03

申请号：US17309432

申请日：2019-11-28

Applicant: Tata Consultancy Services Limited

Inventor： Vishnu TANKASALA VEPARALA ,  Solomon Pushparaj MANUELRAJ ,  Ankit BANSAL ,  Pankaj MALHOTRA ,  Lovekesh VIG ,  Gautam SHROFF ,  Venkataramana RUNKANA ,  Sivakumar SUBRAMANIAN ,  Aditya PAREEK ,  Vishnu Swaroopji MASAMPALLY ,  Nishit RAJ

IPC: G06N3/063 ,  G06N3/10

Abstract: This disclosure relates to optimizing an operation of an equipment by a neural network based optimizer is provided. The method include receiving, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps; training, a plurality of simulation models for each equipment instance to obtain a function (fj); processing, the external input parameters (et) to obtain a fixed-dimensional vector and passed as an input to obtain an vector (it); generating, a modified (it) from the output vector (it) based on a domain constraint value; computing, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj); and iteratively performing the steps of processing, generating, and computing reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.

公开(公告)号：US20210103812A1

公开(公告)日：2021-04-08

申请号：US17005155

申请日：2020-08-27

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Jyoti NARWARIYA ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N3/08 ,  G06F16/28

Abstract: Neural networks can be used for time series data classification. However, in a K-shot scenario in which sufficient training data is unavailable to train the neural network, the neural network may not produce desired results. Disclosed herein are a method and system for training a neural network for time series data classification. In this method, by processing a plurality of task specific data, a system generates a set of updated parameters, which is further used to train a neural network (network) till a triplet loss is below a threshold. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, and so on) such that it can solve a target task from another domain using only a small number of training samples from the target task.

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：US20190057317A1

公开(公告)日：2019-02-21

申请号：US15900482

申请日：2018-02-20

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Vishnu T. V ,  Narendhar GUGULOTHU ,  Lovekesh VIG ,  Puneet AGARWAL ,  Gautam ShROFF

IPC: G06N7/00 ,  G01M15/14

Abstract: This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.

公开(公告)号：US20210232971A1

公开(公告)日：2021-07-29

申请号：US17160197

申请日：2021-01-27

Applicant: Tata Consultancy Services Limited

Inventor： Mayank MISHRA ,  Shruti KUNDE ,  Sharod ROY CHOUDHURY ,  Amey PANDIT ,  Manoj Karunakaran NAMBIAR ,  Siddharth VERMA ,  Gautam SHROFF ,  Pankaj MALHOTRA ,  Rekha SINGHAL

IPC: G06N20/00 ,  G06F16/2458 ,  G06F16/23

Abstract: This disclosure relates generally to data meta model and meta file generation for feature engineering and training of machine learning models thereof. Conventional methods do not facilitate appropriate relevant data identification for feature engineering and also do not implement standardization for use of solution across domains. Embodiments of the present disclosure provide systems and methods wherein datasets from various sources/domains are utilized for meta file generation that is based on mapping of the dataset with a data meta model based on the domains, the meta file comprises meta data and information pertaining to action(s) being performed. Further functions are generated using the meta file and the functions are assigned to corresponding data characterized in the meta file. Further functions are invoked to generate feature vector set and machine learning model(s) are trained using the features vector set. Implementation of the generated data meta-model enables re-using of feature engineering code.

公开(公告)号：US20200012938A1

公开(公告)日：2020-01-09

申请号：US16363038

申请日：2019-03-25

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Priyanka GUPTA ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N3/08 ,  G06F16/28

Abstract: Traditional systems and methods have implemented hand-crafted feature extraction from varying length time series that results in complexity and requires domain knowledge. Building classification models requires large labeled data and is computationally expensive. Embodiments of the present disclosure implement learning models for classification tasks in multi-dimensional time series by performing feature extraction from entity's parameters via unsupervised encoder and build a non-temporal linear classifier model. A fixed-dimensional feature vector is outputted using a pre-trained unsupervised encoder, which acts as off-the shelf feature extractor. Extracted features are concatenated to learn a non-temporal linear classification model and weight is assigned to each extracted feature during learning which helps to determine relevant parameters for each class. Mapping from parameters to target class is considered while constraining the linear model to use only subset of large number of features.

公开(公告)号：US20200012921A1

公开(公告)日：2020-01-09

申请号：US16352587

申请日：2019-03-13

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Vishnu TV ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N3/04 ,  G06F11/22 ,  G06N3/08

Abstract: Estimating Remaining Useful Life (RUL) from multi-sensor time series data is difficult through manual inspection. Current machine learning and data analytics methods, for RUL estimation require large number of failed instances for training, which are rarely available in practice, and these methods cannot use information from currently operational censored instances since their failure time is unknown. Embodiments of the present disclosure provide systems and methods for estimating RUL using time series data by implementing an LSTM-RNN based ordinal regression technique, wherein during training RUL value of failed instance(s) is encoded into a vector which is given as a target to the model. Unlike a failed instance, the exact RUL for a censored instance is unknown. For using the censored instances, target vectors are generated and the objective function is modified for training wherein the trained LSTM-RNN based ordinal regression is applied on an input test time series for RUL estimation.

公开(公告)号：US20180365715A1

公开(公告)日：2018-12-20

申请号：US15781431

申请日：2016-12-02

Applicant: Tata Consultancy Services Limited

Inventor： Pankaj MALHOTRA ,  Gaurangi ANAND ,  Auon HAIDAR KAZMI ,  Lovekesh VIG ,  Puneet AGARWAL ,  Gautam Shroff

IPC: G06Q30/02 ,  G06N5/04 ,  G06Q30/06 ,  G06N7/00

Abstract: A method and a system to enable customer behavior prediction are disclosed. Temporal and aggregate features with respect to purchases made by a customer are extracted from purchase history of customers. Further, temporal and aggregate models are generated corresponding to the features extracted, wherein the temporal and aggregate models are data of a first type and data of a second type respectively. Further, a Mixture of Experts (ME) is used to process the temporal and aggregate models that are of different types of data, to build a combined model, and purchase behavior of the customer is identified based on the combined model.