Patents by Inventor Rajaram Kudli
Rajaram Kudli 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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Publication number: 20240135200Abstract: One or more structural equations modeling a physical process over time may be sampled using simulated parameter values to generate input data signal values. A noise generator may be applied to the input data signal values to generate noise values. The noise values and the input data signal values may be combined to determined noisy data signal values. These noisy data signal values may in turn be used in combination with one or more states to train a prediction model.Type: ApplicationFiled: October 18, 2022Publication date: April 25, 2024Applicant: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Nagarjun Pogakula Surya Prakash, Ananda Shekappa Sonnada
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Patent number: 11954929Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.Type: GrantFiled: March 17, 2023Date of Patent: April 9, 2024Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
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Publication number: 20240054800Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.Type: ApplicationFiled: March 17, 2023Publication date: February 15, 2024Applicant: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
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Publication number: 20240053739Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.Type: ApplicationFiled: March 17, 2023Publication date: February 15, 2024Applicant: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
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Publication number: 20240027974Abstract: A first plurality of predictor values occurring during or before a first time interval may be received. An estimated outcome value may be determined for a second time interval by applying a prediction model via a processor to the first plurality of predictor values. A designated outcome value occurring during the second time interval and a second plurality of predictor values occurring during or before the second time interval may be received. An error value may be determined based on the estimated outcome value and the designated outcome value. A drift value for a second time interval may be determined by fitting a function to the second plurality of predictor values. The prediction model may be updated when it is determined that the drift value exceeds a designated drift threshold or that the error value exceeds a designated error threshold.Type: ApplicationFiled: June 30, 2023Publication date: January 25, 2024Applicant: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Dushyanth Gokhale
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Patent number: 11783233Abstract: A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.Type: GrantFiled: January 11, 2023Date of Patent: October 10, 2023Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Nagarjun Pogakula Surya Prakash, Ananda Shekappa Sonnada
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Patent number: 11740905Abstract: In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.Type: GrantFiled: July 25, 2022Date of Patent: August 29, 2023Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng, Dushyanth Gokhale
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Patent number: 11636697Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.Type: GrantFiled: August 9, 2022Date of Patent: April 25, 2023Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng
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Patent number: 11635753Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.Type: GrantFiled: August 15, 2022Date of Patent: April 25, 2023Assignee: DIMAAG-AI, Inc.Inventors: Rajaram Kudli, Satish Padmanabhan, Fuk Ho Pius Ng