Paypal, Inc.

Michael Charles Todasco

Sumeet Ahuja

Cheng Tian

Braden Christopher Ericson

Gautam Madaan

Dennis Michael Driscoll

Prashanthi Ravanavarapu

Geetha Kuppuswamy

Kevin Ward

Jiri Medlen

Vijeyta Aggarwal

Srivathsan Narasimhan

Timothy Resudek

Anush Vishwanath

Titus Woo

Anand Lakshmanan

Michael Jim Tien Chan

Vladimir Bacvanski

Dushyanth Bharadwaj

John Hastings Granbery

Kent C Griffin

David Edward Eramian

Jeremy Leigh Cattone

Hill Ferguson

Akshay Sanjeevaiah Krishnaiah

Abhishek Chhibber

Michelle Serrano

Adhish Nayanendu Vyas

Fun-Chen Jou

David A Marcus

Michael S Hwang

Nitin S Sharma

Megan Marie Williams

Brandye Sweetnam

Darshankumar Bhadrasinh Desai

Cameron Winter McClees

Vidyut Mukund Naware

Amit Reuven Menipaz

Patrick Wong

Satish Narayan Govindarajan

Geoffrey William Chatterton

Patrick Babcock

Timothy Conrad Nichols

Megan Marie O'Neill

Vidit Nayankumar Maniyar

Charles Gabriel Neale Dalton

Yona Ju

Sandy Godsey

Avik Chatterjee

Eric Byungho Min

Suryatej Gundavelli

Zachary Joseph Berman

Clare Patrice Tandy

Shao-Fei Moy

Gwendolyn Phelan Roberts

David Flemate

Rohan Baddam

Philip Chuang

Haidan Huang

Christina Marie Todasco

Norihiro Edwin Aoki

Saumil Ashvin Gandhi

Lucy Ma Zhao

Jinesh Pravin Patel

Kaili An

Sujay Vijay Purandare

Cynthia L O'Yang

John Lucas Timoney

Megan Marie O′Neill

Maria Mileder

Lars Holger Alexander Steinmetzger

Felipe Albertao

Pushpita Karmakar

Anke Werner

Komalta Rajani

Autumn Paige Crossett

James Fussell

Techniques are disclosed in which a computer system receives, from a plurality of user computing devices, a plurality of device-trained models and obfuscated sets of user data stored at the plurality of user computing devices, where the device-trained models are trained at respective ones of the plurality of user computing devices using respective sets of user data prior to obfuscation. In some embodiments, the server computer system determines similarity scores for the plurality of device-trained models, wherein the similarity scores are determined based on a performance of the device-trained models. In some embodiments, the server computer system identifies, based on the similarity scores, at least one of the plurality of device-trained models as a low-performance model. In some embodiments, the server computer system transmits, to the user computing device corresponding to the low-performance model, an updated model.

Federated machine learning management

Methods and systems are presented for authenticating web content. A request to authenticate web content is received from a user device. A first document object model (DOM) tree representing elements of the web content within a hierarchical structure is accessed. A hash value for each node of the first DOM tree is calculated based on the corresponding element of the web content represented by that node. A second DOM tree associated with a host of the web content is retrieved from a database. The second DOM tree represents predefined elements of the web content. The hash value calculated for each node of the first DOM tree is compared with a hash value associated with a corresponding node of the second DOM tree to determine whether the first DOM tree matches the second DOM tree. The web content is authenticated when the first DOM tree matches the second DOM tree.

Page integrity assurance

There are provided systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. A service provider, such as an online transaction processor, may provide additional services for to users via edge computing systems and edge computing storage nodes. The service may be for data that may be predictively loaded to the edge computing storage node for a particular location, where the edge computing storage node may reside more locally to the location on a network so that data may be served quicker and with less network resource consumption than providing data from a remote cloud computing storage. The data may be predicted to be needed or useful to the user at the location using a user profile for the user, monitored user activities, and/or one or more machine learning models that predict user behaviors at the location.

Edge computing storage nodes based on location and activities for user data separate from cloud computing environments

A system, a medium, and a method are provided to send compensation data to devices at one or more locations where the data is retrievable. For example, a communication interface of a server device accesses request data, where the request data includes an indication of one or more locations where a compensation amount is retrievable. A compensation data component generates compensation data based on the indication of the one or more locations where the compensation amount is retrievable. A location component determines a location of a client device that corresponds to the one or more locations. A transceiver transmits the compensation data to the client device based on the determined location of the client device that corresponds to the one or more locations.

Transmitting data based on retrieval locations

There are provided systems and methods for utilizing a vehicle to determine an identity of a user. A user may establish a vehicle as associated with the user, such as a vehicle owned by the user or used by the user. The vehicle may include a vehicle device that may be configured to establish connections with other devices using short range and network communications. In various embodiments, the other devices may be devices at merchant locations. Thus, a merchant at the merchant location may be informed about settings, preferences, and/or parameters for use of the vehicle established by the user. The merchant may receive these parameters even if another user is utilizing the vehicle, thus, establishing rules for use of the vehicle by the other user. In other embodiments, the other devices may be devices in other vehicles, allowing the users to communicate and set waypoints during trips.

Utilizing a vehicle to determine an identity of a user

There are provided systems and methods for targeted authentication queries based on detected user actions. A user may perform various actions during a day, including online, electronic, or digital actions, such as social networking, messaging, and media consumption, as well as real-life actions, such as exercise, travel, and purchases. The actions may be used to determine a user history for the user by a service provider. When the user wishes to login to an account or otherwise authenticate the identity of the user, the user may provide login or authentication credentials. The credentials may be used to look up the user history and cause the service provider to generate an authentication—query for the user based on events associated with the user in the user history. The query may be utilized to further authenticate the user by requiring the user to respond with the event associated with the user.

Targeted authentication queries based on detected user actions

Methods and systems described herein may implement blockchain cryptocurrency transactions in a variety of environments. An online transaction processor may provide operations for user activity detection for locking cryptocurrency conversions. The transaction processor may detect used activities for a user online or at a physical location and determine that those activities indicate that the user will engage in electronic transaction processing at an upcoming time. The transaction processor may determine that the user has available cryptocurrency and may want to convert at least a portion of the cryptocurrency to another currency for the electronic transaction processing. Using a cryptocurrency exchange, the transaction processor may determine a price to convert the cryptocurrency to the other currency and may lock that price for a time period for the user. A notification may be transmitted to the user with a user interface element to accept this offer.

User activity detection for locking cryptocurrency conversions

Techniques are disclosed in which an edge user computing device pre-processes a stream of user data prior to using the stream of data to train a machine learning model at the edge device. The edge device receives the stream of user data, where the stream of data includes a first set of characteristics associated with the edge device and a second set of characteristics associated with a plurality of user requests received from a user of the edge device. The edge device repeatedly generates, using the stream of data, sets of pre-processed user data by performing pre-processing techniques on characteristics included in the stream of data. The edge device repeatedly trains, using the sets of pre-processed data, a baseline model to generate a device-trained model, where the baseline model is trained at the edge device without providing user data included in the stream of data to a server computer system.

Edge device representation learning

Various systems, mediums, and methods herein describe mechanisms that enable a user to send a request from her smartphone to multiple retriever devices, such that an item may be retrieved for the user. At least one of the users of the retriever devices may accept the request to retrieve the item for the requesting user. The retrieving device may send an acceptance message to the requester's smartphone to confirm that the item may be retrieved for the requesting user. In such instances, a retrieval fee may be transferred to the retrieving user's account accessible with the retriever device that accepted the request.

Aggregation system for item retrieval

Techniques are disclosed in which a computer system receives a transaction request and uses a federated machine learning model to analyze the transaction request. A server computer system may generate a federated machine learning model and distribute portions of the federated machine learning models to other components of the computer system including a user device and/or edge servers. In various embodiments, various components of the computer system apply transaction request evaluation factors to the portions of the federated machine learning model to generate scores. The server computer system uses the scores to determine a response to the transaction request.

Device-side federated machine learning computer system architecture

There are provided systems and methods for on-device data privacy operations to prevent data sharing at user locations. A service provider, such as a merchant location of a merchant and/or associated online transaction processor, may provide additional services for to users via user data that may be tracked or stored of the user. However, the user may not want to share certain data with the merchant or other backend processor for privacy concerns. Thus, on-device data privacy operations may be used to detect when a user is at a location that has a corresponding privacy setting to hide or abstract user data for the location. The privacy setting may designate data to prevent from sharing when the user uses their device with devices associated with the location. Abstracted data associated with recommendations or actions to provide the user may be generated and provided to a merchant for the location.

On-device data privacy operations to prevent data sharing at user locations

There are provided systems and methods for automated configuration of augmented and virtual reality avatars for user specific behaviors. A user may view a virtual environment and/or capture visual data utilizing a device at a location, where the visual data includes one or more real-world objects, or may interact with a virtual reality environment. An augmented or virtual reality experience may be displayed with virtual objects, graphics, avatars, and/or other virtual visualizations and interactions. A service provider may detect real-time data of the user's behaviors and current events or parameters for the corresponding augmented or virtual reality. Using available avatar customizations and configurations, the service provider may automatically configure avatars for presentation in such digital environments. These customizations may be used to automate avatar configurations and presentations without requiring user inputs and based on real-time data, which may be specifically coded and configured dependent on a digital environment.

Automated configuration of augmented and virtual reality avatars for user specific behaviors

Methods and systems are presented for dynamically modifying electronic content presented on a user device by third-party content providers based on goals associated with one or more entities. A content modification system may receive inputs related to goals for a user. The content modification system may synthesize the goals associated with the user. When the user uses a user device to request content from a third-party server, the content modification system may obtain the content and extract items included within the content. The content modification system may modify the content based on the synthesized goals, such as re-arranging the items, highlighting some of the items, or adding and/or removing items. The content modification system may cause the user device to present the modified content.

Goal-based dynamic modifications to user interface content

One or more Bluetooth® low energy (BLE) beacons in communication with a remote server that provides check in capabilities and payment capabilities may be installed at a location. The BLE beacons may connect with a user's mobile device when the user enters the location and allow the user to check in to the location and authorize payments to be made at the location. Once the user is checked in to the location, the user may be provided with additional functionality, benefits, offers, and applications related to the location and facilitated by the check in. Further, the user may be pre-checked in into a next location when the user is at a current location.

Bluetooth low energy (BLE) pre-check in

There are provided systems and methods for load balancing for map application route selection and output. A user may utilize a device application to map or route between two or more endpoints, such as geo-locations entered or detected by the device. During calculation of a travel route between the endpoints, real-time data, user preferences, and requesting entities may provide criteria data that may cause determination of a particular travel route, where the travel route may be longer than a most efficient route but within a pre-defined variable time or distance allotment and match the criteria data. Use of the route may accrue a form of compensation for the user. The user may view an application interface displaying the route, which may further include one or more executable processes to cause recalculation of the route. Recalculation of the route may require the user to provide credits or compensation.

Load balancing for map application route selection and output

Growing community of inventors

Santa Clara, CA, United States of America

Michael Charles Todasco