Intuit, Inc.

Capital One Services, LLC

International Business Machines Corporation

Fedor N Dzegilenko

Igor A Podgorny

Floyd J Morgan

Vineet Singh

Troy D Otillio

Marc J Attinasi

Martha J Fiske

Jason A Greschler

Krishna Venkatraman

Rohit Joshi

Chi-Yi Kuan

Bozhao Tan

Jonathan Weimer

Rob Parkin

Ashish Gupta

Paritosh Desai

Fan Feng

Illiana Reed

France Savard

Vikash Tiwari

Jeffrey Michel

Meenakshi Panda

Vinayak Hulawale

Mohammadamin Dashti Moghaddam

John Crowther

David Davtian

A method includes obtaining an input sequence and first and second word sequences of metadata of a plurality of datasets. The method also includes determining a set of scores including first scores and second scores by determining first scores by determining a score of the first scores based on a count of the word sequence of the first word sequences in the metadata without determining a count of the input sequence. The method also includes determining second scores by determining a score of the second scores based on a count of a second sequence of the second word sequences in the metadata. The method also includes determining an additional word for a predicted sequence including the input sequence based on the set of scores and retrieving a set of datasets based on the predicted sequence.

Sequence prediction for data retrieval

In some implementations, a system may obtain a first model that is trained to identify feature data associated with a client system using one or more services of a service platform. The system may train, based on the feature data, a second model to identify anomalies associated with devices accessing the one or more services in association with a client identifier of the client system. The system may receive access data associated with an acting device accessing a service of the service platform. The system may determine, using the second model, that the acting device accessing the service corresponds to potential anomalous activity based on the access information. The system may obtain, from a verification device, a verification that the acting device accessing the service is anomalous activity. The system may perform, based on obtaining the verification, an action associated with the acting device.

Detection of anomalies associated with fraudulent access to a service platform

Some embodiments provide a system that facilitates use of a question-and-answer system. During operation, the system determines a community value of a question submitted by a first user to the question-and-answer system. Next, the system prioritizes answering of the question by a second user in the question-and-answer system based on the community value.

Question prioritization in community-driven question-and-answer systems

Some embodiments provide a system that facilitates use of a question-and-answer system. During operation, the system determines a business value of a question submitted by a first user to the question-and-answer system. Next, the system prioritizes answering of the question by a second user in the question-and-answer system based on the business value.

An apparatus for providing relative optimized pricing for markdown items is provided. A financial engine for determining revenue for determining sales costs is provided. An optimization engine receiving input from the financial engine and uses the determined sales costs to provide relative optimized pricing for markdown items and provides pricing and a promotion calendar for non-markdown items.

Providing markdown item pricing and promotion calendar

The disclosed embodiments provide a system that facilitates use of an application. During operation, the system obtains an activity history of interaction between the user and the application during use of the application by the user. Next, the system applies a predictive model to the activity history to predict a probability of a user action in the application. Finally, the system facilitates subsequent real-time use of the application by the user based on the probability of the user action.

Real-time user behavior prediction

Some embodiments provide a system that monitors use of an application. During operation, the system uses the application to track a set of interactions between a set of users and the application without impacting the performance of the application. Next, the system stores the interactions as the interactions are detected by the application. Finally, for each user from the set of users, the system periodically processes a subset of the stored interactions associated with the user to obtain an activity history for the user, wherein the activity history is used to facilitate subsequent real-time use of the application by the user.

Real-time tracking of user-application interaction

Growing community of inventors

San Jose, CA, United States of America

Fedor N Dzegilenko