International Business Machines Corporation

Ya Juan Dang

Xiao Lin Sun

Yuan Yuan Li

Zhong Fang Yuan

Tong Liu

Yang Liang

Tian Ji Yang

Nuo Xu

Han Ying Song

Shen Ming Tao

Wen Jie Hao

Ze Wang

Junfeng Yuan

Xiao Wei Hou

Teng Jiao Li

Yuhong Zou

Distributed rendering of digital objects in augmented reality environments includes a method, system, and computer program product that are configured to: receive a first space matrix from a first augmented reality (AR) device; receive a second space matrix from a second AR device; perform distributed rendering of a digital object for the first AR device and the second AR device in response to receiving the first space matrix and the second space matrix; transmit first image data to the first AR device, the first image data defining the first version of the first part of the digital object and the second part of the digital object; and transmit second image data to the second AR device, the second image data defining the second version of the first part of the digital object and the second part of the digital object.

Distributed rendering of digital objects in augmented reality environments

Provided are techniques for identifying and correcting model focus drift during model training. A model is trained using an original dataset with data, and the is classified into clusters. A representation is assigned to each of the clusters. A first visualization dashboard with visualizations is generated, where each visualization represents a first data distribution of an associated cluster using the representation assigned to that cluster. The model is fine-tuned using a fine-tune dataset. A second visualization dashboard is generated by updating each visualization, where each visualization represents a second data distribution of the associated cluster. It is determined that a cluster of the clusters has focus drift based on changes between the first data distribution and the second data distribution. The focus drift is corrected by: adding the data of the cluster to the fine-tune dataset to form a combined dataset and fine-tuning the model using the combined dataset.

Identifying and correcting model focus drift during model training

A method includes performing at least the following operations by one or more processors: receiving a request initiated by a device of a first user for a first service to be provisioned by a first entity, deriving a first user preference data based, at least in part, on a historical interaction information of the first user with a second entity provisioning a second service, and responsive to receiving the request, provisioning an instantiation of the first service, by the first entity, in a manner that is based, at least in part, on the first user preference data.

Assistance in service provision

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Beijing, China

Ya Juan Dang