-
公开(公告)号：US20230153342A1
公开(公告)日：2023-05-18
申请号：US18155553
申请日：2023-01-17
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , ChitraBharathi Ganapathy , Dinesh Kumar Kishorkumar Surapaneni , Tao Fang , Jun Wang
CPC classification number: G06F16/353 , G06F40/30 , G06N5/04 , G06N20/00
Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.
-
2.发明授权公开(公告)号：US11316746B1
公开(公告)日：2022-04-26
申请号：US17145690
申请日：2021-01-11
Applicant: ServiceNow, Inc.
IPC: H04L29/08 , H04L41/12 , H04L41/5009 , H04L41/16 , H04L41/147 , H04L41/0893
Abstract: Identifications of program processes executing on an information technology environment are received. The identified program processes are clustered into a plurality of different groups. Identifications of interactions between at least a portion of the program processes are received. The identified interactions are analyzed to determine one or more interaction metrics between different group pairs in the plurality of different groups. A graph representation that includes at least a portion of the plurality of different groups as graph nodes in the graph representation is generated. The graph representation includes one or more graph edges determined to be included based on the one or more interaction metrics.
-
公开(公告)号：US11080588B2
公开(公告)日：2021-08-03
申请号：US15786177
申请日：2017-10-17
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Thakur , Kannan Govindarajan
Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n−1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n−1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.
-
公开(公告)号：US20200349183A1
公开(公告)日：2020-11-05
申请号：US16434888
申请日：2019-06-07
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , ChitraBharathi Ganapathy , Dinesh Kumar Kishorkumar Surapaneni , Tao Feng , Jun Wang
Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.
-
5.发明授权公开(公告)号：US10671926B2
公开(公告)日：2020-06-02
申请号：US15405041
申请日：2017-01-12
Applicant: SERVICENOW, INC.
Inventor： Baskar Jayaraman , Debashish Chatterjee , Kannan Govindarajan , Ganesh Rajan
Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a likelihood of successfully closing each current opportunity. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.
-
Patent Agency Ranking
公开(公告)号：US20200089652A1
公开(公告)日：2020-03-19
申请号：US16135630
申请日：2018-09-19
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Madhusudan Thakur , Tao Feng , Kannan Govindarajan
Abstract: A database contains a corpus of incident reports, a machine learning (ML) model trained to calculate paragraph vectors of the incident reports, and a look-up set table that contains a list of paragraph vectors respectively associated with sets of the incident reports. A plurality of ML worker nodes each store the look-up set table and are configured to execute the ML model. An update thread is configured to: determine that the look-up set table has expired; update the look-up set table by: (i) adding a first set of incident reports received since a most recent update of the look-up set table, and (ii) removing a second set of incident reports containing timestamps that are no longer within a sliding time window; store, in the database, the look-up set table as updated; and transmit, to the ML worker nodes, respective indications that the look-up set table has been updated.
-
公开(公告)号：US10339441B2
公开(公告)日：2019-07-02
申请号：US15850395
申请日：2017-12-21
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Thakur , Kannan Govindarajan
Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n−1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n−1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.
-
公开(公告)号：US20190102683A1
公开(公告)日：2019-04-04
申请号：US15855227
申请日：2017-12-27
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Thakur , Kannan Govindarajan
Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.
-
公开(公告)号：US20180322462A1
公开(公告)日：2018-11-08
申请号：US15674379
申请日：2017-08-10
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Debashish Chatterjee , Kannan Govindarajan , Aniruddha Thakur
CPC classification number: G06Q10/103 , G06F17/243
Abstract: Systems and methods for using a mathematical model based on historical information to automatically schedule and monitor work flows are disclosed. Prediction methods that use some variables to predict unknown or future values of other variables may assist in reducing manual intervention when addressing incident reports or other task-based work items. For example, work items that are expected to conform to a supervised model built from historical customer information. Given a collection of records in a training set, each record contains a set of attributes with one of the attributes being the class. If a model can be found for the class attribute as a function of the values of the other attributes, then previously unseen records may be assigned a class as accurately as possible based on the model. A test data set is used to determine model accuracy prior to allowing general use of the model.
-
公开(公告)号：US20180322418A1
公开(公告)日：2018-11-08
申请号：US15939609
申请日：2018-03-29
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman
CPC classification number: G06N99/005 , G06F17/243 , G06F17/2785 , G06F17/28 , G06N7/005
Abstract: Systems and methods for using a mathematical model based on historical natural language inputs to automatically complete form fields are disclosed. An incident report may be defined with a set of required parameter fields such as category, priority, assignment, and classification. Incident report submission forms may also have other free text input fields providing information about a problem in the natural vocabulary of the person reporting the problem. Automatic completion of these so-called parameter fields may be based on analysis of the natural language inputs and use of machine learning techniques to determine appropriate values for the parameter fields. The machine learning techniques may include parsing the natural language input to determine a mathematical representation and application of the mathematical representation to “match” historically similar input. Once matched the parameter values from the historically similar input may be used instead of generic default values.
-
-
-
-
-
-
-
-
-
Search Results
61
records
(took 0.027 seconds)
