International Business Machines Corporation

Nuance Communications, Inc.

Ramesh Ambat Gopinath

Krishna Chaitanya Ratakonda

Ashish Jagmohan

Ajay Ashok Deshpande

Ali Koc

Pavithra Harsha

Sabine V Deligne

Elisabeth Claire Paulson

Dimitri Kanevsky

Benoit Maison

Vaibhava Goel

Brian C Wu

Stephane Herman Maes

Edward Emile Kelley

Sara H Basson

Michael Alan Picheny

George Andrei Saon

Ellen Marie Eide

Peder Andreas Olsen

Scott Shaobing Chen

David Nahamoo

Mukesh Kumar Mohania

Nitendra Rajput

Anca Sailer

Mahesh Viswanathan

Andrzej P Kochut

Hidayatullah Habeebullah Shaikh

Lawrence H Koved

Yi-Min Chee

Rakesh Mohan

Lalit R Bahl

Ponani S Gopalakrishnan

Charles Otto Schulz

Karthik Visweswariah

Hong-Kwang Jeff Kuo

Yedendra Shrinivasan

Ea-Ee Jan

Chengjun Julian Chen

Danish Contractor

Upendra V Chaudhari

Sreeram Viswanath Balakrishnan

Sumit Negi

Mukund Padmanabhan

Osamuyimen Stewart

Stanley S Chen

Steven John Rennie

Michael Daniel Monkowski

Lazaros Polymenakos

John Randall Hershey

Nagendra Kumar Goel

Geoffrey G Zweig

Pichappan Pethachi

Yuging Gao

Scott Elliot Axelrod

Benoit Emmanuel Ghislain Maison

Mukund Panmanabhan

Kathiravan M Ramaswami

David Claiborn

Brigid Mcdermott

Trausti Thor Kristjansson

An example operation may include one or more of receiving, by a retailer node, an encrypted inventory of goods data from a plurality of supplier nodes over a blockchain network, computing, by the retailer node, an ordering proportion based on the encrypted inventory of goods data, generating, by the retailer node, an ordering policy based on the ordering proportion, and executing a smart contract to order goods from the plurality of the supplier nodes based on the ordering policy.

Upstream visibility in supply-chain

An example operation may include one or more of acquiring, by a retailer node, an inventory data from a supplier node over a blockchain network, receiving, by the retailer node, outstanding orders data of the supplier node, generating, by the retailer node, an order distribution policy based on the inventory data and the outstanding orders data, and executing a smart contract to order goods from the supplier node based on the ordering policy.

An example operation may include one or more of marking a document, by a user node, to be included into a collection of documents, determining, by the user node, a business process step associated with the document based on a user mark, and executing a transaction to store a hash of the document onto a ledger of a blockchain, wherein a Merkle tree hash is generated and tagged on the ledger with details of the business process step.

Document verification

An example operation may include one or more of receiving one or more transaction requests to complete one or more transactions, recording the one or more transaction requests in a blockchain, requesting one or more transaction confirmations from an out-of-band device to confirm the one or more transactions, and committing the one or more confirmed transactions to the blockchain.

Providing out-of-band verification for blockchain transactions

An example operation may include one or more of storing data via a blockchain ledger based on a consensus of a set of anchor blockchain nodes which are subset of nodes included in larger set of nodes in a blockchain network, receiving an access request from a blockchain member not included in the set of anchor blockchain nodes and which does not maintain a copy of the blockchain ledger, verifying a previously stored portion of the blockchain ledger which is accessible to the blockchain member based on the received access request, and sharing the previously stored portion of the blockchain ledger with the a computing system of the blockchain member based on the verification.

Blockchain trust anchor

Methods and arrangements for generating a learning graph. A contemplated method includes: utilizing at least one processor to execute instructions to perform the steps of: receiving a proficiency input relating to a student; receiving a target knowledge node, wherein the target knowledge node represents at least one skill the student does not currently possess; determining at least one skill requirement of the at least one skill; identifying at least one path between the proficiency input and the target knowledge node based upon the at least one determined skill requirement; calculating a gap between the proficiency input and the target knowledge node at the at least one identified path; and recommending at least one learning content module based upon the calculated gap.

Student specific learning graph

The present invention concerns methods and apparatus for identifying and assigning meaning to words not recognized by a vocabulary or grammar of a speech recognition system. In an embodiment of the invention, the word may be in an acoustic vocabulary of the speech recognition system, but may be unrecognized by an embedded grammar of a language model of the speech recognition system. In another embodiment of the invention, the word may not be recognized by any vocabulary associated with the speech recognition system. In embodiments of the invention, at least one hypothesis is generated for an utterance not recognized by the speech recognition system. If the at least one hypothesis meets at least one predetermined criterion, a sword or more corresponding to the at least one hypothesis is added to the vocabulary of the speech recognition system. In other embodiments of the invention, before adding the word to the vocabulary of the speech recognition system, the at least one hypothesis may be presented to the user of the speech recognition system to determine if that is what the used intended when the user spoke.

Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

A method, an apparatus and an article of manufacture for cloud-driven application execution. The method includes determining a plurality of attributes of a failed application, wherein the plurality of attributes comprises at least one policy context attribute and at least one context attribute, correlating each of the plurality of attributes to at least one alternative asset, wherein the at least one alternative asset is a part of an environment on which the failed application can be executed, using the plurality of attributes correlated to the at least one alternative asset to identify an alternative asset set of alternative assets, wherein the alternative asset set is capable of enabling an alternative environment on which to execute the failed application, and provisioning the alternative assets in the alternative asset set from at least one cloud network to create the alternative environment on which the failed application is executed.

Elastic cloud-driven task execution

A method (and system) of determining confusable list items and resolving this confusion in a spoken dialog system includes receiving user input, processing the user input and determining if a list of items needs to be played back to the user, retrieving the list to be played back to the user, identifying acoustic confusions between items on the list, changing the items on the list as necessary to remove the acoustic confusions, and playing unambiguous list items back to the user.

Method and system for prompt construction for selection from a list of acoustically confusable items in spoken dialog systems

A method (and system) for spoken dialog confirmation classifies a plurality of spoken dialog hypotheses, and assigns a threshold to each class of spoken dialog hypotheses.

Method and system having hypothesis type variable thresholds

A method (and system) of handling out-of-grammar utterances includes building a statistical language model for a dialog state using, generating sentences and semantic interpretations for the sentences using finite state grammar, building a statistical action classifier, receiving user input, carrying out recognition with the finite state grammar, carrying out recognition with the statistical language model, using the statistical action classifier to find semantic interpretations, comparing an output from the finite state grammar and an output from the statistical language model, deciding which output of the output from the finite state grammar and the output from the statistical language model to keep as a final recognition output, selecting the final recognition output, and outputting the final recognition result, wherein the statistical action classifier, the finite state grammar and the statistical language model are used in conjunction to carry out speech recognition and interpretation.

Method and system for using a statistical language model and an action classifier in parallel with grammar for better handling of out-of-grammar utterances

A user interface, and associated techniques, that permit a fast and efficient way of correcting speech recognition errors, or of diminishing their impact. The user may correct mistakes in a natural way, essentially by repeating the information that was incorrectly recognized previously. Such a mechanism closely approximates what human-to-human dialogue would be in similar circumstances. Such a system fully takes advantage of all the information provided by the user, and on its own estimates the quality of the recognition in order to determine the correct sequence of words in the fewest number of steps.

Natural error handling in speech recognition

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White Plains, NY, United States of America

Ramesh Ambat Gopinath