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公开(公告)号：US20180107920A1
公开(公告)日：2018-04-19
申请号：US15786177
申请日：2017-10-17
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Thakur , Kannan Govindarajan
CPC classification number: G06N3/0445 , G06F17/10 , G06F17/11 , G06N3/08 , G06N20/00
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.
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22.发明公开公开(公告)号：US20230229542A1
公开(公告)日：2023-07-20
申请号：US17576490
申请日：2022-01-14
Applicant: ServiceNow, Inc.
CPC classification number: G06F11/0781 , G06K9/6256 , G06K9/6263
Abstract: An embodiment may involve persistent storage containing a machine learning trainer application configured to apply one or more learning algorithms. One or more processors may be configured to: obtain alert data from one or more computing systems; generate training vectors from the alert data, wherein elements within each of the training vectors include: results of a set of statistics applied to the alert data for a particular computing system of the one or more computing systems, and an indication of whether the particular computing system is expected to fail given its alert data; train, using the machine learning trainer application and the training vectors, a machine learning model, wherein the machine learning model is configured to predict failure of a further computing system based on operational alert data obtained from the further computing system; and deploy the machine learning model for production use.
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公开(公告)号：US11595484B2
公开(公告)日：2023-02-28
申请号：US16402800
申请日：2019-05-03
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Madhusudhan Thakur , Kannan Govindarajan , Andrew Kai Chiu Wong , Sriram Palapudi
Abstract: A remote network management platform is provided that includes an end-user computational instance dedicated to a managed network, a training computational instance, and a prediction computational instance. The training instance is configured to receive a corpus of textual records from the end-user instance and to determine therefrom a machine learning (ML) model to determine the numerical similarity between input textual records and textual records in the corpus of textual records. The prediction instance is configured to receive the ML model and an additional textual record from the end-user instance, to use the ML model to determine respective numerical similarities between the additional textual record and the textual records in the corpus of textual records, and to transmit, based on the respective numerical similarities, representations of one or more of the textual records in the corpus of textual records to the end-user computational instance.
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公开(公告)号：US11586659B2
公开(公告)日：2023-02-21
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.
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公开(公告)号：US11574235B2
公开(公告)日：2023-02-07
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.
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Patent Agency Ranking
公开(公告)号：US20220382792A1
公开(公告)日：2022-12-01
申请号：US17885296
申请日：2022-08-10
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Chitrabharathi Ganapathy , Aniruddha Madhusudan Thakur , Jun Wang
Abstract: Systems and methods involving data structures for efficient management of paragraph vectors for textual searching are described. A database may contain records, each associated with an identifier and including a text string and timestamp. A look-up table may contain entries for text strings from the records, each entry associating: a paragraph vector for a respective unique text string, a hash of the respective unique text string, and a set of identifiers of records containing the respective unique text string. A server may receive from a client device an input string, compute a hash of the input string, and determine matching table entries, each containing a hash identical to that of the input string, or a paragraph vector similar to one calculated for the input string. A prioritized list of identifiers from the matching entries may be determined based on timestamps, and the prioritized list may be returned to the client.
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公开(公告)号：US20220019936A1
公开(公告)日：2022-01-20
申请号：US16931906
申请日：2020-07-17
Applicant: ServiceNow, Inc.
Inventor： Gopal Sarda , Sravan Ramachandran , Seganrasan Subramanian , Baskar Jayaraman
Abstract: A specification of a desired target field for machine learning prediction and one or more tables storing machine learning training data are received. Within the one or more tables, eligible machine learning features for building a machine learning model to perform a prediction for the target field are identified. The eligible machine learning features are evaluated using a pipeline of different evaluations to successively filter out one or more of the eligible machine learning features to identify a set of recommended machine learning features among the eligible machine learning features. The set of recommended machine learning features is provided for use in building the machine learning model.
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公开(公告)号：US20210256097A1
公开(公告)日：2021-08-19
申请号：US17167316
申请日：2021-02-04
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , ChitraBharathi Ganapathy , Tao Hong , Rohit Lobo
Abstract: A document is received. The document is analyzed to discover text and structures of content included in the document. A result of the analysis is used to determine intermediate text representations of segments of the content included in the document, wherein at least one of the intermediate text representations includes an added text encoding the discovered structure of the corresponding content segment within a structural layout of the document. The intermediate text representations are used as an input to a machine learning model to extract information of interest in the document. One or more structured records of the extracted information of interest are created.
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公开(公告)号：US10970491B2
公开(公告)日：2021-04-06
申请号：US16809197
申请日：2020-03-04
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Madhusudan Thakur , ChitraBharathi Ganapathy , Shiva Shankar Ramanna
Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.
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30.发明申请公开(公告)号：US20210011936A1
公开(公告)日：2021-01-14
申请号：US17038924
申请日：2020-09-30
Applicant: ServiceNow, Inc.
Inventor： Baskar Jayaraman , Aniruddha Madhusudan Thakur , Chitrabharathi Ganapathy , Kannan Govindarajan , Shiva Shankar Ramanna
IPC: G06F16/33 , G06F16/332 , G06F16/338 , G06F11/30 , G06N3/08 , G06F40/30
Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.
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