Patents by Inventor Sudhakar Kalluri

Sudhakar Kalluri 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).

  • Publication number: 20210287130
    Abstract: Techniques are described for training machine learning (ML) models using one or more electronic lists of items previously used in campaigns and labeled with an engagement rate corresponding to the list. A vocabulary formed from a union of the one or more lists may then be used to generate at least some items of a target recipient list. An engagement rate for the target recipient list may be inferred for the target recipient list. Natural language processing (NLP) techniques may be also be applied to optimize an engagement rate of a target recipient list and/or select content for the list.
    Type: Application
    Filed: March 10, 2020
    Publication date: September 16, 2021
    Applicant: Oracle International Corporation
    Inventor: Sudhakar Kalluri
  • Publication number: 20210264202
    Abstract: The present disclosure generally relates to evaluating communication workflows comprised of tasks using machine-learning techniques. More particularly, the present disclosure relates to systems and methods for generating a prediction of a task outcome of a communication workflow, generating a recommendation of one or more tasks to add to a partial communication workflow to complete the communication workflow, and generating a vector representation of a communication workflow.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Applicant: Oracle International Corporation
    Inventors: Sudhakar Kalluri, Venkata Chandrashekar Duvvuri
  • Publication number: 20210263767
    Abstract: The present disclosure generally relates to evaluating communication workflows comprised of tasks using machine-learning techniques. More particularly, the present disclosure relates to systems and methods for generating a prediction of a task outcome of a communication workflow, generating a recommendation of one or more tasks to add to a partial communication workflow to complete the communication workflow, and generating a vector representation of a communication workflow.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Applicant: Oracle International Corporation
    Inventors: Sudhakar Kalluri, Venkata Chandrashekar Duvvuri
  • Publication number: 20210264251
    Abstract: The present disclosure generally relates to evaluating communication workflows comprised of tasks using machine-learning techniques. More particularly, the present disclosure relates to systems and methods for generating a prediction of a task outcome of a communication workflow, generating a recommendation of one or more tasks to add to a partial communication workflow to complete the communication workflow, and generating a vector representation of a communication workflow.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Applicant: Oracle International Corporation
    Inventors: Sudhakar Kalluri, Venkata Chandrashekar Duvvuri
  • Publication number: 20210158210
    Abstract: Techniques are described herein for training and evaluating machine learning (ML) models for document processing computing applications based on in-domain and out-of-domain characteristics. In some embodiments, an ML system is configured to form feature vectors by mapping unknown tokens to known tokens within a domain based, at least in part, on out-of-domain characteristics. In other embodiments, the ML system is configured to map the unknown tokens to an aggregate vector representation based on the out-of-domain characteristics. The ML system may use the feature vectors to train ML models and/or estimate unknown labels for the new documents.
    Type: Application
    Filed: November 27, 2019
    Publication date: May 27, 2021
    Applicant: Oracle International Corporation
    Inventor: Sudhakar Kalluri
  • Publication number: 20210157983
    Abstract: Techniques are described herein for training and evaluating machine learning (ML) models for document processing computing applications based on in-domain and out-of-domain characteristics. In some embodiments, an ML system is configured to form feature vectors by mapping unknown tokens to known tokens within a domain based, at least in part, on out-of-domain characteristics. In other embodiments, the ML system is configured to map the unknown tokens to an aggregate vector representation based on the out-of-domain characteristics. The ML system may use the feature vectors to train ML models and/or estimate unknown labels for the new documents.
    Type: Application
    Filed: January 13, 2020
    Publication date: May 27, 2021
    Applicant: Oracle International Corporation
    Inventor: Sudhakar Kalluri
  • Publication number: 20210141861
    Abstract: Techniques are described herein for training and evaluating machine learning (ML) models for document processing computing applications using generalized vocabulary tokens. In some embodiments, an ML system determines a set of tokens for non-textual content in a plurality of documents. The ML system generates a fixed-length vocabulary that includes the set of tokens for the non-textual content. The ML system further generates for each respective document in a training dataset of documents, a respective feature vector based at least in part on which tokens in the fixed-length vocabulary occur in the respective document. The ML system trains a ML model based at least in part on the respective feature vector for each respective document in the training dataset.
    Type: Application
    Filed: November 8, 2019
    Publication date: May 13, 2021
    Applicant: Oracle International Corporation
    Inventor: Sudhakar Kalluri
  • Publication number: 20210035025
    Abstract: Techniques for summarizing lists for machine learning operations are disclosed. In some embodiments, a machine learning system generates feature vectors for a set of items based on varying values among a set of feature attributes. The system further generates, based on the feature vectors a set of clusters and generates a summary vector for a list of items as a function of the distribution of the items within the set of clusters, where the summary vector has a length equal to how many clusters are in the set of clusters. Summary vectors may be generated for a plurality of examples within a training dataset. The system may use the summary vectors to train a machine learning model to estimate unknown labels for new examples.
    Type: Application
    Filed: July 29, 2019
    Publication date: February 4, 2021
    Applicant: Oracle International Corporation
    Inventors: Sudhakar Kalluri, Swetha Krishnakumar
  • Patent number: 10680841
    Abstract: The present disclosure generally relates to techniques for determining a segment of a content distribution plan. More specifically, the present disclosure discloses techniques for determining one or more key-value pairs of a content distribution plan by leveraging a trained machine learning model. A plurality of electronic communications may be generated based on completed key-value pairs with a content distribution plan. The plurality of electronic communications may then be distributed to a plurality of devices within a networked environment.
    Type: Grant
    Filed: February 26, 2019
    Date of Patent: June 9, 2020
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Sudhakar Kalluri, Venkata Duvvuri, Swetha Krishnakumar
  • Publication number: 20200143116
    Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.
    Type: Application
    Filed: January 3, 2020
    Publication date: May 7, 2020
    Applicant: AT&T Intellectual Property I, L.P.
    Inventors: Sudhakar Kalluri, Maisam Shahid Wasti
  • Patent number: 10579735
    Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.
    Type: Grant
    Filed: June 7, 2017
    Date of Patent: March 3, 2020
    Assignee: AT&T INTELLECTUAL PROPERTY I, L.P.
    Inventors: Sudhakar Kalluri, Maisam Shahid Wasti
  • Publication number: 20180357218
    Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.
    Type: Application
    Filed: June 7, 2017
    Publication date: December 13, 2018
    Inventors: Sudhakar Kalluri, Maisam Shahid Wasti
  • Publication number: 20180212837
    Abstract: A method may include a processor of a telecommunication service provider network receiving a training data set including service profile trajectories for subscribers of the network, each including a network service profile of a subscriber over a plurality of time periods, where for a given time period, each network service profile includes indications of whether a subscriber is subscribed to a plurality of network services. The processor may further create a predictive model based upon the training data set to predict whether a subject subscriber will be subscribed to a given network service at a designated future time period, receive a service profile trajectory for the subject subscriber, apply the service profile trajectory to the predictive model to generate a prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period, and allocate a network resource based upon the prediction.
    Type: Application
    Filed: January 25, 2017
    Publication date: July 26, 2018
    Inventors: Sudhakar Kalluri, Learie Hercules
  • Patent number: 7881391
    Abstract: Embodiments of an OFDM receiver and methods for decoding OFDM symbols of two or more data streams with reduced multiplication operations are generally described herein. Other embodiments may be described and claimed. In some embodiments, one or more terms of a modified L2-squared-norm cost function are precomputed and stored for predetermined complex symbol values of one or more tones of OFDM symbols prior to performing a searching process. During the searching process, the cost function is computed using the precomputed terms and received data symbols using shifting and adding operations, rather than multiplication operations. In other embodiments, non-L2-squared-norm cost functions are used.
    Type: Grant
    Filed: December 22, 2006
    Date of Patent: February 1, 2011
    Assignee: Intel Corporation
    Inventors: Sudhakar Kalluri, Xiao-Feng Qi, Keith A. Holt
  • Patent number: 7852951
    Abstract: Embodiments of a multicarrier receiver and method for generating soft bits in a multiple-input multiple-output system are generally described herein. In some embodiments, operational parameters for an equalizer and a soft-bit demapper in a multicarrier receiver are determined. Other embodiments may be described and claimed.
    Type: Grant
    Filed: September 30, 2005
    Date of Patent: December 14, 2010
    Assignee: Intel Corporation
    Inventors: Sudhakar Kalluri, Tein Yow Yu
  • Patent number: 7684529
    Abstract: The effects of interference are mitigated in a wireless system by estimating spatial characteristics of an interfering signal, and using those characteristics in the formation of a spatial equalizer.
    Type: Grant
    Filed: May 26, 2005
    Date of Patent: March 23, 2010
    Assignee: Intel Corporation
    Inventors: William J. Chimitt, Sudhakar Kalluri, Keith Holt
  • Publication number: 20080285740
    Abstract: A line card including: a co-channel estimator and a code selector. The line card is configured to couple to digital subscriber lines to support multi-tone modulation of communications channels thereon. The co-channel estimator is configured to estimate co-channel crosstalk coupling coefficients among selected pairs of the subscriber lines at levels for which the total crosstalk into a selected victim line among the plurality of digital subscriber lines substantially corresponds to the sum of the products of the corresponding crosstalk coupling coefficient for each remaining disturber one of the plurality of subscriber lines and a corresponding substantially unique vector transmitted thereon. The code selector couples to the co-channel estimator. The code selector is configured to select a cross-talk estimation code type and to generate substantially unique code vectors derived there from for injection into selected ones of the of subscriber lines.
    Type: Application
    Filed: April 10, 2008
    Publication date: November 20, 2008
    Applicant: IKANOS Communication, Inc., A California Corporation
    Inventors: Sigurd Schelstraete, Sudhakar Kalluri
  • Publication number: 20080152027
    Abstract: Embodiments of an OFDM receiver and methods for decoding OFDM symbols of two or more data streams with reduced multiplication operations are generally described herein. Other embodiments may be described and claimed. In some embodiments, one or more terms of a modified L2-squared-norm cost function are precomputed and stored for predetermined complex symbol values of one or more tones of OFDM symbols prior to performing a searching process. During the searching process, the cost function is computed using the precomputed terms and received data symbols using shifting and adding operations, rather than multiplication operations. In other embodiments, non-L2-squared-norm cost functions are used.
    Type: Application
    Filed: December 22, 2006
    Publication date: June 26, 2008
    Inventors: Sudhakar Kalluri, Xiao-Feng Qi, Keith A. Holt
  • Patent number: 7230976
    Abstract: In one embodiment, an apparatus includes a correlator unit that generates a set of correlator outputs and a codeword selector coupled to the set of correlator outputs to determine a received codeword therefrom. The correlator unit may have reference signals pre-compensated with intra-codeword interference.
    Type: Grant
    Filed: November 20, 2002
    Date of Patent: June 12, 2007
    Assignee: Intel Corporation
    Inventors: Keith Holt, William J. Chimitt, Sudhakar Kalluri
  • Publication number: 20070076805
    Abstract: Embodiments of a multicarrier receiver and method for generating soft bits in a multiple-input multiple-output system are generally described herein. In some embodiments, operational parameters for an equalizer and a soft-bit demapper in a multicarrier receiver are determined. Other embodiments may be described and claimed.
    Type: Application
    Filed: September 30, 2005
    Publication date: April 5, 2007
    Inventors: Sudhakar Kalluri, Tein Yu