International Business Machines Corporation

Globalfoundries Inc.

Airbnb, Inc.

Intematlonal Business Machlnes Corporation

Corville O Allen

Andrew Ronald Freed

Eric Woods

Joseph N Kozhaya

Scott Robert Carrier

Roberto Delima

Albert Alexander Chung

Shikhar Kwatra

Bernadette Alexia Carter

Darryl Maurice Adderly

Robert K Tucker

Aysu Ezen Can

Scott N Gerard

Dana Lea Price

Aris Gkoulalas-Divanis

Laura Janet Rodriguez

Dorian B Miller

Robert E Loredo

Lee Nee Helgeson

David Contreras

Richard A Salmon

Jenny S Li

Robert Christian Sizemore

Aspen L Payton

Thomas John Eggebraaten

Sterling Richardson Smith

Dwi Sianto Mansjur

Henri Fouotsop Meli

David B Styles

Timothy A Bishop

Robert L Nielsen

Donna Karen Byron

John Edward Petri

Rahul Ghosh

Jeffrey Douglas Haggar

Marie Louise Setnes

Roderick Charles Henderson

Christopher Michael Madison

Michael M Skeen

Christina R Carlson

Eileen Jiwon Min

Sorabh Murgai

James E Carey

Richard Joseph Stevens

Adrian Xavier Rodriguez

Adam Clark

Jeffrey K Huebert

Lisa M W Bradley

Mario Alessandro Maldari

Marit L Imsdahl

Michael D Whitley

Kevin B Haverlock

Faheem Altaf

Arthur R Francis

Alexandra D Markello

Suraksha Vidyarthi

Kyle Croutwater

John Stephen Mysak

Paul Anthony Smith

Leah Rae Smutzer

Heather L Duschl

Philip James Sanders

Lisa M Bradley

Anne Elizabeth Gattiker

Travis Eli Nelson

Jeremy R Fox

Paul R Bastide

Fang Lu

Matthew E Broomhall

John David Wilson

Florian Pinel

Timothy P Winkler

Ravi Krishnan Muthukrishnan

Carmine M DiMascio

Francisco Phelan Curbera

Joshua S Allen

Daniel M Jamrog

Malous Melissa Kossarian

Arthur L De Magalhaes

Kristin A Witherspoon

Andrew Dwight Dingsor

Stephan Joseph Roorda

Michael W Schroeder

Sushain Pandit

Nataraj Nagaratnam

Lakshminarayanan Krishnamurthy

Mark Gregory Megerian

Swaminathan Chandrasekaran

Jonathan Bnayahu

Yacov Manevich

Sridhar Sudarsan

Joel Duquene

Artem Barger

Scott A Will

Christopher Shane Claussen

Anita Govindjee

Piotr Przybylski

John Henry Green

Alexander Kofman

Trenton J Johnson

A mechanism is provided to review and verify provenance of analytic execution by a contract analytic binding and provenance system. The mechanism is activated to execute a set of analytics for a contract and verifies outcomes of the analytics before writing them to a blockchain network. The mechanism evaluates the provenance data records stored on peer ledgers and establishes transparency of the outcomes by validating consensus between characteristics of the provenance data.

Computer mechanism for analytics outcome transparency

Disclosed herein are methods and systems for executing verifiable computation modules to process private data at private data owner platform, comprising obtaining a computation module having a unique identifier recorded in a distributed ledger controlled by a plurality of computing nodes, generating a key pair comprising a signing key and a verification key derived from the signing key, recording, in the distributed ledger, an execution record associating an execution instance of the computation module with the verification key, initiating the execution instance of the computation module to process a private dataset incorporated with the signing key. outputting a computation outcome, computing an execution result signature for the execution instance based on the unique identifier and the private data and the signing key, and recording the execution result signature in the distributed ledger to enable verification of the execution instance.

Verifiable privacy preserving computation

Mechanisms are provided for implementing a disambiguation engine for disambiguating content. Electronic content is received from a corpus of electronic content, and analyzed to identify an ambiguous portion of content. The ambiguous portion of content is a portion of the electronic content whose meaning is not made explicit in the ambiguous portion of content. A context associated with the ambiguous portion of content is determined and a set of one or more context based ambiguous content interpretation rules associated with the determined context is applied to the ambiguous portion of content to generate an interpretation of the ambiguous portion of content. The ambiguous portion of content is annotated based on the interpretation to generate disambiguated electronic content which is stored for processing as part of a subsequent operation.

Disambiguation of ambiguous portions of content for processing by automated systems

A mechanism is provided to implement a contract analytic binding and provenance system. The mechanism loads contract rules for an agreement between a plurality of contract parties and creates an analytic entry per rule to form a set of analytic entries, Each analytic entry is associated with an executable analytic function. The mechanism assigns each analytic entry a directed acyclic graph vertex. The mechanism confirms alignment between the contract rules and the set of analytic entries and stores the set of analytic entries in peer ledgers associated with the plurality of contract parties. The mechanism activates the set of analytic entries responsive to confirming provenance of the peer ledgers associated with the plurality of contract parties.

Contract analytic binding and provenance

Mechanisms for generating different lines of questioning based on evaluation of a previous line of questioning are provided. A set of evidential data specifying a plurality of concept entities and input specifying a scenario to be evaluated are received. The scenario specifies a hypothetical link between at least two of the concept entities. A first set of questions corresponding to the at least two information concept entities are evaluated based on the set of evidential data. Based on results of evaluating the first set of questions, a second set of questions is automatically generated to further expand upon and investigate the results of evaluating the first set of questions. The second set of questions are processed and an indication of the scenario and a corresponding measure of support for or against the scenario being a valid scenario is output.

Generating additional lines of questioning based on evaluation of a hypothetical link between concept entities in evidential data

A mechanism is provided to orchestrates entitled execution of a set of analytics. The mechanism retrieves an analytic entry from an immutable data record source and identifies an analytic associated with the analytic entry, and an associated dataset. The analytic and the dataset are encrypted using an owner's key. The mechanism decrypts the analytic using a decryption key and executes the analytic based on the decrypting.

Computer mechanism for analytic orchestration and entitled execution

A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The mechanism receives a plurality of patient electronic medical records (EMRs) for a patient from a plurality of different sources. For a portion of a patient EMR record of the plurality of patient EMRs, the mechanism detects entities and analyzes a document structure of the portion of the patient EMR to identify a hierarchical structure of the portion of the patient EMR. The mechanism generates a container representation of the portion of the patient EMR based on the hierarchical structure. The mechanism placing each of the one or more sentences within the container representation based on relative position within the hierarchical structure. The mechanism generates a knowledge graph using the detected entities and the container representation.

Container-based knowledge graphs for determining entity relations in non-narrative text

A generator categorizes documents in one or more transactions into buckets, each identified by a separate category for an expected time window based on a separate relative age of each of the documents evaluated from one or more dates identified in the documents. The generator trains a document classifier with a model of the separate relative age of each of the documents as a temporal characteristic correlated with the respective category of a respective bucket of the buckets. The document classifier executes on a input documents to classify each of the input documents as a particular logical type identified by a particular category from among multiple logical types.

Classifying digital documents in multi-document transactions based on embedded dates

A mechanism is provided in a data processing system to implement localized device specific limitations on access to patient medical information. An authorizing device receives a request from a requestor device via a dose proximity communication protocol requesting to access an electronic medical record (EMR) associated with a patient. The authorizing device receives user input via a user interface specifying conditions for permitting access to the EMR. The authorizing device transmits an access authorization request to a patient registry system requesting the patient registry system to provide access to the EMR associated with the patient in accordance with the conditions for permitting access specified by the user input. The patient registry system generates a temporary access data structure based on the specified conditions. The patient registry system processes a subsequent request from the requestor device to access the EMR in accordance with the temporary access data structure.

Implementing localized device specific limitations on access to patient medical information

A classifier receives a digital scan of a document and converts the content of the document in the digital scan from an image into text. The classifier analyzes the text to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

Classifying digital documents in multi-document transactions based on signatory role analysis

A processor may receive data regarding a context for a first dialog turn. The processor may monitor a voice input from a user for the first dialog turn. The processor may detect a first pause in the voice input, the first pause having a duration that satisfies a time threshold. The processor may receive, based on the first pause, first voice input data. The processor may analyze the first voice input data. The processor may determine that additional time is recommended for the voice input to be provided by the user.

Dynamic voice input detection for conversation assistants

Text is received from a first user. The text is associated with an electronic communication tool for communication to a second user. Candidate answers are generated based on the text using a question answering system. At least one of the candidate answers is provided to the first user.

Generating answers to text input in an electronic communication tool with a question answering system

A method, computer system, and a computer program product for active machine learning is provided. The present invention may include annotating a plurality of data entries. The present invention may also include building a first dataset based on the annotated plurality of data entries. The present invention may then include receiving user feedback based on the built first dataset. The present invention may further include assigning a plurality of weights to a plurality of data entry subsets. The present invention may also include generating a second weighted dataset based on the received user feedback.

Active learning for concept disambiguation

A phrase that includes a trigger word that modifies a meaning within the phrase is received. The trigger word is identified. The words of the phrase that are modified by the trigger word are identified by analyzing features of the phrase that link the trigger word to other words. The phrase is interpreted by modifying the second subset of words according to the modification of the trigger word.

Contextual span framework

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Morrisville, NC, United States of America

Corville O Allen