Patent ID: 12197827

DETAILED DESCRIPTION

Below are detailed descriptions of various concepts related to, and implementations of, techniques, approaches, methods, apparatuses, and systems for modeling live events. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways, as the described concepts are not limited to any particular manner of implementation. Examples of specific implementations and applications are provided primarily for illustrative purposes.

For purposes of reading the description of the various implementations below, the following descriptions of the sections of the Specification and their respective contents may be helpful:Section A describes a network environment and computing environment which may be useful for practicing embodiments described herein; andSection B describes systems and methods for modeling live events.
A. Computing and Network Environment

Prior to discussing specific implements of the various aspects of this technical solution, it may be helpful to describe aspects of the operating environment as well as associated system components (e.g., hardware elements) in connection with the methods and systems described herein. Referring toFIG.1A, an embodiment of a network environment is depicted. In brief overview, the network environment includes one or more clients102a-102n(also generally referred to as local machine(s)102, client(s)102, client node(s)102, client machine(s)102, client computer(s)102, client device(s)102, endpoint(s)102, or endpoint node(s)102) in communication with one or more agents103a-103nand one or more servers106a-106n(also generally referred to as server(s)106, node106, or remote machine(s)106) via one or more networks104. In some embodiments, a client102has the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients102a-102n.

AlthoughFIG.1Ashows a network104between the clients102and the servers106, the clients102and the servers106may be on the same network104. In some embodiments, there are multiple networks104between the clients102and the servers106. In one of these embodiments, a network104′ (not shown) may be a private network and a network104may be a public network. In another of these embodiments, a network104may be a private network and a network104′ may be a public network. In still another of these embodiments, networks104and104′ may both be private networks.

The network104may be connected via wired or wireless links. Wired links may include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. The wireless links may include BLUETOOTH, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), an infrared channel, or satellite band. The wireless links may also include any cellular network standards used to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, 4G, or 5G. The network standards may qualify as one or more generation of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by International Telecommunication Union. The 3G standards, for example, may correspond to the International Mobile Telecommunications-2000 (IMT-2000) specification, and the 4G standards may correspond to the International Mobile Telecommunications Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTE Advanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standards may use various channel access methods, e.g., FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same types of data may be transmitted via different links and standards.

The network104may be any type and/or form of network. The geographical scope of the network104may vary widely and the network104can be a body area network (BAN), a personal area network (PAN), a local area network (LAN) (e.g., Intranet), a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network104may be of any form and may include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network104may be an overlay network which is virtual and sits on top of one or more layers of other networks104′. The network104may be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network104may utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite may include an application layer, transport layer, internet layer (including, e.g., IPv6), or link layer. The network104may be a type of a broadcast network, a telecommunications network, a data communication network, or a computer network.

In some embodiments, the system may include multiple, logically-grouped servers106. In one of these embodiments, the logical group of servers may be referred to as a server farm (not shown) or a machine farm38. In another of these embodiments, the servers106may be geographically dispersed. In other embodiments, a machine farm38may be administered as a single entity. In still other embodiments, the machine farm38includes a plurality of machine farms38. The servers106within each machine farm38can be heterogeneous-one or more of the servers106or remote machines106can operate according to one type of operating system platform (e.g., WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Washington), while one or more of the other servers106can operate according to another type of operating system platform (e.g., Unix, Linux, or Mac OS X).

In one embodiment, servers106in the machine farm38may be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this embodiment, consolidating the servers106in this way may improve system manageability, data security, the physical security of the system, and system performance by locating servers106and high performance storage systems on localized high performance networks104. Centralizing the servers106and storage systems and coupling them with advanced system management tools allows more efficient use of server resources.

The servers106of each machine farm38do not need to be physically proximate to another server106in the same machine farm38. Thus, the group of servers106logically grouped as a machine farm38may be interconnected using a wide area network (WAN) connection or a metropolitan area network (MAN) connection. For example, a machine farm38may include servers106physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers106in the machine farm38can be increased if the servers106are connected using a local area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm38may include one or more servers106operating according to a type of operating system, while one or more other servers106execute one or more types of hypervisors rather than operating systems. In these embodiments, hypervisors may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors may run directly on the host computer. Hypervisors may include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alto, California; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc.; the HYPER-V hypervisors provided by Microsoft, or others. Hosted hypervisors may run within an operating system on a second software level. Examples of hosted hypervisors may include VMware Workstation and VIRTUALBOX.

Management of the machine farm38may be decentralized. For example, one or more servers106may comprise components, subsystems, and modules to support one or more management services for the machine farm38. In one of these embodiments, one or more servers106provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm38. Each server106may communicate with a persistent store and, in some embodiments, with a dynamic store.

Server106may be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one embodiment, the server106may be referred to as a remote machine or a node. In another embodiment, a plurality of nodes106may be in the path between any two communicating servers.

Referring toFIG.1B, a cloud computing environment is depicted. A cloud computing environment may provide client102with one or more resources provided by a network environment. The cloud computing environment may include one or more clients102a-102n, in communication with respective agents103a-103nand with the cloud108over one or more networks104. Clients102may include, e.g., thick clients, thin clients, and zero clients. A thick client may provide at least some functionality even when disconnected from the cloud108or servers106. A thin client or a zero client may depend on the connection to the cloud108or server106to provide functionality. A zero client may depend on the cloud108or other networks104or servers106to retrieve operating system data for the client device. The cloud108may include back end platforms, e.g., servers106, storage, server farms, or data centers.

The cloud108may be public, private, or hybrid. Public clouds may include public servers106that are maintained by third parties to the clients102or the owners of the clients. The servers106may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds108may be connected to the servers106over a public network104. Private clouds108may include private servers106that are physically maintained by clients102or owners of clients. Private clouds108may be connected to the servers106over a private network104. Hybrid clouds108may include both the private and public networks104and servers106.

The cloud108may also include a cloud based delivery, e.g., Software as a Service (SaaS)110, Platform as a Service (PaaS)112, and Infrastructure as a Service (IaaS)114. IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Washington; RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Texas; Google Compute Engine provided by Google Inc. of Mountain View, California; or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, California. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers, or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Washington; Google App Engine provided by Google Inc.; and HEROKU provided by Heroku, Inc., of San Francisco, California. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources, including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc.; SALESFORCE provided by Salesforce.com Inc. of San Francisco, California; or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g., DROPBOX provided by Dropbox, Inc., of San Francisco, California; Microsoft SKYDRIVE provided by Microsoft Corporation; Google Drive provided by Google Inc.; or Apple ICLOUD provided by Apple Inc. of Cupertino, California.

Clients102may access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards may allow clients access to resources over HTTP and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients102may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages, including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients102may access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g., GOOGLE CHROME, Microsoft INTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, California). Clients102may also access SaaS resources through smartphone or tablet applications, including, e.g., Salesforce Sales Cloud, or Google Drive app. Clients102may also access SaaS resources through the client operating system, including, e.g., Windows file system for DROPBOX.

In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL).

The client102and server106may be deployed as and/or executed on any type and form of computing device, e.g., a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein.FIGS.1C and1Ddepict block diagrams of a computing device100useful for practicing an embodiment of the client102or a server106. As shown inFIGS.1C and1D, each computing device100includes a central processing unit121and a main memory unit122. As shown inFIG.1C, a computing device100may include a storage device128, an installation device116, a network interface118, an I/O controller123, display devices124a-124n, a keyboard126, and a pointing device127, e.g., a mouse. The storage device128may include, without limitation, an operating system, software, and simulation platform120, which can implement any of the features of the data processing system205described herein below in connection withFIG.2. As shown inFIG.1D, each computing device100may also include additional optional elements, e.g., a memory port132, a bridge170, one or more input/output devices130a-130n(generally referred to using reference numeral130), and a cache memory140in communication with the central processing unit121.

The central processing unit121is any logic circuitry that responds to and processes instructions fetched from the main memory unit122. In many embodiments, the central processing unit121is provided by a microprocessor unit, e.g., those manufactured by Intel Corporation of Mountain View, California; those manufactured by Motorola Corporation of Schaumburg, Illinois; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, California; the POWER7 processor manufactured by International Business Machines of White Plains, New York; or those manufactured by Advanced Micro Devices of Sunnyvale, California. The computing device100may be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit121may utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor may include two or more processing units on a single computing component. Examples of a multi-core processors include the AMD PHENOM IIX2, INTEL CORE i5, INTEL CORE i7, and INTEL CORE i9.

Main memory unit122may include one or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor121. Main memory unit122may be volatile and faster than storage128memory. Main memory units122may be dynamic random access memory (DRAM) or any variants, including static random access memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory122or the storage128may be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory122may be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the embodiment shown inFIG.1C, the processor121communicates with main memory122via a system bus150(described in more detail below).FIG.1Ddepicts an embodiment of a computing device100in which the processor communicates directly with main memory122via a memory port132. For example, inFIG.1Dthe main memory122may be DRDRAM.

FIG.1Ddepicts an embodiment in which the main processor121communicates directly with cache memory140via a secondary bus, sometimes referred to as a backside bus. In other embodiments, the main processor121communicates with cache memory140using the system bus150. Cache memory140typically has a faster response time than main memory122and is typically provided by SRAM, BSRAM, or EDRAM. In the embodiment shown inFIG.1D, the processor121communicates with various I/O devices130via a local system bus150. Various buses may be used to connect the central processing unit121to any of the I/O devices130, including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For embodiments in which the I/O device is a video display124, the processor121may use an Advanced Graphics Port (AGP) to communicate with the display124or the I/O controller123for the display124.FIG.1Ddepicts an embodiment of a computer100in which the main processor121communicates directly with I/O device130bor other processors121′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.FIG.1Dalso depicts an embodiment in which local busses and direct communication are mixed: the processor121communicates with I/O device130ausing a local interconnect bus150but communicates with I/O device130bdirectly.

A wide variety of I/O devices130a-130nmay be present in the computing device100. Input devices may include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex cameras (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers.

Devices130a-130nmay include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices130a-130nallow gesture recognition inputs through combining some of the inputs and outputs. Some devices130a-130nprovide for facial recognition which may be utilized as an input for different purposes including authentication and other commands. Some devices130a-130nprovides for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for IPHONE by Apple, Google Now, or Google Voice Search.

Additional devices130a-130nhave both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices may use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices may allow two or more contact points with the surface, allowing advanced functionality, including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may have larger surfaces, such as on a table-top or on a wall, and may also interact with other electronic devices. Some I/O devices130a-130n, display devices124a-124nor group of devices may be augmented reality devices. The I/O devices130a-130nmay be controlled by an I/O controller123as shown inFIG.1C. The I/O controller123may control one or more I/O devices130a-130n, such as, e.g., a keyboard126and a pointing device127, e.g., a mouse or optical pen. Furthermore, an I/O device130may also provide storage and/or an installation medium116for the computing device100. In still other embodiments, the computing device100may provide USB connections (not shown) to receive handheld USB storage devices. In further embodiments, an I/O device130may be a bridge between the system bus150and an external communication bus, e.g., a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus.

In some embodiments, display devices124a-124nmay be connected to I/O controller123. Display devices may include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays may use, e.g., stereoscopy, polarization filters, active shutters, or autostereoscopic. Display devices124a-124nmay also be a head-mounted display (HMD). In some embodiments, display devices124a-124nor the corresponding I/O controllers123may be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries.

In some embodiments, the computing device100may include or connect to multiple display devices124a-124n, which each may be of the same or different type and/or form. As such, any of the I/O devices130a-130nand/or the I/O controller123may include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices124a-124nby the computing device100. For example, the computing device100may include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect, or otherwise use the display devices124a-124n. In one embodiment, a video adapter may include multiple connectors to interface to multiple display devices124a-124n. In other embodiments, the computing device100may include multiple video adapters, with each video adapter connected to one or more of the display devices124a-124n. In some embodiments, any portion of the operating system of the computing device100may be configured for using multiple displays124a-124n. In other embodiments, one or more of the display devices124a-124nmay be provided by one or more other computing devices100aor100bconnected to the computing device100via the network104. In some embodiments, software may be designed and constructed to use another computer's display device as a second display device124afor the computing device100. For example, in one embodiment, an Apple iPad may connect to a computing device100and use the display of the device100as an additional display screen that may be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and embodiments that a computing device100may be configured to have multiple display devices124a-124n.

Referring again toFIG.1C, the computing device100may comprise a storage device128(e.g., one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the simulation platform120. Examples of storage device128include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices may include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device128may be non-volatile, mutable, or read-only. Some storage device128may be internal and connect to the computing device100via a bus150. Some storage device128may be external and connect to the computing device100via an I/O device130that provides an external bus. Some storage device128may connect to the computing device100via the network interface118over a network104, including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices100may not require a non-volatile storage device128and may be thin clients or zero clients102. Some storage device128may also be used as an installation device116, and may be suitable for installing software and programs. Additionally, the operating system and the software110can be run from a bootable medium, for example, a bootable CD, e.g., KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net.

Client device100may also install software110or application from an application distribution platform112. Examples of application distribution platforms112include the App Store for iOS provided by Apple, Inc.; the Mac App Store provided by Apple, Inc.; GOOGLE PLAY for Android OS provided by Google Inc.; Chrome Webstore for CHROME OS provided by Google Inc.; and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform112may facilitate installation of software110on a client device102. An application distribution platform112may include a repository of applications on a server106or a cloud108, which the clients102a-102nmay access over a network104. An application distribution platform112may include an application developed and provided by various developers. A user of a client device102may select, purchase, and/or download an application via the application distribution platform112.

Furthermore, the computing device100may include a network interface118to interface to the network104through a variety of connections, including, but not limited to, standard telephone lines, LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.11a/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the computing device100communicates with other computing devices100′ via any type and/or form of gateway or tunneling protocol, e.g., Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc., of Ft. Lauderdale, Florida. The network interface118may comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem, or any other device suitable for interfacing between the computing device100and any type of network capable of communication and performing the operations described herein.

A computing device100of the sort depicted inFIGS.1B and1Cmay operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device100can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to, WINDOWS 2000, WINDOWS Server 2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, and WINDOWS 8 all of which are manufactured by Microsoft Corporation of Redmond, Washington; MAC OS and iOS, manufactured by Apple, Inc., of Cupertino, California; and Linux, a freely-available operating system, e.g., Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingdom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google, of Mountain View, California, among others. Some operating systems, including, e.g., the CHROME OS by Google, may be used on zero clients or thin clients, including, e.g., CHROMEBOOKS.

The computer system100can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system100has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device100may have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface.

In some embodiments, the computing device100is a gaming system. For example, the computer system100may comprise a PLAYSTATION 3, a PLAYSTATION 4, PLAYSTATION 5, or PERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA device manufactured by the Sony Corporation of Tokyo, Japan, a NINTENDO DS, NINTENDO 3DS, NINTENDO WII, NINTENDO WII U, or a NINTENDO SWITCH device manufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX 360, an XBOX ONE, an XBOX ONE S, an XBOX ONE X, an XBOX SERIES S, or an XBOX SERIES X, manufactured by the Microsoft Corporation of Redmond, Washington.

In some embodiments, the computing device100is a digital audio player such as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices, manufactured by Apple Computer of Cupertino, California. Some digital audio players may have other functionality, including, e.g., a gaming system or any functionality made available by an application from a digital application distribution platform. For example, the IPOD Touch may access the Apple App Store. In some embodiments, the computing device100is a portable media player or digital audio player supporting file formats, including, but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audible audiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4 MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the computing device100is a tablet, e.g., the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, by Amazon.com, Inc., of Seattle, Washington. In other embodiments, the computing device100is an eBook reader, e.g., the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes & Noble, Inc., of New York City, New York.

In some embodiments, the communications device102includes a combination of devices, e.g., a smartphone combined with a digital audio player or portable media player. For example, one of these embodiments is a smartphone, e.g., the IPHONE family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc.; or a Motorola DROID family of smartphones. In yet another embodiment, the communications device102is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g., a telephony headset. In these embodiments, the communications devices102are web-enabled and can receive and initiate phone calls. In some embodiments, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call.

In some embodiments, the status of one or more machines102,106in the network104are monitored, generally as part of network management. In one of these embodiments, the status of a machine may include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these embodiments, this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein.

B. Modeling Live Events

The systems and methods of this technical solution provide techniques for modeling live events using results produced from simulations of the live events. Existing technical solutions that utilize computers to model live events implement brute-force approaches to calculate probabilities of combinations of outcomes that may occur during the live events. The brute-force approaches include individually and iteratively processing and computing the probability of each combination of all possible outcomes that may occur during the live event. Calculating the probability of combinations of events typically exhausts computational resources, and therefore using such brute-force methods each time a request is received is computationally intensive and becomes impracticable to perform as the number of conditional events or the number of requests increases. To address these and other issues, the systems and methods can execute simulations of live events in an offline (e.g., non-real-time) process to generate data structures that include one or more outcomes of different actions (e.g., points scored, other actions described herein, etc.) that may occur during the live events. Rather than brute-force computing the probability of the combination of events each time a request is received, the simulation results can then be filtered in real-time or near real-time to estimate probabilities of requested combinations of actions or occurrences during the live event. This provides a significant improvement in computational performance and significantly reduces the computational resources required to process requests from large numbers of client devices. These and other improvements are described in further detail herein.

Referring now toFIG.2, illustrated is a block diagram of an example system200for modeling live events, in accordance with one or more implementations. The system200can include at least one data processing system205, at least one network210, and one or more client devices220A-220N (sometimes generally referred to as client device(s)220). The data processing system205can include a simulation maintainer230, a selection receiver235, a subset identifier240, an odds value generator245, a user interface presenter250, and at least one database215. The storage215can include one or more player profiles270, one or more conditional events275(individual conditional events of which are sometimes referred to as a “conditional event275”), and one or more simulation results280(individual results of which are sometimes referred as a “simulation result280”). Although shown here as internal to the data processing system205, the storage215can be external to the data processing system205, for example, as a part of a cloud computing system or an external computing device in communication with the devices (e.g., the data processing system205, the client devices220, etc.) of the system200via the network210.

Each of the components (e.g., the data processing system205, the network210, the client devices220, the simulation maintainer230, the selection receiver235, the subset identifier240, the odds value generator245, the user interface presenter250, the storage215, etc.) of the system200can be implemented using the hardware components or a combination of software with the hardware components of a computing system, such as the computing system100detailed herein in conjunction withFIGS.1A-ID, or any other computing system described herein. Each of the components of the data processing system205can perform the functionalities detailed herein.

The data processing system205can include at least one processor and a memory (e.g., a processing circuit). The memory can store processor-executable instructions that, when executed by a processor, cause the processor to perform one or more of the operations described herein. The processor may include a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a graphics processing unit (GPU), a tensor processing unit (TPU), etc., or combinations thereof. The memory may include, but is not limited to, electronic, optical, magnetic, or any other storage or transmission device capable of providing the processor with program instructions. The memory may further include a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ASIC, FPGA, read-only memory (ROM), random-access memory (RAM), electrically erasable programmable ROM (EEPROM), erasable programmable ROM (EPROM), flash memory, optical media, or any other suitable memory from which the processor can read instructions. The instructions may include code from any suitable computer programming language. The data processing system205can include one or more computing devices or servers that can perform various functions as described herein. The data processing system205can include any or all of the components and perform any or all of the functions of the computer system100described herein in conjunction withFIGS.1A-1D.

In some implementations, the data processing system205may communicate with the client devices220, for example, to provide user interfaces (e.g., the user interfaces described in connection withFIGS.3A-3H, etc.) and to receive user input, via the network210. In one example, the data processing system205can be or can include an application server or webserver, which may include software modules allowing various computing devices (e.g., the client devices220, etc.) to access or manipulate data stored by the data processing system205. For example, the data processing system205may include a webserver allowing the client devices220to access data (e.g., via one or more web-based user interfaces, such as those described in connection withFIGS.3A-3H) that is collected and manipulated by the data processing system205. For example, a player accessing the functionality of the data processing system205using a corresponding player profile270may execute a web browser application and access a website hosted on the webserver in order to access data (e.g., one or more interfaces to select combinations of conditional events275for one or more live events as described herein, etc.).

The network210can include computer networks such as the Internet, local, wide, metro or other area networks, intranets, satellite networks, other computer networks such as voice or data mobile phone communication networks, or combinations thereof. The data processing system205of the system200can communicate via the network210with one or more computing devices, such as the one or more client devices220. The network210may be any form of computer network that can relay information between the data processing system205, the one or more client devices220, and one or more information sources, such as web servers or external databases, amongst others. In some implementations, the network210may include the Internet and/or other types of data networks, such as a local area network (LAN), a wide area network (WAN), a cellular network, a satellite network, or other types of data networks. The network210may also include any number of computing devices (e.g., computers, servers, routers, network switches, etc.) that are configured to receive or transmit data within the network210.

The network210may further include any number of hardwired or wireless connections. Any or all of the computing devices described herein (e.g., the data processing system205, the one or more client devices220, the computer system100, etc.) may communicate wirelessly (e.g., via Wi-Fi, cellular communication, radio, etc.) with a transceiver that is hardwired (e.g., via a fiber optic cable, a CAT5 cable, etc.) to other computing devices in the network210. Any or all of the computing devices described herein (e.g., the data processing system205, the one or more client devices220, the computer system100, etc.) may also communicate wirelessly with the computing devices of the network210via a proxy device (e.g., a router, network switch, or gateway). In some implementations, the network210can be similar to or can include the network104or the cloud108described herein above in conjunction withFIGS.1A and1B.

Each of the client devices220can include at least one processor and a memory (e.g., a processing circuit). The memory can store processor-executable instructions that, when executed by the processor, cause the processor to perform one or more of the operations described herein. The processor can include a microprocessor, an ASIC, an FPGA, a GPU, a TPU, etc., or combinations thereof. The memory can include, but is not limited to, electronic, optical, magnetic, or any other storage or transmission device capable of providing the processor with program instructions. The memory can further include a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ASIC, FPGA, ROM, RAM, EEPROM, EPROM, flash memory, optical media, or any other suitable memory from which the processor can read instructions. The instructions can include code from any suitable computer programming language. The client devices220can include one or more computing devices or servers that can perform various operations as described herein. The one or more client devices220can include any or all of the components and perform any or all of the functions of the computer system100described herein in conjunction withFIGS.1A-1D. The client devices220can be, or can be similar to, the client devices102described herein above in connection withFIGS.1A-1D.

Each client device220can be a personal computer, a laptop computer, a television device, a smart phone device, a mobile device, or another type of computing device. Each client device220can be implemented using hardware or a combination of software and hardware. Each client device220can include a display or display portion. The display can include a display portion of a television, a display portion of a computing device, a GUI, or another type of interactive display (e.g., a touchscreen, a display, etc.) and one or more I/O devices (e.g., a mouse, a keyboard, digital key pad). The display can present one or more user interfaces described in connection withFIGS.3A-3H, which relate to the selection of one or more combinations of conditional events275, as described herein. The display can include a touch screen displaying an application, such as a web browser application or a native application, which may be used to access the functionality of the data processing system205, as described herein.

The display may include a border region (e.g., side border, top border, bottom border). The display can include a touch screen display, which can receive interactions from a player. The client device220may also receive interactions via any other type of I/O device. The interactions can result in interaction data, which can be stored and transmitted by the processing circuitry of the client device220. The interaction data can include, for example, interaction coordinates, an interaction type (e.g., drag, click, swipe, scroll, tap, etc.), and an indication of an actionable object (e.g., an interactive user interface element, such as a button, hyperlink, etc.) with which the interaction occurred. The interaction data can identify a user interface element (e.g., including any of the user interface elements described in connection withFIGS.3A-3H) with which the interaction occurred.

Each client device220can include an input device that couples or communicates with the display of each client device220to enable a player to interact with or select one or more actionable objects as described herein. The display can enable interaction with one or more visual indications provided through the display of each client device220, and responsive to an interaction (e.g., select, click-on, touch, hover), the client device220can generate an indication identifying a user input or selection of a wager, a live event, one or more selections of combinations of conditional events275, an indication to place a wager, or an interaction to request odds values for a selected combination of conditional events275, among others.

Each client device220can include or be identified by a device identifier, which can be specific to each respective client device220. The device identifier can include a script, code, label, or marker that identifies a particular client device220. In some implementations, the device identifier can include a string or plurality of numbers, letters, characters, or any combination numbers, letters, and characters. In some embodiments, each client device220can have a unique device identifier. Each client device220can include a client application, which can be a web browser or a native application that communicates with the data processing system205to present user interfaces (e.g., the user interfaces300A-300H ofFIGS.3A-3H), generate one or more data records corresponding to a live event (e.g., a post, a wager, etc.), or other functionality described herein. The client application can be executing on each client device220, and may be provided to the client device220by the data processing system205. The application can include a web application, a server application, a resource, a desktop, or a file.

The application can include a local application (e.g., local to a client device220), hosted application, a SaaS application, a virtual application, a mobile application, or other forms of content. In some implementations, the application can include or correspond to applications provided by remote servers or third-party servers. In some implementations, the application can access the player profiles270or the conditional events275stored and maintained in the storage215, and generate one or more interactive user interface elements, such as the interactive user interface elements described in connection withFIGS.3A-3H, to a player when executed by a client device220. Some example interactive user interface elements include user-selectable hyperlinks, buttons, graphics, videos, images, or other application features. Interactions with such interactive user interface elements (sometimes referred to as “actionable objects”) can cause the application executing on the respective client device220to generate a signal, which can cause the application to perform further operations corresponding to the actionable object.

In some implementations, one or more client devices220can establish one or more communication sessions with the data processing system205. A communication session can include a channel or connection between the data processing system205and a respective client device220. The one or more communication sessions can each include an application session (e.g., virtual application), an execution session, a desktop session, a hosted desktop session, a terminal services session, a browser session, a remote desktop session, a URL session or a remote application session. Each communication session can include encrypted or secure sessions, which can include an encrypted file, encrypted data, or traffic.

In some implementations, in response to interactions with corresponding user interface elements, the application executing on a client device220can transmit information, such as player profile270information (e.g., changing player profile270parameters, changing login information, etc.), interaction information, selections of wager amounts, selections to request odds for combinations of conditional events275of live events (e.g., live sporting events, etc.), or selections to request conditional events275for current or upcoming live events, selections to request players, plays, or other attributes or potential actions that may occur during an identified live event, among other selections described herein. For example, the client device220can transmit a request for one or more participants of a live event (e.g., athletes playing for a particular team during a live event, etc.). Additional requests may also be transmitted, including requests to place one or more wagers, execute one or more simulations to generate the simulation results280, or requests for information (e.g., available wagers, odds information, etc.) related to one or more live events, among others. The requests can be hypertext transfer protocol (HTTP or HTTPS) request messages, file transfer protocol messages, email messages, text messages, or any other type of message that can be transmitted via the network210.

As described herein, a client device220can receive one or more interactions with actionable objects presented on the display of the client device. Interactions can be tap interactions, click interactions, or other types of indications that a user is engaged with a particular user interface element. Upon detecting an interaction with a particular user interface element, the client device220can execute instructions (e.g., processor-readable code, a script, etc.) that cause the client device220to transmit an appropriate signal to the data processing system205. Additionally, the client devices220may receive and display one or more content items or interactive user interface elements that may correspond to one or more conditional events275, one or more live events, one or more odds values, or one or more live event participants, as described herein.

In some implementations, the application interfaces can present a selectable list of live events (e.g., current or upcoming sporting events, etc.). Upon selection of a live event in the list, or upon a receiving a request to perform a parlay wager, the data processing system205can provide one or more user interfaces similar to the user interfaces shown inFIGS.3A-3H, which enable a user to select one or more combinations of conditional events275, as described herein, to assemble parlay wagers. Upon receiving one or more selections of the conditional events275, the data processing system205can perform the various operations described herein to generate odds values in real-time or near real-time, which may be presented on the application interfaces presented at the respective client device.

The storage215can be a computer-readable memory that can store or maintain any of the information described herein. The storage215can store or maintain one or more data structures, which may contain, index, or otherwise store each of the values, pluralities, sets, variables, vectors, numbers, or thresholds described herein. The storage215can be accessed using one or more memory addresses, index values, or identifiers of any item, structure, or region maintained in the storage215. The storage215can be accessed by the components of the data processing system205, or any other computing device described herein, via the network210. In some implementations, the storage215can be internal to the data processing system205. In some implementations, the storage215can exist external to the data processing system205, and may be accessed via the network210. The storage215can be distributed across many different computer systems or storage elements, and may be accessed via the network210or a suitable computer bus interface. The data processing system205can store, in one or more regions of the memory of the data processing system205, or in the storage215, the results of any or all computations, determinations, selections, identifications, generations, constructions, or calculations in one or more data structures indexed or identified with appropriate values.

Any or all values stored in the storage215may be accessed by any computing device described herein, such as the data processing system205, to perform any of the functionalities or functions described herein. In some implementations, a computing device, such as a client device220, may utilize authentication information (e.g., username, password, email, etc.) to show that the client device220is authorized to access requested information in the storage215. The storage215may include permission settings that indicate which users, devices, or profiles are authorized to access certain information stored in the storage215. The storage215may be similar to or include the storage128described herein above in conjunction withFIG.1C. In some implementations, instead of being internal to the data processing system205, the storage215can form a part of a cloud computing system. In such implementations, the storage215can be a distributed storage medium in a cloud computing system and can be accessed by any of the components of the data processing system205, by the one or more client devices220(e.g., via one or more user interfaces, etc.), or any other computing devices described herein.

The storage215can store one or more player profiles270in one or more data structures. Each player profile270can be associated with a corresponding player (e.g., a user) of a client device220that accesses the functionality of the data processing system205. In implementations where the data processing system205can operate without using a client device220(e.g., a slot machine, a video game machine, a standalone wagering kiosk, etc.), a player profile270may correspond to a player that accesses the data processing system205to place wagers. Each player profile270can be a user profile that includes information about a user. Each player profile270may include information about one or more of the client devices220used to access the data processing system205using the player profile270. For example, identifiers of a player profile270can be used to access the functionality of the data processing system205via the network210.

The identifiers of player profiles270can include a username, a password, an e-mail address, a phone number, a personal identification number (PIN), a secret code-word, or device identifiers for use in a two-factor authentication technique, among others. The player profile270can store information about historic wagers (e.g., including parlay wagers), historic selections of conditional events275, historic games viewed or wagered upon using the player profile270, and historic live event outcomes, or other information. The player profile270can store a credit balance or wager information (e.g., an amount of a wager, a timestamp associated with a wager, information about the presence of an indication to participate in a bonus opportunity using the wager, a client device identifier of a client device that was used to place the wager, wager types, parlay wager complexity such as the number of conditional events275in each parlay wager placed using the player profile270, etc.).

The player profile270can store information about a client device220used to access the data processing system205such as an internet protocol (IP) address, a media access control (MAC) address, a global unique identification (GUID), a player profile270name (e.g., the name of a user of the client device220, a player-chosen username, etc.), device name, among others. In some implementations, a player profile270can be created by the data processing system205in response to a player profile270, creation request transmitted by a client device220. The player profile270creation request can include any of the player profile270information described herein. In some implementations, a client device220accessing the data processing system205may not be associated with a player profile270. In such implementations, the data processing system205can automatically create a player profile270using an identifier of the client device220provided by the client device220.

The storage215can store or maintain one or more conditional events275associated with one or more simulation results275for one or more live events (e.g., current or upcoming sporting events). The conditional events275can be stored in one or more data structures. The conditional events275can correspond to any action or event that may occur in a live game (e.g., a live event) that may be mutually exclusive (e.g., have one outcome). Once example of a conditional event275is “who will score the first touchdown in an upcoming football game” (e.g., there can be only one first touchdown scorer). Each conditional event275may be generated or predetermined and stored in the storage215such that they are accessible by the components of the data processing system205. In an embodiment, one or more conditional events275may be generated based upon one or more conditional event templates for a live event type (e.g., a type of sporting event, etc.). Prior to an upcoming live event, the data processing system205(or any components thereof) may generate one or more conditional events275for the upcoming live event by applying one or more templates to the attributes of the upcoming live event (e.g., which athletes are participating in the live event, which teams are playing in the live event, possible outcomes of the live event, etc.). Respective sets of conditional events275can be stored in the storage for each live event. Each set of conditional events275can correspond to potential outcomes that may occur during the live event. Some example conditional events275that may correspond to an example football game include which athlete will score the first touchdown, which athlete will score the second touchdown, which team will win the game, or which team will have a greater score at halftime, among others. It should be understood that other conditional events are also possible for other types of live events (e.g., baseball games, hockey games, basketball games, other types of live events, etc.). A conditional event275can be any type of event or potential, mutually exclusive outcome on which a wager can be placed.

The data processing system205can store one or more data records in the storage215, for example, in one or more data structures. As described herein, the client device220can transmit interaction data to the data processing system205in response to placing a wager via one or more application interfaces (e.g., a parlay wager using the selections described in connection withFIGS.3A-3H). The wager information in the interaction data can include, for example, any input information as part of the wager (e.g., wager amounts, selections of outcomes for one or more conditional events275, etc.), the player profile270associated with the client device220, a wager type (e.g., parlay wager, etc.), an identifier of a live event corresponding to the wager, a wager identifier, or any other information relating to the wager. Upon receiving the interaction data, the data processing system205can store the interaction data as a data record, in association with the player profile270that was used to provide the interaction data. The data records can be records of wagers, requests to modify a player profile270, or any other data relating to live events that may be provided via an interaction at a client device220. In some implementations, the data processing system205can store the data record in association with one or more identifiers of a live event or a wagering opportunity of the live event identified in the interaction received from the client device220.

The storage215can store or maintain one or more simulation results280associated with one or more corresponding conditional events275, for example, in one or more data structures. The simulation results280can be generated from simulations of one or more live events (e.g., sports game simulations), which may be executed by the data processing system205or by another external computing system (not pictured). Each live event can be associated with multiple sets of simulation results280, with each set of simulation results280including respective outcomes for each conditional event275that may occur during the live event. Each set of simulation results280can be generated by a respective simulation of the live event.

Each simulation used to generate the simulation results280may depend partially on an element of randomness, and may be based on various aspects of the particular live event, the teams and athletes participating in the live event, and any other attribute or characteristic that may influence any outcome that may occur during the live event. As such, each set of simulation results280generated from each simulation may include different outcomes for the conditional events275of the simulated live event. An example data structure representation of the simulation results280of conditional events275of a live event is shown below in Table 1.

TABLE 1Simulation Number12345678910ConditionalABCDABCDACEvent #1ConditionalBBABACDACAEvent #2

As shown in Table 1 above, the simulation results280for a live event may be organized according to various conditional events275that may occur during the live event. In Table 1, each simulation 1, 2, 3 . . . corresponds to a respective simulation of the live event, and the values A, B, C, and D correspond to simulated outcomes of the respective conditional event275for that row of the table. The components of the data processing system205can utilize the simulation results280to calculate odds values for parlay wagers involving multiple conditional events275. Although only ten simulation results are shown in Table 1, it should be understood that hundreds, thousands, tens of thousands, hundreds of thousands, or millions of simulations may be executed for a live event, with corresponding outcomes for each conditional event275that occur in each simulation being recorded as part of the simulation results280. Likewise, the example A, B, C, and D outcomes are provided purely for example purposes. It should be understood that any number of potential outcomes can occur for a conditional event275, and that different conditional events275may have different numbers of potential outcomes.

Referring now to the operations of the data processing system205, the simulation maintainer230can maintain the simulation results280corresponding to outcomes of the conditional events275that can occur during the simulations to which the simulation results280correspond, as described herein. For example, the simulation maintainer230can maintain one or more data structures that include the simulation results280, which may be stored in association with respective identifiers of respective conditional events, as described herein. In some implementations, the simulation results280for a particular conditional event275can include respective counter values that correspond to a count of each outcome for each simulation of the corresponding live event. The simulation maintainer230can calculate or generate the counter values for each conditional event275of a live event by scanning through the simulation results280of the simulations of the live event, or may receive (or retrieve, from the storage215) the counter values as part of the simulation results280from an external computing system that executed the simulations of the live event.

In some implementations, the simulation maintainer230can itself generate the simulation results280for a live event by executing simulations of the live event. A simulation of a live event can be a model of the event and can produce one or more outputs that correspond to various outcomes of the live event. For example, if the live event is a sporting event, the outcomes may include timestamps of simulated game events (e.g., points scored, passes made, etc.), along with identifiers of participants (e.g., athletes) that performed the simulated game event. Executing the simulations can include executing processor-readable instructions that cause the simulation maintainer230to carry out a model of the live event, with the outputs of the simulation being respective simulation results280for each conditional event275of the live event, as described herein. Each simulation used to generate the simulation results280may depend partially on an element of randomness (e.g., one or more random numbers generated using one or more random number generators), and may be based on various aspects of the particular live event, such as the teams and athletes participating in the live event or any other attribute or characteristic that may influence any outcome that may occur during the live event. As such, the simulation results280generated from each simulation may include different outcomes for the conditional events275of the simulated live event.

The simulation maintainer230can execute a predetermined number of simulations of the live event, for example. The predetermined number may be specified in one or more requests from an external computing device (e.g., a client device220, an administrative computing system, an operator of the data processing system205, etc.). In some implementations, one or more of the simulations of the live event can be executed by an external computing system (not pictured), which can store corresponding simulation results280in the storage215, as described herein. The simulation maintainer230can generate counter values for each outcome of each conditional event275of the live event to which the simulations correspond. To do so, the simulation maintainer can update a respective counter value for each conditional event275based on the various outcomes indicated in the simulation results280for the live event. The simulations of the live event can be executed as an offline process (e.g., prior to the live event, not in real-time or near real-time, etc.). The simulation results280can be maintained to perform the estimation of odds values for parlay wagers or other combinations of candidate outcomes of conditional events275, as described herein.

The selection receiver235can receive one or more selections identifying respective selected outcomes for one or more conditional events275to occur during a live event. The selections can be received, for example, via a user interface (e.g., user interfaces provided by the data processing system205similar to those described in connection withFIGS.3A-3H, etc.) presented at a client device220. As described herein, particular wagers, such as parlay wagers, can include wagers on multiple mutually exclusive conditional events. The odds values for such wagers can be calculated across two correlated markets, containing a number of mutually exclusive outcomes of conditional events275. The client device220can access a webpage or application resource provided by the data processing system205to make selections of combinations of outcomes for one or more conditional events275to occur during a live sporting event. These combinations can be utilized to place a parlay wager on the combinations. The selections of one or more conditions events may include Boolean operations (e.g., player A OR player B will score the first touchdown in the live event, player A will score the first touchdown AND player B will score the second touchdown in the live event, etc.). The odds values for these mutually exclusive events may be based upon characteristics of the players or the live event, or the simulation results280of the live event. Example selections of combinations of conditional events275are shown inFIGS.3A-3H.

Referring toFIGS.3A-3Hin the context of the components described in connection withFIG.2, illustrated are example user interfaces300A-300H showing selected combinations of outcomes of specified conditional events275and corresponding odds values as generated by the system ofFIG.2, in accordance with one or more implementations.FIG.3Ashows an example user interface300A that enables a user to select one or more outcomes for a conditional event275. In the example of the user interface300A, the conditional event275is the player who will score the first touchdown in an upcoming live event (e.g., an upcoming football game). Although not shown here, it should be understood that the conditional events275for which the user can make selections may be selected via a client device220using a different user interface, or by interacting with a corresponding interactive user interface element (e.g., an actionable object) of the user interface300A. Some example actionable objects that enable the user to specify the conditional events275for which to select outcomes can include drop-down menus, interactive buttons, hyperlinks, search bars, or other types of graphical user interface elements.

As shown, the user interface300A includes a list of players305A-305N (sometimes referred to as the list of players305or the players305). Each of the players305can be athletes participating in the live event to which the conditional event corresponds. The table310A shows the conditional event (e.g., who will score the first touchdown in the live event). Each entry in the table310A (shown as the boxes in the table) can correspond to a respective player. When an entry (e.g., a box, sometimes referred to as an “actionable object”) in the table is selected, it can indicate a user selection of the outcome for the conditional event275corresponding to the box row. In this example user interface300A, the candidate outcomes indicate each player that the user has selected will perform the first touchdown of the football game. Multiple players can be selected by interacting with multiple boxes in the table310A, indicating a Boolean OR between the possible outcomes for the corresponding conditional event275. In this example, this can indicate that the win condition of the corresponding leg of the parlay wager is satisfied if any of the selected players have performed the first touchdown.

In some implementations, certain actionable objects may not be selectable, for example, if the corresponding player is incapable of satisfying the corresponding conditional event275. The un-selectable actionable objects can include an indicator that the respective actionable object is un-selectable (e.g., displayed in a different color, a message or symbol, etc.). As shown, the corresponding actionable objects for each outcome are arranged in columns, with each column corresponding to a respective conditional event275.

In the user interface300A, only the player Cordarrelle Patterson has been selected in the table310A as performing the respective touchdown. As shown, each athlete in the player list305includes corresponding “anytime odds,” which in this example indicate the odds of the respective player performing a touchdown at any time in the game. These “anytime odds” can be generated by the data processing system205using techniques similar to those described herein for the odds of parlay wagers (e.g., by filtering the simulation results280and dividing by the total number of simulations of the live event, etc.). Once selections have been made in the table310A, the data processing system205can generate a bet slip315A that includes the odds values for the combination of selected outcomes for the specified conditional events275. As shown in the bet slip315A, only one selection has been made with odds of 9.67. The odds values for the selected player are different from the “anytime odds” for the selected player, because the anytime odds indicate the odds of the player making a touchdown at anytime during the game, rather than the first touchdown (which in this example is the conditional event275).

FIG.3Bshows an example user interface300B, which can be similar to the user interface300A. The user interface300B can be the user interface300A after a user has made multiple selections of players to perform the first touchdown during the game. As shown in the table310B, three players have been selected. Additionally, the bet slip has been updated to reflect the selections of the players for the parlay wager. Note that the odds for the parlay wager shown inFIG.3Bare lower than the odds of the wager inFIG.3A, because the there is a greater chance that any one of the selected athletes will score the first touchdown than a single athlete.

FIG.3Cshows an example user interface300C, which can be similar to the user interface300A. The user interface300C shows the list of players305, and a table310C that includes multiple mutually exclusive conditional events. As shown in the table310C, three conditional events275are shown (e.g., who will score the first touchdown, who will score the second touchdown, and who will score the third touchdown). In this example, one respective player has been selected as a candidate outcome for each of the three conditional events275. As shown in the corresponding bet slip315C, this results in very high odds, because there is a very low probability of each of the selected athletes performing the first, second, and third touchdowns in the specified order. The conditional events275of the table310C may be displayed in response to a corresponding selection of the conditional events via one or more respective user interface elements (not shown). In some implementations, certain conditional events275may not have a possible outcome for every athlete, and in such implementations, the actionable objects corresponding to invalid players may be “grayed out” or otherwise not selectable.

FIG.3Dshows an example user interface300D, which can be similar to the user interface300C. The user interface300D can be the user interface300C after a user has made multiple selections of players to perform the first, second, and third touchdowns during the football game. As shown in the table310D, additional players have been selected for each of the respective touchdowns. Additionally, the bet slip315D has been updated to reflect the selections of the players for the parlay wager. Note that the odds for the parlay wager shown inFIG.3Dare lower than the odds of the wager inFIG.3C, because the there is a greater chance that any one of the selected athletes will score the first, second, and third touchdowns than a single athlete scoring all three touchdowns.

AlthoughFIG.3Dshows selections of multiple athletes performing the first second, and third touchdowns, it should be understood that any combination of athletes can be selected as performing one or more of the specified conditional events275. An example of this is shown inFIG.3E, which shows an example user interface300E. The user interface300E shows the list of players305and the table310E, which shows differing selections for each of the first, second, and third touchdowns. As shown, different combinations of athletes can be selected for each of the first, second, and third touchdowns, and the bet slip has been updated to list both the player selections and the odds value for the parlay wager.

FIGS.3F,3G, and3Hshow the user interfaces300F,300G, and300H, each of which include different numbers of possible conditional events275(in these examples, who will score the first, second, third, or fourth touchdowns in a football game) in corresponding tables310F,310G, and310H. Based on the corresponding selections, the bet slips315F,315G, and315H have been generated or updated by the data processing system205to include the odds values for the respective parlay wagers as described herein. Although not shown here for visual simplicity, each of the user interfaces ofFIGS.3A-3Hcan include one or more actionable objects that cause the client device220to transmit a request to place the selected parlay wager indicated on the respective bet slip315A-315H. The actionable objects can include any wager information described herein, including a wager amount, additional wager conditions, or an identifier of a player profile270, among other features. In response to the request, the data processing system205can update the player profile270via which the wager was placed.

The data processing system205can credit an account specified in the player profile270based on the odds and an actual outcome of the conditional events275in the parlay wager as they occur in the game. For example, the data processing system205may monitor one or more outcomes of the conditional events275in the game based upon wagers placed via one or more client devices220. In some implementations, the actual outcomes of the conditional events275can be monitored by an external source (e.g., a third party, an external computing system, etc.) that communicates the actual outcomes of the conditional events275to the data processing system205.

Although not shown inFIGS.3A-3H, it should be understood that similar user interfaces may be utilized to assemble parlay wagers based on conditional events275of multiple games. For example, a parlay wager may be assembled based upon who scores the first touchdown in two or more football games. The live events, conditional events275, and candidate outcomes for the conditional events275can be selected by a player using techniques similar to those described herein, and may be utilized to assemble parlay wagers for multiple conditional events275, for multiple outcomes, and for multiple live events that may be concurrent or subsequent to one another. Any type of possible parlay wager may be generated using the present techniques, and the data processing system205can utilize the selected candidate outcomes of the conditional events275and the simulation results280to generate odds values for the parlay wager, as described in further detail herein.

Referring back toFIG.2, once the candidate outcomes of the conditional events275of one or more live events have been selected, the subset identifier240can identify, from the simulation results280, a subset of the simulation results280that include the outcomes selected via the user interfaces. In doing so, the subset identifier240can identify the number of simulations, from a superset of simulations of the one or more live events, which include the selected outcome for the parlay wager. To identify the subset of the simulations, the subset identifier240can filter the data structure(s) storing the simulation results280for the one or more live events based on a first candidate outcome of a first conditional event275of the parlay wager to generate an initial subset of simulations. Then, the subset identifier240can repeatedly filter the initial subset to generate further subsets (which are also iteratively filtered) based on additional selected candidate outcomes of any additional conditional events in the parlay wager. In some implementations, if the simulation results280are structured as a database, the subset identifier240can generate one or more database queries (e.g., an SQL query, a NoSQL query, etc.) that return the simulation results280(and therefore the number of simulations) that corresponds to the selected outcomes of the conditional events275in the parlay wager.

In implementations where the simulation results include a counter for each possible outcome of the candidate events275, the subset identifier240can identify the respective counter value for each outcome in the simulation results280that indicates the selected outcomes of the candidate event275. If the outcomes correspond to a Boolean OR operation, the subset identifier240can increment the counters. If the outcomes correspond to a Boolean AND operation, the subset identifier240can recalculate the counter values for each subset of simulation results280in each subset. The subset identifier240can iteratively filter each subset as described herein, and recalculate the corresponding counter value, to determine the number of simulations that satisfy the outcomes selected by the player for the parlay wager via the user interfaces described herein. For example, the subset identifier can determine the subset of simulations in which the selected players selected by the user scored the first touchdown (e.g., if the first touchdown is the conditional event275in the parlay wager).

Upon determining the number of simulations that satisfy the selected outcomes of the parlay wager based on the simulation results280, the odds value generator245can generate an odds value for the parlay wager. The odds value can be calculated as a function of the number of the simulations having outcomes that satisfy the parlay wager (e.g., the number of the subset of simulations that satisfy the subset) and the total number simulations of the one or more live events indicated in the parlay wager. For example, the probability of the parlay wager being satisfied can be calculated by dividing the number of the subset of simulations in which the parlay bet was satisfied by the total number of simulations of the corresponding live events. The odds value (e.g., a payout multiplier, etc.) can then be calculated based on this probability. Removing entire simulations with each evaluation implicitly deals with the correlation across markets, without requiring brute-force probability determinations for every combination of possible outcomes in the parlay wager. The combinations of possible outcomes in brute-force techniques increase exponentially as the number of conditional events275(and the selected outcomes corresponding thereto) increase. Therefore, the techniques described herein that utilize filtering of the simulation results280provide a technical improvement to probability determination systems due to the increased efficiency of filtering the simulation results280. Filtering the simulation results280can be used to calculate the probability of combinations of outcomes of conditional events275of arbitrary complexity with little or no decrease in performance. This enables the real-time or near real-time calculation of odds values for complex parlay wagers.

Once the odds value for the selected parlay wager has been generated, the user interface presenter250can provide the odds value for presentation at the user interface (e.g., user interfaces similar to those described in connection withFIGS.3A-3H) at the client device220. The odds value may be presented in a bet slip, as shown in the user interfaces300A-300H ofFIGS.3A-3H. In some implementations, the odds values can be provided to the client device220by dynamically updating the user interface, for example, in response to an interaction with a corresponding button displayed in the user interface (e.g., a calculate odds button, etc.). In some implementations, the odds value can be stored in association with an identifier of the parlay wager. Displaying the odds value can include updating the user interface (e.g., by transmitting display instructions, etc.) to include a button that, when interacted with, causes the client device220to transmit a request to place the parlay wager assembled based the selections of candidate outcomes for the specified conditional events275. Upon receiving the request, the data processing system205can update the player profile270used to access the functionality of the data processing system205with a record of the parlay wager and can monitor the conditional events275of the parlay wager as they occur in the corresponding live event(s), as described herein.

Referring now toFIG.4, depicted is an illustrative flow diagram of a method400for modeling live events. The method400can be executed, performed, or otherwise carried out by the data processing system205, the computer system100described herein in conjunction withFIGS.1A-1D, or any other computing devices described herein. In brief overview of the method400, the data processing system (e.g., the data processing system205, etc.) can maintain simulation results (e.g., the simulation results280) corresponding to conditional events (e.g., the conditional events275) for simulations of a live event (STEP402), receive selections of candidate outcomes of one or more conditional events of the live event (STEP404), identify a subset of the simulations that have results including the selected candidate outcomes (STEP406), generate an odds value for a parlay wager including the selected outcomes based on a number of the subset of simulations and the (STEP408), and provide the odds value for presentation at a user interface (STEP410).

In further detail of the method400, the data processing system (e.g., the data processing system205, etc.) can maintain simulation results (e.g., the simulation results280) corresponding to conditional events (e.g., the conditional events275) for simulations of a live event (STEP402). For example, the data processing system can maintain one or more data structures that include the simulation results, which may be stored in association with respective identifiers of respective conditional events, as described herein. In some implementations, the simulation results for a particular conditional event can include respective counter values that correspond to a count of each outcome for each simulation of the corresponding live event. The data processing system can calculate or generate the counter values for each conditional event of a live event by scanning through the simulation results of the simulations of the live event, or may receive (or retrieve, from a storage (e.g., the storage215) of the data processing system) the counter values as part of the simulation results from an external computing system that executed the simulations of the live event.

In some implementations, the data processing system can itself generate the simulation results for a live event by executing simulations of the live event. A simulation of a live event can be a model of the event and can produce one or more outputs that correspond to various outcomes of the live event. For example, if the live event is a sporting event, the outcomes may include timestamps of simulated game events (e.g., points scored, passes made, etc.), along with identifiers of participants (e.g., athletes) that performed the simulated game event. Executing the simulations can include executing processor-readable instructions that cause the data processing system to carry out a model of the live event, with the outputs of the simulation being respective simulation results for each conditional event of the live event, as described herein. Each simulation used to generate the simulation results may depend partially on an element of randomness (e.g., one or more random numbers generated using one or more random number generators), and may be based on various aspects of the particular live event, such as the teams and athletes participating in the live event or any other attribute or characteristic that may influence any outcome that may occur during the live event. As such, the simulation results generated from each simulation may include different outcomes for the conditional events of the simulated live event.

The data processing system can execute a predetermined number of simulations of the live event, for example. The predetermined number may be specified in one or more requests from an external computing device (e.g., a client device (e.g., a client device220), an administrative computing system, an operator of the data processing system, etc.). In some implementations, one or more of the simulations of the live event can be executed by an external computing system (not pictured), which can store corresponding simulation results in the storage of the data processing system, as described herein. The data processing system can generate counter values for each outcome of each conditional event of the live event to which the simulations correspond. To so, the data processing system can update a respective counter value for each conditional event based on the various outcomes indicated in the simulation results for the live event. The simulations of the live event can be executed as an offline process (e.g., prior to the live event, not in real-time or near real-time, etc.). The simulation results can be maintained to perform the estimation of odds values for parlay wagers or other combinations of candidate outcomes of conditional events, as described herein.

The data processing system can receive selections of candidate outcomes of one or more conditional events of the live event (STEP404). The selections can be received, for example, via a user interface (e.g., user interfaces provided by the data processing system similar to those described in connection withFIGS.3A-3H, etc.) presented at a client device. As described herein, particular wagers, such as parlay wagers, can include wagers on multiple mutually exclusive conditional events. The odds values for such wagers can be calculated across two correlated markets, containing a number of mutually exclusive outcomes of conditional events. The client device can access a webpage or application resource provided by the data processing system to make selections of combinations of outcomes for one or more conditional events to occur during a live sporting event. These combinations can be utilized to place a parlay wager on the combinations. The selections of one or more conditions events may include Boolean operations (e.g., player A OR player B will score the first touchdown in the live event, player A will score the first touchdown AND player B will score the second touchdown in the live event, etc.). The odds values for these mutually exclusive events may be based upon characteristics of the players or the live event, or the simulation results of the live event. Example selections of combinations of conditional events are shown inFIGS.3A-3H.

The data processing system can identify a subset of the simulations that have results including the selected candidate outcomes (STEP406). Once the candidate outcomes of the conditional events of one or more live events have been selected, the data processing system can identify, from the simulation results, a subset of the simulation results that include the outcomes selected via the user interfaces. In doing so, the data processing system can identify the number of simulations, from a superset of simulations of the one or more live events, which include the selected outcome for the parlay wager. To identify the subset of the simulations, the data processing system can filter the data structure(s) storing the simulation results for the one or more live events based on a first candidate outcome of a first conditional event of the parlay wager to generate an initial subset of simulations. Then, the data processing system can repeatedly filter the initial subset to generate further subsets (which are also iteratively filtered) based on additional selected candidate outcomes of any additional conditional events in the parlay wager. In some implementations, if the simulation results are structured as a database, the data processing system can generate one or more database queries (e.g., an SQL query, a NoSQL query, etc.) that return the simulation results (and therefore the number of simulations) that corresponds to the selected outcomes of the conditional events in the parlay wager.

In implementations where the simulation results include a counter for each possible outcome of the candidate events, the data processing system can identify the respective counter value for each outcome in the simulation results that indicates the selected outcomes of the candidate event. If the outcomes correspond to a Boolean OR operation, the data processing system can increment the counters. If the outcomes correspond to a Boolean AND operation, the data processing system can recalculate the counter values for each subset of simulation results in each subset. The data processing system can iteratively filter each subset as described herein, and recalculate the corresponding counter value, to determine the number of simulations that satisfy the outcomes selected by the player for the parlay wager via the user interfaces described herein. For example, the data processing system can determine the subset of simulations in which the selected players selected by the user scored the first touchdown (e.g., if the first touchdown is the conditional event in the parlay wager).

The data processing system can generate an odds value for a parlay wager including the selected outcomes based on a number of the subset of simulations and the (STEP408). Upon determining the number of simulations that satisfy the selected outcomes of the parlay wager based on the simulation results, the data processing system can generate an odds value for the parlay wager. The odds value can be calculated as a function of the number of the simulations having outcomes that satisfy the parlay wager (e.g., the number of the subset of simulations that satisfy the subset) and the total number simulations of the one or more live events indicated in the parlay wager. For example, the probability of the parlay wager being satisfied can be calculated by dividing the number of the subset of simulations in which the parlay bet was satisfied by the total number of simulations of the corresponding live events. The odds value (e.g., a payout multiplier, etc.) can then be calculated based on this probability. Removing entire simulations with each evaluation implicitly deals with the correlation across markets, without requiring brute-force probability determinations for every combination of possible outcomes in the parlay wager. The combinations of possible outcomes in brute-force techniques increase exponentially as the number of conditional events (and the selected outcomes corresponding thereto) increase. Therefore, the techniques described herein that utilize filtering of the simulation results provide a technical improvement to probability determination systems due to the increased efficiency of filtering the simulation results. Filtering the simulation results can be used to calculate the probability of combinations of outcomes of conditional events of arbitrary complexity with little or no decrease in performance. This enables the real-time or near real-time calculation of odds values for complex parlay wagers.

The data processing system can provide the odds value for presentation at a user interface (STEP410). Once the odds value for the selected parlay wager has been generated, the data processing system can provide the odds value for presentation at the user interface (e.g., user interfaces similar to those described in connection withFIGS.3A-3H) at the client device. The odds value may be presented in a bet slip, as shown in the user interfaces300A-300H ofFIGS.3A-3H. In some implementations, the odds values can be provided to the client device by dynamically updating the user interface, for example, in response to an interaction with a corresponding button displayed in the user interface (e.g., a calculate odds button, etc.). In some implementations, the odds value can be stored in association with an identifier of the parlay wager. Displaying the odds value can include updating the user interface (e.g., by transmitting display instructions, etc.) to include a button that, when interacted with, causes the client device to transmit a request to place the parlay wager assembled based the selections of candidate outcomes for the specified conditional events. Upon receiving the request, the data processing system205can update the player profile used to access the functionality of the data processing system205with a record of the parlay wager and can monitor the conditional events of the parlay wager as they occur in the corresponding live event(s), as described herein.

Implementations of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software embodied on a tangible medium, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer programs, e.g., one or more components of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. The program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can include a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).

The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

The terms “data processing apparatus,” “data processing system,” “client device,” “computing platform,” “computing device,” or “device” encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatuses can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The elements of a computer include a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. A computer, however, need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can include any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system such as the data processing system205can include clients and servers. For example, the data processing system205can include one or more servers in one or more data centers or server farms. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving input from a user interacting with the client device). Data generated at the client device (e.g., a result of an interaction, computation, or any other event or computation) can be received from the client device at the server, and vice versa.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of the systems and methods described herein. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.

In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. For example, the data processing system205could be a single module, a logic device having one or more processing modules, one or more servers, or part of a search engine.

Having now described some illustrative implementations and implementations, it is apparent that the foregoing is illustrative and not limiting, having been presented by way of example. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements, and features discussed only in connection with one implementation are not intended to be excluded from a similar role in other implementations.

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing,” “involving,” “characterized by,” “characterized in that,” and variations thereof herein, is meant to encompass the items listed thereafter, equivalents thereof, and additional items, as well as alternate implementations consisting of the items listed thereafter exclusively. In one implementation, the systems and methods described herein consist of one, each combination of more than one, or all of the described elements, acts, or components.

Any references to implementations or elements or acts of the systems and methods herein referred to in the singular may also embrace implementations including a plurality of these elements, and any references in plural to any implementation, element, or act herein may also embrace implementations including only a single element. References in the singular or plural form are not intended to limit the presently disclosed systems or methods, their components, acts, or elements to single or plural configurations. References to any act or element being based on any information, act, or element may include implementations where the act or element is based at least in part on any information, act, or element.

Any implementation disclosed herein may be combined with any other implementation, and references to “an implementation,” “some implementations,” “an alternate implementation,” “various implementation,” “one implementation,” or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the implementation may be included in at least one implementation. Such terms as used herein are not necessarily all referring to the same implementation. Any implementation may be combined with any other implementation, inclusively or exclusively, in any manner consistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any terms described using “or” may indicate any of a single, more than one, and all of the described terms.

Where technical features in the drawings, detailed description or any claim are followed by reference signs, the reference signs have been included for the sole purpose of increasing the intelligibility of the drawings, detailed description, and claims. Accordingly, neither the reference signs nor their absence have any limiting effect on the scope of any claim elements.

The systems and methods described herein may be embodied in other specific forms without departing from the characteristics thereof. Although the examples provided may be useful for modeling live events, the systems and methods described herein may be applied to other environments. The foregoing implementations are illustrative rather than limiting of the described systems and methods. The scope of the systems and methods described herein may thus be indicated by the appended claims, rather than the foregoing description, and changes that come within the meaning and range of equivalency of the claims are embraced therein.