公开(公告)号：US20170200101A1

公开(公告)日：2017-07-13

申请号：US15400297

申请日：2017-01-06

Applicant: Tata Consultancy Services Limited

Inventor： Rahul KUMAR ,  Anand SRIRAMAN ,  Mandar Shrikant KULKARNI ,  Kanika KALRA ,  Shirish Subhash KARANDE ,  Sachin Premsukh LODHA

IPC: G06Q10/06 ,  G06N3/08

CPC classification number: G06Q10/06311 ,  G06N5/003 ,  G06N7/005 ,  G06Q10/067

Abstract: Optimizing task allocation requires taking into account cognitive load on workers and their response time to allocated tasks. The present disclosure provides for allocation of task by receiving data pertaining to current activity of workers; receiving data pertaining to at least one task to be allocated and determining activity-task pairs based on an activity feature vector corresponding to at least one human body part used during the current activity and a task feature vector corresponding to at least one human body part required for the at least one task to be performed by the workers. Cognitive load on the workers is then estimated for the determined activity-task pairs. An optimum activity-task pair based on the estimated cognitive load is determined and at least one task is allocated to the workers based on the determined optimum activity-task pair.

公开(公告)号：US20190026604A1

公开(公告)日：2019-01-24

申请号：US15895429

申请日：2018-02-13

Applicant: Tata Consultancy Services Limited

Inventor： Monika SHARMA ,  Lovekesh VIG ,  Shirish Subhash KARANDE ,  Anand SRIRAMAN ,  Ramya Sugnana Murthy HEBBALAGUPPE

IPC: G06K9/62 ,  G06T7/00 ,  G06K9/00

Abstract: The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).