Applied Materials, Inc.

Microsoft Technology Licensing, LLC

Other

Scintera Networks, Inc.

Samsara Inc.

Delphix Corporation

Photon Dynamics, Inc.

Ejoule, Inc.

Yan Wang

Liang-Yuh Chen

Alain Duboust

Stan D Tsai

Feng Quan Liu

Yongqi Hu

Siew Neo

Qian Yu

Paul D Butterfield

Rashid Mavliev

Arun Sacheti

Antoine P Manens

Lu Yang

Nathan Hurst

Yuchun Wang

Ralph M Wadensweiler

Manoocher Birang

Shou-Sung Chang

Alok Kumar Srivastava

Jedidiah Yueh

Daniel A Carl

Kevin Lai

Xavier David Luiz

Abhijit G Shanbhag

Margaret Irene Finch

Surendra Ulabala

Cole Jurden

Akshay Raj Dhamija

Rajeev Krishnamoorthy

Rachel Demerly

Lizhong Sun

Donald J K Olgado

Houdong Hu

Sharan Srinivasan

Yuan Tian

Shirish Nair

Rohit Annigeri

Ye Wu

Sen-Hou Ko

Aastha Jain

Sung Chun Lee

Li Huang

Jing Wang

Parag Agrawal

Vladimir Galburt

Renhe Jia

Kenshiro Nakagawa

Maisie Wang

Kuang-Huei Lee

Saurajit Mukherjee

Vishal Chhabilbhai Thakkar

Jose Cazarin

Fnu Yokesh Kumar

Nikesh Srivastava

Shipeng Li

Xi Chelsea Chen

Sasson Roger Somekh

Xian-Sheng Hua

Ratson Morad

Wei-Yung Hsu

Alan Eli Willner

Tao Mei

Lakshmanan Karuppiah

Wen-Chiang Tu

Wei Guang Lu

Kaushal Gangakhedkar

Linjun Yang

You Wang

Daxin Mao

Ming-Kuei Tseng

Martin S Wohlert

Ankan Saha

Jie Diao

Eric M Lawrence

Youling Lin

David L Baldwin

Nile Alexander Light

Keqing Liang

Ashwin Narasimha Murthy

Meenaz Merchant

Da Xu

Huyen Karen Tran

Olivier Charlon

Brian Westphal

Zhongqi Pan

Aastha Nigam

Kent Nguyen

Nikita Astrakhantsev

Yongsik Moon

Thomas H Bailey

Ravi Theja Yada

Suresh M Shrauti

Robert A Ewald

Suryakant Kaushik

Narendran Rajan

Yafei Wang

Steve Aochi

Atila Ersahin

Hai Tran

Techniques for a social networking system involve analyzing members' interactions to enhance follow recommendations. The techniques include calculating the number of follows generated by a first member and the number of follows received by a second and third member. The platform then computes a weighted follow utility score for two member pairs: the first comprising the first and second members, and the second comprising the first and third members. These scores are determined by equally weighing the follows generated and received, along with a probability of the first member following the second or third member. While the probability suggests a preference for the second member, the ranking system places the third member pair higher. Consequently, the first member is advised to follow the third member, based on this ranking. Finally, this recommendation is displayed to the first member on an electronic device, guiding their social interactions on the platform.

Augmented follow probability for social networking system follow recommendations

A processing system and method of producing a particulate material are provided. The processing system includes a system inlet connected to one or more gas lines to deliver one or more gases into the processing system, a buffer chamber, a dispersion chamber, a heating assembly, a reaction chamber and a system outlet for delivering particulate material out of the processing system. The method includes delivering one or more gases via a system inlet into a buffer chamber of a processing system, jetting a liquid mixture into one or more streams of droplets using one or more power jet modules into the processing system, delivering flows of one or more heated gases via a heating assembly, forming a reaction mixture and processing the reaction mixture at a reaction temperature into a product material inside the reaction chamber.

System and process with assisted gas flow inside a reaction chamber

Techniques are presented for detecting when drivers drive while drowsy. In some implementations, a drowsiness model is trained with data associated with inward videos and outward videos captured during a trip. The inward videos capture the inside of the cabin with the driver, and the outward videos capture the view in front of the vehicle in the direction of travel. Further, a device at the vehicle periodically calculates a drowsiness scale index value that indicates the level of drowsiness of the driver. Calculating the drowsiness scale index value includes obtaining a set of inward frames from the inward videos; for each inward frame, creating a face image by cropping the inward frame; obtaining a set of outward frames from the outward videos; calculating inward embeddings of the face images and outward embeddings of the outward frames; and calculating, by the drowsiness model, the drowsiness scale index value.

Drowsy driving detection

Systems and methods can be implemented to conduct a visual search as a service in a variety of applications. In various embodiments, a system is configured to provide searching capabilities of content provided by a first entity in response to a search request by a second entity. An image provided by the second entity can be used by the system as a query image to search the content of the first entity. In an embodiment, the first entity can be a commercial entity providing such a system with image related content regarding its products and services such that any number of individual consumers can search for particular products and services of the commercial entity via their communication enabled devices. In addition, such systems can be arranged for other embodiments to provide customized searches of a single source by many individual devices. Additional systems and methods are disclosed.

Visual search services for multiple partners

Techniques are presented for the detection and management of collision warning (CW) events. A training dataset comprising videos of vehicle collisions and non-collisions, sensor readings, environmental conditions, and more is utilized to train a CW classification model for detecting potential collision events in vehicles. A backend CW classification model, with greater computational resources, employs a more complex neural network to review CW events received by the Behavioral Monitoring System (BMS) based on video data, achieving higher precision and reducing false positives. The CW model is installed in vehicles for real-time detection, while the backend model is deployed at the BMS. The BMS validates detected CW events, filters out false positives, and streamlines the review process for fleet administrators and customers. Additional BMS filtering operations include assessing non-proximity-related CW events and camera impairments, with the filtered CW events presented for review in the safety inbox.

Forward collision warning

Methods, systems, and computer programs are presented for monitoring tailgating when a vehicle follows another vehicle at an unsafe distance. A method for enhancing a Following Distance (FD) machine learning (ML) model is disclosed. The method includes providing a management user interface (UI) for configuring FD parameters, followed by receiving FD events. A UI for manual FD annotation and another for customer review of filtered FD events are also provided. Annotations and customer review information are collected to improve the training set for the FD ML model. The FD model is then trained with the new data and downloaded to a vehicle. Once installed, the FD model is utilized to detect FD events within the vehicle, thereby enhancing the vehicle's safety and performance in driving scenarios by improving the accuracy and reliability of FD event predictions or detections.

Monitoring the safe distance between vehicles while driving

A computer-implemented method for machine learning model operation can include, by one or more processors executing program instructions: providing a first training dataset comprising a plurality of images and associated object detection labels, providing a second training dataset comprising a plurality of images and associated classification labels, and providing a machine learning model comprising a model backbone, an object detection task head, and a classification task head. The method can further include training the machine learning model by training the object detection task head using the first training dataset and training the classification task head using the second training dataset. The method can further include deploying the trained machine learning model that includes the trained model backbone and the trained object detection task head but does not include the trained classification task head.

Multi-task machine learning model for event detection

Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.

Detection of camera with impaired view

A processing system for producing a product material from a liquid mixture includes an array of one or more power jet modules adapted to jet the liquid mixture into one or more streams of droplets and force the one or more streams of droplets into the processing system adapted to process the one or more streams of droplets into the product material. A method for producing a product material from a liquid mixture on a processing system includes moving each of the one or more power jet modules and be connected to an opening of a dispersion chamber, opening one or more doors of the one or more power jet modules, processing the one or more streams of droplets inside a reaction chamber, closing the one or more doors of the power jets modules and moving each of the one or more power jet modules in a second direction.

System with power jet modules and method thereof

A processing system and method of producing a particulate material from a liquid mixture are provided. The processing system generally includes a system inlet connected to one or more gas lines to deliver one or more gases into the processing system, one or more power jet modules adapted to jet a liquid mixture into one or more streams of droplets and to force the one or more streams of droplets into the processing system, and a reaction chamber adapted to deliver the one or more streams of droplets in the presence of the one or more gases and process the one or more streams of droplets into the particulate material. The method includes delivering one or more gases into a processing system, jetting the liquid mixture into one or more first droplets streams using one or more power jet modules of the processing system and into the processing system, and reacting the one or more first droplets streams delivered from the processing chamber inside a reaction chamber of the processing system in the presence of the one or more gases into the particulate material at a first temperature.

Processing system and method for producing a particulate material

A processing system for producing a product material from a liquid mixture includes an array of one or more power jet modules adapted to jet the liquid mixture into one or more streams of droplets and force the one or more streams of droplets into the processing system adapted to process the one or more streams of droplets into the product material. A method for producing a product material, from a liquid mixture on a processing system includes moving each of the one or more power jet modules and be connected to an opening of a dispersion chamber, opening one or more doors of the one or more power jet modules, processing the one or more streams of droplets inside a reaction chamber, closing the one or more doors of the power jets modules and moving each of the one or more power jet modules in a second direction.

Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

Layout-aware, scalable recognition system

Growing community of inventors

Sunnyvale, CA, United States of America

Yan Wang