Patents by Inventor Sathiya Keerthi Selvaraj
Sathiya Keerthi Selvaraj 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).
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Patent number: 9141966Abstract: A system is disclosed for obtaining and aggregating opinions generated by multiple sources with respect to one or more objects. The disclosed system uses observed variables associated with an opinion and a probabilistic model to estimate latent properties of that opinion. With those latent properties, the disclosed system may enable publishers to reliably and comprehensively present object information to interested users.Type: GrantFiled: December 23, 2009Date of Patent: September 22, 2015Assignee: Yahoo! Inc.Inventors: Srujana Merugu, Arun Shankar Iyer, Ashwin Kumar V. Machanavajjhala, Sathiya Keerthi Selvaraj, Philip L. Bohannon
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Publication number: 20150100877Abstract: Methods and systems are provided that may be utilized to extract hyper-local event information from one or more web pages.Type: ApplicationFiled: June 29, 2012Publication date: April 9, 2015Inventors: Chong Long, Xin Li, Zhaohul Zheng, Sathiya Keerthi Selvaraj, Xiubo Geng
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Patent number: 8903800Abstract: Methods, systems and computer readable mediums are provided for indexing network resources. One method includes accessing, using one or more computer systems, a data store of menu items. The method further includes accessing identification information associated with one or more food providers from one or more data sources. One or more network resources are crawled based on the identification information to search for one or more menu items in the data store of menu items associated with corresponding ones of the food providers. Using the one or more computing systems, an index feed is generated, the index feed comprising the identification information of one or more of the food providers, and one or more menu items associated with the identification information of corresponding food providers based on the crawl and search.Type: GrantFiled: June 2, 2010Date of Patent: December 2, 2014Assignee: Yahoo!, Inc.Inventors: Vinay Kakade, Sathiya Keerthi Selvaraj, Philip Bohannon
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Patent number: 8849790Abstract: A classifier development process seamlessly and intelligently integrates different forms of human feedback on instances and features into the data preparation, learning and evaluation stages. A query utility based active learning approach is applicable to different types of editorial feedback. A bi-clustering based technique may be used to further speed up the active learning process.Type: GrantFiled: December 24, 2008Date of Patent: September 30, 2014Assignee: Yahoo! Inc.Inventors: Kedar Bellare, Srujana Merugu, Sathiya Keerthi Selvaraj
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Patent number: 8793239Abstract: Techniques are provided for the efficient location, processing, and retrieval of local product information derived from web pages generally locatable through form queries submitted to web pages often referred to as the “deep” or “hidden” web. In an embodiment, information such as product information and dealer-location information is located on a web page form such as a dealer-locator form. After location of a suitable web page form, editorial wrapping is performed to create an automated information extraction process. Using the automated information extractor, deep-web crawling is performed. A grid-based extraction of individual business records is performed, and matching and ingestion are performed in conjunction with a business listing database. Finally, metadata tags are added to entries in the business listing database. Metadata tags also may be added to entries in other databases.Type: GrantFiled: October 8, 2009Date of Patent: July 29, 2014Assignee: Yahoo! Inc.Inventors: Nilesh Dalvi, Raghu Ramakrishnan, Vinay Kakade, Arup Kumar Choudhury, Sathiya Keerthi Selvaraj, Philip Bohannon, Mani Abrol, David Ciemiewicz, Arun Shankar Iyer, Vipul Agarwal, Alok S. Kirpal
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Patent number: 8719096Abstract: An improved system and method for generating a maximum utility slate of advertisements for online advertisement auctions is provided. Various utility factors for each advertisement that may be a candidate in a slate of advertisements may be applied within a framework in order to generate a maximum utility slate of advertisements. Either backward or forward dynamic programming may be applied to recursively evaluate the utility of subslates of advertisements in order to generate a maximum utility slate of advertisements. In an embodiment, a network with directed edges and associated costs may be defined, and the longest path may be found in the directed network for constructing a maximum utility slate of advertisements. Various utility factors may be applied for different objectives of an auctioneer and the framework presented may be extended for revenue ordering, exclusion of bidders, ordering slates according to first and second price utilities, and so forth.Type: GrantFiled: December 20, 2006Date of Patent: May 6, 2014Assignee: Yahoo! Inc.Inventors: Sathiya Keerthi Selvaraj, John Anthony Tomlin
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Patent number: 8606564Abstract: Methods and apparatus for performing computer-implemented extraction of temporal information for business entities and events are disclosed. In one embodiment, a sequence of text is obtained. A label is assigned to one or more of a plurality of segments of the text such that each of the one or more of the plurality of segments of the text is classified as temporal data in one of a plurality of classes of temporal data. One or more rules are applied to the one or more segments of the text that have been classified as temporal data to generate a structured representation of the temporal data, where the rules include one or more schematic rules. Each of the schematic rules pertains to one or more of the plurality of classes of temporal data and indicates a structure in which temporal data in the corresponding one or more of the plurality of classes is to be stored.Type: GrantFiled: November 1, 2010Date of Patent: December 10, 2013Assignee: Yahoo! Inc.Inventors: Srujana Merugu, Sathiya Keerthi Selvaraj, Vipul Agarwal, Arup Kumar Choudhury
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Patent number: 8280829Abstract: In one embodiment, training a ranking model comprises: accessing the ranking model and an objective function of the ranking model; accessing one or more preference pairs of objects, wherein for each of the preference pairs of objects comprising a first object and a second object, there is a preference between the first object and the second object with respect to the particular reference, and the first object and the second object each has a feature vector comprising one or more feature values; and training the ranking model by minimizing the objective function using the preference pairs of objects, wherein for each of the preference pairs of objects, a difference between the first feature vector of the first object and the second feature vector of the second object is not calculated.Type: GrantFiled: July 16, 2009Date of Patent: October 2, 2012Assignee: Yahoo! Inc.Inventors: Olivier Chapelle, Sathiya Keerthi Selvaraj
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Patent number: 8271408Abstract: The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive.Type: GrantFiled: October 22, 2009Date of Patent: September 18, 2012Assignee: Yahoo! Inc.Inventors: Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj, Priyanka Garg
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Publication number: 20120109637Abstract: Methods and apparatus for performing computer-implemented extraction of temporal information for business entities and events are disclosed. In one embodiment, a sequence of text is obtained. A label is assigned to one or more of a plurality of segments of the text such that each of the one or more of the plurality of segments of the text is classified as temporal data in one of a plurality of classes of temporal data. One or more rules are applied to the one or more segments of the text that have been classified as temporal data to generate a structured representation of the temporal data, where the rules include one or more schematic rules. Each of the schematic rules pertains to one or more of the plurality of classes of temporal data and indicates a structure in which temporal data in the corresponding one or more of the plurality of classes is to be stored.Type: ApplicationFiled: November 1, 2010Publication date: May 3, 2012Applicant: YAHOO! INC.Inventors: Srujana Merugu, Sathiya Keerthi Selvaraj, Vipul Agarwal, Arup Kumar Choudhury
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Publication number: 20110302148Abstract: Methods, systems and computer readable mediums are provided for indexing network resources. One method includes accessing, using one or more computer systems, a data store of menu items. The method further includes accessing identification information associated with one or more food providers from one or more data sources. One or more network resources are crawled based on the identification information to search for one or more menu items in the data store of menu items associated with corresponding ones of the food providers. Using the one or more computing systems, an index feed is generated, the index feed comprising the identification information of one or more of the food providers, and one or more menu items associated with the identification information of corresponding food providers based on the crawl and search.Type: ApplicationFiled: June 2, 2010Publication date: December 8, 2011Applicant: YAHOO! INC.Inventors: Vinay Kakade, Sathiya Keerthi Selvaraj, Philip Bohannon
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Patent number: 7949622Abstract: Generally, the present invention provides a method and computerized system for generating a classifier model, wherein the classifier model is operative to classify web content. The method and computerized system includes a first step of defining a plurality of predictive performance measures based on a leave one out (LOO) cross validation in terms of selectable model parameters. Exemplary predictive performance measures includes smoothened predictive measures such as F-measure, weighted error rate measure, area under curve measure, by way of example. The method and computerized system further includes deriving efficient analytical expressions for predictive performance measures to compute the LOO predictive performance and their derivatives. The next step is thereupon selecting a classifier model based on the LOO predictive performance.Type: GrantFiled: December 13, 2007Date of Patent: May 24, 2011Assignee: Yahoo! Inc.Inventors: Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj
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Publication number: 20110113063Abstract: A method for identifying a brand name is described herein. The method involves obtaining category keywords associated with a category, designating a subgroup of the category keywords as brand name keywords for a particular brand name, receiving a search term, determining that the search term is a brand name keyword, and identifying the particular brand name corresponding to the brand name keyword.Type: ApplicationFiled: November 9, 2009Publication date: May 12, 2011Inventors: Bob Schulman, Sathiya Keerthi Selvaraj, Vinay Kakade, Mani Abrol, Amit Basu, Arun Shankar Iyer, Philip Bohannon
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Publication number: 20110099131Abstract: The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive.Type: ApplicationFiled: October 22, 2009Publication date: April 28, 2011Applicant: Yahoo! Inc.Inventors: Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj, Priyanka Garg
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Publication number: 20110087646Abstract: Techniques are provided for the efficient location, processing, and retrieval of local product information derived from web pages generally locatable through form queries submitted to web pages often referred to as the “deep” or “hidden” web. In an embodiment, information such as product information and dealer-location information is located on a web page form such as a dealer-locator form. After location of a suitable web page form, editorial wrapping is performed to create an automated information extraction process. Using the automated information extractor, deep-web crawling is performed. A grid-based extraction of individual business records is performed, and matching and ingestion are performed in conjunction with a business listing database. Finally, metadata tags are added to entries in the business listing database. Metadata tags also may be added to entries in other databases.Type: ApplicationFiled: October 8, 2009Publication date: April 14, 2011Inventors: Nilesh Dalvi, Raghu Ramakrishnan, Vinay Kakade, Arup Kumar Choudhury, Sathiya Keerthi Selvaraj, Philip Bohannon, Mani Abrol, David Ciemiewicz, Arun Shankar Iyer, Vipul Agarwal, Alok S. Kirpal
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Publication number: 20110016065Abstract: In one embodiment, training a ranking model comprises: accessing the ranking model and an objective function of the ranking model; accessing one or more preference pairs of objects, wherein for each of the preference pairs of objects comprising a first object and a second object, there is a preference between the first object and the second object with respect to the particular reference, and the first object and the second object each has a feature vector comprising one or more feature values; and training the ranking model by minimizing the objective function using the preference pairs of objects, wherein for each of the preference pairs of objects, a difference between the first feature vector of the first object and the second feature vector of the second object is not calculated.Type: ApplicationFiled: July 16, 2009Publication date: January 20, 2011Applicant: Yahoo! Inc.Inventors: Olivier Chapelle, Sathiya Keerthi Selvaraj
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Patent number: 7836000Abstract: An improved system and method is provided for training a multi-class support vector machine to select a common subset of features for classifying objects. A multi-class support vector machine generator may be provided for learning classification functions to classify sets of objects into classes and may include a sparse support vector machine modeling engine for training a multi-class support vector machine using scaling factors by simultaneously selecting a common subset of features iteratively for all classes from sets of features representing each of the classes. An objective function using scaling factors to ensure sparsity of features may be iteratively minimized, and features may be retained and added until a small set of features stabilizes. Alternatively, a common subset of features may be found by iteratively removing at least one feature simultaneously for all classes from an active set of features initialized to represent the entire set of training features.Type: GrantFiled: December 10, 2007Date of Patent: November 16, 2010Assignee: Yahoo! Inc.Inventors: Olivier Chapelle, Sathiya Keerthi Selvaraj
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Publication number: 20100274770Abstract: Disclosed are methods and apparatus for segmenting and labeling a collection of token sequences. A plurality of segments of one or more tokens in a token sequence collection are partially labeled with labels from a set of target labels using high precision domain-specific labelers so as to generate a partially labeled sequence collection having a plurality of labeled segments and a plurality of unlabeled segments. Any label conflicts in the partially labeled sequence collection are resolved. One or more of the labeled segments of the partially labeled sequence collection are expanded so as to cover one or more additional tokens of the partially labeled sequence collection. A statistical model, for labeling segments using local token and segment features of the sequence collection, is trained based on the partially labeled sequence collection. This trained model is then used to label the unlabeled segments and the labeled segments of the sequence collection so as to generate a labeled sequence collection.Type: ApplicationFiled: April 24, 2009Publication date: October 28, 2010Applicant: Yahoo! Inc.Inventors: Rahul Gupta, Sathiya Keerthi Selvaraj, Daniel Kifer, Srujana Merugu
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Publication number: 20100241639Abstract: Disclosed are methods and apparatus for extracting (or annotating) structured information from web content. Web content of interest from a particular domain is represented as one or more tree instances having a plurality of branching nodes that each correspond to a web object such that the tree instances correspond to one or more structured data instances. The particular domain is associated with domain knowledge that includes one or more presentation rulesets that each specifies a particular structure for a set of data instances, a domain-specific concept labeler, one or more specified properties of the web objects in the tree instances, and a concept schema that specifies a representation of the data to be extracted from the web content. A structured data instance that conforms to the concept schema is extracted from the one or more tree instances based on the domain knowledge for the particular domain.Type: ApplicationFiled: March 20, 2009Publication date: September 23, 2010Applicant: YAHOO! INC.Inventors: Daniel Kifer, Srujana Merugu, Ankur Jain, Sathiya Keerthi Selvaraj, Alok S. Kirpal, Philip L. Bohannon, Raghu Ramakrishnan
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Publication number: 20100161652Abstract: A classifier development process seamlessly and intelligently integrates different forms of human feedback on instances and features into the data preparation, learning and evaluation stages. A query utility based active learning approach is applicable to different types of editorial feedback. A bi-clustering based technique may be used to further speed up the active learning process.Type: ApplicationFiled: December 24, 2008Publication date: June 24, 2010Applicant: YAHOO! INC.Inventors: Kedar BELLARE, Srujana MERUGU, Sathiya Keerthi SELVARAJ