Ford Global Technolgoies, LLC

Retail Capital LLC

Zhen Zhao

Mostafa Parchami

Codrin Cionca

Juan Enrique Castorena Martinez

Sourabh Jha

Jack Mansueti

Jeffrey Thomas Remillard

Linjun Zhang

Bryan Roger Goodman

Danny M Yelle

Ishan Patel

Jerome M Pender

Satyajit Samal

Brad Garon

Kp Unnikrishnan

Ashley Naomi Kleinhans

Guoyu Lu

Minyang Jiang

Gursharan Sandhu

Haley Lyon

A computer server system and method are disclosed for personalization and customization of search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a primary query and transmit a plurality of secondary queries and search results; a data storage device storing a vector database; and one or more processors configured to generate the secondary queries, using the primary query and using responses received to prior secondary queries; to select and concatenate responses to the secondary queries; using a vector formed from the response concatenation, to perform a vector search of the vector database and obtain associated classifications or categories; to filter and rank the associated classifications or categories to generate and output personalized search results and search result rankings comprising one or more associated classifications or categories corresponding to the primary query.

System and methods for personalization and customization of search results and search result ranking in an internet-based search engine

A computer server system and method are disclosed for personalization and customization of search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a primary query and transmit secondary queries and search results; and one or more processors configured to generate the secondary queries; to extract or transform responses into text variables; to use trained, supervised multi-class neural networks to classify the text variables to form initial classifications or categories and combine the initial classifications or categories to form resulting classifications or categories; to filter and rank the resulting classifications or categories; and to use the filtered and ranked resulting classifications or categories to generate and output the personalized search results and search result rankings, the personalized search results and search result rankings comprising one or more associated classifications or categories corresponding to the primary query.

A sensor system is disclosed. The sensor system may comprise a housing; an emitter, carried by the housing, that emits a beam comprising depth-data signals; a beam-distribution adjustment system; and a processor programmed to control the adjustment system by selectively changing an angular distribution of the depth-data signals emitted from the housing.

Beam distribution adjustment for a sensor

A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to detect and locate an object by processing video camera data with a capsule network, wherein training the capsule network includes determining routing coefficients with a scale-invariant normalization function. The computer can be further programmed to receive the detected and located object.

Vehicle capsule networks

A map of an area is generated from first sensor data of a first field of view including a blocked area and second sensor data of a second field of view having an unblocked view of the blocked area.

Sensor field of view mapping

A computing system receives first sensor data including moving objects and multi-modal LiDAR map data wherein the first sensor is calibrated based on a cost function that alternates between LiDAR map point cloud data and LiDAR map ground reflectivity data as input. The computing system can operate the vehicle based on the moving objects.

Stationary camera localization

A system, comprising a processor; and a memory, the memory including instructions to be executed by the processor to acquire a first image of a scene, acquire a second image of the scene while illuminating the scene, identify pixel blooming in a subtracted image determined by subtracting the first image from the second image, remediating the pixel blooming based on empirically determined parameters, and operate a vehicle based on the subtracted image.

Bloom removal for vehicle sensors

Growing community of inventors

Farmington Hills, MI, United States of America

Zhen Zhao