Servicenow, Inc.

International Business Machines Corporation

Sriram Palapudi

Baskar Jayaraman

Firat Karakusoglu

Kannan Govindarajan

Aniruddha Madhusudan Thakur

Rainer Wolafka

Nikhil Prashant Bendre

Maria Savarimuthu Rajakannimariyan

Fernando Ros

Omer Anil Turkkan

Nikhil Bendre

Edwin Vito Sapugay

Anil Kumar Madamala

Maxim Naboka

Sagar Davasam Suryanarayan

Sathees Babu Kodi

Venu Madhav Matcha

Rammohan Narendula

Hongqiao Li

Andrew Kai Chiu Wong

Aniruddha Madhusudhan Thakur

Virendra Kumar Mehta

Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.

Determining semantic similarity of texts based on sub-sections thereof

A natural language understanding (NLU) framework includes a lookup source framework, which enables a lookup source system to be defined having one or more lookup sources. Each lookup source of the lookup source system includes a respective source data representation that is compiled from respective source data. For example, a source data representation may include source data arranged in a finite state transducer (IFST) structure as a set of finite-state automata (FSA) states, wherein each state is associated with a token that represents underlying source data. Different producers can be applied during compilation of a source data representation to derive additional states within the source data representation from the source data. Certain states of the source data representation that contain sensitive data can be selectively protected through encryption and/or obfuscation, while other portions of the source data representation that are not sensitive may remain in clear-text form.

Lookup source framework for a natural language understanding (NLU) framework

A request to perform a prediction using a machine learning model of a specific entity is received. A specific security key for the machine learning model of the specific entity is received. At least a portion of the machine learning model is obtained from a multi-tenant machine learning model storage. The machine learning model is unlocked using the specific security key and the requested prediction is performed. A result of the prediction is provided from a prediction server.

Machine learning platform with model storage

A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Machine learning with distributed training

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.

Centralized machine learning predictor for a remote network management platform

A system may include memory containing: (i) a master data set representable in columns and rows, and (ii) a query expression. The system may include a software application configured to apply a machine learning (ML) pipeline to an input data set. The system may include a computing device configured to: obtain the master data set and the query expression; apply the query expression to the master data set to generate a test data set, where applying the query expression comprises, based on content of the query expression, generating the test data set to have one or more columns or one or more rows fewer than the master data set; apply the ML pipeline to the test data set, where applying the ML pipeline results in either generation of a test ML model from the test data set or indication of an error in the test data set; and delete the test data set from the memory.

Data set generation for testing of machine learning pipelines

Shared machine learning

A determination is made that an archive that includes at least one file is present in association with an operating system file system. An additional file system is generated for accessing the archives. The generated additional file system is included in the operating system file system. An application is allowed to access the at least one file via the generated additional file system.

Managing an archived file system

Provided are a method, system, and program for generating executable objects implementing methods for an information model. A file including code defining a class implementing at least one method in an information model is received. The file is translated to produce an object oriented implementation of the class and the at least one method in an object oriented programming (OOP) language file. Protocol statements of the information model are added to the OOP file to enable a client application to invoke the at least one method on a server. The OOP file is compiled to produce an executable object capable of being invoked by a call to a method invocation statement, wherein the client application calling the method invocation statement causes execution of the protocol statements and the at least one method in the executable object to invoke the at least one method on the server.

Generating executable objects implementing methods for an information model

An apparatus, system, and program are disclosed for automatically backing up groups of related data. The apparatus includes a plurality of data cells capable of storing data. The apparatus also includes a grouping module configured to group one or more of the data cells into a related group. A tracking module tracks activity on one or more predetermined data cells in the group. A backup module backs up the data stored in the group of data cells upon the occurrence of a predetermined triggering event identified by the tracking module. The triggering event may include I/O activity to one or more predetermined data cells in the group or it may be a cumulative total of I/O activity to the group as a whole.

Apparatus, system, and program for automatic backup of related data

A determination is made that an archive that includes at least one file is present in association with an operating system file system. An additional file system is generated for accessing the archive. The generated additional file system is included in the operating system file system. An application is allowed to access the at least one file via the generated additional file system.

An apparatus, system, and method are disclosed for verifying backup data. A storage mapping module maps storage data that is to be backed up to an application module. A storage copy module copies the storage data to a backup copy and a backup copy module copies the backup copy to a verification copy. The backup copy and the verification copy are identical copies of an instance of the storage data. A verification mapping module maps the verification copy to the application module. A verification module verifies the verification copy using the application module. If the verification copy is valid, the backup copy is also valid. A control module may mark the backup copy as valid.

Growing community of inventors

Santa Clara, CA, United States of America

Sriram Palapudi