Patents by Inventor Joshua M. Rosenkranz

Joshua M. Rosenkranz 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: 20240126630
    Abstract: An embodiment includes detecting a set of anomalies recorded during a first predefined window of time in log entries for a computer environment. The embodiment also includes generating cluster data representative of a cluster of anomalies from among the set of anomalies, where the cluster is formed using a lattice clustering algorithm that spatially distinguishes the cluster of anomalies from other anomalies in the set of anomalies. The embodiment also includes composing an explanation using log templates generated from log entries associated with the cluster of anomalies.
    Type: Application
    Filed: October 12, 2022
    Publication date: April 18, 2024
    Applicant: International Business Machines Corporation
    Inventors: Seema Nagar, Mudhakar Srivatsa, Pooja Aggarwal, Joshua M Rosenkranz, Dipanwita Guhathakurta, Amitkumar Manoharrao Paradkar, Rohan R. Arora
  • Patent number: 11954085
    Abstract: A computer implemented method performs data skipping in a hierarchically organized computing system. A group of processor units determines leaf node data sketches for data in leaf nodes in the hierarchically organized computing system. The leaf node data sketches summarize attributes of data in the leaf nodes. The group of processor units aggregates the leaf node data sketches at intermediate nodes in the hierarchically organized computing system to form aggregated data sketches at the intermediate nodes and retains data sketches received at the intermediate nodes from a group of child nodes to form retained data sketches. The retained data sketches are one of leaf node data sketches and the aggregated data sketches. The group of processor units searches the data using the retained data sketches and the data skipping within the hierarchically organized computing system in response to queries made to the intermediate nodes in the hierarchically organized computing system.
    Type: Grant
    Filed: September 22, 2022
    Date of Patent: April 9, 2024
    Assignee: International Business Machines Corporation
    Inventors: Mudhakar Srivatsa, Raghu Kiran Ganti, Joshua M. Rosenkranz, Linsong Chu, Tuan Minh Hoang Trong, Utpal Mangla, Satishkumar Sadagopan, Mathews Thomas
  • Publication number: 20240104075
    Abstract: A computer implemented method performs data skipping in a hierarchically organized computing system. A group of processor units determines leaf node data sketches for data in leaf nodes in the hierarchically organized computing system. The leaf node data sketches summarize attributes of data in the leaf nodes. The group of processor units aggregates the leaf node data sketches at intermediate nodes in the hierarchically organized computing system to form aggregated data sketches at the intermediate nodes and retains data sketches received at the intermediate nodes from a group of child nodes to form retained data sketches. The retained data sketches are one of leaf node data sketches and the aggregated data sketches. The group of processor units searches the data using the retained data sketches and the data skipping within the hierarchically organized computing system in response to queries made to the intermediate nodes in the hierarchically organized computing system.
    Type: Application
    Filed: September 22, 2022
    Publication date: March 28, 2024
    Inventors: MUDHAKAR SRIVATSA, RAGHU KIRAN GANTI, Joshua M. Rosenkranz, Linsong Chu, Tuan Minh HOANG TRONG, Utpal Mangla, SATISHKUMAR SADAGOPAN, Mathews Thomas
  • Patent number: 11797842
    Abstract: Aspects of the present disclosure relate to identifying friction points in customer data. In some embodiments, identifying friction points can include receiving a set of input sequence data and predicted class labels for the set of input sequence data; selecting input sequences, from the set of input sequence data, that have class labels matching a ground truth class label; reducing the selected sequences to anchor points; and grouping the reduced selected sequences into critical data set signatures using discriminatory subsequence mining.
    Type: Grant
    Filed: August 2, 2019
    Date of Patent: October 24, 2023
    Assignee: International Business Machines Corporation
    Inventors: Linsong Chu, Pranita Sharad Dewan, Raghu Kiran Ganti, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Patent number: 11727266
    Abstract: Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.
    Type: Grant
    Filed: August 2, 2019
    Date of Patent: August 15, 2023
    Assignee: International Business Machines Corporation
    Inventors: Linsong Chu, Pranita Sharad Dewan, Raghu Kiran Ganti, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Publication number: 20230252297
    Abstract: Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.
    Type: Application
    Filed: April 18, 2023
    Publication date: August 10, 2023
    Inventors: Linsong Chu, Pranita Sharad Dewan, Raghu Kiran Ganti, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Publication number: 20230176939
    Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.
    Type: Application
    Filed: December 3, 2021
    Publication date: June 8, 2023
    Inventors: Joshua M. Rosenkranz, Pranita Sharad Dewan, Mudhakar Srivatsa, Praveen Jayachandran, Chander Govindarajan, Priyanka Prakash Naik, Kavya Govindarajan
  • Publication number: 20230169354
    Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.
    Type: Application
    Filed: November 30, 2021
    Publication date: June 1, 2023
    Applicant: International Business Machines Corporation
    Inventors: Mudhakar SRIVATSA, Raghu Kiran GANTI, Carlos Henrique ANDRADE COSTA, Linsong CHU, Joshua M. ROSENKRANZ
  • Publication number: 20230169408
    Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is implemented over a data flow graph (DFG) with nodes configured to support rich semantics. The rich semantics include two or more operational semantics, and at least one lineage semantic to selectively combine features that trace lineage to a common input object. The lineage semantic is leveraged to associate training and testing data set pairs in cross validation of the trained ML models produced from parallelizing the selection of ML pipelines.
    Type: Application
    Filed: November 30, 2021
    Publication date: June 1, 2023
    Applicant: International Business Machines Corporation
    Inventors: Carlos Henrique Andrade Costa, RAGHU KIRAN GANTI, MUDHAKAR SRIVATSA, Linsong Chu, Joshua M. Rosenkranz, Tuan Minh HOANG TRONG
  • Patent number: 11656927
    Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.
    Type: Grant
    Filed: December 3, 2021
    Date of Patent: May 23, 2023
    Assignee: International Business Machines Corporation
    Inventors: Joshua M Rosenkranz, Pranita Sharad Dewan, Mudhakar Srivatsa, Praveen Jayachandran, Chander Govindarajan, Priyanka Prakash Naik, Kavya Govindarajan
  • Patent number: 11526800
    Abstract: Method and apparatus for exchanging corpora via a data broker are provided. One example method generally includes receiving, at the data broker from a holder of a first corpus application, a coreset for the first corpus and transmitting the coreset to a set of data providers. The method further includes receiving, from a first data provider of the set of data providers, a value with respect to the coreset of a second corpus associated with the first data provider and transmitting, from the data broker to the holder of the first corpus, the value. The method further includes receiving, at the data broker from the holder of the first corpus, a request to receive the second corpus and receiving the second corpus from the first data provider. The method further includes validating the value of the second corpus and transmitting the second corpus to the holder of the first corpus.
    Type: Grant
    Filed: May 17, 2019
    Date of Patent: December 13, 2022
    Assignee: International Business Machines Corporation
    Inventors: Mudhakar Srivatsa, Shiqiang Wang, Joshua M Rosenkranz, Supriyo Chakraborty, Bong Jun Ko
  • Patent number: 11481267
    Abstract: Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.
    Type: Grant
    Filed: May 28, 2020
    Date of Patent: October 25, 2022
    Assignee: International Business Machines Corporation
    Inventors: Ramya Raghavendra, Mudhakar Srivatsa, Joshua M. Rosenkranz, Pranita Sharad Dewan
  • Publication number: 20210373987
    Abstract: Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.
    Type: Application
    Filed: May 28, 2020
    Publication date: December 2, 2021
    Inventors: Ramya Raghavendra, MUDHAKAR SRIVATSA, Joshua M. Rosenkranz, Pranita Sharad Dewan
  • Patent number: 11010384
    Abstract: A request may be received to join one or more attributes of at least two independent sets of data into a data structure. The one or more attributes may include a time attribute. The two independent sets of data may be included within a data store. It may be determined that there are one or more null values associated with the join to the data structure. In response to at least the determining that there are one or more null values associated with the join, one or more values may be imputed into one or more fields corresponding to the one or more null values, wherein there are no null values in the one or more fields subsequent to the imputing.
    Type: Grant
    Filed: September 3, 2019
    Date of Patent: May 18, 2021
    Assignee: International Business Machines Corporation
    Inventors: Ramya Raghavendra, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Publication number: 20210034964
    Abstract: Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.
    Type: Application
    Filed: August 2, 2019
    Publication date: February 4, 2021
    Inventors: Linsong Chu, Pranita Sharad Dewan, Raghu Kiran Ganti, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Publication number: 20210034963
    Abstract: Aspects of the present disclosure relate to identifying friction points in customer data. In some embodiments, identifying friction points can include receiving a set of input sequence data and predicted class labels for the set of input sequence data; selecting input sequences, from the set of input sequence data, that have class labels matching a ground truth class label; reducing the selected sequences to anchor points; and grouping the reduced selected sequences into critical data set signatures using discriminatory subsequence mining.
    Type: Application
    Filed: August 2, 2019
    Publication date: February 4, 2021
    Inventors: Linsong Chu, Pranita Sharad Dewan, Raghu Kiran Ganti, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Publication number: 20200364613
    Abstract: Method and apparatus for exchanging corpora via a data broker are provided. One example method generally includes receiving, at the data broker from a holder of a first corpus application, a coreset for the first corpus and transmitting the coreset to a set of data providers. The method further includes receiving, from a first data provider of the set of data providers, a value with respect to the coreset of a second corpus associated with the first data provider and transmitting, from the data broker to the holder of the first corpus, the value. The method further includes receiving, at the data broker from the holder of the first corpus, a request to receive the second corpus and receiving the second corpus from the first data provider. The method further includes validating the value of the second corpus and transmitting the second corpus to the holder of the first corpus.
    Type: Application
    Filed: May 17, 2019
    Publication date: November 19, 2020
    Inventors: MUDHAKAR SRIVATSA, Shiqiang Wang, Joshua M. Rosenkranz, SUPRIYO CHAKRABORTY, Bong Jun KO
  • Publication number: 20200287923
    Abstract: Data associated with performances of microservices functioning in a distributed computing environment is clustered by executing an unsupervised machine learning algorithm. A representative data is selected from a cluster, selecting performed for a plurality of the clusters. Based on time series data of the representative data associated with the plurality of the clusters, causal extraction is performed. Based on the causal extraction and the plurality of the clusters, a causal graph is constructed. The causal graph is embedded into vector space. Based on the embedded vector space, an artificial neural network model can be trained for managing the distributed computing environment.
    Type: Application
    Filed: March 8, 2019
    Publication date: September 10, 2020
    Inventors: Ramya Raghavendra, Mudhakar Srivatsa, Joshua M. Rosenkranz, Christopher Streiffer
  • Publication number: 20190384768
    Abstract: A request may be received to join one or more attributes of at least two independent sets of data into a data structure. The one or more attributes may include a time attribute. The two independent sets of data may be included within a data store. It may be determined that there are one or more null values associated with the join to the data structure. In response to at least the determining that there are one or more null values associated with the join, one or more values may be imputed into one or more fields corresponding to the one or more null values, wherein there are no null values in the one or more fields subsequent to the imputing.
    Type: Application
    Filed: September 3, 2019
    Publication date: December 19, 2019
    Inventors: Ramya Raghavendra, Joshua M. Rosenkranz, Mudhakar Srivatsa
  • Patent number: 10409813
    Abstract: A request may be received to join one or more attributes of at least two independent sets of data into a data structure. The one or more attributes may include a time attribute. The two independent sets of data may be included within a data store. It may be determined that there are one or more null values associated with the join to the data structure. In response to at least the determining that there are one or more null values associated with the join, one or more values may be imputed into one or more fields corresponding to the one or more null values, wherein there are no null values in the one or more fields subsequent to the imputing.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: September 10, 2019
    Assignee: International Business Machines Corporation
    Inventors: Ramya Raghavendra, Joshua M. Rosenkranz, Mudhakar Srivatsa