Accenture Global Solutions Limited

Eaton Intelligent Power Limited

Chahrazed Bouhini

Freddy Lecue

Nicholas McCarthy

Jeremiah Patrick Hayes

Qurrat Ul Ain

Mykhaylo Zayats

Benedikt M Golla

Adi Ionel Botea

Medb Corcoran

Bogdan Eugen Sacaleanu

Nut Limsopatham

Jitesh Goyal

Nóirín Duggan

Ascanio Afan De Rivera Costaguti

A method for classifying records by relevance to nodes of a hierarchical structure representative of a classification scheme for different classification options. The method includes receiving an input record having a plurality of record content features, and a contextual attribute indicative of a context of the receipt of the input record, retrieving relational data indicative of one or more nodes of the hierarchical structure that are associated with the contextual at-tribute of the received input record, and determining a relevance score for one or more of the nodes of the hierarchical structure to classify the input record. The relevance score of each node is determined in dependence on a comparison of the plurality of record content features of the input record relative to the classification option represented by said node, and further wherein the relevance score of each node depends on the retrieved relational data.

Method for efficient re-ranking and classification of ambiguous inputs in deep hierarchy

Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Compound discovery via information divergence with knowledge graphs

Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Density-based computation for information discovery in knowledge graphs

An example implementation described herein involves identifying an artificial intelligence module to train a user; selecting, using the artificial intelligence module, a set of tasks from a plurality of tasks to provide to the user; providing the set of tasks to the user; monitoring a performance parameter associated with the user performing the tasks; identifying a machine learning model to determine a level of expertise of the user; determining, using the performance parameter as an input to the machine learning model, whether the level of expertise of the user satisfies an expertise threshold; obtaining a configuration update to the artificial intelligence module from the user, determining that the level of expertise of the user satisfies the expertise threshold; and updating the artificial intelligence module to use the configuration update in association with training one or more users or selecting a subsequent set of tasks from the plurality of tasks based on determining that the level of expertise of the user satisfies the expertise threshold.

Adaptive artificial intelligence for user training and task management

A device receives occupational activity descriptions and occupational role attributes, and processes the occupational activity descriptions to generate estimated occupational activity attribute values. The device trains a neural network model based on the estimated occupational activity attribute values to generate a trained neural network model, and receives a new activity description for a new role in an organization. The device processes the new activity description, with the trained neural network model, to generate estimated new activity attribute values, and processes the estimated new activity attribute values, with the logistic regression model, to generate probabilities that the new role is suitable for different workforce types. The device determines a workforce recommendation for the new role based on the probabilities that the new role is suitable for the different workforce types.

Determining optimal workforce types to fulfill occupational roles in an organization based on occupational attributes

Implementations include receiving a predicted value and confidence level from a first ML model, and determining that the confidence level is below a threshold, and in response: providing an encoding based on input data and non-textual information to the first ML model, the encoding representing characteristics of the input data relative to the predicted value, the characteristics including respective gradients of features of the input data, injecting the encoding into a textual knowledge graph that corresponds to a domain of the first ML model to provide an encoded knowledge graph, receiving supplemental data based on the encoded knowledge graph, and providing a supplemental predicted value from a second ML model based on the input data and the supplemental data, the second ML model having a higher number of features than the first ML model, and the supplemental predicted value having a supplemental confidence level that exceeds the threshold.

Machine learning with small data sets

A device may generate a product delivery map that includes route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries. The device may generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations that are candidates to engage in collaborative logistics. The device may determine, based on the set of location constraints and the set of collaborative constraints, a set of delivery schedules that are to be used to perform the set of deliveries. The device may provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries.

System for collaborative logistics using a collaborative logistics map and a knowledge graph

According to an example, with respect to software product development defect and issue prediction and diagnosis, worker profile information and worker state information for a plurality of workers involved in development of a product may be ascertained. A general worker signature that includes a plurality of clusters for all of the plurality of workers may be generated. For each of the plurality of workers, an individual worker signature vector that represents at least one cluster of the plurality of clusters that an individual worker is aligned to may be generated. A product signature vector may be generated based on product state information. Further, an output that includes an explanation for a defect associated with the development of the product may be generated based on a neural network model based analysis of the individual worker signature vectors and the product signature vector over a temporal dimension.

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Chahrazed Bouhini