Accenture Global Solutions Limited

Zhijie Wang

Teresa Sheausan Tung

Colin Anil Puri

Matthew Bernard Kujawinski

Kirby James Linvill

Malek Ben Salem

William Richard Gatehouse

Sarah Crabb

Louis Gerald Farfan

Robert A Kovach

Steven Robert Roberts

Peter Daniel Corkum

Anant Sharma

Brian H Richards

Mohamed Aftkhar

Brian J Green

Aritomo Shinozaki

Ramoj Kumar Paruchuri

Jordan Thomas

Hao Zhou

Manas R Satapathy

Prashant Kumar Mehrotra

Prashant K Mehrotra

Peter D Corkum

A model retraining tool is provided for utilizing a knowledge graph to retrain analytical models used in production. The model retraining tool retrains the analytical models to improve performance of the analytical models in an efficient and resource conserving manner.

Automated analytical model retraining with a knowledge graph

Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Approaches for knowledge graph pruning based on sampling and information gain theory

A device may receive content data identifying content created by users and metadata associated with the content. The device may receive rules data identifying rules associated with utilization of the content. The device may utilize the metadata to generate digital DNA signatures for the content in near-real time. The device may store, in a repository, the rules data, the content, the digital DNA signatures, and relationships between the digital DNA signatures. The device may receive, from a client device, new content that is generated based on particular content of the content data and new metadata associated with the new content. The device may utilize the new metadata to generate a new digital DNA signature for the new content. The device may process the new digital DNA signature, the rules data, and the digital DNA signatures to determine whether the new content violates one or more rules of the rules data.

Utilizing machine learning to prevent intellectual property issues for content during different stages of a project lifecycle

Embodiments of the present disclosure provide systems and methods for controlling a manufacturing process in a manner that protects sensitive information from misuse by different entities involved in the manufacturing process. According to the present disclosure, a blueprint providing information regarding subcomponents of a product to be manufactured may be provided to a synthesizer device. The synthesizer device may engage in two-party computation with IP providers to generate a set of machine commands, which may be encrypted, and then provide a message including the set of machine commands to a manufacturer device. The manufacturer device may obtain authorization from the IP provider(s) based on the message, where the authorization may enable the manufacturer device to configure a manufacturing process in accordance with the set of machine commands to manufacture the subcomponents of the product.

Systems and methods for distributed control of manufacturing processes

A model management tool is provided for performing analysis on data in a knowledge graph representation and enforcing data standardization to increase performance when reusing existing models to develop new artificial intelligence applications.

Knowledge engineering and reasoning on a knowledge graph

A knowledge enabled data management system ingests data and stores the data as an instance in a knowledge graph according to a domain specific ontology. The instance includes stored relationships of the entities in the instance. A query regarding the data may be parsed to derive a first query entity that is used to search the knowledge graph for a first graph entity corresponding to the first query entity. Results may be returned including a first identification of the first graph entity, a second identification of at least a second graph entity related to the first graph entity stored within the knowledge graph, and additional data corresponding to the first graph entity and to the second graph entity. The additional data may include a probabilistic measure of the relationship of the first graph entity to the second graph entity.

Knowledge enabled data management system

A knowledge graph virtualization system (KGVS) implements data virtualization techniques using semantic knowledge when parsing a knowledge graph to return results to a query. The data virtualization using semantic knowledge enable end users and applications to describe desired output without detailed knowledge of underlying data storage, schema, and relationships.

Data virtualization using leveraged semantic knowledge in a knowledge graph

A device may receive video of a facility from an image capture system. The video may show an individual within the facility, an object within the facility, or an activity being performed within the facility. The device may process the video using a technique to identify the individual within the facility, the object within the facility, or the activity being performed within the facility. The device may track the individual, the object, or the activity through the facility to facilitate an analysis of the individual, the object, or the activity. The device may perform the analysis of the individual, the object, or the activity using information related to tracking the individual, the object, or the activity. The device may perform an action related to the individual, the object, or the activity based on a result of the analysis. The action may positively impact operations of the facility.

Tracking and/or analyzing facility-related activities

A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.

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Fremont, CA, United States of America

Zhijie Wang