Accenture Global Solutions Limited

International Business Machines Corporation

Walmart Apollo, LLC

Jadran Sirotkovic

Matthew Allen Jones

Dadong Wan

Michael D Whitley

John A Riendeau

Nicholas McCarthy

Le Zhang

Xin Zuo

Jingguang Han

Britto Michaelsamy

Erinc Merdivan

Jody P Cook

Maxwell A Yardley

Asim S Alvi

Christopher R Jescheling

Kshitiz Gupta

Sung H Kim

Rahul S Kala

Kumar Chandan

Peter M Terlecky

Tirui Wu

Mirko A Arnold

Methods and systems for managing inventory at a retail facility include an image capture device having a field of view that includes a product display shelf of the retail facility, an electronic database that stores a planogram of product display shelves at the retail facility and a computing device. The computing device obtains an image of a product display shelf, detects the individual packages of each of the different products captured in the image, defines different product display areas for the different products on the product display shelf captured in the image, and generates virtual boundary lines that surround each of the defined product display areas. The computing device also determines an identity of a product contained located in each defined product display area, and associates the virtual boundary lines surrounding each defined product display area with an identifier unique to the product contained in the defined product display area.

Systems and methods of defining and identifying product display areas on product display shelves

This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.

Cognitive searches based on deep-learning neural networks

A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Using a mixture model to generate simulated transaction information

Mechanisms for processing a knowledge canvassing request receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Knowledge canvassing using a knowledge graph and a question and answer system

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Dublin, Ireland

Jadran Sirotkovic