International Business Machines Corporation

Robin Lougee

James R Kraemer

Robert R Friedlander

Kirill M Osipov

Levente Klein

Richard Thomas Goodwin

Fernando Jimenez Marianno

German Sergio Goldszmidt

Richard B Segal

Petar Ristoski

Jing Fu

Kimberly C Lang

Christian Harris

Tenzin Yeshi

A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.

Method, system, and computer program product for representational machine learning for product formulation

A weighing station device, a computer-implemented method and a computer program product for estimating a weight of an animal. The weighing station device includes a stall for the animal, a camera in the stall that is activated to capture a plurality of digital images of the animal when a presence of the animal is detected at the stall. A processor obtains the plurality of digital images from the camera, constructs a three-dimensional digital image of the animal from the plurality of digital images, determines a dimension of the animal from the three-dimensional digital image, and estimates the weight of the animal from the determined dimension and a model relating the dimension of the animal to the weight of the animal.

Imaging and three dimensional reconstruction for weight estimation

A computer implemented method, program product, and/or system allocate human resources to a cohort. At least one attribute held by each member of a group of human resources is identified. A request is received, from a planned cohort, for multiple human resources that collectively possess a set of predefined attributes, wherein no single human resource possesses all of the predefined attributes. The set of human resources that satisfies the request is identified and assigned to the planned cohort.

Generating candidate inclusion/exclusion cohorts for a multiply constrained group

A processor-implemented method, system and/or computer program product allocates multiple resources from multiple organizations. A series of requests for multiple resources from multiple organizations is received. The multiple resources are required to accomplish a specific task, and each of the multiple resources is assigned a probability of consumption. Probabilities of availability of the multiple resources are then determined and transmitted to the organizations.

Probabilistic optimization of resource discovery, reservation and assignment

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Robin Lougee