Guanglei C Xiong

20130297265, Nov 7, 2013

Apr 26, 2013

13/871675

Sajjad Hussain Baloch - Monmouth Junction NJ, US
Suraj Ravi Musuvathy - Lawrence NJ, US
Guanglei Xiong - Plainsboro NJ, US
Lawrence Spivey - Channahon IL, US
James B. Thompson - St. Charles IL, US
Tong Fang - Morganville NJ, US

SIEMENS PRODUCT LIFECYCLE MANAGEMENT SOFTWARE INC. - Plano TX

A61B 19/00

703 1

The design process for the surgical plan in orthopedics and/or the design of a personalized cutting guide and/or implant are automated in a workflow frame work. Abstracted rules are scripted through a sequence of operations to alter a bone surface or model for fitting an implant. Using bone information for a specific patient, the proper implant and series of cuts are determined using the rules. A corresponding cutting guide may be fitted to the bone information for the specific patient. Surgical planning of bone replacement implants is performed automatically.

20220318546, Oct 6, 2022

Apr 2, 2021

17/221564

- Dublin, IE
Guanglei XIONG - Pleasanton CA, US
Ullas Balan NAMBIAR - Bangalore, IN
Emmanuel MUNGUIA TAPIA - San Jose CA, US
Ditty MATHEW - Vaikom, IN
Omar RAZI - Bangalore, IN
David Alfonso GUERRA - San Antonio TX, US
Thyagarajan DELLI - Bangalore, IN

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

G06K 9/00
G06K 9/32
G06F 40/169
G06F 40/177

The validity of engineering drawings is automatically determined based on compliance of the specifications of the engineering drawings with automatically generated rules. A document package including images of the engineering drawings and related documents is received. Rules codifying the requirements to be fulfilled by the engineering drawings are automatically generated from the related documents. Data such as specifications of the various parts of the entities in the engineering drawings are automatically extracted. The extracted data is analyzed to determine compliance with the rules to validate the engineering drawings.

20220309332, Sep 29, 2022

Mar 23, 2021

17/210153

- Dublin 4, IE
Swati TATA - Bangalore, IN
Soujanya SONI - Bangalore, IN
Madhur BHASINI CHAINI - Bangalore, IN
Anjani KUMARI - Jharkhand, IN
Omar RAZI - Bangalore, IN
Thyagarajan DELLI - Bangalore, IN
Ullas Balan NAMBIAR - Bangalore, IN
Guanglei XIONG - Pleasanton CA, US
Sivasubramanian ARUMUGAM JALAJAM - Chennai, IN
Srinivasan Krishnan RAJAGOPALAN - Chennai, IN
Venkatesan KAMALAKANNAN - Chennai, IN
Harbhajan SINGH - Chennai, IN

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4

G06N 3/08
G06N 3/04
G06F 16/35
G06F 40/247
G06K 9/00
G06K 9/62

A system for providing automated and domain specific contextual processing for context based verification may classify a plurality of extracted parameters from a set of digitized training document to assign a document similarity score with respect to a set of reference documents. The system may automatically detect a domain for the set of digitized training documents based on the document similarity score. The system may load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters. The system may receive a set of input documents and perform a contextual processing of the received set of documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank. The context based verification may be performed based on the plurality of filtered snippets and the corresponding rank.

20220300804, Sep 22, 2022

Jun 17, 2021

17/350460

- Dublin, IE
Guanglei Xiong - Pleasanton CA, US
Christopher Yen-Chu Chan - Jersey City NJ, US
Jayashree Subrahmonia - San Jose CA, US
Aaron James Sander - Silver Spring MD, US
Sukryool Kang - Dublin CA, US
Wenxian Zhang - San Jose CA, US

G06N 3/08
G06N 3/04

Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

20220300854, Sep 22, 2022

Jul 8, 2021

17/370434

- Dublin, IE
Guanglei Xiong - Pleasanton CA, US
Wenxian Zhang - San Jose CA, US
Sukryool Kang - Dublin CA, US
Jing Su Brewer - San Jose CA, US
Ivan A. Wong - Edison NJ, US
Christopher Yen-Chu Chan - Jersey City NJ, US
Danielle Moffat - Highlands Ranch CO, US
Jayashree Subrahmonia - San Jose CA, US
Louise Noreen Barrere - Torrance CA, US

G06N 20/00
G06N 5/02
G06Q 10/06

Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

20230111633, Apr 13, 2023

Oct 8, 2021

17/497494

- Dublin 4, IE
Guanglei XIONG - Pleasanton CA, US
Lan GUAN - Johns Creek CA, US
Jayashree SUBRAHMONIA - San Jose CA, US
Yuan HE - Los Angeles CA, US
Louise Noreen BARRERE - Torrance CA, US

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4

G06Q 30/06
G06T 17/20
G06T 13/20
G06T 13/40
G06F 40/35
G06F 40/279

A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead. The processor causes CVAP to process responses to determine action and/or state, which includes whether to issue additional product-attribute based questions through Virtual Avatar (VA) using Response To Motion Module (RTME) or to, through recommender engine that uses recommendation model, in real-time, recommend products associated with first entity to respective at least one lead based on any or combination of responses received from lead, information pertaining to lead, and products ordered by entities similar to lead.

20200219040, Jul 9, 2020

Jan 7, 2019

16/241749

- 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

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin

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

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.

20200126641, Apr 23, 2020

Oct 17, 2018

16/163252

- Dublin 4, IE
Guanglei XIONG - Pleasanton CA, US
Emmanuel MUNGUIA TAPIA - San Jose CA, US
Jingyun FAN - Berkeley CA, US
Sukryool KANG - Sunnyvale CA, US
Neeru NARANG - San Jose CA, US
Michael C. PETERSEN - Austin TX, US
Dennis P. DELANEY - Cedar Park TX, US

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4

G16H 10/00
G06Q 40/08
G06F 17/30

Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.