Colin F Connors

US Patent:

20200219040, Jul 9, 2020

Filed:

Jan 7, 2019

Appl. No.:

16/241749

Inventors:

- Dublin, IE
Guruprasad Dasappa - Bangalore, IN
Krishna Kummamuru - Bangalore, IN
Colin Connors - Campbell CA, US
Guanglei Xiong - Pleasanton CA, US
Christopher Banschbach - Wilmington DE, US
Thomas Michael Fahey - Wynnewood PA, US

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

International Classification:

G06Q 10/08
G06Q 30/02
G06Q 10/06
G06F 16/903
G06N 5/04

Abstract:

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.

US Patent:

20200118195, Apr 16, 2020

Filed:

Oct 12, 2018

Appl. No.:

16/159428

Inventors:

- Dublin 4, IE
Emmanuel Munguia Tapia - San Jose CA, US
Jingyun Fan - Berkeley CA, US
Danielle Moffat - Highlands Ranch CO, US
Colin Connors - Campbell CA, US
Kayhan Moharreri - San Jose CA, US

Assignee:

Accenture Global Solutions Limited - Dublin 4

International Classification:

G06Q 30/06
G06Q 10/08
G06Q 10/06
G06F 15/18

Abstract:

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.

US Patent:

20200089962, Mar 19, 2020

Filed:

Jul 16, 2019

Appl. No.:

16/513254

Inventors:

- Dublin, IE
Guanglei XIONG - Pleasanton CA, US
Colin CONNORS - Campbell CA, US
Sukryool KANG - Sunnyvale CA, US
Chung-Sheng LI - San Jose CA, US

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

International Classification:

G06K 9/00
G06T 7/11
G06T 7/73
G06T 7/246
G06T 11/20
G06K 9/62

Abstract:

Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

US Patent:

20190244121, Aug 8, 2019

Filed:

Jul 17, 2018

Appl. No.:

16/037807

Inventors:

- Dublin 4, IE
Guanglei Xiong - Pleasanton CA, US
Mohammad Ghorbani - Foster City CA, US
Emmanuel Munguia Tapia - San Jose CA, US
Sukryool Kang - Sunnyvale CA, US
Benjamin Nathan Grosof - Mercer Island WA, US
Ashish Jain - Chennai, IN
Colin Connors - Campbell CA, US

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4

International Classification:

G06N 5/04

Abstract:

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.

US Patent:

20190244300, Aug 8, 2019

Filed:

Feb 2, 2018

Appl. No.:

15/887813

Inventors:

- Dublin, IE
Guanglei XIONG - Pleasanton CA, US
Sukryool KANG - Sunnyvale CA, US
Ashish JAIN - Chennai, IN
Colin CONNORS - Campbell CA, US
Benjamin Nathan GROSOF - Mercer Island WA, US
Neeru NARANG - San Jose CA, US

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

International Classification:

G06Q 40/08
G06F 17/30
G06N 99/00

Abstract:

A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

US Patent:

20190005590, Jan 3, 2019

Filed:

Jun 30, 2017

Appl. No.:

15/640223

Inventors:

- Dublin, IE
Suraj Govind JADHAV - Mumbai IN, US
Saurabh MAHADIK - Mumbai, IN
Prakash GHATAGE - Bangalore, IN
Guanglei XIONG - Fremont CA, US
Emmanuel MUNGUIA TAPIA - Newark CA, US
Mohammad Jawad GHORBANI - Foster City CA, US
Kyle JOHNSON - San Francsico CA, US
Colin Patrick CONNORS - Campbell CA, US
Benjamin Nathan GROSOF - Mercer Island WA, US

International Classification:

G06Q 40/00

Abstract:

A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.

US Patent:

20180374051, Dec 27, 2018

Filed:

Jun 21, 2017

Appl. No.:

15/629140

Inventors:

- Dublin, IE
Suraj Govind JADHAV - Mumbai, IN
Saurabh MAHADIK - Mumbai, IN
Prakash GHATAGE - Bangalore, IN
Guanglei XIONG - Fremont CA, US
Emmanuel Munguia TAPIA - Newark CA, US
Mohammad Jawad GHORBANI - Foster City CA, US
Kyle JOHNSON - San Francsico CA, US
Colin Patrick CONNORS - Campbell CA, US
Benjamin Nathan GROSOF - Mercer Island CA, US

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

International Classification:

G06Q 10/10
G06Q 10/06

Abstract:

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.

US Patent:

20180241881, Aug 23, 2018

Filed:

Feb 21, 2018

Appl. No.:

15/901049

Inventors:

- Dublin, IE
Guanglei Xiong - Pleasanton CA, US
Emmanuel Munguia Tapia - San Jose CA, US
Kyle P. Johnson - Lawrence KS, US
Christopher Cole - San Anselmo CA, US
Sachin Aul - San Francisco CA, US
Suraj Govind Jadhav - Navi Mumbai, IN
Saurabh Mahadik - Mumbai, IN
Mohammad Ghorbani - Foster City CA, US
Colin Connors - Campbell CA, US
Chinnappa Guggilla - Bangalore, IN
Naveen Bansal - Delhi, IN
Praveen Maniyan - Kayamkulam, IN
Sudhanshu A. Dwivedi - Bangalore, IN
Ankit Pandey - Bangalore, IN
Madhura Shivaram - Bangalore, IN
Sumeet Sawarkar - Nagpur, IN
Karthik Meenakshisundaram - Bangalore, IN
Nagendra Kumar M R - Bangalore, IN
Hariram Krishnamurth - Chennai, IN
Karthik Lakshminarayanan - Bengaluru, IN

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

International Classification:

H04M 3/51
H04M 3/523
G06N 99/00

Abstract:

A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.