Patents by Inventor Maziyar Baran Pouyan
Maziyar Baran Pouyan 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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HIERARCHICAL DATA LABELING FOR MACHINE LEARNING USING SEMI-SUPERVISED MULTI-LEVEL LABELING FRAMEWORK
Publication number: 20240062051Abstract: Implementations are directed to receiving a plurality of data samples comprising a first set of data samples associated with respective labels and a second set of data samples to be labeled; generating a random forest structure comprising a set of decisions trees, each decision tree including nodes corresponding to the first set of data samples; adding the second set of data samples into each decision tree as additional nodes of each decision tree; merging the set of decision trees to obtain a universal graph, wherein each node corresponds to a data sample; extracting, using a graph embedding algorithm, an embedding feature for each data sample that corresponds to each node included in the universal graph; determining a distance between any pair of two data samples using respective embedding features of the two data samples; and determining a label for each of the second set of data samples using the distance.Type: ApplicationFiled: August 17, 2022Publication date: February 22, 2024Inventors: Maziyar Baran Pouyan, Mary A. Ohara, Manish Khati, Srikant Vilas Khole, Hrishikesh Satbhai, Vivek Kumar Khetan, Elena Stoyanova Eneva -
Patent number: 11625621Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.Type: GrantFiled: January 16, 2020Date of Patent: April 11, 2023Assignee: Accenture Global Solutions LimitedInventors: Maziyar Baran Pouyan, Yao A. Yang, Saeideh Shahrokh Esfahani, Andrew E. Fano, David William Vinson, Timothy M. Shea
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Patent number: 11574216Abstract: A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.Type: GrantFiled: June 19, 2020Date of Patent: February 7, 2023Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Teresa Sheausan Tung, Mohamad Mehdi Nasr-Azadani, Yao A. Yang, Zaid Tashman, Maziyar Baran Pouyan
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Patent number: 11544491Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.Type: GrantFiled: January 15, 2020Date of Patent: January 3, 2023Assignee: Accenture Global Solutions LimitedInventors: Maziyar Baran Pouyan, Yao A. Yang, Saeideh Shahrokh Esfahani, Andrew E. Fano, David William Vinson, Timothy M. Shea, Jesus Sanchez-Macias
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Publication number: 20220358336Abstract: An Artificial Intelligence (AI)-based data matching and alignment system identifies similar data sources for a target data source from a data corpus and generates a knowledge graph that enables downstream applications seamless access to data in the data corpus. The system extracts column features at different levels for the target data source and a plurality of data sources from the data corpus. Feature matrices are built from the features of the target data source and the plurality of data sources. Candidate data sources similar to the target data source are filtered from the plurality of data sources using the feature matrices. The tree-based similarity is estimated and K Nearest Neighbor (KNN) graphs are built to identify columns from the candidate data sources that are similar to columns of the target data source to build the knowledge graph.Type: ApplicationFiled: August 25, 2021Publication date: November 10, 2022Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Neda ABOLHASSSANI, Maziyar Baran POUYAN, Teresa Sheausan TUNG, Andrew FANO, Sayantan MITRA
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Patent number: 11412305Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.Type: GrantFiled: July 15, 2021Date of Patent: August 9, 2022Assignee: Accenture Global Solutions LimitedInventors: Andrew E. Fano, Maziyar Baran Pouyan, Milind Savagaonkar, Saeideh Shahrokh Esfahani, David William Vinson, Ritesh Dhananjay Nikose
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Publication number: 20220030309Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.Type: ApplicationFiled: July 15, 2021Publication date: January 27, 2022Inventors: Andrew E. Fano, Maziyar Baran Pouyan, Milind Savagaonkar, Saeideh Shahrokh Esfahani, David William Vinson, Ritesh Dhananjay Nikose
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Publication number: 20210264306Abstract: A device may receive unlabeled data associated with a particular domain and may select sets of data from the unlabeled data. The device may calculate Gaussian kernel densities and minimum distances for data points in each of the sets of data and may calculate anomaly scores for the data points based on the Gaussian kernel densities and the minimum distances for the data points. The device may train a machine learning model, with the anomaly scores for the data points, to generate a trained machine learning model that determines a single anomaly score for the data points, wherein a plurality of single anomaly scores is determined for the sets of data. The device may calculate a final anomaly score for the unlabeled data based on a combination of the plurality of single anomaly scores and may perform one or more actions based on the final anomaly score.Type: ApplicationFiled: February 10, 2021Publication date: August 26, 2021Inventors: Maziyar BARAN POUYAN, Saeideh SHAHROKH ESFAHANI, Vivek Kumar KHETAN, Andrew E. FANO
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Patent number: 11071494Abstract: Multi-modal sensing relating to joint acoustic emission and joint bioimpedance. Custom-design analog electronics and electrodes provide high resolution sensing of bioimpedance, microphones and their front-end electronics for capturing sound signals from the joints, rate sensors for identifying joint motions (linear and rotational), and a processor unit for interpretation of the signals. These components are packed into a wearable form factor, which also encapsulates the hardware required to minimize the negative effects of motion artifacts on the signals.Type: GrantFiled: May 14, 2018Date of Patent: July 27, 2021Assignee: Georgia Tech Research CorporationInventors: Omer T. Inan, Michael N. Sawka, Jennifer O. Hasler, Hakan Toreyin, Mindy L. Millard-Stafford, Geza Kogler, Sinan Hersek, Caitlin Teague, Hyeon Ki Jeong, Maziyar Baran Pouyan
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Publication number: 20210224584Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.Type: ApplicationFiled: January 16, 2020Publication date: July 22, 2021Inventors: Maziyar Baran Pouyan, Yao A. Yang, Saeideh Shahrokh Esfahani, Andrew E. Fano, David William Vinson, Timothy M. Shea
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Publication number: 20210216813Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.Type: ApplicationFiled: January 15, 2020Publication date: July 15, 2021Inventors: Maziyar Baran Pouyan, Yao A. Yang, Saeideh Shahrokh Esfahani, Andrew E. Fano, David William Vinson, Timothy M. Shea, Jesus Sanchez-Macias
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UTILIZING A NEURAL NETWORK MODEL AND HYPERBOLIC EMBEDDED SPACE TO PREDICT INTERACTIONS BETWEEN GENES
Publication number: 20210158901Abstract: In some implementations, a prediction system may receive a gene regulatory network associated with genes. The prediction system may determine interactions between the genes associated with the gene regulatory network. The prediction system may generate a hyperbolic embedded space based on the gene regulatory network and the interactions between the genes. The prediction system may determine a hyperbolic distance measure based on the hyperbolic embedded space. The prediction system may process the hyperbolic embedded space and the hyperbolic distance measure, with a neural network model, to generate predictions of interactions between the genes. The prediction system may perform one or more actions based on the predictions of interactions between the genes.Type: ApplicationFiled: November 16, 2020Publication date: May 27, 2021Inventors: Vivek Kumar KHETAN, Maziyar BARAN POUYAN -
Publication number: 20200401915Abstract: A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.Type: ApplicationFiled: June 19, 2020Publication date: December 24, 2020Applicant: Accenture Global Solutions LimitedInventors: Teresa Sheausan Tung, Mohamad Mehdi Nasr-Azadani, Yao A. Yang, Zaid Tashman, Maziyar Baran Pouyan
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Publication number: 20180289313Abstract: Multi-modal sensing relating to joint acoustic emission and joint bioimpedance. Custom-design analog electronics and electrodes provide high resolution sensing of bioimpedance, microphones and their front-end electronics for capturing sound signals from the joints, rate sensors for identifying joint motions (linear and rotational), and a processor unit for interpretation of the signals. These components are packed into a wearable form factor, which also encapsulates the hardware required to minimize the negative effects of motion artifacts on the signals.Type: ApplicationFiled: May 14, 2018Publication date: October 11, 2018Inventors: Omer T. Inan, Michael N. Sawka, Jennifer O. Hasler, Hakan Toreyin, Mindy I. Millard-Stafford, Geza Kogler, Sinan Hersek, Caitlin Teague, Hyeon Ki Jeong, Maziyar Baran Pouyan