International Business Machines Corporation

Accenture Global Solutions Limited

Pwc Product Sales LLC

Other

Columbia University

Ibm Thomas J. Watson Search Center

Eastman-kodak Company

At+t Corp.

Chung-Sheng Li

John Richard Smith

Vittorio R Castelli

Yuan-Chi Chang

Hubertus Franke

Lawrence David Bergman

Guanglei Xiong

Emmanuel Munguia Tapia

Pratap Chandra Pattnaik

Madhura Shivaram

Colin Connors

Nagui Halim

Matt Richard Hogstrom

Brad L Brech

Michael D Williams

Matthew Leon Hill

Scott W Crowder

Radha P Ratnaparkhi

Rakesh Mohan

Sukryool Kang

Shih-Fu Chang

Josyula R Rao

Francisco Phelan Curbera

Ana Belen Benitez

Bulent Abali

Dimitrios Pendarakis

David Bruce Lindquist

Jeffrey Alan Frey

Yurdaer Nezihi Doganata

Milind R Naphade

Kevin Patrick McAuliffe

Aleksander A Slominski

Balachandar Rajaraman

Nirmal K Mukhi

Axel Martens

Joseph David Voyles

Rodney A Smith

Stefan Pappe

Atul Puri

Manoj N Kumar

Ivan Matthew Milman

Charles Daniel Wolfson

Christos J Georgiou

Krishna Kummamuru

Guenter Anton Sauter

Swati Tata

Murray Scott Campbell

Colin James Parris

Harald Clyde Smith

Guruprasad Dasappa

Rick A Hamilton, Ii

Philip Shi-lung Yu

Jenny S Li

Hui Lei

Sriram Kolinjivadi Padmanabhan

Ching-Yung Lin

Seetharami R Seelam

Rajiv R Ramaswami

Youssef Drissi

Maroun Touma

Qian Huang

Ming-Syan Chen

Paul Gregory Crumley

Mitchell Adam Cohen

Fenno F Heath, Iii

Shahram Ebadollahi

Anant Deep Jhingran

Franklin F Tong

Josef Schiefer

Seungyup Paek

Abhishek Gunjan

Gregory J Boss

Renato John Recio

Charu Chandra Aggarwal

Kun-Lung Wu

Philip Shilung Yu

Yu-Cheng Hsu

Jorjeta Gueorguieva Jetcheva

Ornan A Gerstel

Hui-I Hsiao

Min Wang

George Wayne Nation

John Timothy Robinson

Colin George Harrison

Prakash Ghatage

Dilip Dinkar Kandlur

Apostol Ivanov Natsev

David Mun-Hien Choy

Harold S Stone

John Joseph Turek

Jy-Jine James Lin

Chinnappa Guggilla

Karen Liu

Jack Lawrence Kouloheris

Lexing Xie

Gene Y C Fuh

Ashish Jain

Douglas Charles Lovell

Deepak Kumar

Ting D Cheng

An entity trust index relating to an entity may indicate a trustworthiness level of the entity. To generate an entity trust index, entity information may be received from one or more information sources. Using the entity information and one or more machine learning models, a performance index, an innovation index, and a sustainability index for the entity may be generated. The entity trust index may be generated based on the performance index, the innovation index, and the sustainability index. One or more actions for the entity that will modify the entity trust index may then be determined.

Systems and methods for measuring trust

A system for automatically generating responses to user queries includes one or more processors, operable when executing instructions to: receive a query from a user, determine a first set of similarity scores between the query and a plurality of stored queries stored in a data store, and determine whether a first similarity score of the first set of similarity scores meets a first threshold. In accordance with a determination that the first similarity score meets the first threshold, the processors are further operable to obtain a set of case information for the current case; retrieve case information for at least one precedent case; determine a second set of similarity scores between the current case and the at least one precedent case; determine whether a second similarity score of the second set of similarity scores meets a second threshold; and generate a response to the query.

Method and apparatus for an AI-assisted virtual consultant

Provided herein are methods and systems for identifying a related data values in a plurality of datasets. The method can include extracting data from the plurality of datasets, identifying potential linking categories, and determining a validity of the potential linking categories. One or more linking categories can be selected from the potential linking categories based on the validity, and related data values can be identified between the plurality of datasets based on the selected linking categories. Also provided herein are methods and systems for classifying data values as reconciled or non-reconciled. The method can include identifying a plurality of related data values in a plurality of datasets. For each of the plurality of related data values, a similarity score and confidence score can be determined, and the plurality of related data values can be classified as reconciled or non-reconciled based on the similarity score and/or the confidence score.

AI-augmented composable and configurable microservices for record linkage and reconciliation

An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

Method and apparatus for knowledge representation and reasoning in accounting

Systems and methods for automated document processing for use in AI-augmented auditing platforms are provided. A system for determining the composition of document bundles extracts substantive content information and metadata information from a document bundle and generates, based on the extracted information regarding a composition of the document bundle. A system for validating signatures in documents extracts data representing a spatial location for respective signatures and generates a confidence level for respective signatures, and determines, based on location and confidence level, whether signature criteria are met. A system for extracting information from documents applies a set of data conversion processing steps to a plurality received documents to generate structured data, and then applies a set of knowledge-based modeling processing steps to the structured data to generating output data extracted from the plurality of electronic documents.

AI-augmented auditing platform including techniques for automated document processing

Performing an operation comprising generating, by a computing system based on data received from a plurality of data sources, a data model describing a patient, wherein the data comprises medical history data of the patient and observation data received by the computing system during observation of at least one medical professional, generating, by the computing system and based on the model applied to the received data, a plurality of candidate diagnoses for the patient, executing, by the computing system, a plurality of simulations of a plurality of candidate treatments generated by the computing system based on the model, and identifying a first candidate treatment of the plurality of candidate treatments that reduces an uncertainty value of the data model below a threshold.

Medical diagnosis system with continuous learning and reasoning

Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Explainable artificial intelligence

In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Ontology and rule based adjudication

Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.

Cognitive procurement

Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Employee concierge

Examples of analyzing documents are defined. In an example, a request to analyze a document may be received. A knowledge model corresponding to a guideline associated with the document may be obtained. The knowledge model may include at least one of a hypothetical question and a logical flow to determine an inference to the hypothetical question. The hypothetical question relates to an element of the guideline. Based on the knowledge model, data from the document may be extracted for analysis using an artificial intelligence (AI) component. The Ai component may be configured to extract and analyze data, based on the knowledge model. Based on the analysis, a report indicating whether the document falls within a purview of the guideline may be generated.

Document analyzer

In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Image content moderation

Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.

Automated order troubleshooting

Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Process orchestration

Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.

Growing community of inventors

Scarsdale, NY, United States of America

Chung-Sheng Li