Toyota Jidosha Kabushiki Kaisha

Toyota Motor Engineering & Manufacturing North America, Inc.

Microsoft Technology Licensing, LLC

Ondot Systems Inc.

Qi Chen

Onur Altintas

Vaduvur Bharghavan

John P Kenney

Hongsheng Lu

Kyungtae Han

Rohit Gupta

Akila C Ganlath

Nejib Ammar

Dawei Chen

Alexandru Croicu

Jiajun Hua

Zhiqiang Zhang

Eray Aydin

Jose Julian Argil Torres

Richard Mar

David Moreno

Abhishek Kumar Sharma

Sudheer Maremanda

Wesley Hargrove

Arvind Kalyan

Apoorvi Kapoor

Brenton Michael Moffit

Ajay Subramanian

Zhiling Liu

Kun Qian

Yuriy Grinberg

Systems and methods for selecting an edge node include a processor operable to send a request of bidding to edge nodes regarding performing a task for an inquiry vehicle, receive one or more bids from one or more bidding edge nodes of the edge nodes, wherein each bid includes a resource allocation offer and a price, calculate a preference value based on a weighted sum of preference factors for each bidding edge node, wherein the preference factors are determined based on the resource allocation offer, and the weight assigned to each preference factor is determined based on the task, select a performing edge node based on the preference values and the prices, and operate the inquiry vehicle based on a result of the task performed by the performing edge node.

Systems and methods for selecting edge nodes for task performance

Systems, methods, and other embodiments described herein relate to reconstructing a dynamic scene using Neural Radiance Field (NeRF) technology. In one embodiment, a method includes receiving, from one or more sensors, a plurality of video clips of an environment. The method includes generating a second plurality of video clips based on the plurality of video clips, and reconstructing, using NeRF technology, a scene as a continuous function based on the second plurality of video clips.

Dynamic scene reconstruction using neural radiance field technology

Logo candidates for a specific ISO data can be identified from external resources based on the enriched merchant data. Low quality images of the logo candidates are filtered out with image analysis including entropy ratio evaluations of the logo candidates. Also, the logo candidates are processed with high quality filtering including classification of the logo candidates with a deep learning classifier for distinguishing logos from non-logos. A logo from the logo candidates is selected to associate with the ISO data packets. A display having the selected logo associated with a transaction of the ISO data packets can be generated for display to users.

Merchant logo detection artificial intelligence (AI) for injecting user control to ISO back-end transaction approvals between acquirer processors and issuer processors over data communication networks

Techniques for performing near real-time analytics are provided. In one technique, different client devices generate different sets of events pertaining to the same or different media item. Events generated by one client device are assigned to one process (or machine) while events generated by another client device are assigned to a different process (or machine). Each process aggregates events assigned to it based on a time window to generate an aggregated event. The aggregated events are stored in persistent storage. In response to a request for a report that is associated with a media item, one or more of the aggregated events are retrieved from persistent storage and report data is generated based on the aggregated events. The report data is sent to the requestor for visual presentation.

Near real time analytics

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San Jose, CA, United States of America

Qi Chen