International Business Machines Corporation

Boe Technology Group Co., Ltd.

Visa International Service Association

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Beijing University of Posts and Telecommunications

Yu Gu

Su Liu

Jing Wang

Hongli Ding

Kai Zhao

Yifei Zhang

Eric Rozner

Inseok Hwang

Yaoguang Wei

Hongqin Song

Si Bin Fan

Kanak Behari Agarwal

Cheng Xu

Wesley Michael Felter

John Bruce Carter

Keqiang He

Yinhe Cheng

Kai Liu

Yaoping Ruan

Peter A Walker

Ranglin Lu

Dingcheng Li

Debbie A Anglin

Igor V Karpenko

Zhongcheng Gui

Hiroki Nakano

Vishal Chittranjan Aslot

Chungkuk Yoo

Takayuki Kushida

Yuji Sugiyama

Jing Min Xu

Hao Yang

Qimei Cui

Xiao Xi Liu

Xiaofeng Tao

Yulong Shi

Pei Ni Liu

William Joseph Leddy, Iii

Daniel Dean

Yuhai Sun

Robert Brian Christensen

Tianpeng Yuan

Shawn Johnson

Subir Roy

Peijie Li

Biswajit Das

Gary Denitus Dougan

Fangfang Fu

Ajay Raman Rayapati

Xiaohui Sun

Wenzhi Cui

Peng Hui Jiang

Hui Wang

Yue Li

Romelia H Flores

Xitong Ma

Johnny Menh-Han Shieh

Mahesh Viswanathan

Xiao Ming Ma

Harigovind Venkatraj Ramasamy

Hui Lei

Xiaojun Tang

Shuai Liu

Junmin Sun

Hongbo Feng

Chao Yuan Huang

Balaji Viswanathan

Wenchu Dong

Mudit Verma

Seep Goel

Kalpana Jogi

Heliang Di

Sam P Hamilton

Zandro Luis Gonzalez

Dan Wang

Yongguo Kang

Jiashuang Zong

Dirk Reinshagen

Yun Xiang

Ran Xu

Uttam Thakore

Xiang Yu Song

Shizhuo Yu

Raghunandan Surapaneni

Vishwas Siravara

Dingcheng Lil

Checed A Rodgers

Roger Cheng-Chung Huang

Ankit Talati

Provided is a method for aggregating data from real-time events (e.g., payment transactions). The method may include receiving event (e.g., transaction) data associated with a plurality of events (e.g., payment transactions). First aggregation of interest data associated with a type of aggregation of interest may be received. A first key associated with each event (e.g., transaction) may be determined based on a first portion of the event (e.g., transaction) data associated with each event (e.g., transaction) and the first aggregation of interest data. A first value based at least partially on a first plurality of the first keys associated with a first subset of the plurality of payment transactions may be communicated based on a first user request. A system and computer program product are also disclosed.

Method, system, and computer program product for real-time data aggregation

A plurality of wireless charging systems may be employed within a network at a venue or other physical area to precisely locate a battery-operated node of the network. The location of the node may then be used to manage a transaction between that node and another node within the network when the transaction is associated with that precise location.

System and method for location-based node management in a wireless charging network

A method for dynamically assigning an inference request is disclosed. A method for dynamically assigning an inference request may include determining at least one model to process an inference request on a plurality of computing platforms, the plurality of computing platforms including at least one Central Processing Unit (CPU) and at least one Graphics Processing Unit (GPU), obtaining, with at least one processor, profile information of the at least one model, the profile information including measured characteristics of the at least one model, dynamically determining a selected computing platform from between the at least one CPU and the at least one GPU for responding to the inference request based on an optimized objective associated with a status of the computing platform and the profile information, and routing, with at least one processor, the inference request to the selected computing platform. A system and computer program product are also disclosed.

Method, system, and computer program product for dynamically assigning an inference request to a CPU or GPU

A real-time geo-intelligent aggregation engine and related methods are disclosed. The real-time geo-intelligent aggregation engine can be used to provide interaction event data corresponding to a geographic coordinate point and a boundary element to requestors or requestor computers. After receiving a request from a requestor computer, the real-time geo-intelligent aggregation engine can determine one or more geohashes corresponding to a geographic region. The one or more geohashes can be used to query a geographic resource provider database to determine a list of resource providers acting within the geographic region. The list of resource providers can be used to query a resource provider interaction database to determine interaction event data corresponding to those resource providers. The interaction event data can subsequently be provided to the requestor computer. The requestor computer and/or a requestor associated with the requestor computer can perform subsequent action based on the interaction event data.

Real-time geo-intelligent aggregation engine

A method may include obtaining feature data associated with a set of feature inputs of a machine learning model, determining a probability that a subset of the feature data is concurrently used as the set of feature inputs for the machine learning model, and storing the subset of the feature data on a same cache node or server of a plurality of cache nodes or servers based on the probability.

System, method, and computer program product for distributed cache data placement

A method, system, and computer program product for predicting future transactions may obtain merchant data associated with a merchant; determine a geographic location associated with the merchant based on the merchant data; determine one or more other merchants within a first threshold distance of the geographic location; obtain transaction data associated with the one or more other merchants and the merchant; and predict, based on the transaction data, at least one of a future number of transactions for the merchant in a future time period and a future transaction amount for the merchant in the future time period.

Method, system, and computer program product for predicting future transactions

Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

System, method, and computer program product for time-based ensemble learning using supervised and unsupervised machine learning models

Embodiments of the invention are directed to systems and methods for utilizing a multi-tiered caching architecture in a multi-tenant caching system. A portion of the in-memory cache may be allocated as dedicated shares (e.g., dedicated allocations) that are each dedicated to a particular tenant, while another portion of the in-memory cache (e.g., a shared allocation) can be shared by all tenants in the system. When a threshold period of time has elapsed since data stored in a dedicated allocation has last been accessed, the data may be migrated to the shared allocation. If data is accessed from the shared allocation, it may be migrated back to the dedicated allocation Utilizing the techniques for providing a multi-tiered approach to a multi-tenant caching system can increase performance and decrease latency with respect to conventional caching systems.

Techniques for multi-tiered data storage in multi-tenant caching systems

Provided is a system that includes at least one processor programmed or configured to receive an XML data file, wherein the XML data file includes data associated with one or more input parameters of a machine learning model, generate a code generation template based on the data associated with one or more input parameters of the machine learning model included in the XML file, where the code generation template includes one or more keys associated with one or more parameters of a transaction aggregate for an account of a user, and generate a file of executable code based on the code generation template, wherein the file of executable code includes instructions that, when executed by at least one processor, causes at least one processor to retrieve transaction aggregate data associated with the transaction aggregate for the account of the user. A method and computer program product are also provided.

System, method, and computer program product for generating code to retrieve aggregation data for machine learning models

Described are a method, system, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes generating a shadow testing environment operating at least two transaction services. The method also includes receiving a plurality of transaction authorization requests. The method further includes determining a first percentage associated with a first testing policy of the first transaction service and a second percentage associated with a second testing policy of the second transaction service. The method further includes replicating in the shadow testing environment, in real-time with processing the payment transactions, a first portion of the plurality of transaction authorization requests and a second portion of the plurality of transaction authorization requests. The method further includes testing the first transaction service using the first set of replicated transaction data and the second transaction service using the second set of replicated transaction data.

Method, system, and computer program product for operating dynamic shadow testing environments

Systems and methods of reconfiguring machine learning models are disclosed. A method includes receiving, by a server computer, external data and determining, by the server computer, a machine learning model from a plurality of machine learning models, based on the external data. The plurality of machine learning models are in standby mode. The server computer configures an AI engine with the machine learning model. The method also includes receiving, by the server computer, a prediction request from a client computer, processing the prediction request with the AI engine to form a prediction response, and sending the prediction response to the client computer.

Method for dynamically reconfiguring machine learning models

A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

Method, system, and computer program product for dynamically scheduling machine learning inference jobs with different quality of services on a shared infrastructure

Creating a summary of a plurality of texts includes tokenizing each of a plurality of texts to obtain tokens; generating a vector space using a first set of vectors having one or more obtained feature scores equal to or larger than a predefined value; executing non-hierarchical clustering using the vector space to generate a first plurality of clusters; choosing a first representative text in each of the plurality of clusters; generating a second set of vectors from each of the arrays generated based on a number of characters included in tokens of the representative texts; executing hierarchical clustering using the second set of vectors to generate a second plurality of clusters; and in response to a determining a number of clusters included in the second plurality of clusters, determining a second representative text for each of the clusters included in the second plurality of clusters.

Creation of a summary for a plurality of texts

Methods and systems are provided to efficiently update account profiles based on a predicted likelihood of use, including by ranking the account profiles according to the likelihood of use. The disclosed system can considerably improve the processing time to update account profiles with the most recent information available, including new access requests. An authentication platform receives a plurality of new access requests, including request data and account identifiers associated with account profiles. The request data is transmitted to a prediction engine that determines a ranking of the account identifiers based on a predicted likelihood of use during a next time interval. A profile batch scheduler retrieves a first set of access requests based on the ranking. The system updates a first set of account profiles based on the ranking, and stores the updated account profiles for use by the authentication platform.

Predictive update of profile database with update logic

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Cedar Park, TX, United States of America

Yu Gu