Patent Publication Number: US-9846990-B2

Title: Visualizing wagering game establishment patron flow

Description:
RELATED APPLICATIONS 
     This application is a continuation application that claims priority benefit of U.S. application Ser. No. 12/995,940 which is a National Stage application of PCT/US09/46326 filed 4 Jun. 2009, which claims priority benefit of Provisional U.S. Application No. 61/059,487 filed 6 Jun. 2008. 
    
    
     LIMITED COPYRIGHT WAIVER 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. Copyright 2015, WMS Gaming, Inc. 
     FIELD 
     Embodiments of the inventive subject matter relate generally to data processing, and more particularly to generating data that represents flow of patrons through a wagering game establishment. 
     BACKGROUND 
     Wagering game machines, such as slot machines, video poker machines and the like, have been a cornerstone of the gaming industry for several years. Generally, the popularity of such machines depends on the likelihood (or perceived likelihood) of winning money at the machine and the intrinsic entertainment value of the machine relative to other available gaming options. Where the available gaming options include a number of competing wagering game machines and the expectation of winning at each machine is roughly the same (or believed to be the same), players are likely to be attracted to the most entertaining and exciting machines. Shrewd operators consequently strive to employ the most entertaining and exciting machines, features, and enhancements available because such machines attract frequent play and hence increase profitability to the operator. 
     In addition to the wagering games, the floor layout of a wagering game establishment affects player experience. Floor layout influences traversal across the floor and exposes various non-gaming aspects, as well as gaming aspects, of the wagering game establishment to players, such as restaurants, events, etc. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       Embodiments of the invention are illustrated in the Figures of the accompanying drawings in which: 
         FIG. 1  depicts a conceptual example of visualization of patron flow through a wagering game establishment. 
         FIG. 2  depicts a conceptual example of generating patron flow visualization based on wagering game data and non-wagering game data and corresponding times. 
         FIG. 3  depicts a conceptual example of aggregating data for patron flow visualization at an individual wagering game machine and individual patron level of granularity. 
         FIG. 4  depicts a flowchart of example operations for generating patron flow visualization data. 
         FIG. 5  depicts an example use of patron flow data for suggesting a social group. 
         FIG. 6  depicts a flowchart of example operations for suggesting social groups based on patron flow data. 
         FIG. 7  is a block diagram illustrating a wagering game network  700 , according to example embodiments of the invention. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     The description that follows includes exemplary systems, methods, techniques, instruction sequences and computer program products that embody techniques of the present inventive subject matter. However, it is understood that the described embodiments may be practiced without these specific details. For instance, although examples refer to flow of patrons, a slightly different perspective could visualize flow of money from players throughout a wagering game establishment. In other instances, well-known instruction instances, protocols, structures and techniques have not been shown in detail in order not to obfuscate the description. 
     A tool or set of tools can aggregate data over a time period from a variety of different sources within a wagering game establishment to determine player flow or probable player flow through the wagering game establishment. The tool can aggregate data from wireless access points, player account activity, purchases, room access activity based on card keys, RFID interrogators, etc. Some of the aggregated data directly indicates location within the wagering game establishment. The tool processes other data that does not directly indicate location to derive location within the wagering game establishment of the multiple players. The tool synthesizes locations and times to generate player flow data based on the aggregated data, which can indicate actual flow or probable flow. A variety of utilities arise with this player flow data. For instance, a wagering game establishment can use player flow data to evaluate layout of their floor, event schedules, maintenance schedules, strategic placement of advertisements and/or notices, etc. Evaluation of the various aspects of a wagering game establishment with player flow data provides opportunities to target advertisements, to enhance player experience, to enhance revenues, etc. 
       FIG. 1  depicts a conceptual example of patron flow visualization through a wagering game establishment. A wagering game establishment in  FIG. 1  includes group games, wagering game machines, and restaurants. The wagering game establishment includes a poker room  101 , roulette tables  105  and  107 , craps tables  109  and  111 , and pai gow tables  119  and  125 . The wagering game establishment also includes several banks of slots: a bank of $5 slot machines  103 , banks of penny slots  113  and  127 , banks of $50 slot machines  115  and  123 , a bank of dollar slot machines  131 , and a bank of $0.50 slot machines  121 . For the restaurants, the wagering game establishment includes an American cuisine buffet style restaurant  131  and a sushi restaurant  117 . 
     Several lines of different lengths and different patterns indicate flows of patrons through the wagering game establishment. Each of the different lines represents a trail of a particular group. A series of one or more trails indicates a flow. Line density represents group size and line pattern represents different root groups. The groups can be defined with different techniques and various criteria. For example, a root group could be a group of tourists registered as a group (i.e., a pre-defined group). As another example, a system generating patron flow data (“patron flow system”) can create an ad-hoc root group and child groups based on criteria (e.g., detecting a threshold number of patrons flowing from a first set of wagering game machines to a second set of wagering game machines within a given time period, detecting transition of patrons to same or different destination locations from a same source location within a given time period, etc.). 
     Lines  133 ,  135 ,  137 , and  138  represent trails of a root group A and child groups A 1  and A 2 . The line  133  indicates a trail for a root group A from the bank of penny slots  127  to the bank of $0.50 slots  121 . The line  135  represents a trail from the bank of $0.50 slots  121  to the restaurant  131  for a child group A 1 , which is a substantial portion of the root group A but not all. The lines  137  and  138  represent trails for a child group A 2  from the bank of $0.50 slots  123  to the bank of dollar slots  131  and then to their rooms. Although flow for every individual and/or small group could be tracked, child groups that fall below a given threshold and/or outside of a given time period are not tracked in this example. 
     The flow of groups A, A 1 , and A 2  suggest various characteristics about the group&#39;s flocking behavior, which can be used to enhance player experience and revenue for the wagering game establishment. The wagering game establishment can evaluate the flows of group A and the child groups A 1  and A 2  to perhaps change denomination of the bank  123  from $50 to $1 since the flow indicates all of the members of group A skipped the bank  123 . A flow of groups A, A 1 , and A 2  suggests that the larger child group A 1  may have played the dollar slots if they encountered a bank of dollar slots instead of the bank  123  of $50 slots. The flow of group A 1  also suggests that the revenue may be increased if smaller denomination wagering game machines are located proximate to the restaurant  131 . 
     Lines  139 ,  141 ,  143 , and  145  represent trails of a root group B and child groups B 1 , B 2 , and B 3 . The line  139  represents a trail for a root group B from the pai gow table  119  to the poker room  101 . The line  141  represents a trail for a child group B 2  from the poker room  101  to the craps table  109 . The line  143  represents a trail for a child group B 1  from the poker room  101  to the sushi restaurant  117 . The line  145  represents a trail from the sushi restaurant  117  to the bank  115  of $50 slots. 
     The flows of groups B, B 1 , B 2 , and B 3  suggest a correlation between patrons that participate in group wagering games and higher denomination wagering game machines. In addition, assuming the sushi restaurant  117  can be categorized as fine dining, the flows for the groups B, B 1 , B 2 , and B 3  suggest a preference for fine dining over a buffet style restaurant by patrons who play group wagering games and higher denomination wagering game machines. These suggestions by the flows can lead the wagering game establishment to make modifications to their floor layout, such as placing higher denomination wagering game machines near group wagering game tables and changing the denomination of the bank  113  from penny to a higher denomination. In addition, the lack of patron flow with respect to the pai gow table  125  may indicate poor placement of the pai gow table  125 . 
     Lines  147  and  149  represent trails of respective groups C and D. The line  147  represents a flow of group C from the roulette table  107  to the craps table  111 . The line  149  represents a flow of group D from the roulette table  105  to the bank  103  of $5 slots. 
     The single hop flows of groups C and D suggest a tendency to remain at a location for longer periods of time. The single hop flows can suggest more when evaluated with the flows of groups with similar behaviors, such as groups B, B 1 , B 2 , and B 3 . The flow of group C in view of the flow of groups B and B 2  reinforce the suggestion that these groups of patrons prefer group wagering games. The suggestion by the flow of group B 3  that patrons that participate in group wagering games also prefer higher denomination wagering game machines can be used to motivate the wagering game establishment to increase the denomination of the bank  103 . 
       FIG. 1  only illustrates a few possibilities for visualization of patron flow data. Many different conditions, thresholds, parameters, and filters can be applied to the data that indicates patron flow (“patron flow data”) to gain a range of perspectives. For example, patron flow data can be visualized for patrons that spent a minimum amount of money on wagering game machines. As another example, patron flow data can be visualized for wagering game machines near a particular exit for a given time period that corresponds to the end of a performance or show.  FIG. 1  also illustrates that patron flow data can be derived from both wagering game data and non-wagering game data. 
       FIG. 2  depicts a conceptual example of generating patron flow visualization based on wagering game data and non-wagering game data and corresponding times.  FIG. 2  depicts a portion of the example wagering game establishment depicted in  FIG. 1 .  FIG. 2  depicts an American cuisine buffet style restaurant  201 , and banks of slot machines  203 ,  205 ,  207 , and  209 .  FIG. 2  also depicts a timeline  200  of group activity. The patron flow visualization is generated based on the timeline  200 . 
     A series of data points along the timeline  200  are the basis for the trail generated for patron flow visualization. Wagering game machine activity was detected from 9:17 to 9:53 at the bank  203  of penny slots by the patrons that comprise group A. Wagering game machine activity is then detected from 10:00 to 11:54 at the bank  205  of fifty cent slots by the patrons that comprise group A. A line  233  that represents a trail for group A from the bank  203  to the bank  205  is generated from these two data transitions on the timeline  200 . Non-wagering game activity (e.g., purchase on a player card, walking by an RFID interrogator in a restaurant, etc.) indicates patrons that comprise group A 1  eating (or at least entering and remaining) in the restaurant  201  from 12:08 to 13:15. A line  235  that represents a trail for group A 1  from the bank  205  to the restaurant  201  is generated based on this transition on the timeline  200 . Data from the timeline  200  indicates wagering game activity detected from 12:11 to 13:02 at the bank  209  of dollar slots by patrons that comprise group A 2 . A line  237  that represents a trail for group A 2  from the bank  205  to the bank  209  is generated based on this data from the timeline  200 . Data from the timeline  200  indicates room access at 13:42, 14:05, and 14:12 by respective patrons A 2 . 1 , A 2 . 2 , and A 2 . 3  from group A 2 . A line  238  that represents a trail for group A 2  from the bank  209  to their rooms is generated based on this room access data illustrated on the timeline  200 . 
     Although  FIG. 2  depicts time ranges and banks of wagering game machines, embodiments are not so limited. Patron flow data can be visualized based on a series of points in a time range, disparate points in time aggregated together, blocks of time, etc. In addition, patron flow data can be visualized at different levels of granularity. For example, patron flow data can be visualized for individual wagering game machines instead of or in addition to visualizing patron flow from/to banks of wagering game machines. Furthermore, visualization of patron flow data can be depicted with any of a variety of graphical possibilities. Simple lines are utilized in the Figures, but patron flow can be visualized with animation, images, etc. 
       FIG. 3  depicts a conceptual example of aggregating data for patron flow visualization at an individual wagering game machine and individual patron level of granularity. An example wagering game establishment includes X-Treme Reels gaming machines  301  and  303 , Lucky Meerkats gaming machines  315  and  317 , Big Event gaming machines  337  and  339 , and Compressed Coal Jackpots gaming machines  331  and  333 . The example wagering game establishment also includes a roulette table  305 , a craps table  311 , a sushi restaurant  329 , and a café  327 . 
     A timeline  343  illustrates when patron activity is detected based on data aggregated from different sources throughout the wagering game establishment. The different sources include security cameras  307  and  309  near the roulette table  305  and the craps table  311 , all of the gaming machines, and wireless access points/RFID interrogators  313 ,  319 , and  341 . At 9:08, the security camera  309  captures data used to identify a patron Foo  323  at the craps table  311 . The patron Foo  323  also has a portable wagering game machine  325 . The wireless access point  313  detects the portable wagering game machine  325  at  9 : 08  and provides data that indicates presence of Foo near the wireless access point  313 , which is near the craps table  311 . Data from the security camera  309  and from the wireless access point  313  can collectively be used to ascertain location of Foo  323  at the craps table  311 , as redundant data indicating location of Foo  323  at the craps table  311 , etc. In addition, the access point data from the portable wagering game machine  325  can be used to identify Foo  323  and be associated with an image of Foo  323  captured by the security camera  309 . At 9:11, the security camera  307  captures data used to identify a patron Stu  321  at the roulette table  305 . Although not depicted, location of patrons can also be determined with player account activity data generated from use of player cards and/or determined with location data generated from RFID chips embedded in player cards and/or wagering chips. At 10:04, the gaming machine  337  generates data that indicates gaming activity by a patron Lou  335 . At 10:10, the wagering game machine  301  generates data that indicates gaming activity by Stu  321 . At 10:29, the wagering game machine  315  generates data that indicates gaming activity by Foo  323 . At 10:45, the RFID interrogator  319  generates data that indicates Lou  335 , assumed to have a player card with an RFID chip for this example, is near the café  327 . In addition or alternatively, data from the café  327  (e.g., payment data, reservation data, etc.) indicates and/or confirms presence of Lou  335  in the café  327 . At 11:28, the gaming machine  337  generates data that indicates gaming activity by Foo  323 . At 12:13 and 12:16, the RFID interrogator  341  generates data that respectively indicates Stu  321  and Foo  323 , assumed to have player cards with RFID chips, are near the sushi restaurant  329 . In addition or alternatively, data from the sushi restaurant  329  (e.g., payment data, reservation data, etc.) indicates and/or confirms presence of Stu  321  and Foo  323  in the sushi restaurant  329 . At 13:11, the gaming machine  337  generates data that indicates gaming activity by Stu  321 . At 13:48, the gaming machine  317  generates data that indicates gaming activity by Lou. 
     A back-end  345  (e.g., one or more servers in communication with databases of the aggregated data) determines patron flow with the data aggregated across these different sources, and generates player flow data  347  used for visualizing the determined patron flow. The back-end  345  determines a flow for Stu  321  from the roulette table  305 , to the gaming machine  301 , to the sushi restaurant  329 , and then to the gaming machine  337 . The back-end  345  determines a flow for Foo  323  from the craps table  311 , to the gaming machine  315 , to the gaming machine  337 , and then to the sushi restaurant  329 . The back-end  345  also determines a flow for Lou  335  from the gaming machine  337 , to the café  327 , and then to the gaming machine  317 . 
     From these flows, a tool could flag the Compressed Coal Jackpots gaming machine  331  and  333  as low use since none of the flows involved them. A user or tool could also use the flow visualization of Foo, Stu, and Lou to realize the attractiveness of the Big Event gaming machines  337  and  339 . 
       FIG. 4  depicts a flowchart of example operations for generating patron flow visualization data. At block  401 , data associated with patrons of a wagering game establishment during a time period are aggregated. For example, data from wagering game activity databases and RFID interrogator databases over an eight hour time period are aggregated and chained. At block  403 , location of the patrons during the time period within the wagering game establishment are determined based on the aggregated data. For instance, identifiers of individual wagering game machines are mapped to locations within the wagering game establishment. At block  405 , times that correspond to the derived locations are determined based on the aggregated data. For instance, time stamps are extracted from wagering game events. At block  407 , it is determined if any events during the time period occurred that could affect patron flow. For example, a patron flow system accesses a schedule of performances to determine if a performance began or ended during the time period. If no events are found, then control flows to block  413 . If events that could impact patron flow are discovered, then control flows to block  409 . 
     At block  409 , the time(s) of the event(s) is determined. At block  411 , patron flow data that represents flow of the patrons during the time period based on the derived location, corresponding times, and the time(s) of the event(s) are generated. For example, a patron flow system generates a data structure representation of a graph with edges that correspond to trails and nodes that correspond to locations. Control flows from block  411  to block  415 . 
     If a patron flow impacting event was not discovered at block  407 , then patron flow data that represents flow of patrons based on the derived locations and determined times are generated. For example, the patron flow system generates a hash table indexed by patron identifier with each entry indexing into a linked list of nodes that represent trails of the indexing patron. At block  415 , flow visualization data that graphically represents the patron flow data are generated. For instance, graphic elements are generated for elements of the patron flow data. 
     Although examples refer to generating player flow data, such as data structures that represent patron flow, these examples should not be used to limit embodiments or scope of the claims. Embodiment can generate player flow data by tagging entries in databases, cloning portions of databases and reorganizing the cloned portions, copying entries from databases to build a player flow database, etc. Embodiments can construct queries and/or search commands to extract data based on the tagging, for example, that represent player flow. 
     In addition to evaluating floor layout, targeting advertising, scheduling maintenance, maximizing flow, etc., using patron flow data, wagering game establishments can engineer or infer social groups based on player flow data. Social interaction and larger social groups may enhance patron experience and excite patron activity. Overlapping flows among patrons can indicate similar behavior (e.g., eating times, sleeping times, preferred cuisine, etc.) and similar wagering game preferences (e.g., preferences for high denomination wagering game machine, lower denomination wagering game machine, group wagering games, particular brands of gaming machines, etc.). 
       FIG. 5  depicts an example use of patron flow data for suggesting a social group.  FIG. 5  uses the timeline  343  from  FIG. 3 . At a stage A, a patron flow system  501  determines a candidate social group with patron flow data. From the patron flow data represented by the timeline  343 , the patron flow system  501  can determine a candidate social group should be comprised of Stu and Foo. The flows of Stu and Foo overlap at the group wagering game area early in the morning, and the sushi restaurant within three minutes of each other. The patron flow system  501  may also suggest a candidate social group based on flow intersections indicated in the patron flow data. Although not overlapping, the flows of Foo, Stu, and Lou intersect at the Big Event gaming machines. The patron flow system  501  can suggest a candidate social group based on patrons having degrees of overlap in their flows, a threshold number of intersections in their flows, etc. The patron flow system  501  can go further and suggest social group based on inferences derived from patron flow data. Referring back to  FIG. 1 , the patron flow system  501  could suggest a candidate social group comprised of the patrons of groups B, C, and D even though their flows do not intersect or overlap. The patron flow system  501  can analyze the patron flow data of these groups and determine that the patrons of these groups share an interest in group wagering games. The patron flow system  501  suggests the social group based on this determination. 
       FIG. 6  depicts a flowchart of example operations for suggesting social groups based on patron flow data. At block  601 , data associated with patrons of a wagering game establishment during a time period are aggregated. At block  603 , location of the patrons during the time period within the wagering game establishment are determined based on the aggregated data. At block  605 , times that correspond to the derived locations are determined based on the aggregated data. At block  607 , patron flow data that represents flow of the patrons through the wagering game establishment based on the derived locations and the determined times are generated. At block  609 , it is determined if multiple of the patrons have sufficiently overlapping and/or intersecting flows. For example, the patron flow system searches for patrons with flows that overlap (e.g., patrons who were at a first bank of wagering game machines during a first time window and moved to a second bank of wagering game machines during a second time window) and/or patrons with at least x flow intersections as indicated by the patron flow data. If multiple patrons are found to have sufficiently overlapping and/or intersecting flows, then control flows to block  611 . Otherwise, flow ends. 
     At block  611 , the patrons with the sufficiently overlapping and/or intersecting flows are indicated as a possible social group. For example, account identifiers for the patrons are associated with a candidate social group identifier. At block  613 , it is determined if demographic data is available for the indicated patrons. Examples of demographic data include country of origin, residence, age, frequency of patronage, cumulative wagering, etc. If demographic data is available, then control flows to block  617 . If demographic data is not available, then control flows to block  615 . 
     At block  617 , the available demographic data is evaluated against social group constraints. At block  619 , the candidate social group is updated based on the evaluation. For example, social group constraints can indicate limit to age gaps among patrons of a social group. Another social group constraint may indicate that patrons of a social group should speak a common language. For example, if the indicated patrons include 3 patrons who speak Japanese and one patron who does not speak Japanese, then the non-Japanese speaking patron would be removed. At block  621 , it is determined if multiple patrons are still indicated for the candidate social group. If not, then the flow ends. If multiple candidates are still indicated, then control flows to block  615 . 
     At block  615 , the suggestion of the candidate social group is indicated to the remaining indicated patrons. The suggestion of the candidate social group can be indicated directly or indirectly. For instance, e-mail messages can be sent to the indicated patrons to notify them that other patrons have similar interests. As another example, group events can be orchestrated to place the indicated patrons in proximity (e.g., a special wagering game event only for the indicated patrons) without specifically suggesting the social group. 
     Although  FIG. 6  depicts operations for using patron flow data to engineer a social group, patron flow data can be used to infer a social group. For instance, the operations can be performed to generate a data structure of an inferred social group. Subsequent operations can then be performed to validate the inferred social group instead of suggesting the social group. In addition, whether a social group is engineered or inferred, a wagering game establishment can enhance the experience of the social group with various offers, invitations, information, etc. 
     A social group inference/engineering system can provide information about an event affecting one of the members of the inferred/engineered social group to the other members. For example, if Foo hits a jackpot then a notification can be sent to the Stu and Lou (assumed to be in an inferred/engineered social group with Foo) (e.g., via text messaging, messaging to wagering games machines being played by Stu and Lou, phone calls, etc.). A system that provides this information about wins can deliver different granularities of information (e.g., a simple message that Foo won, a message that indicates amount of the jackpot and location of Foo, etc.). The system that provides this information can also limit recipients to those who opt-in to the social group inference/engineering system, those who indicate a preference in their player accounts for social interaction, etc. 
     The social group inference/engineering system can provide invitations to members of an inferred/engineered social group to participate in a social gaming event, such as a communal gaming event or a tournament, and excite their gaming activity. For example, the system can send invitations to Foo, Stu, and Lou to play Big Event together. The system can condition sending the invitation on the profiles of Foo, Stu, and Lou. For instance, the system may not send an invitation to Lou because Lou&#39;s profile indicates a preference for card games. As another example, the system can send an invitation to a poker game to the members because Lou&#39;s profile indicates a preference for card games. The system can also monitor for occurrences of events that affect at least one of the members to trigger an invitation. They system may detect that Foo and Stu have won several times and then send an invitation to Foo and Stu to participate in a tournament type game. 
     The social group inference/engineering system can provide offers to members of an inferred/engineered social group to drive use of amenities, distribute marketing information, etc. The system can notify members of a sweepstakes for a group vacation, free spins, meals, etc., and automatically register the members of the inferred/engineered social group if permitted by the members (e.g., based on their profiles, responses to prompts, etc.). The system can send a message to the members for one free appetizer at a restaurant, reduced green fees, etc. The system can use acceptance of the offers to validate an inferred/engineered social group. For example, if Foo and Stu accept the offer then the system can update the structure that represents the social group to validate Foo and Stu as members of the inferred/engineered social group. 
     They social group inference/engineering system can contact less than all members of a social group with offers, invitations, etc., and motivate the contacted social group subset to involve the other members. For example, the system can send an invitation to Foo for a poker game with an offer of a free steak and/or $20 of chips if Foo brings along 3 friends. The system can generically refer to Foo bringing along friends, suggest that Foo ask Stu and Lou. Embodiments of such a system can also ask an individual is the individual would like the system to recommend some patrons to invite. If the individual accepts, then the system can recommend other patrons based on an engineered/inferred social group, assuming those other patrons are participating in the system. 
     Utilizing inferred/engineered social groups is not limited to use in isolation. A system can create combinations of offers, invitations, notifications, etc. to enhance the gaming experience, improve consumption of amenities, etc. For example, Foo and Stu can be invited to participate in a slot tournament when the system detects that both have won beyond a given threshold amount along with an offer of two free tickets to a show if one of them wins. The system can also send Foo and Stu an offer of reduced green fees if they persuade Lou to join them in the tournament. 
     The described operations can be performed by logic not described in the block diagrams. In addition, embodiments can perform operation by executing instructions residing on machine-readable media (e.g., software), while in other embodiments, the operations can be performed by hardware and/or other logic (e.g., firmware). In addition, the operations can be performed in series, while in other embodiments, one or more of the operations can be performed in parallel. Moreover, some embodiments can perform less than all the operations shown in any flow diagram. For example, with reference to  FIGS. 4 and 6 , operations to derive location are not necessary. Operations can be performed to base patron flow on trails between wagering game machines, for example, without regard to actual physical location within the wagering game establishment. Even though the physical trail may not be visualized, this patron flow among the wagering game machines can still be visualized. 
     Wagering Game Networks 
       FIG. 7  is a block diagram illustrating a wagering game network  700 , according to example embodiments of the invention. As shown in  FIG. 7 , the wagering game network  700  includes a plurality of casinos  712  connected to a communications network  714 . 
     Each casino  712  includes a local area network  716 , which includes an access point  704 , a patron flow system  706 , and wagering game machines  702 . The access point  704  provides wireless communication links  710  and wired communication links  708 . The wired and wireless communication links can employ any suitable connection technology, such as Bluetooth, 802.11, Ethernet, public switched telephone networks, SONET, etc. The patron flow system  706  embodies functionality for determining patron flow based on data collected, at least, from the wagering game network  700 . In some embodiments, the patron flow system  706  can embody wagering game server functionality to serve wagering games and distribute content to devices located in other casinos  712  or at other locations on the communications network  714 . In some embodiments, a back-end server embodies a patron flow unit that performs at least some of the functionality described herein for determining patron flow in a wagering game establishment. 
     The wagering game machines  702  described herein can take any suitable form, such as floor standing models, handheld mobile units, bartop models, workstation-type console models, etc. Further, the wagering game machines  702  can be primarily dedicated for use in conducting wagering games, or can include non-dedicated devices, such as mobile phones, personal digital assistants, personal computers, etc. In one embodiment, the wagering game network  700  can include other network devices, such as accounting servers, wide area progressive servers, player tracking servers, and/or other devices suitable for use in connection with embodiments of the invention. 
     In some embodiments, wagering game machines  702  and the patron flow system  706  work together such that a wagering game machine  702  can be operated as a thin, thick, or intermediate client. For example, one or more elements of game play may be controlled by the wagering game machine  702  (client) or the patron flow system  706  (server). Game play elements can include executable game code, lookup tables, configuration files, game outcome, audio or visual representations of the game, game assets or the like. In a thin-client example, the patron flow system  706  can perform functions such as determining game outcome or managing assets, while the wagering game machine  702  can present a graphical representation of such outcome or asset modification to the user (e.g., player). In a thick-client example, the wagering game machines  702  can determine game outcomes and communicate the outcomes to the patron flow system  706  for recording or managing a player&#39;s account. 
     In some embodiments, either the wagering game machines  702  (client) or the patron flow system  706  can provide functionality that is not directly related to game play. For example, account transactions and account rules may be managed centrally (e.g., by the patron flow system  706 ) or locally (e.g., by the wagering game machine  702 ). Other functionality not directly related to game play may include power management, presentation of advertising, software or firmware updates, system quality or security checks, etc. 
     Any of the wagering game network components (e.g., the wagering game machines  702 ) can include hardware and machine-readable media including instructions for performing the operations described herein. 
     Embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments of the inventive subject matter may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium. The described embodiments may be provided as a computer program product, or software, that may include a machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic device(s)) to perform a process according to embodiments, whether presently described or not, since every conceivable variation is not enumerated herein. A machine readable medium includes any mechanism for storing or transmitting information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions. In addition, embodiments may be embodied in an electrical, optical, acoustical or other form of propagated signal (e.g., carrier waves, infrared signals, digital signals, etc.), or wireline, wireless, or other communications medium. 
     GENERAL 
     This detailed description refers to specific examples in the drawings and illustrations. These examples are described in sufficient detail to enable those skilled in the art to practice the inventive subject matter. These examples also serve to illustrate how the inventive subject matter can be applied to various purposes or embodiments. Other embodiments are included within the inventive subject matter, as logical, mechanical, electrical, and other changes can be made to the example embodiments described herein. Features of various embodiments described herein, however essential to the example embodiments in which they are incorporated, do not limit the inventive subject matter as a whole, and any reference to the invention, its elements, operation, and application are not limiting as a whole, but serve only to define these example embodiments. This detailed description does not, therefore, limit embodiments of the invention, which are defined only by the appended claims. For instance, examples refer to flow within a wagering game establishment, but patron flow data can also be generated for visualization of flow among different wagering game establishments. Each of the embodiments described herein are contemplated as falling within the inventive subject matter, which is set forth in the following claims.