Patents by Inventor Ashwin Srinivasan

Ashwin Srinivasan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11449362
    Abstract: The present invention relates to a method for distributing resources to different data sources based on their knowledge contribution. In addition, the invention relates to a resource distribution data system for distributing resources, a computer program product for distributing resources and a computer readable medium. It may comprise the steps of receiving values from a plurality of different data sources, blending the received values for the attributes into a dataset, assigning data lineages to the values for the attributes, receiving a query for providing a data subset, providing the data subset based on the query, determining a knowledge contribution of each of the data sources to the data subset based on the data lineages of the values and instructing a distribution of shares of resources to the different data sources based on the knowledge contribution of each of the data sources to the data subset.
    Type: Grant
    Filed: May 20, 2020
    Date of Patent: September 20, 2022
    Assignee: zeotap GmbH
    Inventors: Sathish Kumar K S, Saurabh Verma, Ashwin Srinivasan, Chaitanya Bendre, Projjol Banerjea, Daniel Heer
  • Publication number: 20220284215
    Abstract: This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 8, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Mouli RASTOGI, Syed Afshan ALI, Mrinal RAWAT, Lovekesh VIG, Puneet AGARWAL, Gautam SHROFF, Ashwin SRINIVASAN
  • Patent number: 11372105
    Abstract: A system including one or more waveguides to receive a first returned reflection having a first lag angle and generate a first waveguide signal, receive a second returned reflection having a second lag angle different from the first lag angle, and generate a second waveguide signal. The system includes one or more photodetectors to generate a first output signal within a first frequency range, and generate, based on the second waveguide signal and a second LO signal, a second output signal within a second frequency range. The system includes an optical frequency shifter (OFS) to shift a frequency of the second LO signal to cause the second output signal to shift from within the second frequency range to within the first frequency range to generate a shifted signal. The system includes a processor to receive the shifted signal to produce one or more points in a point set.
    Type: Grant
    Filed: October 6, 2021
    Date of Patent: June 28, 2022
    Assignee: Aeva, Inc.
    Inventors: Brian J. Roxworthy, Pradeep Srinivasan, Ashwin Samarao
  • Patent number: 11238383
    Abstract: Methods and systems for suggesting electronic collaborative user groups to a user account based on user account activity. The method includes identifying one or more event records corresponding to a user account. Each of the one or more event records identifying an interaction between a client device of the user account and a server computing system and corresponding to one or more themes associated with a given team. The method further includes calculating a theme score for the user account based on the retrieved one or more event records. The theme score based at least in part on the number of identified event records. The method also includes determining whether the calculated theme score exceeds a predetermined threshold score, and in response to determining that the calculated theme score exceeds the predetermined threshold score, facilitating connection of the user account and the team associated with the theme.
    Type: Grant
    Filed: June 14, 2017
    Date of Patent: February 1, 2022
    Assignee: ATLASSIAN PTY LTD.
    Inventors: Sherif Mansour, Sidney Gee-Lake Shek, Ashwin Srinivasan, Roaan Vos, Ernest Wong
  • Publication number: 20210365292
    Abstract: The present invention relates to a method for distributing resources to different data sources based on their knowledge contribution. In addition, the invention relates to a resource distribution data system for distributing resources, a computer program product for distributing resources and a computer readable medium. It may comprise the steps of receiving values from a plurality of different data sources, blending the received values for the attributes into a dataset, assigning data lineages to the values for the attributes, receiving a query for providing a data subset, providing the data subset based on the query, determining a knowledge contribution of each of the data sources to the data subset based on the data lineages of the values and instructing a distribution of shares of resources to the different data sources based on the knowledge contribution of each of the data sources to the data subset.
    Type: Application
    Filed: May 20, 2020
    Publication date: November 25, 2021
    Applicant: zeotap GmbH
    Inventors: Sathish Kumar K S, Saurabh Verma, Ashwin Srinivasan, Chaitanya Bendre, Projjol Banerjea, Daniel Heer
  • Patent number: 10936897
    Abstract: Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: March 2, 2021
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Lovekesh Vig, Gautam Shroff, Arindam Chowdhury, Rohit Rahul, Gunjan Sehgal, Vishwanath Doreswamy, Monika Sharma, Ashwin Srinivasan
  • Publication number: 20200175304
    Abstract: Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.
    Type: Application
    Filed: March 14, 2019
    Publication date: June 4, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Lovekesh VIG, Gautam SHROFF, Arindam CHOWDHURY, Rohit RAHUL, Gunjan SEHGAL, Vishwanath DORESWAMY, Monika SHARMA, Ashwin SRINIVASAN
  • Patent number: 10579931
    Abstract: 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.
    Type: Grant
    Filed: December 16, 2015
    Date of Patent: March 3, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Puneet Agarwal, Gautam Shroff, Sarmimala Saikia, Ashwin Srinivasan
  • Publication number: 20190220470
    Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.
    Type: Application
    Filed: March 25, 2019
    Publication date: July 18, 2019
    Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
  • Patent number: 10332030
    Abstract: 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.
    Type: Grant
    Filed: March 2, 2016
    Date of Patent: June 25, 2019
    Assignee: Tata Consultancy Services Limited
    Inventors: Puneet Agarwal, Gautam Shroff, Sarmimala Saikia, Ashwin Srinivasan
  • Patent number: 10311086
    Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.
    Type: Grant
    Filed: March 15, 2016
    Date of Patent: June 4, 2019
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Publication number: 20190080225
    Abstract: Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.
    Type: Application
    Filed: March 5, 2018
    Publication date: March 14, 2019
    Applicant: Tata Consultancy Services Limited
    Inventors: Puneet AGARWAL, Prerna KHURANA, Gautam SHROFF, Lovekesh VIG, Ashwin SRINIVASAN
  • Publication number: 20180365627
    Abstract: Methods and systems for suggesting electronic collaborative user groups to a user account based on user account activity. The method includes identifying one or more event records corresponding to a user account. Each of the one or more event records identifying an interaction between a client device of the user account and a server computing system and corresponding to one or more themes associated with a given team. The method further includes calculating a theme score for the user account based on the retrieved one or more event records. The theme score based at least in part on the number of identified event records. The method also includes determining whether the calculated theme score exceeds a predetermined threshold score, and in response to determining that the calculated theme score exceeds the predetermined threshold score, facilitating connection of the user account and the team associated with the theme.
    Type: Application
    Filed: June 14, 2017
    Publication date: December 20, 2018
    Applicant: ATLASSIAN PTY LTD
    Inventors: Sherif Mansour, Sidney Gee-Lake Shek, Ashwin Srinivasan, Roaan Vos, Ernest Wong
  • Publication number: 20180365626
    Abstract: Systems and methods for creating and/or managing dynamic user teams. The method includes retrieving event records corresponding to a theme, each event record identifying an interaction between a user computing device and a server computing system, the theme being a common factor underlying each of the interactions; calculating a team creation score for the theme based on the retrieved event records, wherein the team creation score is based at least in part on the number of retrieved event records corresponding to the theme; determining whether the theme meets an implicit team creation criteria by comparing the team creation score with a threshold team creation score; in response to determining that the implicit team creation criteria are met, automatically creating the implicit team; and automatically adding at least two members to the implicit team, the at least two members meeting a member addition criteria associated with the implicit team.
    Type: Application
    Filed: June 14, 2017
    Publication date: December 20, 2018
    Applicant: ATLASSIAN PTY LTD
    Inventors: Sherif Mansour, Sidney Gee-Lake Shek, Ashwin Srinivasan, Roaan Vos, Ernest Wong
  • Publication number: 20180352430
    Abstract: Systems and methods for automatically creating electronic access accounts at a service provider system are disclosed.
    Type: Application
    Filed: May 30, 2017
    Publication date: December 6, 2018
    Applicant: ATLASSIAN PTY LTD
    Inventors: Sherif Mansour, Sidney Gee-Lake Shek, Ashwin Srinivasan
  • Patent number: 9996617
    Abstract: 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.
    Type: Grant
    Filed: June 17, 2016
    Date of Patent: June 12, 2018
    Assignee: Tata Consultancy Services Limited
    Inventors: Ehtesham Hassan, Mohit Yadav, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan
  • Publication number: 20180157977
    Abstract: System and method for training inductive logic programming enhanced deep belief network models for discrete optimization are disclosed. The system initializes (i) a dataset comprising values and (ii) a pre-defined threshold, partitions the values into a first set and a second set based on the pre-defined threshold. Using Inductive Logic Programming (ILP) engine and a domain knowledge associated with the dataset, a machine learning model is constructed on the first set and the second set to obtain Boolean features, and using the Boolean features that are being appended to the dataset, a deep belief network (DBN) model is trained to identify an optimal set of values between the first set and the second set. Using the trained DBN model, the optimal set of values are sampled to generate samples. The pre-defined threshold is adjusted based on the generated samples, and the steps are repeated to obtain optimal samples.
    Type: Application
    Filed: May 9, 2017
    Publication date: June 7, 2018
    Applicant: Tata Consultancy Services Limited
    Inventors: Sarmimala SAIKIA, Lovekesh Vig, Gautam Shroff, Puneet Agarwal, Richa Rawat, Ashwin Srinivasan
  • Publication number: 20170109653
    Abstract: 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.
    Type: Application
    Filed: March 2, 2016
    Publication date: April 20, 2017
    Applicant: Tata Consultancy Services Limited
    Inventors: Puneet AGARWAL, Gautam Shroff, Sarmimala Saikia, Ashwin Srinivasan
  • Publication number: 20160371376
    Abstract: 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.
    Type: Application
    Filed: June 17, 2016
    Publication date: December 22, 2016
    Applicant: Tata Consultancy Services Limited
    Inventors: Ehtesham HASSAN, Mohit Yadav, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan
  • Publication number: 20160196328
    Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.
    Type: Application
    Filed: March 15, 2016
    Publication date: July 7, 2016
    Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA