Yahoo Assets LLC

Verizon Patent and Licensing Inc.

Excalibur Ip, LLC

Avaya Inc.

Other

Yahoo! Inc.

Verizon Media Inc.

Applied Invention, LLC

Oath Inc.

Yahoo Ad Tech LLC

Applied Minds, Inc.

Ispheres Corporation

Eric Theodore Bax

Lisa Giaffo

Tanisha Sharma

John Donald

Kimberly Williams

Melissa Gerber

W Daniel Hillis

Mathias L Kolehmainen

Augusto Callejas

Tarun Bhatia

John Peng

Arun Autuchirayll

Harry Kao

Charlotte Bax

Nikki Thompson

Dante John Pacella

Manuel Enrique Caceres

Ayman Farahat

Mun Wei Low

Darshan Vishwanath Kantak

Ian Swett

Teppo Veikko Salonen

Nikki Mia Williams

Will Wagner

Scott Ard

Nikki M Thompson

Dipika Bhatia

Mohammad Mahdian

Sergei Vassilvitskii

Prasad Chalasani

Nicola Barbieri

R Preston McAfee

William W Wagner

Louis Cisnero, Jr

Joseph Greg Billock

Natalie Bax

Julian Pellico

Kimberley Williams

Jitesh Mehta

David Garcia Soriano

Arundhyoti Sarkar

Roxana Iuliana Marcu Forbes

Sam Fishman

Charless C Fowlkes

In some implementations, the techniques described herein relate to a method including: parsing, by a processor, a generated text to identify statements included within a generated text; querying, by the processor, a remote data source to identify sources for each statement in the statements; determining, by the processor, trustworthiness values for each statement, a trustworthiness value for a given statement determined by computing trustworthiness labels for each source corresponding to a given statement: generating, by the processor, a label for the generated text based on an aggregated trustworthiness of each of the statements; and displaying, by the processor, the generated text and the label within a user interface displayed to a user.

Automated citations and assessment for automatically generated text

Disclosed are systems and methods for initiating communication between users of a user group based on machine learning techniques. The disclosed systems and methods provide a novel framework for automating communication scheduling and communication initiation based on user communication objectives and machine learning techniques. The disclosed framework operates by leveraging available user provided communication parameters, user provided objectives, and various real-time data associated with the users, and using the aforementioned data as inputs for machine learning models, in order to schedule communication between the users, automatically initiate communication between the users, or transmit communication notifications to the users.

Systems and methods for initiating communication between users based on machine learning techniques

Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Automatic privacy-aware machine learning method and apparatus

The present teaching relates to communication related to predictions. A communication chain is created with multiple chain units, each operating independently to provide prediction services related to a prompt with embedded information therein associated with predictions for the prompt to enable prediction services. A request is received for a requested operation related to a query prompt on one of prediction solicitation, prediction entry, verification entry, and prediction access. If a chain unit for the query prompt, the request is directed to the chain unit to perform the requested operation. If not, a new chain unit is created for the query prompt and the request is forwarded to the new chain unit to carry out the requested operation.

System and method for verified predictions and performance

One or more computing devices, systems, and/or methods for content item selection for goal achievement are provided herein. A goal of a user is identified. A model is utilized to evaluate the goal, user information, and a set of content items to generate predictions for the content items of how likely each content item will be a causation factor of the user making progress towards the goal in response to the user being provided with each content item. A target content item is selected from the set of content items based upon the target content item having a predicted likelihood of being the causation factor above a threshold. The target content item is provided through a registered media channel accessible through a device of the user.

Content item selection for goal achievement

In an example, a first content item is provided for display on a first client device. A first feedback signal is received. The first feedback signal is indicative of one or more first user reactions to display of the first content item on the first client device. The first content item is provided for display on a second client device. A second feedback signal is received. A first popularity score associated with the first content item is determined based upon the first feedback signal and the second feedback signal. A first popularity label is assigned to the first content item based upon the first popularity score. An enhanced content presentation interface including the first content item is generated based upon the first popularity label assigned to the first content item. The enhanced content presentation interface including the first content item is presented on a third client device.

System and method for labeling and presenting content

Methods are disclosed that include receiving electronic content data associated with an electronic content provider comprising: a content campaign identification associated with a virtual token, and provider-identified platform identifications that identify platforms on which the content campaign may be initiated; receiving input from the user comprising: a user identification, a virtual token identification, a campaign identification that identifies the content campaign associated with the virtual token, and user-identified platform identifications that identify the platforms on which the user consents to be targeted by the content campaign; determining, based on the received electronic content data and user data, the content campaign and platforms the user consents to; and generating a notification to initiate a content campaign targeting the user on the platforms on which the user consents to be targeted; and outputting the generated notification to the platforms on which the user consents to be targeted.

Systems and methods for targeting electronic content using virtual tokens on user devices

A user device may invoke, for a user associated with an unavailable user device, a guest mode, and may connect the user device with a network device based on invoking the guest mode. The user device may provide credentials of the user and a secure input of the user to the network device based on invoking the guest mode, and may receive an identity service and an emergency service for the user when the secure input is authenticated by the network device. The user device may associate, via the identity service, the user with the user device to enable the user to utilize the emergency service, and may provide, via the emergency service, one or more emergency notifications. The user device may receive an indication of the user exiting the guest mode, and may remove the credentials of the user from a memory based on the indication.

Systems and methods for providing surrogate credentials and a secure guest mode for mobile devices

Disclosed are a method and apparatus for limiting the number of results returned by a data store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.

Data store with lock-free stateless paging capacity

One or more computing devices, systems, and/or methods for executing feedback actions based upon vehicle practices are provided. For example, sensor data is collected from sensors configured to collect sensor data associated with vehicles. The sensor data is synthesized to attribute sensor data to vehicle motion of the vehicle in relation to other objects. The vehicle motion is synthesized to determine driving actions of the vehicle, such as switching lanes, making a left hand turn, running a red light, etc. Vehicle practices of the vehicle are defined based upon the driving actions, such as a vehicle practice of a driver running red lights. A feedback action is executed to either reward the vehicle practices or incentivize a change in the vehicle practices.

Systems and methods for executing feedback actions based upon vehicle practices

One or more computing devices, systems, and/or methods for classifier validation are provided. A set of in-sample examples are partitioned into a reduced in-sample set and a remaining in-sample set. The reduced in-sample set is processed using a set of classifiers. A subset of classifiers are identified as having error counts, over the reduced in-sample set, below a threshold number of errors. A training procedure is executed to select a classifier having a minimum error rate over the set of in-sample examples. If the classifier is within the subset of classifiers, then an out-of-sample error bound is determined for the classifier.

Classifier validation

Growing community of inventors

Sierra Madre, CA, United States of America

Eric Theodore Bax