Accenture Global Services Gmbh

Accenture Global Solutions Limited

Accenture Corporation

Ac Properties B.v.

Accenture Properties (2) Bv

Andrew E Fano

Marko Krema

Rayid Ghani

Chad Michael Cumby

Scott Kurth

Anatole Vitaly Gershman

David William Vinson

Christopher K Chung

Yan Liu

Katharina Probst

Fredrik Linaker

Agata Opalach

Robert Bernard (Robin) Groenevelt

Shubhashis Sengupta

Kishore S Swaminathan

Jurgen Albert Weichenberger

James L Meyers

Maziyar Baran Pouyan

Saeideh Shahrokh Esfahani

Abhisek Mukhopadhyay

Daniel Garrison

Timothy M Shea

Yao A Yang

Joshua Neland

Allan Enemark

Phillip Henry Rogers

Sai P Kandallu

Pier Paolo Campari

Michele Sassano

Marco Dini

Simone Morandi

Tripti Saxena

Sneha Singhania

Jana A Thompson

Chad M Cumby

Kelly L Dempski

David T Nguyen

Roshni Ramesh Ramnani

Michael E Bechtel

Milind Ravikiran Savagaonkar

Jordan K Buller

Vivek Kumar Khetan

Mayuresh Anand

Ritesh Dhananjay Nikose

Jesus Sanchez-Macias

Patrick Lai

Scott W Kurth

Chad Cumby

A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Utilizing machine learning models and in-domain and out-of-domain data distribution to predict a causality relationship between events expressed in natural language text

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Data clustering

Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

System for time-efficient assignment of data to ontological classes

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as 'Violence', 'Conversation', &#x201c;Street&#x201d;, &#x201c;Nudity&#x201d;, &#x201c;Animation&#x201d;. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Enhanced digital content review

Methods, systems, and apparatus for solving optimization tasks. In one aspect, a system includes one or more classical processors and one or more quantum computing resources, wherein the one or more classical processors and one or more quantum computing resources are configured to perform operations comprising receiving input data comprising data specifying a computational task to be solved; processing the received input data using a first quantum computing resource to generate data representing a reduced computational task, wherein the reduced computational task has lower dimensionality that the computational task; and processing the data representing the reduced computational task to obtain a solution to the computational task.

Solving computational tasks using quantum computing

In some examples, artificial intelligence based music playlist reordering and song performance assessment may include ascertaining listening data for a plurality of tracks, and generating a plurality of sessions. Tracks that have been played more than a specified play threshold may be identified and retained. Sessions that are greater than a minimum session length threshold and less than a maximum session length threshold may be retained. Input-output track sequences may be generated for the retained sessions. Unique identifiers may be assigned to each of the tracks present across the retained sessions. Each input-output track sequence may be vectorized based on associated unique identifiers. A neural network model may be trained based on the vectorized input-output track sequences. For an input playlist, the trained neural network model may be used to generate a modified playlist. Additionally or alternatively, a user-song interaction graph may be used to generate another modified playlist.

Artificial intelligence based music playlist reordering and song performance assessment

In some examples, artificial intelligence based music playlist curation may include ascertaining listening data for a plurality of tracks, and generating a plurality of embeddings that represent the plurality of tracks. A replacement track for an existing track in an input playlist may be generated. Alternatively or additionally, at least one additional track may be added to the input playlist. Alternatively or additionally, based on a seed set of tracks, an output playlist that includes a specified number of tracks that is greater than a number of tracks in the seed set of tracks may be generated. Alternatively or additionally, based on a plurality of specified attributes, the plurality of embeddings may be partitioned into a plurality of clusters corresponding to the plurality of specified attributes.

Artificial intelligence based music playlist curation

Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Multi-state quantum optimization engine

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining retraining predictive models. One of the methods includes maintaining, by a computer system of an enterprise, one or more predictive models. The computer system receives operational data and uses each of the one or more predictive models to generate predictions using the received operational data. An indication of a systemic change in the computer system is received. The method includes determining that one or more retraining rules specify that at least one of the one or more predictive models should be retrained due to the systemic change, and in response, obtaining updated training data and retraining the predictive model using the updated training data.

Determining retraining of predictive models

A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment's operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).

System for individualized customer interaction

A digital point-of-sale system for determining key performance indicators (KPIs) at a point-of-sale includes a product identification unit and a realogram creation unit. The product identification unit is configured to receive a captured image of a product display and to identify products in the captured image by comparing features determined from the captured image to features determined from products templates. The realogram creation unit is configured to create a realogram from the identified products and product templates. A product price KPI unit is configured to identify a product label proximally located to each identified product, and to recognize the product price on each product label. Each product price is compared to a predetermined range of prices to determine whether the product label proximally located to the identified product is a correct product label for the identified product.

Digital point-of-sale analyzer

Growing community of inventors

Lincolnshire, IL, United States of America

Andrew E Fano