Paypal, Inc.

Life Technologies Corporation

Chun Kiat Ho

Lin Song

Kai Xie

Kim Dung Bui

Rey Neo

Edwin Boaz Soenaryo

Yuehao Wu

Harsha Singalreddy

Fei Li

Jonathan M Schultz

Todd Roswech

Srinivasan Manoharan

Vinesh Chirakkil

Karl Anton Hennig

Thomas Anthony Doran

Junhua Zhao

Meng Zang

Shek Hei Wong

Xin Wu

Li Wei Lu

Xiaoying Han

Premila Viswanathan

Jun Zhang

Jun Yang

Yuzhen Zhuo

Quan Jin Ferdinand Tang

Xiaohan Yun

Hewen Wang

Pengshan Zhang

Zhenyin Yang

Sandro Cavallari

Van Hoang Nguyen

Jyothish Kozhipurath

Quin Zuo

Ke Zheng

Vipul Jain

Nilotpal Paul

Anurag Singh

Bipin Kesapur

Wen Lu

Jerry Tso

Lei Xu

Jesslyn Sze Yu Chew

Jintao Guo

Chinmayee Rathi

Mihir Sirohi

Jie How Ko

Huynh Phi Long

RaviKumar PhoolChand Vishwakarma

Anu Kumari

Atul Kumar Agrawal

Methods and systems are presented for providing a framework to securely integrate third-party logic into electronic transaction processing workflow. Third-party programming code that implements different third-party logic may be obtained and stored in a repository. A transaction processing request is received from a third-party server, and an instance of a transaction processing module is instantiated within an operating runtime environment to process a transaction according to a workflow. When the instance of the transaction processing module has reached an interruption point, the instance of the transaction processing module is suspended, and a third-party programming code is executed within an isolated runtime environment. The third-party programming code is configured to provide an output value based on attributes of the transaction. The instance of the transaction processing module then determines whether to authorize or deny the transaction based in part on the output value.

Secured integration of third-party logic in electronic transaction processing

Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Container orchestration framework

Techniques are disclosed relating to the automated capture and reporting of user-verification metric data. In various embodiments, a server system may maintain a set of user-verification rules usable to verify an identity of a user requesting to perform operations via a service provided by the server system. In response to receiving an updated rule definition for a particular user-verification rule, the server system may automatically generate an updated schema for the particular user-verification rule, where the updated schema specifies a structure of event messages used to documents uses of the particular user-verification rule by a user-verification service. The server system may then receive information corresponding to a particular use of the particular user-verification rule and, using the updated schema, generate an event message to document the particular use of the particular user-verification rule.

Techniques for automated capture and reporting of user-verification metric data

A computer system receives a set of user-defined rules are that useable by a computer service to automate a decision flow. The computer system generates a graph model from the user-defined rules. From the graph model, the computer system determines an input dependency model that is indicative of a set of inputs referred to in the graph model. The input dependency model is useable by an orchestrator to coordinate accesses to the one or more data stores in which the set of inputs is stored. The computer system receives the set of inputs and determines one or more automated decisions by applying the set of inputs to the graph model.

Automated decision platform

Methods and systems for content distribution and management are presented. A transaction flow for conducting different stages of a transaction is determined in response to a request for the transaction from an application executable at a user device. The transaction flow includes a sequence of content pages to be displayed within a graphical user interface (GUI) of the application over the different stages of the transaction. The content associated with a tagged UI element of at least one content page is identified. The content is validated for the tagged UI element of the at least one content page, based on a software and hardware configuration of the user device. The validated content is provided via a network to the application at the user device to be displayable with the tagged UI element on the at least one content page of the transaction flow during a corresponding stage of the transaction.

Artificial intelligence (AI) engine for dynamic content distribution and management

Techniques are disclosed relating to using machine learning techniques to predict storage configurations for historical data. In some embodiments, a computer system stores representations of historical data according to a current set of storage parameters. The representations may include snapshots of historical data in a data repository at different points in time. The computer system may receive queries for historical data specifying points in time from which to retrieve the historical data. In some embodiments, the computer system responds to the queries using the stored representations and determines performance metrics for the responses. In some embodiments, the computer system trains a machine learning model using the performance metrics. Based on output of the trained model, the computer system updates the current set of storage parameters. The updating may affect subsequent storage of representations in the data repository, which may advantageously improve query response times and decrease repository storage size.

Alteration of historical data representations using data repository performance metrics

Techniques are disclosed relating to data validation using encode values. In various embodiments, a data monitoring system may retrieve a plurality of datasets from a live database at a non-production datacenter. The data monitoring system may perform encoding operations on one or more of the plurality of datasets to generate encode values that correspond to the plurality of datasets. The data monitoring system may then retrieve an updated dataset, for example from an experimental database at the non-production datacenter, and perform validation operations to validate one or more characteristics of the updated dataset. For example, in some embodiments, the data monitoring system may retrieve the encode values corresponding to the plurality of datasets and use the encode values to validate the updated dataset. The data monitoring system may then generate a validation output indicative of a result of the validation operations.

Data validation using encode values

Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Systems and methods for determining a response to a user query

Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

System and method for generating highly scalable temporal graph database

Techniques are disclosed relating to detecting matching datasets using encode values. In various embodiments, a data monitoring system may perform encoding operations on a first dataset to generate a first encode value that corresponds to a particular one of one or more fields included in the first dataset. The data monitoring system may then determine whether the first dataset matches a previously analyzed dataset. For example, in some embodiments, data monitoring system may compare the first encode value to a previous encode value that corresponds to a second field of the previously analyzed dataset. Based on this comparison, the data monitoring system may generate an output value that is indicative of a similarity between the first encode value and the previous encode value. The data monitoring system may then determine whether the first dataset matches the previously analyzed dataset based on this output value.

Detection of matching datasets using encode values

A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.

Real time application error identification and mitigation

Techniques are disclosed relating to data versioning in a multi-datacenter topology. In various embodiments, a computer system at a first datacenter may execute a query at a first point in time against a particular dataset that is maintained at the first datacenter. The particular dataset includes a first data record with a corresponding version number, where the version number is associated with a value of the first data record at the first point in time. In response to the first query, the computer system may receive a first results dataset that includes the first data record with its corresponding version number. The computer system may then store information indicative of the results dataset that is usable to retrieve, at a subsequent time from a historical version of the particular dataset at a second datacenter, the first data record with the same value as at the first point in time.

Data versioning in a multi-datacenter topology

Aspects of the present disclosure involve systems, methods, devices, and the like for an end-to-end solution to auto-identifying features. In one embodiment, a novel architecture is presented that enables the identification of optimal features and feature processes for use by a machine learning model. The novel architecture introduces a feature orchestrator for managing, routing, and retrieving the data and features associated with analytical job request. The novel architecture also introduces a feature store designed to identify, rank, and store the features and data used in the analysis. To aid in identifying the optimal features and feature processes, a training system may also be included in the solution which can perform some of the training and scoring of the features.

Solution to end-to-end feature engineering automation

Growing community of inventors

Singapore, Singapore

Chun Kiat Ho