Accenture Global Solutions Limited

Nitish Bhardwaj

Alpana A Dubey

Kumar Abhinav

Sakshi Jain

Suma Mani Kuriakose

Mary Elizabeth Hamilton

Manish Mehta

Veenu Arora

Michael L Duncan

Niyati Singal

Dhruv Bajpai

Aakanksha Saini

Methods, systems, and computer-readable storage media for receiving, by an aromatic simulation platform, a recipe including descriptions indicative of ingredients of a consumable, processing the recipe through a first neural network to provide a recipe embedding, processing an ingredients profile to determine an aroma compounds profile representing the ingredients of the ingredients profile, processing the aroma compounds profile through a second neural network to provide an aroma embedding, and processing the recipe embedding and the aroma embedding through a third neural network to provide an aroma profile representative of the consumable of the recipe.

Intelligent aromatic simulation of food recipe

An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs. After the most suitable action is performed, the interactive troubleshooting assistant considers new information from multimodal inputs so that the interactive troubleshooting assistant can provide the next recommended action. The interactive troubleshooting assistant may repeat these operations until the repair is completed.

Interactive assistant

Examples of article designing are described herein. In an example, image data, margin data, and sales data corresponding to a plurality of articles may be obtained. The obtained data may be analyzed to identify a first article image of a first article and a second article image of a second article. The first article image is integrated with the second article image, based on an article attribute to generate a transformed article image. The article attribute may be an attribute having a maximum likelihood of making the article popular. The transformed article image may be filtered based on predefined filtering rules to obtain a curated article design image. The curated article design image is assessed to generate a design score indicative of a popularity and/or a sellability of an article, and a design of the article may be selected for a post design selection process, based on the design score.

Article design

Implementations of the present disclosure include generating, by a design generation assistant, a design image representing a design subject, the design subject having one or more regulations applicable thereto, querying, by a regulation assistant, an answer extractor to provide a query result based on a query, the answer extractor including at least one deep learning model that processes the query to provide the query result, the query being descriptive of at least a portion of the design subject, the query result being representative of at least one regulation applicable to the design subject, and displaying, within a graphical user interface (GUI), the query result.

AI-driven design platform

Implementations of the present disclosure include receiving a content image depicting a first set of objects and a style image depicting a second set of objects and a collective style, processing, by the design generation assistant, the content image using one or more ML models to determine a first sub-set of objects, to which a style is to be transferred from the style image, the first sub-set of objects including fewer objects than the first set of objects, processing, by the design generation assistant, the style image using the ML models to determine a second sub-set of objects, from which the style is to be transferred, and generating, by the design generation assistant, an output image with stylized objects, each stylized object including an object in the first sub-set of objects having the style of the one or more objects in the second sub-set of objects.

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Bengaluru, India

Nitish Bhardwaj