Patents by Inventor Raghav Bali
Raghav Bali 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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Cybersecurity for sensitive-information utterances in interactive voice sessions using risk profiles
Patent number: 11900927Abstract: An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.Type: GrantFiled: December 23, 2020Date of Patent: February 13, 2024Assignee: OPTUM TECHNOLOGY, INC.Inventors: Devikiran Ramadas, Gregory J Boss, Ninad Sathaye, Raghav Bali, Nitin Dwivedi -
Patent number: 11886824Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.Type: GrantFiled: January 28, 2022Date of Patent: January 30, 2024Assignee: Optum Technology, Inc.Inventors: Ninad D. Sathaye, Raghav Bali, Piyush Gupta, Krishnamohan Nandiraju
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Patent number: 11854553Abstract: A method comprises obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user during the interactive voice session will contain sensitive information, the subsequent utterance following the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction, whether to transmit the second audio data; and based on a determination not to transmit the second audio data: replacing, by the computing system, the second audio data with third audio data that is based on a voice of the user; and transmitting, by the computing system, the third audio data.Type: GrantFiled: December 23, 2020Date of Patent: December 26, 2023Assignee: OPTUM TECHNOLOGY, INC.Inventors: Devikiran Ramadas, Gregory J Boss, Ninad Sathaye, Raghav Bali, Nitin Dwivedi
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Patent number: 11853700Abstract: There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.Type: GrantFiled: January 31, 2023Date of Patent: December 26, 2023Assignee: Optum, Inc.Inventors: Nathan H. Funk, Eric D. Tryon, Amy L. Jensen, Sudheer Ponnala, M. P. S. Jagannadha Rao, Raghav Bali, Veera Raghavendra Chikka, Subhadip Maji, Anudeep Srivatsav Appe
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Publication number: 20230325690Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical intervention recommendation machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations using at least one of the techniques using real-time sensory timeseries data object, techniques using global baseline sensory feature data object, techniques using intermediate intervention operations, techniques using real-time risk scores, techniques using intermediate risk scores, and/or the like.Type: ApplicationFiled: April 12, 2022Publication date: October 12, 2023Inventors: Ninad D. Sathaye, Manoj Kapoor, Gregory J. Boss, Raghav Bali
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Patent number: 11741381Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.Type: GrantFiled: July 14, 2020Date of Patent: August 29, 2023Assignee: OPTUM TECHNOLOGY, INC.Inventors: V Kishore Ayyadevara, Sree Harsha Ankem, Raghav Bali, Rohan Khilnani, Vineet Shukla, Saikumar Chintareddy, Ranraj Rana Singh
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Publication number: 20230269291Abstract: An example method includes obtaining first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system (IVS); generating, based on the first audio data, a prediction regarding whether a subsequent utterance of the user in the interactive voice session will contain sensitive information; obtaining second audio data representing the subsequent utterance; determining, based on the prediction, whether to transmit the second audio data to the IVS via a first communication channel; based on a determination not to transmit the second audio data to the IVS via the first communication channel: transmitting third audio data to the IVS via the first communication channel in place of the second audio data; and transmitting the second audio data to a server via a second communication channel that bypasses the IVS.Type: ApplicationFiled: February 22, 2022Publication date: August 24, 2023Inventors: Devikiran Ramadas, Ninad D. Sathaye, Gregory J. Boss, Raghav Bali
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Publication number: 20230252338Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing intervention recommendation operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform intervention recommendations by using at least one of reinforcement learning machine learning models and event scoring machine learning models.Type: ApplicationFiled: February 10, 2022Publication date: August 10, 2023Inventors: V. Kishore Ayyadevara, Rohan Khilnani, Swaroop S. Shekar, Raghav Bali, Joseph C. Cremaldi, Fritz T. Wilhelm, Vinod Burugupalli
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Patent number: 11663790Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.Type: GrantFiled: August 18, 2021Date of Patent: May 30, 2023Assignee: Optum, Inc.Inventors: Kartik Chaudhary, Sudeep Choudhary, Raghav Bali, Anurag Das, Subhadip Maji
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Publication number: 20230059399Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.Type: ApplicationFiled: August 18, 2021Publication date: February 23, 2023Inventors: Kartik Chaudhary, Sudeep Choudhary, Raghav Bali, Anurag Das, Subhadip Maji
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Publication number: 20230059947Abstract: A method for managing sleep of a user comprises obtaining, by a computing system, sleep data and environmental data for the user; determining, by the computing system, a sleep state of the user based on the sleep data; determining, by the computing system, one or more awakening actions based on the sleep state of the user and the environmental data; and causing one or more devices in an environment of the user to perform the one or more awakening actions to awaken the user.Type: ApplicationFiled: August 10, 2021Publication date: February 23, 2023Inventors: Raghav Bali, Ninad D. Sathaye, Swapna Sourav Rout
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Publication number: 20220199093Abstract: An example method comprises obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user during the interactive voice session will contain sensitive information, the subsequent utterance following the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction, whether to transmit the second audio data; and based on a determination not to transmit the second audio data: replacing, by the computing system, the second audio data with third audio data that is based on a voice of the user; and transmitting, by the computing system, the third audio data.Type: ApplicationFiled: December 23, 2020Publication date: June 23, 2022Inventors: Devikiran Ramadas, Gregory J. Boss, Ninad Sathaye, Raghav Bali, Nitin Dwivedi
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CYBERSECURITY FOR SENSITIVE-INFORMATION UTTERANCES IN INTERACTIVE VOICE SESSIONS USING RISK PROFILES
Publication number: 20220199073Abstract: An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.Type: ApplicationFiled: December 23, 2020Publication date: June 23, 2022Inventors: Devikiran Ramadas, Gregory J Boss, Ninad Sathaye, Raghav Bali, Nitin Dwivedi -
Publication number: 20220164543Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.Type: ApplicationFiled: January 28, 2022Publication date: May 26, 2022Inventors: Ninad D. Sathaye, Raghav Bali, Piyush Gupta, Krishnamohan Nandiraju
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Patent number: 11256874Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.Type: GrantFiled: June 16, 2020Date of Patent: February 22, 2022Assignee: Optum Technology, Inc.Inventors: Ninad D. Sathaye, Raghav Bali, Piyush Gupta, Krishnamohan Nandiraju
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Publication number: 20220019913Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.Type: ApplicationFiled: July 14, 2020Publication date: January 20, 2022Inventors: V Kishore Ayyadevara, Sree Harsha Ankem, Raghav Bali, Rohan Khilnani, Vineet Shukla, Saikumar Chintareddy, Ranraj Rana Singh
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Publication number: 20210390264Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.Type: ApplicationFiled: June 16, 2020Publication date: December 16, 2021Inventors: Ninad D. Sathaye, Raghav Bali, Piyush Gupta, Krishnamohan Nandiraju
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Publication number: 20210326631Abstract: There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.Type: ApplicationFiled: August 21, 2020Publication date: October 21, 2021Inventors: Kartik Chaudhary, Raghav Bali, V Kishore Ayyadevara, Yerraguntla Yeshwanth Reddy