Yahoo! Inc.

Other

Ebay, Incorporated

Honda Motor Co., Ltd.

University of California

Excalibur IP, LLC

John Canny

Pavel Berkhin

Scott J Gaffney

Ye Chen

George H John

Chad Brower

Aaron S Wallack

Randolph Brost

Kenneth Y Goldberg

Shi Zhong

Teruhisa Misu

Yi-Ting Chen

Ashish Tawari

Dmitry Pavlov

Jinkyu Kim

Shi Zhonog

Aspects of the present disclosure may include methods, apparatuses, and computer readable media for receiving one or more images having a plurality of objects, receiving a notification from an occupant of the self-driving vehicle, generating an attention map highlighting the plurality of objects based on at least one of the one or more images and the notification, and providing at least one of a steering control or a velocity control to operate the self-driving vehicle based on the attention map and the notification.

Methods and apparatuses for operating a self-driving vehicle

A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.

Probabilistic clustering of an item

A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Granular data for behavioral targeting

Probabilistic recommendation of an item

Method and system for generating a linear machine learning model for predicting online user input actions

A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.

Large-scale behavioral targeting for advertising over a network

A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Granular data for behavioral targeting using predictive models

Methods and apparatus are provided for developing a complete set of all admissible Type I and Type II fixture designs for a workpiece. The fixture processor generates the set of all admissible designs based on geometric access constraints and expected applied forces on the workpiece. For instance, the fixture processor may generate a set of admissible fixture designs for first, second and third locators placed in an array of holes on a fixture plate and a translating clamp attached to the fixture plate for contacting the workpiece. In another instance, a fixture vise is used in which first, second, third and fourth locators are used and first and second fixture jaws are tightened to secure the workpiece. The fixture process also ranks the set of admissible fixture designs according to a predetermined quality metric so that the optimal fixture design for the desired purpose may be identified from the set of all admissible fixture designs.

Processor and method for developing a set of admissible fixture designs

A fixture process and method is provided for developing a complete set of all admissible fixture designs for a workpiece which prevents the workpiece from translating or rotating. The fixture processor generates the set of all admissible designs based on geometric access constraints and expected applied forces on the workpiece. For instance, the fixture processor may generate a set of admissible fixture designs for first, second and third locators placed in an array of holes on a fixture plate and a translating clamp attached to the fixture plate for contacting the workpiece. In another instance, a fixture vice is used in which first, second, third and fourth locators are used and first and second fixture jaws are tightened to secure the workpiece. The fixture process also ranks the set of admissible fixture designs according to a predetermined quality metric so that the optimal fixture design for the desired purpose may be identified from the set of all admissible fixture designs.

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Berkeley, CA, United States of America

John Canny