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1. 发明授权

US10579931B2 Interpretation of a dataset for co-occurring itemsets using a cover rule and clustering 有权
公开(公告)号：US10579931B2
公开(公告)日：2020-03-03
申请号：US14970726
申请日：2015-12-16
申请人： Tata Consultancy Services Limited
发明人： Puneet Agarwal , Gautam Shroff , Sarmimala Saikia , Ashwin Srinivasan
IPC分类号： G06N5/04 , G06Q30/02 , G06F17/16
摘要： A method and system for interpreting a dataset is described herein. The method include computing a rule set pertaining to the dataset, followed by generating a rule cover pertinent to a subset of the rule set. Further, a plurality of distances between the plurality of rule pairs in the rule cover is calculated and a distance matrix based on the calculated plurality of distances is generated. Consequently, the overlapping rules within the rule cover are clustered using the distance matrix and a representative rule from each cluster is selected. Further, at least one exception for each representative rule is determined and the dataset is interpreted using the representative rules and the at least one exception. Thereby, the method provides succinct results in terms of rules and exceptions along with multiple interpretations of the same set of transactions from the dataset, thereby providing a holistic view about the dataset.

2. 发明授权

US10288653B2 Time series analytics 有权
公开(公告)号：US10288653B2
公开(公告)日：2019-05-14
申请号：US14538999
申请日：2014-11-12
申请人： TATA CONSULTANCY SERVICES LIMITED
发明人： Puneet Agarwal , Gautam Shroff , Rishabh Gupta
IPC分类号： G01R13/00 , G06K9/00 , G01R23/00 , G06K9/62
摘要： A method for identifying frequently occurring waveform patterns in time series comprises segmenting each of one or more time series into a plurality of subsequences. Further, a subsequence matrix comprising each of the plurality of subsequences is generated. Further, the subsequence matrix is processed to obtain a candidate subsequence matrix comprising a plurality of non-trivial subsequences. Further, the plurality of non-trivial subsequences is clustered into a plurality of spherical clusters of a predetermined diameter. Further, a plurality of sub-clusters for each of one or more spherical clusters is obtained based on a mean of each of the plurality of non-trivial subsequences present in the spherical cluster. Further, one or more frequent waveform clusters, depicting frequently occurring waveform patterns, are ascertained from amongst the one or more spherical clusters based on a number of non-trivial subsequences present in each of the plurality of sub-clusters of the spherical cluster.

3. 发明申请

US20160371376A1 METHODS AND SYSTEMS FOR SEARCHING LOGICAL PATTERNS 有权
标题翻译：搜索逻辑图案的方法和系统
公开(公告)号：US20160371376A1
公开(公告)日：2016-12-22
申请号：US15185980
申请日：2016-06-17
申请人： Tata Consultancy Services Limited
发明人： Ehtesham HASSAN , Mohit Yadav , Puneet Agarwal , Gautam Shroff , Ashwin Srinivasan
IPC分类号： G06F17/30 , G06N99/00
CPC分类号： G06F17/30864 , G06F17/30525 , G06F17/3053 , G06F17/30551 , G06F17/30598 , G06K9/00536 , G06K9/00785 , G06K9/6218 , G06N99/005
摘要： Methods and systems for searching logical patterns in voluminous multi sensor data from the industrial internet is provided. The method retrieves instances of patterns in time-series data where patterns are specified logically, using a sequence of symbols. The logical symbols used are a subset of the qualitative abstractions specifically, the concepts of steady, increasing, decreasing. Patterns can include symbol-sequences for multiple sensors, approximate duration as well as slope values for each symbol. To facilitate efficient querying, each sensor time-series is pre-processed into a sequence of logical symbols. Each position in the resulting compressed sequence is registered across a TRIE-based index structure corresponding to the multiple logical patterns it may belong to. Logical multi-sensor patterns are efficiently retrieved and ranked using such a structure. This method of indexing and searching provides an efficient mechanism for exploratory analysis of voluminous multi-sensor data.
摘要翻译：提供了用于从工业互联网搜索大量多传感器数据中的逻辑模式的方法和系统。该方法使用符号序列检索时间序列数据中的模式实例，其中模式是逻辑地指定的。使用的逻辑符号是定性抽象的一个子集，特别是稳定，增加，减少的概念。模式可以包括多个传感器的符号序列，每个符号的近似持续时间以及斜率值。为了方便有效的查询，每个传感器时间序列被预先处理成一系列逻辑符号。所得到的压缩序列中的每个位置都跨越与其可能属于的多个逻辑模式相对应的基于TRIE的索引结构登记。使用这样的结构有效地检索和排序逻辑多传感器图案。这种索引和搜索方法为探索性分析大量多传感器数据提供了有效的机制。

4. 发明授权

US10460477B2 System and method for interactively visualizing rules and exceptions 有权
公开(公告)号：US10460477B2
公开(公告)日：2019-10-29
申请号：US14731756
申请日：2015-06-05
申请人： Tata Consultancy Services Limited
发明人： Geetika Sharma , Gautam Shroff , Aditeya Pandey , Puneet Agarwal
IPC分类号： G06T11/00 , G06F16/26 , G06T11/20 , G06N5/02
摘要： 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.

5. 发明授权

US10332030B2 Multi-sensor data summarization 有权
公开(公告)号：US10332030B2
公开(公告)日：2019-06-25
申请号：US15058837
申请日：2016-03-02
申请人： Tata Consultancy Services Limited
发明人： Puneet Agarwal , Gautam Shroff , Sarmimala Saikia , Ashwin Srinivasan
IPC分类号： G06N7/02 , G06N7/04 , G06N7/06 , G06N7/08 , G06N20/00 , G06F16/28 , G06F16/22 , H04Q9/00 , G06N5/02
摘要： This disclosure relates generally to multi-sensor data, and more particularly to summarizing multi-sensor data. In one embodiment, the method includes computing plurality of histograms from sensor data associated with a plurality of sensors. The respective histograms of each sensor are clustered into a first plurality of sensor-clusters, and a first set of rules is extracted therefrom. First set of rules defines patterns of histograms of a set of sensors occurring frequently over a time-period. Two or more sensor-clusters from amongst the first plurality of sensor-clusters are merged selectively to obtain a second plurality of sensor-clusters. Second set of rules are extracted from the second plurality of sensor-clusters, and a set of correlated sensors are identified therefrom based on the second set of rules. Third set of rules are extracted from the set of correlated sensors, the third set of rules summarizes the multi-sensor data to represent prominent co-occurring sensor behaviors.

6. 发明授权

US10311093B2 Entity resolution from documents 有权
公开(公告)号：US10311093B2
公开(公告)日：2019-06-04
申请号：US14533866
申请日：2014-11-05
申请人： TATA Consultancy Services Limited
发明人： Puneet Agarwal , Gautam Shroff , Pankaj Malhotra
IPC分类号： G06F16/35 , G06F16/93 , G06F17/22 , G06F17/27 , G06F16/901
摘要： 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.

7. 发明授权

US10013634B2 Multi-sensor visual analytics 有权
公开(公告)号：US10013634B2
公开(公告)日：2018-07-03
申请号：US15350962
申请日：2016-11-14
申请人： Tata Consultancy Services Limited
发明人： Geetika Sharma , Gautam Shroff , Puneet Agarwal , Aditeya Pandey , Gunjan Sehgal , Kaushal Ashokbhai Paneri , Brijendra Singh
IPC分类号： G06K9/62 , G06N99/00 , G06T11/20 , G06F17/30
CPC分类号： G06K9/6212 , G06F16/26 , G06K9/6221 , G06K9/6265 , G06N20/00 , G06T11/206
摘要： 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.

8. 发明申请

US20160299938A1 ANOMALY DETECTION SYSTEM AND METHOD 审中-公开
标题翻译：异常检测系统和方法
公开(公告)号：US20160299938A1
公开(公告)日：2016-10-13
申请号：US15019681
申请日：2016-02-09
申请人： Tata Consultancy Services Limited
发明人： Pankaj MALHOTRA , Gautam Shroff , Puneet Agarwal , Lovekesh Vig
IPC分类号： G06F17/30
CPC分类号： G06F17/30371 , G06F17/18 , G06F17/30324 , G06K9/0055 , G06K9/6284 , G06N3/0445
摘要： 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.
摘要翻译：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。

9. 发明申请

US20160019567A1 WARRANTY COST ESTIMATION BASED ON COMPUTING A PROJECTED NUMBER OF FAILURES OF PRODUCTS 审中-公开
标题翻译：基于计算产品故障数量的保证成本估算
公开(公告)号：US20160019567A1
公开(公告)日：2016-01-21
申请号：US14798892
申请日：2015-07-14
申请人： Tata Consultancy Services Limited
发明人： Puneet Agarwal , Gautam Shroff , Karamjit Singh
IPC分类号： G06Q30/02 , G06Q30/00
CPC分类号： G06Q30/0202 , G06Q10/06 , G06Q30/012
摘要： Estimating warranty cost of products having multiple parts is described. In an implementation, part-failure data indicative of number of cycles at which each part fails in and after a first predefined time period is determined Sensor data and service records data are obtained to determine DTC occurrence data and DTC observance data. The DTC occurrence data and the DTC observance data are indicative of number of cycles at which each DTC associated with each part occurs and is observed for first time in the first predefined time period, respectively. Dependency parameters between the part-failure data, the DTC occurrence data and the DTC observance data are identified based on Bayesian Network that represents probabilistic relationships between the part-failure data, the DTC occurrence data and the DTC observance data. Number of failures of products in a second predefined time period is computed based on the dependency parameters for estimating the warranty cost.
摘要翻译：描述具有多个部件的产品的保修成本估算。在实现中，确定指示每个部件在第一预定时间段内和之后故障的周期数的部分故障数据被确定获得传感器数据和服务记录数据以确定DTC发生数据和DTC遵循数据。 DTC发生数据和DTC遵循数据指示与每个部分相关联的每个DTC发生并在第一预定义时间段中首次观察的周期数。基于表示部分故障数据，DTC发生数据和DTC遵循数据之间的概率关系的贝叶斯网络来识别部分故障数据，DTC发生数据和DTC遵循数据之间的依赖性参数。基于用于估计保固成本的依赖参数来计算第二预定时间段内的产品的故障数量。

10. 发明申请

US20150378963A1 DETECTING AN EVENT FROM TIME-SERIES DATA SEQUENCES 审中-公开
标题翻译：从时间序列数据序列中检测事件
公开(公告)号：US20150378963A1
公开(公告)日：2015-12-31
申请号：US14749881
申请日：2015-06-25
申请人： Tata Consultancy Services Limited
发明人： Ehtesham Hassan , Puneet Agarwal , Guatam Shroff
IPC分类号： G06F17/18
CPC分类号： G06F17/18 , G06F17/16 , G07C5/0808
摘要： The present subject matter discloses a system and a method for detecting an event from time-series data sequences. The system receives time-series data sequences generated by sensors, wherein the time-series data sequences comprise sample points. The system pairs the sample points with one another for determining pairs of the sample points. The system computes Euclidean distances and angles between the sample points for determining distance matrix and angle matrix corresponding to the sample points. Further, the system determines global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points represent 2D shape histogram for the time-series data sequence. Further, the system concatenates the 2D shape histogram for each time-series data sequence to generate a concatenated shape histogram. Finally the system matches the concatenated shape histogram to pre-stored shape histograms for determining the event.
摘要翻译：本主题公开了一种用于从时间序列数据序列检测事件的系统和方法。系统接收由传感器产生的时间序列数据序列，其中时间序列数据序列包括采样点。系统将采样点彼此对齐，以确定采样点的对。系统计算样本点之间的欧几里德距离和角度，用于确定与样本点相对应的距离矩阵和角度矩阵。此外，系统确定多对采样点的全局分布，其中多对采样点的全局分布表示时间序列数据序列的2D形状直方图。此外，系统连接每个时间序列数据序列的2D形状直方图以生成级联的形状直方图。最后，系统将级联形状直方图与预先存储的形状直方图相匹配，以确定事件。

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