公开(公告)号：US11593651B2

公开(公告)日：2023-02-28

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：US11379717B2

公开(公告)日：2022-07-05

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：US20210406603A1

公开(公告)日：2021-12-30

申请号：US17180976

申请日：2021-02-22

Applicant: Tata Consultancy Services Limited

Inventor： Jyoti NARWARIYA ,  Pankaj Malhotra ,  Vibhor Gupta ,  Vishnu Tankasala Veparala ,  Lovekesh Vig ,  Gautam Shroff

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Several applications capture data from sensors resulting in multi-sensor time series. Existing neural networks-based approaches for such multi-sensor/multivariate time series modeling assume fixed input-dimension/number of sensors. Such approaches can struggle in practical setting where different instances of same device/equipment come with different combinations of installed sensors. In the present disclosure, neural network models are trained from such multi-sensor time series having varying input dimensionality, owing to availability/installation of different sensors subset at each source of time series. Neural network (NN) architecture is provided for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions/sensors at test time. Such combinatorial generalization is achieved by conditioning layers of core NN-based time series model with “conditioning vector” carrying information of available sensors combination for each time series and is obtained by summarizing learned “sensor embedding vectors set” corresponding to available sensors in time series.

公开(公告)号：US12051099B2

公开(公告)日：2024-07-30

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: G06Q30/00 ,  G06F16/22 ,  G06N3/04 ,  G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06F16/2237 ,  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 are 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：US11568203B2

公开(公告)日：2023-01-31

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 ,  G06N3/08 ,  G06N20/00 ,  G06F11/22

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：US10346439B2

公开(公告)日：2019-07-09

申请号：US14635709

申请日：2015-03-02

Applicant: Tata Consultancy Services Limited

Inventor： Puneet Agarwal ,  Gautam Shroff ,  Pankaj Malhotra

IPC: G06F16/28 ,  G06F16/27 ,  G06F16/951 ,  G06F17/27 ,  G06F16/215

Abstract: The present subject matter relates to entity resolution, and in particular, relates to providing an entity resolution from documents. The method comprises obtaining a plurality of documents corresponding to a plurality of entities, from at least one data source. Upon receiving the plurality of documents, the plurality of documents is blocked into at least one bucket based on textual similarity. Further, a graph including a plurality of record vertices and at least one bucket vertex is created. The plurality of record vertices and the at least one bucket vertex are indicative of the plurality of documents and the at least one bucket, respectively. Subsequently, a notification is provided to a user for selecting one of a Bucket-Centric Parallelization (BCP) technique and a Record-Centric Parallelization (RCP) technique for resolving entities from the plurality of documents. Based on the selection, a resolved entity-document for each entity is created.

公开(公告)号：US10223403B2

公开(公告)日：2019-03-05

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 ,  G06F17/18 ,  G06K9/00 ,  G06K9/62 ,  G06N3/04

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.

公开(公告)号：US12039434B2

公开(公告)日：2024-07-16

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

CPC classification number: 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：US10719774B2

公开(公告)日：2020-07-21

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 ,  G06F11/34 ,  G06F11/07 ,  G05B23/02

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：US20150205803A1

公开(公告)日：2015-07-23

申请号：US14533866

申请日：2014-11-05

Applicant: TATA Consultancy Services Limited

Inventor： Puneet Agarwal ,  Gautam Shroff ,  Pankaj Malhotra

IPC: G06F17/30

CPC classification number: G06F17/3071 ,  G06F17/2211 ,  G06F17/2229 ,  G06F17/278 ,  G06F17/30 ,  G06F17/30011 ,  G06F17/30958

Abstract: The present subject matter relates to entity resolution, and in particular, relates to providing an entity resolution from documents. The method comprises obtaining the plurality of documents from at least one data source. The plurality of documents is blocked into at least one bucket based on textual similarity and inter-document references among the plurality of documents. Further, within each bucket, a merged document for each entity may be created based on an iterative match-merge technique. The iterative match-merge technique identifies, from the plurality of documents, at least one matching pair of documents and merges the at least one matching pair of documents to create the merged document for each entity. The merged documents may be merged to generate a resolved entity-document for each entity based on a graph clustering technique.

Abstract translation: 本主题涉及实体决议，特别涉及从文件中提供实体决议。 该方法包括从至少一个数据源获得多个文档。 基于多个文档中的文本相似性和文档间参考，将多个文档阻塞到至少一个桶中。 此外，在每个桶中，可以基于迭代匹配合并技术来创建每个实体的合并文档。 迭代匹配合并技术从多个文档中识别至少一个匹配的文档对，并且合并至少一个匹配的文档对以为每个实体创建合并的文档。 可以合并合并的文档以基于图形聚类技术为每个实体生成解析的实体文档。