Patents by Inventor Shubha Nabar
Shubha Nabar 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: 20230334367Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.Type: ApplicationFiled: May 12, 2023Publication date: October 19, 2023Inventors: Sara Beth Asher, John Emery Ball, Vitaly Gordon, Till Christian Bergmann, Fai Kan, Chalenge Masekera, Shubha Nabar, Nihar Dandekar, James Reber Lewis
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Patent number: 11663517Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.Type: GrantFiled: January 31, 2018Date of Patent: May 30, 2023Assignee: Salesforce, Inc.Inventors: Sara Beth Asher, John Emery Ball, Vitaly Gordon, Till Christian Bergmann, Kin Fai Kan, Chalenge Masekera, Shubha Nabar, Nihar Dandekar, James Reber Lewis
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Publication number: 20230110057Abstract: A method for generating a model for recommendations from an item data set for a target data set includes embedding a set of targets from the target data set in a shared coordinate space using a first embedding function, embedding a first set of items from the item data set in the shared coordinate space using a second embedding function, selecting at least one target from the set of targets, and identifying a second set of items from the first set of items that are proximate to the at least one target as candidates from the recommendations.Type: ApplicationFiled: October 7, 2021Publication date: April 13, 2023Applicant: salesforce.com, inc.Inventors: Kin Fai Kan, Chaney Lin, Mayukh Bhaowal, Shubha Nabar, Seiji J. Yamamoto
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Patent number: 11526799Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.Type: GrantFiled: January 31, 2019Date of Patent: December 13, 2022Assignee: Salesforce, Inc.Inventors: Kevin Moore, Leah McGuire, Eric Wayman, Shubha Nabar, Vitaly Gordon, Sarah Aerni
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Patent number: 10984283Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.Type: GrantFiled: September 10, 2019Date of Patent: April 20, 2021Assignee: salesforce.com, inc.Inventors: Sarah Aerni, Natalie Casey, Shubha Nabar, Melissa Runfeldt, Sara Beth Asher
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Publication number: 20210073579Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.Type: ApplicationFiled: September 10, 2019Publication date: March 11, 2021Inventors: Sarah Aerni, Natalie Casey, Shubha Nabar, Melissa Runfeldt, Sara Beth Asher
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Patent number: 10778628Abstract: A method for improving mass messaging in an electronic messaging system includes receiving recipient data describing a response of each of one or more recipients to receiving a prior message, generating predictor data based on the recipient data, where the predictor data indicates a plurality of predictors of recipient behavior in response to a message, identifying one or more top predictors of recipient behavior, the one or more top predictors being selected from among the plurality of predictors based on preferred recipient behaviors, generating, for each of the one or more recipients and from the recipient data, one or more predictive scores for each combination of top predictor and recipient, and assigning, based on one or more predictive scores of a specific recipient, the specific recipient to a specific persona, wherein the specific persona describes an expected behavior of the recipient.Type: GrantFiled: October 3, 2017Date of Patent: September 15, 2020Assignee: salesforce.com, inc.Inventors: Brian Brechbuhl, John Grotland, Rick Munoz, Leslie Fine, Leah McGuire, Shubha Nabar, Vitaly Gordon, Xiuchai (Meko) Xu
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Publication number: 20200057958Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.Type: ApplicationFiled: January 31, 2019Publication date: February 20, 2020Inventors: Kevin Moore, Leah McGuire, Eric Wayman, Shubha Nabar, Vitaly Gordon, Sarah Aerni
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Publication number: 20200057959Abstract: Instances of data associated with hindsight bias in a training set of data for a machine learning system can be reduced. A first set of data, having a first set of fields, can be received. Data in a first field can be analyzed with respect to data in a second field corresponding to an event to be predicted. A result can be that the data in the first field is associated with hindsight bias. A second set of data, having a second set of fields, can be produced. The second set of fields can exclude the first field. One or more features associated with the second set of data can be generated. A third set of data, having the second set of fields and fields that correspond to the one or more features, can be produced. The training set of data can be produced using the third set of data.Type: ApplicationFiled: January 31, 2019Publication date: February 20, 2020Inventors: Kevin Moore, Leah McGuire, Matvey Tovbin, Mayukh Bhaowal, Shubha Nabar
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Publication number: 20190138946Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.Type: ApplicationFiled: January 31, 2018Publication date: May 9, 2019Inventors: Sara Beth Asher, John Emery Ball, Vitaly Gordon, Till Christian Bergmann, Kin Fai Kan, Chalenge Masekera, Shubha Nabar, Nihar Dandekar, James Reber Lewis
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Publication number: 20180096267Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating single model-based behavior predictions in an on-demand services environment in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises collecting, by a model selection and application server device (“model device”), information associated with customers of a tenant, and extracting, from the information, behavior traits of the customers as they relate to products or services offered by the tenant.Type: ApplicationFiled: September 22, 2017Publication date: April 5, 2018Inventors: Chalenge Masekera, Vitaly Gordon, Leah McGuire, Shubha Nabar
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Publication number: 20180097759Abstract: A method for improving mass messaging in an electronic messaging system includes receiving recipient data describing a response of each of one or more recipients to receiving a prior message, generating predictor data based on the recipient data, where the predictor data indicates a plurality of predictors of recipient behavior in response to a message, identifying one or more top predictors of recipient behavior, the one or more top predictors being selected from among the plurality of predictors based on preferred recipient behaviors, generating, for each of the one or more recipients and from the recipient data, one or more predictive scores for each combination of top predictor and recipient, and assigning, based on one or more predictive scores of a specific recipient, the specific recipient to a specific persona, wherein the specific persona describes an expected behavior of the recipient.Type: ApplicationFiled: October 3, 2017Publication date: April 5, 2018Inventors: Brian Brechbuhl, John Grotland, Rick Munoz, Leslie Fine, Leah McGuire, Shubha Nabar, Vitaly Gordon, Xiuchai (Meko) Xu
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Patent number: 9773045Abstract: Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.Type: GrantFiled: September 17, 2013Date of Patent: September 26, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Sandy Wong, Wei Mu, Jun Yin, Rahul Nair, Simon King, Srinivasan Badrinarayanan, Xavier Legros, Michael Ching, Kevin Haas, Shubha Nabar
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Publication number: 20140181101Abstract: Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.Type: ApplicationFiled: September 17, 2013Publication date: June 26, 2014Applicant: Microsoft CorporationInventors: Sandy Wong, Wei Mu, Jun Yin, Rahul Nair, Simon King, Srinivasan Badrinarayanan, Xavier Legros, Michael Ching, Kevin Haas, Shubha Nabar
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Patent number: 8538960Abstract: Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.Type: GrantFiled: August 5, 2011Date of Patent: September 17, 2013Assignee: Microsoft CorporationInventors: Sandy Wong, Wei Mu, Jun Yin, Rahul Nair, Simon King, Srinivasan Badrinarayanan, Xavier Legros, Michael Ching, Kevin Haas, Shubha Nabar
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Publication number: 20130110827Abstract: Systems, computer-readable media, and methods for utilizing information pertaining to one or more individuals or entities with which a user has at least one social networking relationship are provided. A search engine is configured to receive a query, to identify matching electronic documents, to rank the electronic documents, and to transmit the matching electronic documents and/or advertisements to the user in response to receiving a query. Upon receiving the query from a user, the search engine obtains a social network identifier of the user and utilizes information about the user's social networking relationships to augment the query with nonretrieval modifiers. The search engine processes the nonretrieval modifiers matching the electronic documents included in search results and ranks the results but does not use the nonretrieval modifiers to identify or retrieve results matching the query. The ranked electronic documents are included in the results and displayed in rank order to the user.Type: ApplicationFiled: October 26, 2011Publication date: May 2, 2013Applicant: MICROSOFT CORPORATIONInventors: SHUBHA NABAR, RAJESH KRISHNA SHENOY
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Publication number: 20130036114Abstract: Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.Type: ApplicationFiled: August 5, 2011Publication date: February 7, 2013Applicant: MICROSOFT CORPORATIONInventors: Sandy Wong, Wei Mu, Jun Yin, Rahul Nair, Simon King, Srinivasan Badrinarayanan, Xavier Legros, Michael Ching, Kevin Haas, Shubha Nabar