Blue Yonder Group, Inc.

Arun Raj Parwana Adiraju

Vineet Chaudhary

Abhinav Kishore

Machiraju Pakasasana Rama Rao

Pawan Kumar Singh

Ramakrishna Perla

Ankit Goel

Vaibhav Sharma

A system and method are disclosed for generating intelligent multi-modal system actions based, at least in part, on predicting a user action and one or more stored user inputs. Embodiments include a database and a computer comprising a processor and memory, the computer is configured to monitor user inputs using one or more sensors and one or more tactile interface devices, detect at least two modes of user input and store the user inputs in the database. The computer is further configured to evaluate the stored user inputs in the database and the at least two modes of user input to generate a system action and generate a system action based, at least in part, on predicting a user action and one or more stored user inputs.

System and method for intelligent multi-modal interactions in merchandise and assortment planning

A system and method are disclosed for aggregating product trends. Embodiments include receiving an initial set of images from one or more data feeds by a trend aggregation system comprising a server, identifying one or more attribute values from the initial set of images, and quantifying a social affinity score of one or more items of an inventory of a supply chain entity based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment. Embodiments may further include receiving an image of at least one additional item, identifying a product attribute from the image, and assigning an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

System and method of integrating social trends in assortment planning

A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.

System and method of visual attribute recognition

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Hyderabad Telangana, India

Arun Raj Parwana Adiraju