Nancy L Greco

US Patent:

20230126457, Apr 27, 2023

Filed:

Oct 26, 2021

Appl. No.:

17/452247

Inventors:

- Armonk NY, US
Raghu Ramaswamy - Bangalore, IN
Nirmit V Desai - Yorktown Heights NY, US
Dhiraj Joshi - Edison NJ, US
Satish Rajani - Dewas, IN
Nancy Anne Greco - Lagrangeville NY, US
Shiva G - Nagar, IN
Aakash Praliya - Kota, IN
Wei-Han Lee - White Plains NY, US
Luis Angel Bathen - Placentia CA, US
Tova Roth - Woodmere NY, US
Sujoy Kumar Roy Chowdhury - Kolkata, IN
Prakriti Pritmani - Ahmedabad, IN
Kay Murphy - Nebo NC, US
Shilpa Shenai - Bengaluru, IN
Arun Yashwant Ingale - Pune, IN
Ajjay Ratnakar - Bangalore, IN
Gwilym Benjamin Lee Newton - Winchester, GB

International Classification:

B25J 9/16
B25J 13/08

Abstract:

Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

US Patent:

20210150315, May 20, 2021

Filed:

Nov 14, 2019

Appl. No.:

16/683649

Inventors:

- Armonk NY, US
Syed Yousaf Shah - Yorktown Heights NY, US
Petros Zerfos - New York NY, US
Nancy Anne Greco - Lagrangeville NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06N 3/04
G06N 20/00
G06F 17/27
G06F 17/18
G06F 17/16

Abstract:

Embodiments relate to a system, program product, and method for employing deep learning techniques to fused data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.