International Business Machines Corporation

Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Maxim I Sviridenko

Tracy Jay Kimbrel

Justin Thaler

Ron Belmarch

Jerry Shen

Prerit Uppal

Edo Liberty

Jayram S Thathachar

Baruch M Schieber

Robert Krauthgamer

Maria Minkoff

Asser Nasreldin Tantawi

Malgorzata Steinder

Shaunak Mishra

Michael Lazarus

Tarun Kumar

Yifan Hu

Kevin McKenzie

Prabhakar Krishnamurthy

Richard D Prewitt, Jr

Changwei Hu

Kevin Yen

Chao-Hung Wang

Gaurav Srivastava

Max Edward Beech

Kamil Michal Zasadzinski

Wei Yu Lin

Hua-Ying Tsai

Debra Tomkowid

Yu Tian

Darja Krushevskaja

Robert Krauthgarner

Chris Wilkens

Mikhail Kuznetsov

Manisha Verma

Ruggiero Cavallo

Avinash Chukka

In an aspect, fantasy sports data is analyzed to generate and/or manage the delivery of and user experience of fantasy sports games and contests, including timing and payout structure(s) for short-term fantasy contests, such as daily, weekly, etc. fantasy sports contests, that motivate and appeal to contestants and is/are reasonably structured and concisely represented. In yet another aspect, a daily fantasy contest system is provided to automatically adjust to user demand without overshooting profitable levels and offer a variety of different contests, as well as offer contests with guaranteed and non-guaranteed payouts.

Fantasy sports data analysis for game structure development

In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.

Search query generation based upon received text

One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Performance metric prediction and content item text suggestion based upon content item text

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 automatic fantasy sports data analysis, including analysis of data to equalize player attractiveness in contestant composition of a fantasy sports team.

Automatic fantasy sports data analysis method and apparatus

Systems, devices, and methods generate a more accurate search advertising auction that considers heterogeneous attributes of advertisements for inclusions into an available advertisement space. Each advertisement considered for inclusion into the available advertisement space may be further analyzed to generate a truthful estimated price for reference during an online bidding for the advertisement space.

Local-search optimization and pricing for rich ad allocation

An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Dynamic application placement under service and memory constraints

A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.

Scheduling heterogeneous partitioned resources with sharing constraints

A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server 'farm' given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server 'farm' problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

Dynamic resource allocation using known future benefits

A method for server allocation in a Web server 'farm' is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of 'discounting the future'. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

Dynamic resource allocation using projected future benefits

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Mohegan Lake, NY, United States of America

Maxim I Sviridenko