Patents by Inventor Chetan Bansal
Chetan Bansal 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: 20240095237Abstract: Identification of content gaps based on relative user-selection rates between multiple discrete content sources. A system analyzes search log activity to determine whether users that are conducting particular types of search activities are ultimately selecting and relying upon content resources from a predefined content source of interest or, alternatively, whether such users are unsatisfied with the predefined content source of interest and are instead relying upon other third-party content sources. This particular type of analysis provides valuable insights into whether content gaps exist within the predefined content source of interest.Type: ApplicationFiled: November 28, 2023Publication date: March 21, 2024Inventors: Junia Anna GEORGE, Chetan BANSAL, Nikitha RAO, Casey Jo GOSSARD, Dung NGUYEN, David Boyd LUDWIG, IV, Curtis Dean ANDERSON
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Publication number: 20240078107Abstract: Techniques are described herein that are capable of performing quality-based action(s) regarding engineer-generated documentation associated with code and/or an API. Features are extracted from data associated with the engineer-generated documentation, which includes engineer-generated document(s). Weights are assigned to the features. The quality-based action(s) are performed. The quality-based action(s) include generating quality score(s) for the engineer-generated document(s) and/or providing a recommendation to revise a subset of the engineer-generated document(s). Each quality score is based at least in part on the weights assigned to the features that correspond to the respective engineer-generated document. The recommendation recommends performance of an operation to increase the quality of each engineer-generated document in the subset based at least in part on the weights assigned to the features that correspond to the subset.Type: ApplicationFiled: August 26, 2021Publication date: March 7, 2024Inventors: Anurag GUPTA, Chetan BANSAL, Manish Shetty MOLAHALLI
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Publication number: 20240039874Abstract: A technique is described herein for capturing signals that indicate when any calling BOT delegates control to a called BOT, or when a calling BOT is preconfigured to contact a called BOT (e.g., as conveyed by a manifest file associated with the calling BOT). The technique can leverage these signals to facilitate the selection of BOTs. For example, the technique can use the signals to improve searches performed by a search engine and/or recommendation engine. The technique can also use the signals to generate metadata items that describe the properties of the available BOTs.Type: ApplicationFiled: August 23, 2023Publication date: February 1, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Anantha Deepthi UPPALA, Chetan BANSAL
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Patent number: 11875233Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.Type: GrantFiled: July 10, 2020Date of Patent: January 16, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Manish Shetty Molahalli, Chetan Bansal, Sumit Kumar, Nikitha Rao, Nachiappan Nagappan, Thomas Michael Josef Zimmermann
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Patent number: 11868341Abstract: Identification of content gaps based on relative user-selection rates between multiple discrete content sources. A system analyzes search log activity to determine whether users that are conducting particular types of search activities are ultimately selecting and relying upon content resources from a predefined content source of interest or, alternatively, whether such users are unsatisfied with the predefined content source of interest and are instead relying upon other third-party content sources. This particular type of analysis provides valuable insights into whether content gaps exist within the predefined content source of interest.Type: GrantFiled: December 15, 2020Date of Patent: January 9, 2024Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Junia Anna George, Chetan Bansal, Nikitha Rao, Casey Jo Gossard, Dung Nguyen, David Boyd Ludwig, IV, Curtis Dean Anderson
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Publication number: 20230401103Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.Type: ApplicationFiled: June 9, 2022Publication date: December 14, 2023Inventors: Soumya RAM, Preston Tapley STEPHENSON, Alexander David FISCHER, Mahmoud SAYED, Robert Edward MINNEKER, Eli Cortex Custodio VILARINHO, Felipe VIEIRA FRUJERI, Inigo GOIRI PRESA, Sidhanth M. PANJWANI, Yandan WANG, Camille Jean COUTURIER, Jue ZHANG, Fangkai YANG, Si QIN, Qingwei LIN, Chetan BANSAL, Bowen PANG, Vivek GUPTA
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Patent number: 11777875Abstract: A technique is described herein for capturing signals that indicate when any calling BOT delegates control to a called BOT, or when a calling BOT is preconfigured to contact a called BOT (e.g., as conveyed by a manifest file associated with the calling BOT). The technique can leverage these signals to facilitate the selection of BOTs. For example, the technique can use the signals to improve searches performed by a search engine and/or recommendation engine. The technique can also use the signals to generate metadata items that describe the properties of the available BOTs.Type: GrantFiled: September 15, 2017Date of Patent: October 3, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Anantha Deepthi Uppala, Chetan Bansal
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Patent number: 11734156Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.Type: GrantFiled: September 23, 2021Date of Patent: August 22, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Chetan Bansal, Manish Shetty Molahalli, Suman Kumar Nath, Siamak Ahari, Haitao Wang, Sean A. Bowles, Kamil Ozgur Arman
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Publication number: 20230244965Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.Type: ApplicationFiled: April 10, 2023Publication date: August 3, 2023Applicant: Microsoft Technology Licensing, LLCInventors: Peeyush KUMAR, Ranveer CHANDRA, Chetan BANSAL, Dang Khoa TRAN, Emmanuel AZUH MENSAH, Michael Raymond GRANT
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Patent number: 11625627Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.Type: GrantFiled: June 30, 2020Date of Patent: April 11, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Peeyush Kumar, Ranveer Chandra, Chetan Bansal, Dang Khoa Tran, Emmanuel Azuh Mensah, Michael Raymond Grant
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Publication number: 20230091899Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.Type: ApplicationFiled: September 23, 2021Publication date: March 23, 2023Inventors: Chetan BANSAL, Manish Shetty MOLAHALLI, Suman Kumar NATH, Siamak AHARI, Haitao WANG, Sean A. BOWLES, Kamil Ozgur ARMAN
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Patent number: 11599354Abstract: Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configuration files using a machine learning algorithm that discovers change rules using a support parameter, and, a confidence parameter, and, a refinement algorithm that refines the discovered change rules. The correlated change model comprising the change rules is stored. The correlated change model can be used to identify potential issue(s) regarding a particular file (e.g., changed code or configuration file(s)). Information regarding the identified potential issue(s) can be provided to a user.Type: GrantFiled: July 18, 2019Date of Patent: March 7, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Ranjita Bhagwan, Chandra Sekhar Maddila, Aditya Kumar, Sumit Asthana, Rahul Kumar, Sonu Mehta, Chetan Bansal, Balasubramanyan Ashok, Christian Alma Bird
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Patent number: 11599814Abstract: A computer implemented method includes receiving an exception generated based on programming code, generating exception features from the received exception, the generated exception features being generated based on a set exception features derived from search logs, and executing a machine learning model on the received exception and generated exception features to provide information from the search logs identified as most helpful to resolve the received exception, wherein the machine learning model was trained on training data comprising extracted exceptions and the set of exception features derived from the search logs.Type: GrantFiled: October 21, 2019Date of Patent: March 7, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Foyzul Hassan, Chetan Bansal, Thomas Michael Josef Zimmermann, Nachiappan Nagappan, Ahmed Awadallah
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Publication number: 20230063880Abstract: Techniques are described herein that are capable of performing quality-based action(s) regarding engineer-generated documentation associated with code and/or an API. Features are extracted from data associated with the engineer-generated documentation, which includes engineer-generated document(s). Weights are assigned to the features. The quality-based action(s) are performed. The quality-based action(s) include generating quality score(s) for the engineer-generated document(s) and/or providing a recommendation to revise a subset of the engineer-generated document(s). Each quality score is based at least in part on the weights assigned to the features that correspond to the respective engineer-generated document. The recommendation recommends performance of an operation to increase the quality of each engineer-generated document in the subset based at least in part on the weights assigned to the features that correspond to the subset.Type: ApplicationFiled: August 26, 2021Publication date: March 2, 2023Inventors: Anurag GUPTA, Chetan BANSAL, Manish Shetty MOLAHALLI
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Publication number: 20220414463Abstract: The technology described herein generates automated workflows from trouble shooting guides. The automated workflow generation process described herein starts with existing TSGs as the input. A first step in the process may be identifying the computer commands in the TSG. In one aspect, the commands are identified using a sequence-to-sequence model. Once a command is identified as a command, the command is associated with an application of origin. In aspects, a second model is used to identify the application associated with the command. The second model may be a metric-based meta-learning approach to associate a command with an application. Once the commands are identified and associated with an application, they may be parsed or extracted using a regular expression, which is a special text string describing a search pattern. The structure of the natural text is then parsed to build an executable decision tree and merged with the parsed commands.Type: ApplicationFiled: May 12, 2022Publication date: December 29, 2022Inventors: Rahul MITTAL, Manish Shetty MOLAHALLI, Puneet KAPOOR, Chetan BANSAL, Tarun SHARMA, Abhilekh MALHOTRA, Sunil SINGHAL
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Patent number: 11379227Abstract: Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics.Type: GrantFiled: October 3, 2020Date of Patent: July 5, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Nikitha Rao, Chetan Bansal, Zhongyan Guan, Mark Alistair Wilson-Thomas, Nachiappan Nagappan, Thomas Michael Josef Zimmermann
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Patent number: 11336595Abstract: A system is configured to replay a selected conversation with a selected agent. The selected conversation may be selected from a plurality of previously conducted conversations with other agents. The selected agent may be selected from a plurality of available agents. The system determines various tasks, named entities, and user preferences from the selected conversation. During a replay of the selected conversation with the selected agent, the system generates responses to messages received from the selected agent based on the determined tasks, named entities, and user preferences. The system also allows a user to select whether the replayed conversation should be conducted in a passive mode or in an active mode. In a passive mode, the system generally conducts the replayed conversation autonomously whereas, in an active mode, the system requests user input before sending a response to the selected agent.Type: GrantFiled: June 26, 2020Date of Patent: May 17, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Anantha Deepthi Uppala, Chetan Bansal
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Publication number: 20220121660Abstract: Identification of content gaps based on relative user-selection rates between multiple discrete content sources. A system analyzes search log activity to determine whether users that are conducting particular types of search activities are ultimately selecting and relying upon content resources from a predefined content source of interest or, alternatively, whether such users are unsatisfied with the predefined content source of interest and are instead relying upon other third-party content sources. This particular type of analysis provides valuable insights into whether content gaps exist within the predefined content source of interest.Type: ApplicationFiled: December 15, 2020Publication date: April 21, 2022Inventors: Junia Anna GEORGE, Chetan BANSAL, Nikitha RAO, Casey Jo GOSSARD, Dung NGUYEN, David Boyd LUDWIG, IV, Curtis Dean ANDERSON
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Publication number: 20220107802Abstract: Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics.Type: ApplicationFiled: October 3, 2020Publication date: April 7, 2022Inventors: Nikitha RAO, Chetan BANSAL, Zhongyan GUAN, Mark Alistair WILSON-THOMAS, Nachiappan NAGAPPAN, Thomas Michael Josef ZIMMERMANN
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Publication number: 20220012633Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.Type: ApplicationFiled: July 10, 2020Publication date: January 13, 2022Inventors: Manish Shetty MOLAHALLI, Chetan BANSAL, Sumit KUMAR, Nikitha RAO, Nachiappan NAGAPPAN, Thomas Michael Josef ZIMMERMANN