International Business Machines Corporation

Dell Products Inc.

Abhijit Mishra

Hung The Dinh

Karthik Sankaranarayanan

Parag Jain

Satish Ranjan Das

Kuntal Dey

Seema Nagar

Enara C Vijil

Amar Prakash Azad

Krishna Mohan Akkinapalli

Vinod Kumar

Sabu K Syed

Panguluru Vijaya Sekhar

Anirban Laha

Tousif Mohammed

Vijaya Panguluru Sekhar

Vinay Sathyanarayana

Nagireddy Bonthu

Manikandan Pammal Rathinavelu

Jatin Kamlesh Thakkar

Saravanan Kannan

Bijan Kumar Mohanty

Md Faisal Mahbub Chowdhury

Kannappan Ramu

Rajesh Krishnan

Dan Gutfreund

Reddeppa Kollu

Saravanan Krishnan

Vinayak Sastri

Joydeep Mondal

Craig J Van Der Bogart

Pallavi Jaini

Geetha Venkatesan

John Kenneth Maxi

Pradeepta Ranjan Choudhury

Vellore Mohammed Imran

Karan Kapoor

Mohammed Imran V

Gnanesh Gowda

Reshma Nair

Farook Shaik

Nilay Mishra

Vishnu Prabhu Sundarrajan

Jonathan Andrew Hernandez

Peter John Sarkis

Puttaraju Bommanna Chikkanna

Vineet Kumar

Vijaya Sekhar P

Akanksha Bansal

Sharath Kumar Mudigere Yathiraj

Amirthraj Ramakrishnan

Sagar Manohar

Sethukarasi Sockalingam

Gaurav Suwal

Bala Kumar

Narendra Kumar

SivaMohan Nimmakayala

A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Message oriented middleware cluster synchronization

Methods, computer program products, and systems are presented. The methods include, for instance: training of a machine learning model for predicting a rating in certain content rating systems based on training data. The machine learning model includes a plurality of maturity classifiers corresponding to individual features of the previously rated contents. An input content is obtained and features of the input content are extracted by use of respective content analysis tools and subsequently classified by the maturity classifiers of the machine learning model.

Dynamic content rating assistant

Methods, apparatus, and processor-readable storage media for implementing extensible upgrade and modification as a service are provided herein. An example computer-implemented method includes processing one or more modifiers, wherein each modifier includes an independent processing unit having a given canonical structure and is configured to execute one or more automated actions related to at least one of application modification and application migration; obtaining data pertaining to multiple applications across multiple computing environments; determining, based at least in part on processing at least a portion of the obtained data, at least one of the one or more modifiers applicable for use in executing at least one of the one or more automated actions in connection with at least a portion of the multiple applications; and executing the at least one of the one or more automated actions using the at least one determined modifier.

Extensible upgrade and modification as a service

One embodiment of the invention provides a method of training a chatbot. The method comprises identifying one or more chat logs that exhibit a trait. The method further comprises identifying one or more labels associated with the trait based on the one or more chat logs. The method further comprises training the chatbot to generate a response that models the trait based on the one or more chat logs. The method further comprises labeling the chatbot with the one or more labels.

Trait-modeled chatbots

Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes selecting at least one set of style specifications for transforming at least a portion of input text. The at least one set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text, and weights for each of a set of linguistic aspects for transformation of at least a portion of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.

Controllable style-based text transformation

Provided are systems, methods, and media for handling dialogs based on user behavior data. An example method includes receiving an input paragraph having one or more factual sentences, in which each of the one or more factual sentences includes one or more words. Receiving an input question comprising one or more words. Performing word-level gaze prediction on the input paragraph to identify one or more predicted gaze attributes for the input paragraph. Extracting an answer to the input question based, at least in part, on the input paragraph, the input question, and the one or more predicted gaze attributes of the input paragraph. Transmitting the extracted answer.

Question answering system influenced by user behavior and text metadata generation

An embodiment of the present invention generates natural language content from a set of keywords in accordance with a template. Keyword vectors representing a context for the keywords are generated. The keywords are associated with language tags, while the template includes a series of language tags indicating an arrangement for the generated natural language content. Template vectors are generated from the series of language tags of the template and represent a context for the template. Contributions from the contexts for the keywords and the template are determined based on a comparison of the series of language tags of the template with the associated language tags of the keywords. One or more words for each language tag of the template are generated to produce the natural language content based on combined contributions from the contexts for the keywords and the template.

Natural language text generation from a set of keywords using machine learning and templates

Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying amendable portions of input text by processing at least a portion of the input text using at least one neural network; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein said determining comprises processing at least a portion of the set of stylistic parameters using at least one neural network; generating a stylized output set of text by transforming at least a portion of the input text, wherein said transforming comprises modifying at least one of the amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to at least one user.

Unsupervised tunable stylized text transformations

In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Use machine learning to verify and modify a specification of an integration interface for a software module

A method includes retrieving vendor specific data from one or more message oriented middleware servers of a message oriented middleware infrastructure, and inputting the vendor specific data from the one or more message oriented middleware servers into a back-end database. The vendor specific data is converted into commonly formatted data, and the commonly formatted data is inputted into a front-end database. The method also includes retrieving the commonly formatted data from the front-end database, and displaying the commonly formatted data on a user interface providing a visualization of a topology of the message oriented middleware infrastructure.

Message oriented middleware topology explorer

Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Device manufacturing cycle time reduction using machine learning techniques

Text suggestions are generated. A document is received, and a portion score for at least one portion of the document is generated. A global assessment score for at least two portions of the document is also generated. A variation between the portion score and the global assessment score is calculated. It is determined that the variation is above a threshold variation, and at least one text change suggestion is generated.

Real-time assessment of text consistency

One embodiment provides a method, including: receiving, from a user, input for generating an article, wherein the input comprises (i) an indication of topic-specific requirements for the article that are dependent upon a topic of the article and (ii) an indication of non-topic-specific requirements for the article that are independent of the topic of the article; creating at least one query from the input; retrieving, from secondary sources by performing a search utilizing the query, structured information contextually relevant to the topic; and from the structured information, generating the article, wherein the article comprises a coherent and grammatically fluid structured article generated in view of the topic-specific requirements and the non-topic-specific requirements.

Structured article generation

According to an embodiment of the present invention, a video is generated from a communication session pertaining to support for use of a user interface. A scenario is identified within a document including content of the communication session. One or more items corresponding to the identified scenario and associated with the user interface are extracted from the document. The extracted items are mapped to corresponding aspects of the user interface, wherein at least one of the extracted items remains unmapped to the user interface. At least one question is determined for a user to receive information to map an unmapped item to a corresponding aspect of the user interface. A video is generated based on the mapped aspects of the user interface to reproduce one or more activities performed during the use of the user interface.

Generating a video for an interactive session on a user interface

In an approach to generating HTML based on a plurality of content and design controls, one or more computer processors crawl one or more conforming websites. The one or more computer processors create a training set of crawled conforming webpages, wherein each crawled conforming webpage in the training set of crawled conforming webpages includes associated web code labeled a combination of respective one or more content controls and respective one or more design controls; encode the combination of the respective one or more design controls and the respective one or more content controls based on one or more user preferences utilizing a created design encoder and a created content encoder; create a decoder with the training set of crawled conforming webpages and associated encoded content and design vectors; generate web code based on the encoded design and content controls utilizing the created decoder; implement generated web code.

Supervised environment controllable auto-generation of HTML

Growing community of inventors

Bengaluru, India

Abhijit Mishra