Patents by Inventor Rajneesh Patil

Rajneesh Patil 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: 20250124529
    Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
    Type: Application
    Filed: December 20, 2024
    Publication date: April 17, 2025
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil, Nithyanandan Thangavelu, Jian Huang, Vijay Pratap A
  • Publication number: 20250087372
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for source data review and document compliance. The computer obtains a plurality of source documents, each source document comprising clinical trial information of the one or more clinical studies. The computer identifies, for each source document and by a natural language processing (NLP) model, a plurality of entities of the one or more clinical studies from the information related to the participants of the one or more clinical studies in the source document. The computer generates an updated NLP models configured to detect one or more events likely to have occurred among the plurality of entities, each event being associated with at least one entity from the plurality of entities. The updated NLP model is configured to update parameters in response to receiving a user input representing feedback to a model output from the updated NLP model.
    Type: Application
    Filed: September 12, 2024
    Publication date: March 13, 2025
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil, Jonas Renström, Jonathan Hill
  • Publication number: 20250087315
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating operational plans. The method includes grounding a natural language processing (NLP) model in clinical trial context documents containing information related to execution of clinical trials; receiving a request to generate a clinical trial operational plan; by the NLP model and responsive to the received request, generating the clinical trial operational plan such that the generated plan includes content based on the clinical trial context documents; providing the generated clinical trial operational plan as an output; receiving revisions to the generated clinical trial operational plan, wherein the revisions applied to the generated clinical trial operational plan correspond to a revised clinical trial operational plan; and grounding the NLP model in the revised clinical trial operational plan.
    Type: Application
    Filed: September 13, 2024
    Publication date: March 13, 2025
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil
  • Patent number: 11526953
    Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
    Type: Grant
    Filed: June 25, 2019
    Date of Patent: December 13, 2022
    Assignee: IQVIA Inc.
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil, Nithyanandan Thangavelu, Jian Huang, Vijay Pratap A
  • Publication number: 20220375560
    Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
    Type: Application
    Filed: August 8, 2022
    Publication date: November 24, 2022
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil, Nithyanandan Thangavelu, Jian Huang, Vijay Pratap A
  • Publication number: 20220359048
    Abstract: Methods and systems to automatically construct a clinical study site visit report (SVR), conduct the SVR, evaluate the SVR in real-time, and provide feedback while the SVR is being conducted. Responses to the SVR include user-selectable answers and natural language notes. Each response is evaluated as it is submitted based on a combination of pre-configured rules and a computer-trained model. If an anomaly is detected and is not already captured in the SVR, an alert is generated during performance of the SVR. The alert may include recommended remedial action.
    Type: Application
    Filed: May 5, 2021
    Publication date: November 10, 2022
    Inventors: Rajneesh Patil, Virupaxkumar Bonageri, Gargi Shastri
  • Publication number: 20200410614
    Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
    Type: Application
    Filed: June 25, 2019
    Publication date: December 31, 2020
    Inventors: Virupaxkumar Bonageri, Rajneesh Patil, Nithyanandan Thangavelu, Jian Huang, Vijay Pratap A