Patents by Inventor Saran Prasad

Saran Prasad 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: 20220012654
    Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.
    Type: Application
    Filed: August 20, 2020
    Publication date: January 13, 2022
    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Balaji JANARTHANAM, Anil KUMAR, Abhishek PATNI, Vinu VARGHESE, Hari Kumar KARNATI, Saran PRASAD, Nirav Jagdish SAMPAT
  • Publication number: 20210374304
    Abstract: A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.
    Type: Application
    Filed: May 27, 2020
    Publication date: December 2, 2021
    Inventors: Balaji JANARTHANAM, Abhishek PATNI, Anil KUMAR, Vinu VARGHESE, Hari Kumar KARNATI, Saran PRASAD, Nirav Jagdish SAMPAT
  • Patent number: 11113475
    Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.
    Type: Grant
    Filed: April 15, 2019
    Date of Patent: September 7, 2021
    Assignee: Accenture Global Solutions Limited
    Inventors: Nirav Jagdish Sampat, Saran Prasad, Manish Jain, Sriram Lakshminarasimhan, Dharmesh Dhirajlal Barochia, Purnanga Prema Borah, Deepali Jain, Suhas Vinod Sane
  • Publication number: 20200342302
    Abstract: Examples of a cognitive forecasting system are defined. In an example, the system receives a forecasting requirement from a user. The system obtains parameter data from a plurality of data sources associated with the forecasting requirement and identify a parameter set therein. The system implements an artificial intelligence component to sort the parameter data into a plurality of data domains and identify a set of preponderant data domains therein. The system may update the preponderant data domains based on a modification in the plurality of data domains. The system may establish a forecasting model corresponding to the forecasting requirement by performing a cognitive learning. The system may update the forecasting model corresponding to the update in the parameter data. The system may generate a forecasting result corresponding to the forecasting requirement. The system may generate the cognitive forecasting model that may account for real time fluctuations in the data.
    Type: Application
    Filed: April 24, 2019
    Publication date: October 29, 2020
    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Priyanka PRATIHAR, Vinu VARGHESE, Anil KUMAR, Soundar RAJAN, Mukund KUMAR, Saran PRASAD, Nirav SAMPAT
  • Publication number: 20200327196
    Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.
    Type: Application
    Filed: April 15, 2019
    Publication date: October 15, 2020
    Inventors: Nirav Jagdish SAMPAT, Saran PRASAD, Manish JAIN, Sriram LAKSHMINARASIMHAN, Dharmesh DHIRAJLAL BAROCHIA, Purnanga Prema BORAH, Deepali JAIN, Suhas Vinod SANE
  • Patent number: 8689187
    Abstract: A test object can be selectively included in a test run based on predicting the behavior of the test object. In one embodiment, the present invention includes predicting how likely the test object is to produce a failure in a test run and deciding whether to include the test object in the test run based on the predicted likelihood. This likelihood of producing a failure may be based on any number of circumstances. For example, these circumstances may include the history of prior failures and/or the length of time since the test object was last included in a test run.
    Type: Grant
    Filed: May 7, 2007
    Date of Patent: April 1, 2014
    Assignee: Cadence Design Systems, Inc.
    Inventors: Steven G. Esposito, Kiran Chhabra, Saran Prasad, D. Scott Baeder
  • Publication number: 20080282124
    Abstract: A test object can be selectively included in a test run based on predicting the behavior of the test object. In one embodiment, the present invention includes predicting how likely the test object is to produce a failure in a test run and deciding whether to include the test object in the test run based on the predicted likelihood. This likelihood of producing a failure may be based on any number of circumstances. For example, these circumstances may include the history of prior failures and/or the length of time since the test object was last included in a test run.
    Type: Application
    Filed: May 7, 2007
    Publication date: November 13, 2008
    Inventors: Steven G. Esposito, Kiran Chhabra, Saran Prasad, D. Scott Baeder