WO2016149766A1

WO2016149766A1 - A gaming machine entitlement reallocation centralised server and associated server-implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed venue gaming machines across a data network for gaming machine revenue optimisation

Info

Publication number: WO2016149766A1
Application number: PCT/AU2016/050224
Authority: WO
Inventors: Trevor CALLAWAY; Wes MADYCKI
Original assignee: Horizon Technology Systems Pty Limited; T Callaway And Associates Pty Limited
Priority date: 2015-03-24
Filing date: 2016-03-24
Publication date: 2016-09-29
Also published as: AU2016236853A1

Abstract

There is provided a gaming machine entitlement reallocation centralised server implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation. The method comprises receiving historical gaming data comprising at least historical gaming machine revenue data from a plurality of gaming machines at a respective plurality of gaming venues; generating a time series revenue predictive model for the at least one of the plurality of gaming machines and gaming venues using the historical gaming data; calculating predicted revenue for the at least one of the plurality of gaming machines and gaming venues using the time series revenue predictive model for a plurality of future time slots; calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues using the predicted revenue; and dynamically reallocating a plurality of gaming machine entitlement across the data network for at least a subset of each the plurality of future time slots so as to selectively enable and disable a corresponding number of a subset of the gaming machines.

Description

A gaming machine entitlement reallocation centralised server and associated server-implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed venue gaming machines across a data network for gaming machine revenue optimisation

Field of the Invention

[1] The present invention relates generally to a system that dynamically allocates gaming machine entitlements between venues in order to optimize the overall performance of a wide area network which may extend statewide and in particular, but not necessarily entirely, to gaming machine entitlement reallocation centralised server and associated server-implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed venue gaming machines across a data network for gaming machine revenue optimisation.

Background of the Invention

[2] Electronic gambling machines, also known as poker machines and the like, are a popular form of gambling in many countries throughout the world.

[3] In order to control the proliferation of gaming machines within regions or jurisdictions the numbers of gaming machines, or in particular their entitlements, are controlled with defined number of entitlements permitted within defined regions or jurisdictions.

[4] The regulatory controls of the number of gaming machine entitlements have been segmented into specific regions or venues and these entitlements cannot be changed unless there is a permanent change to the gaming machine licensing arrangements.

[5] As such, a venue operator must permanently remove one or more gaming machine entitlements and sell that / those entitlements to another venue.

[6] Furthermore, some regulators impose restrictions of the movement of gaming machine entitlements to various venue locations based on demographic conditions and also impose restrictions on the reintroduction of the subsequent replacement of gaming machine entitlements to venues that have removed and sold those entitlements.

[7] The currently regulatory requirements are effective in maintaining the predefined numbers of gaming machine entitlements within a region or jurisdiction. However, the currently regulatory requirements severely limits the optimum use of the numbers of gaming machines, when evaluated over an entire venue. [8] As such, a need therefore exists for a system that is able to automatically determine future requirements of machine entitlements at any prescribed time in the future at any specified venue so as to optimise the usage of the finite number of gaming entitlements. Ideally, the system would increase or decrease numbers of entitlements for various venues dynamically and in substantial real time at differing venues. Ideally, the system 1 would predict the required number of entitlements based on optimizing the revenues received by venues that require additional gaming machine entitlements and also to those venues that are prepared or can to release (temporarily) gaming machine entitlements for prescribed periods.

[9] US 20070155490 Al (Dl) discloses a system and method for casino reconfiguration. Specifically, Dl discloses the ability to reconfigure available games on a plurality of gaming machines. In one embodiment Dl provides a user interface for use by an administrative user for manually configuring the available games. In other embodiments, Dl discloses automated reconfiguration of game availability in accordance with a schedule or the like.

[10] However, Dl differs from the embodiments described below in several important respects.

[11] Firstly, Dl is directed to the reconfiguration of available games and game types from a stored data base of games within the venue to electronic gaming machines within the same venue whereas the claimed invention as provided herein relates to the automated revenue optimising dynamic reallocation of gaming entitlements based on revenue predicting models for future timeslots based on historical revenue gaming data.

[12] As such, Dl is directed to a different problem to the claimed invention in that Dl is directed to venue specific localised game-type availability reconfiguration whereas the claimed invention relates to statewide dynamic reallocation of gaming entitlements across a data network.

[13] Furthermore, reallocating gaming entitlements is not obvious at least for the reasons alluded to above in that currently regulatory requirements maintain predefined numbers of gaming machine entitlements within a region or jurisdiction. In other words, whereas gaming availability reconfiguration may be known, at least manually in accordance with human operator input, gaming entitlement reallocation between venues is not, even manually. As such, reallocating gaming machine entitlements across regions in the manner claimed is counterintuitive when considering the present location/venue locked arrangements.

[14] Furthermore, reconfiguring gaming systems to allow for the dynamic reallocation of gaming entitlements involves technical difficulties beyond what may be referred to as routine steps, workshop improvements and the like. Specifically, the reconfiguration of gaming machines, enablement controllers and gaming systems to make allowance for being able to dynamically transfer game entitlements has in itself several technical obstacles which must be overcome. [15] Furthermore, the claimed invention generates revenue predictive models in accordance with at least historical revenue data. Other data may also be used by the system such as historical usage and demographic data. Furthermore, the predictive revenue models may be generated by a machine learning training algorithm having as input the multivariable historical revenue data and other data which may be utilised by a revenue predicting neural network for predicting future revenue for future timeslots for venues/machines.

[16] Dl refers loosely to "historical" data in places but does not fairly teach or disclose how such historical data is used. Furthermore, Dl never refers to historical revenue data. Furthermore, Dl does not refer to generating future revenue predictive models for predicting future revenue of gaming machines/venues let alone the dynamic reallocation of gaming machine entitlements across a data network automatically in accordance with the future revenue predictive models in anticipation of optimising revenue.

[17] Furthermore, Dl does not disclose sophisticated revenue optimisation as described herein in accordance with an embodiment preferably utilising a revenue predicting neural network trained using a machine learning training algorithm having as input historical revenue data and other data such as historical usage data, historical demographic data and other data which may be extended to even taking into account whether data.

[18] As can be appreciated, the ability of the sophisticated revenue optimisation ability of the system to optimise revenue in accordance with a potential plurality of data inputs (such as historical revenue, usage, demographic and other data) is able to tease out complex revenue optimising strategies that extend far beyond the simplistic gaming availability reconfiguration of Dl such as wherein the complexity of Dl ostensibly extends only to the level of gaming availability reconfiguration in accordance with a schedule or the like. Furthermore, the claimed system is able to take into account variable covariance as opposed to the input variables of Dl which are treated in isolation (such as user preferences, time schedule and the like) wherein, for example, the system 1 is able to optimise revenue in accordance with both historical and predicted, revenue, usage, demographic and other data.

[19] The present invention seeks to provide a system that dynamically allocates gaming machine entitlements between venues in order to optimize the overall performance of a wide area network. This network may extend statewide, which will overcome or substantially ameliorate at least some of the deficiencies of the prior art, or to at least provide an alternative.

[20] It is to be understood that, if any prior art information is referred to herein, such reference does not constitute an admission that the information forms part of the common general knowledge in the art, in Australia or any other country. Summary of the Disclosure

[21] There is provided a method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation. In general terms, there is provided a system that determines the appropriate number of electronic gaming machines that are required to be activated at a venue via the transfer of entitlements from other venues. In embodiments, the system allows venues to programmatically release their entitlements for use by other venues based on dynamic revenue optimization performed by the system. Furthermore, in embodiments, the system is able to identify and target electronic gaming machines within venues for enablement. The venues to which entitlements are transferred must have a corresponding number of "dormant" or nonoperative electronic gaming machines which become activated for use when the entitlements are transferred to that venue. Similarly the venue that is transferring entitlements to another venue will have the corresponding electronic gaming machine made dormant until the corresponding entitlement is reinstated to that venue

[22] In embodiments, system will facilitate automated electronic voting for entitlements between participating venues and automated determination of real time entitlement pricing for the entitlements being transferred across a computer network between gaming machines of participating venues.

[23] Ideally, the system uses sophisticated neural network algorithms which have been trained using multivariable inputs including historical gaming data such as demographic, player behaviour, size of wager and past performance of players and the characteristics of the EGMs to generate a dynamic model of each participating venue which will allow for optimised entitlement reallocation including, in embodiments, their location on the gaming floor and game type to be installed in the venue for optimum revenue performance. As the availability and allocation of entitlements will be optimized for the entire network of participating venues and EGMs based on neural network computations, all dynamic allocation and acceptance will be venue neutral in that the network will dynamically and automatically revenue optimize itself, even to the possibility of voting and allocation between gaming machines at the same venue.

[24] As such, with the foregoing in mind, according to one aspect, there is provided a gaming machine entitlement reallocation centralised server implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation, the method comprising: receiving historical gaming data comprising at least historical gaming machine revenue data from a plurality of gaming machines at a respective plurality of gaming venues: generating a time series revenue predictive model for the at least one of the plurality of gaming machines and gaming venues; calculating predicted revenue for the at least one of the plurality of gaming machines and gaming venues using the time series revenue predictive model for a plurality of future time slots; calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues using the predicted revenue; and reallocating a plurality of gaming machine entitlement across the data network for at least a subset of each the plurality of future time slots.

[25] Calculating predicted revenue may comprise training a revenue predictive neural network utilising a machine learning training algorithm.

[26] Training the revenue predictive neural network may comprise optimising at least one of a structure of the neural network and weightings of neurons of the neural network.

[27] Calculating revenue optimising gaming machine entitlement reallocations further may comprise receiving substantially real time gaming data from the plurality of gaming machines and calculating the revenue optimising gaming machine entitlement reallocations further in accordance with the real time gaming data.

[28] The historical gaming data further may comprise historical usage data and wherein the machine learning training algorithm has as input the historical usage data.

[29] The method may further comprise generating a timeseries usage predictive model for the at least one of the plurality of gaming machines and gaming venues using the machine learning training algorithm and calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues further using the usage predictive model.

[30] The historical usage data may comprise time period utilisation ratio data and wherein the machine learning training algorithm has as input the time period utilisation ratio data.

[31] The historical gaming data further may comprise historical demographic data and wherein the machine learning training algorithm has as input the historical demographic data.

[32] The method may further comprise generating a demographic predictive model for the at least one of the plurality of gaming machines and gaming venues using the machine learning training algorithm and calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues further using the demographic predictive model.

[33] The historical demographic data may comprise at least one of the race, gender and age demographic data.

[34] The demographic data may be received from a gaming machine user interface. [35] The gaming machine user interface may be configured for receiving the data from a user identification gaming card.

[36] The gaming machine user interface may comprise a demographic identifying biometric reader.

[37] The biometric reader may comprise an image capture device configured for identifying at least one of skin colour, hair colour and facial features.

[38] Calculating revenue optimising gaming machine entitlement allocations may comprise feeding back candidate gaming machine entitlement reallocations into the neural network.

[39] The method may further comprise generating the candidate gaming machine entitlement reallocations in accordance with a revenue optima finding gradient ascent algorithm.

[40] Reallocating a plurality of gaming machine entitlements may comprise updating a relationship between an entitlements table and a venues table of a database of a server in operable communication with the plurality of gaming machines.

[41] Reallocating a plurality of gaming machine entitlements may comprise transmitting gaming machine entitlement data across a data network to the plurality of gaming machines.

[42] The gaming machine entitlement data may comprise cryptographically signed gaming machine digital certificates.

[43] Reallocating a plurality of gaming machine entitlements may comprise transmitting entitlement reallocation instruction data to an entitlement authority server.

[44] According to another aspect, there is provided a gaming machine entitlement reallocation server for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation, the server comprising: a processor for processing digital data; a memory device for storing digital data including computer program code, the memory device being in operable communication with the processor; a data network interface for communicating with a plurality of gaming machines across a computer network; and a database comprising a historical gaming data table, wherein the computer program code comprises a plurality of software modules comprising: a gaming data receiver module configured for receiving gaming data from the plurality of gaming machines across a computer network and populating the historical gaming data table; and a revenue optimising module configured for generating a gaming entitlement reallocation for revenue optimisation in accordance with the historical gaming data from the historical gaming data table; and an entitlement reallocation module configured for reallocating gaming machine entitlements across the data network.

[45] The revenue optimising module may comprise a revenue predictive neural network. [46] The revenue optimising module further may comprise a machine learning training algorithm configured for training the revenue predictive neural network in accordance with the historical gaming data.

[47] The machine learning training algorithm may be configured for optimising at least one of the structure and neuron weightings of the revenue predictive neural network.

[48] Other aspects of the invention are also disclosed.

Brief Description of the Drawings

[49] Notwithstanding any other forms which may fall within the scope of the present invention, preferred embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

[50] Figure 1 shows a system dynamically reallocating gaming machine entitlements;

[51] Figure 2 shows exemplary historical revenue, usage and demographic data for utilisation by the system of Figure 1 in accordance with an embodiment of the present disclosure;

[52] Figure 3 shows a method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation in accordance with an embodiment of the present disclosure; and

[53] Figure 4 shows an exemplary functional/data schematic for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation in accordance with an embodiment of the present disclosure.

Description of Embodiments

[54] For the purposes of promoting an understanding of the principles in accordance with the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Any alterations and further modifications of the inventive features illustrated herein, and any additional applications of the principles of the disclosure as illustrated herein, which would normally occur to one skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the disclosure.

[55] Before the structures, systems and associated methods relating to the gaming machine entitlement reallocation centralised server and associated server-implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation are disclosed and described, it is to be understood that this disclosure is not limited to the particular configurations, process steps, and materials disclosed herein as such may vary somewhat. It is also to be understood that the terminology employed herein is used for the purpose of describing particular embodiments only and is not intended to be limiting since the scope of the disclosure will be limited only by the claims and equivalents thereof.

[56] In describing and claiming the subject matter of the disclosure, the following terminology will be used in accordance with the definitions set out below.

[57] It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[58] As used herein, the terms "comprising," "including," "containing," "characterised by," and grammatical equivalents thereof are inclusive or open-ended terms that do not exclude additional, unrecited elements or method steps.

[59] It should be noted in the following description that like or the same reference numerals in different embodiments denote the same or similar features.

System 1 for dynamic gaming machine entitlement reallocation for revenue

optimisation

[60] Turning now to figure 1, there is shown a system 1 for dynamic gaming machine entitlement reallocation for revenue optimisation. As will become apparent from the ensuing description, as opposed to conventional arrangements were gaming machine entitlements are fixed by location/venue, the system 1 is configured for the dynamic reallocation of gaming machine entitlements across venues and different geographic locations so as to optimise their usage and revenue.

[61] Specifically, and as will be described in further detail below, the system 1 is configured to generate a timeseries revenue predictive model in accordance with historical game data, preferably utilising a neural network predictive model trained on the historical game data. In this manner, for future timeslots, the system 1 is able to predict the expected revenue takings for the gaming machines 11 or venues 10 so as to be able to reallocate the gaming entitlements in advance of the timeslots so as to optimise the revenue of the machines 11 or venues 10 for the future timeslots.

[62] Now, as can be seen, the system 1 comprises a plurality of gaming machines 11 at a plurality of venues 10. In accordance with existing arrangements, gaming machines 11 are enabled or disabled in accordance with gaming machine entitlements. In a preferred embodiment, the gaming machines 11 are electronic gaming machines such that their enablement and disablement in accordance with gaming entitlement reallocation by the system 1 may occur autonomously.

[63] Generally, each venue 10 may be allocated a set number of gaming entitlements for a set number of gaming machines. For example, Venue A operator may have been allocated three gaming entitlements and may therefore activate three gaming machines 11 accordingly. Further generally, gaming entitlements are venue specific and therefore may be used on various gaming machines at the venue 10 irrespective of the type of gaming machines or games provided thereon. However, it should be appreciated that within the purposive scope of the embodiments described herein of the dynamic reallocation of gaming entitlements, the gaming entitlements may further be gaming machine or game type specific. However, for illustrative convenience, the embodiment wherein gaming entitlements are venue specific will be used herein.

[64] The number of gaming machines 11 exceeds the number of available gaming entitlements. In this manner, as gaming entitlements are transferred across the data network 26 in the manner described herein, certain of the gaming machines 11 without corresponding gaming entitlements are deactivated so as to be rendered inoperable to gamers. In embodiments, the system 1 may be configured for determining the number of gaming machines at each venue 10 which may be conducted in embodiments by queries via the computer network 26. In this manner, when determining an optimised entitlement reallocation, the system 1 knows the capacity of available gaming machines at particular venues.

[65] Each gaming machine 11 may comprise computer componentry including for the purposes of allowing gameplay and gaming entitlement verification and activation. For example, each gaming machine 11 may comprise a processor for processing digital data, the processor being in operable communication with a memory device for storing digital data including computer program code. In this manner, the processor, executing the computer program code retrieved from the memory device, may implement the features and functionality described herein, including in allowing gameplay, gaming entitlement verification, gaming machine activation and the like.

[66] Specifically, as can be seen from figure 1, each gaming machine 11 may comprise a gameplay controller 14 comprising the requisite hardware and software components for allowing gameplay. The gaming machine 11 may further comprise a user interface 15 for interfacing with the user. In embodiments, the user interface 15 is configured for receiving demographic data as will be described in further detail below.

[67] Furthermore, each gaming machine 11 may comprise an enablement controller 13. The enablement controller 13 is configured for verifying whether a particular gaming machine 11 has an associated valid gaming machine entitlement so as to control the operability of the gaming machine 11.

[68] There are differing manners by which gaming machine enablement in accordance with gaming machine entitlements may be performed within the purposive scope of the embodiments described herein. For example, the enablement controller 13 may be a hardware dongle or the like interfacing with the gaming machine so as to enable or disable the operation thereof. In further embodiments, the enablement controller 13 may be software implemented within the computer program code of the gaming machine 11 itself.

[69] In embodiments, and as is shown for Venue A for illustrative purposes, the enablement controller 13 may be a standalone network connected hardware device connected to a plurality of gaming machines 11.

[70] In embodiments, the enablement controller 13 may be configured with the entitlements. For example, the gaming machine entitlements may comprise cryptographic digital certificates issued by an entitlement authority. In this manner, the enablement controller 13 is configured for verifying the authenticity of the provided cryptographic digital certificate so as to enable the operation of the associated gaming machine 11.

[71] In other embodiments, the enablement controller 13 may communicate with an entitlement authority server 16 so as to be able to receive or ascertain the validity of digitally issued gaming machine entitlements across a data network 26.

[72] Now, as will be described in further detail below, the system 1 comprises the modification of the enablement controller 13 so as to allow for the dynamic reallocation of entitlements as described herein.

[73] Specifically, as can be seen, the system 1 further comprises a server 2 for dynamically reallocating gaming machine entitlements.

[74] In one embodiment, the enablement controller 13 is issued with digital gaming machine entitlements dynamically across the data network 26. In alternative embodiments, the digital gaming machine entitlements are held in a database or memory device of the server 2 such that the enablement controller 13 is able to query the allocation of an entitlement to an associated gaming machine 11. It should be appreciated that other technical manners for the dynamic allocation of gaming machine entitlements for a plurality of gaming machines for a plurality of venues across a data network may be employed within the purposive scope of the embodiments described herein.

[75] Now, as can be seen, the server 2 may comprise a database 45. In the embodiments described herein the database 45 is a relational database.

[76] As such, the database 45 may comprise an entitlements table 24 configured for storing the gaming machine entitlements. As alluded to above, this described embodiment is the embodiment wherein the server 2 holds the cryptographic gaming machine entitlement digital certificates were a representation thereof for querying by the various enablement controllers 13 of the various venues 10. However, in other embodiments as described above, the server 2 may alternatively be configured for digitally transmitting the cryptographic gaming machine entitlement digital certificates across the data network 26 to the relevant enablement controllers 13 or gaming machines 11. [77] However, for the embodiment wherein the database 45 stores the cryptographic gaming machine entitlement digital certificates, the entitlements table 24 may be stored in relation to an entitlements holder table 25 so as to record ownership of the various gaming machine entitlements.

[78] For example, for a venue operator having 50 entitlements, the venue operator may register all of or a subset the entitlements with the server 2 so as to allow for the dynamic reallocation to other operators for optimising the revenue of the venue operator.

[79] In embodiments, the operator may impose restrictions as to when the made available entitlements may be reallocated, such as restrictions on the number of entitlements that may be simultaneously reallocated, times of day at which entitlements may be reallocated and the like. For example, an entitlement owner may specify that a maximum of 10 entitlements may be reallocated on Tuesdays whereas for the remainder of the week up to 20 entitlements may be simultaneously reallocated.

[80] In further embodiments, the entitlement owners may impose revenue restrictions. For illustrative convenience, the owners are referred to herein as transferor owners and transferee owners.

[81] For example, transferor owners may stipulate that the system 1 may only reallocate their gaming entitlements if the predicted revenue expected to be attained from such a reallocation would increase the predicted revenue were the entitlement left in place. For example, a gaming owner may impose a limitation that a gaming entitlement be transferred by the system 1 only should the expected revenue be increased by 10% from the reallocation. In alternative embodiments, such a revenue threshold increase may be uniformly set across the system 1.

[82] Conversely, a transferee owner may stipulate that their venue should only receive an entitlement if it would increase their revenue by a threshold, such as more than $10 for example.

[83] In embodiments, the system 1 may utilise a fixed or a determined dynamic revenue split between transferor and transferee owners. For example, for a gaming entitlement that is predicted by the system 1 as only having the ability to generate $50 for a future timeslot, but, if transferred to another venue would have the ability to generate $100 for a future timeslot, the transferor owner may be recompensed (such as by way of automated electronic funds transfer by the system) $55 (being $50 plus the expected revenue increase threshold of 10%) and the transferee recompensed $10, being the minimum threshold amount specified by the transferee.

[84]

[85] In embodiments, the system 1 is configured for automated entitlement voting and dynamic entitlement pricing determination. For example, for a potentially available entitlement, the system 1 may allow for automated voting by venues wherein venues place bids in accordance with bid constraints, such as maximum bid amounts, total amount bid, gaming entitlements on hand, revenue expectations and the like.

[86] Furthermore, the system 1 may be configured for dynamically adjusting entitlement pricing in accordance with demand. For example, for an entitlement which has received a large number of bids by meeting the bid constraints of a plurality of gaming venues, the system 1 may allocate a higher pricing or revenue split as opposed to an entitlement that has not. For example, for a highly bid for entitlement, the system 1 may require that the receiving venue pay $10 per timeslot or 90% commission of the revenue as opposed to a less bid for entitlement wherein the system 1 may require that the receiving venue pay $5 dollars per timeslot or 50% commission of the revenue received.

[87] Such entitlement owner configuration may be performed by way of a web interface served by the server 2, for example.

Method 30 for dynamic gaming machine entitlement reallocation

[88] Turning now to figure 3, there is shown a method 30 for dynamic gaming machine entitlement reallocation.

[89] The method comprises step 31 wherein the server 2 is configured for receiving historical gaming data from a plurality of gaming machines 11 at a respective plurality of gaming venues 10.

[90] In a preferred embodiment, the historical gaming data comprises at least historical gaming machine revenue data. As will be described in further detail below, the historical gaming machine revenue data is utilised by the server 2 for predicting future revenue so as to be able to optimise entitlement allocations accordingly.

[91] Each gaming machine 11 may be configured for transmitting the gaming data to the server 2 either in substantial real time or at predetermined intervals.

[92] Turning now to figure 2A there is shown an exemplary time series historical revenue data 27 for two venues 10. Specifically, the revenue amount, ranging from zero dollars to $1000 is shown for two exemplary venues 10 wherein Venue A is shown by the unshaded bar graph and Venue B is shown by the shaded bar graph.

[93] As can be seen, the historical revenue dated 27 is divided into a series of timeslots comprising a representative 12 hour period starting at 1 PM in the afternoon for example. In the embodiment shown, the timeslots are hours of the day. However, it should be appreciated that differing timeslots may be equally applicable within the purposive scope of the embodiments described herein.

[94] As can be seen, the revenue for venue B optimises between 4 and 5 PM in the afternoon whereas the revenue for venue A has a minor peak between the hours of 4 and 5 PM and a major peak between the hours of 10 and 11 PM in the evening. [95] In further embodiments, the historical gaming data may comprise other data which may be used for the purposes of optimising revenue. In one embodiment, the historical gaming data may further comprise gaming machine usage data representing a timeseries utilisation ratio such as from 0 to 1 per machine.

[96] Specifically, and as alluded to above, for a venue comprising three gaming machines, a gaming machine utilisation ratio of 1.5 typically means that for any time period (such as an hour of the day) a first one of the gaming machines is fully used, a second one of the gaming machines is used for half the time and a third one of the gaming machines is not utilise at all.

[97] Figure 2B shows exemplary timeseries utilisation ratio data for two venues. In this exemplary embodiment, Venue B comprises 10 gaming machines and venue A comprises six gaming machines. As such, as can be seen, the utilisation for venue A peaks at a value of 6 at 7 PM meaning that all of the six gaming machines are fully utilised. Furthermore, the utilisation for Venue B peaks at 6 PM when all 10 gaming machines are being used.

[98] As can be appreciated at 6 PM, there is an opportunity to transfer a gaming entitlement from venue A to venue B and similarly, at 7 PM, there is an opportunity to transfer a gaming entitlement from venue B to venue A.

[99] Such utilisation data may be further utilised by the system 1 for optimising revenue. For example, the system 1 may avoid allocating entitlements to a venue having insufficient utilisation and vice versa.

[100] In further embodiments, the system 1 may be configured to optimise a plurality of parameters such as, in this embodiment, revenue and usage which, in further embodiments may be hierarchical optimisation being optimising revenue then usage. Other combinations of data input may be utilised including those other data inputs which are described herein and others.

[101] In further embodiments, the historical gaming machine data may comprise demographic data.

Demographic data may further be utilised by the system for optimising revenue.

[102] Specifically, turning to Figure 2C there is shown an exemplary demographic correlation data being for Asian males (A), Asian females (B), Caucasian males (C)) and Caucasian females (D) at two venues.

[103] As can be seen, there is a strong relation and venue A for Asian males indicating that at venue A, Asian males are high rollers whereas Asian females are less so.

[104] As can also be seen, Asian males are high rollers at both venue A and venue B whereas Asian females tend to spend more and venue A as compared to venue B.

[105] As such, in this manner, by ascertaining a demographic of the user, the system 1 may further optimise revenue by reallocating entitlements in accordance with demographic. [106] In the exemplary embodiment described above, race demographic is described as one example. However, it should be appreciated that other demographics may be utilised also, such as age, income, residence location and more.

[107] As alluded to above, in one embodiment, demographic information may be received via the user interface 15 of the gaming machine 11. For example, the user may insert a gaming card or the like comprising personal details which may be utilised for the purposes of ascertaining demographic.

[108] In further embodiments the gaming machine 11 may comprise a biometric reader configured for reading bio variables for the purposes of identifying demographics. For example, gaming machines in 11 may be equipped with image recording devices to recognise skin colour, hair colour, facial features and the like. Voice sampling inputs may recognise language.

[109] In additional or alternative embodiments, biometric data may be received from other systems such as venue entry gates, turnstiles, front desk reception information and the like.

[110] In further embodiments other types of historical gaming data may be utilised within the purposive scope of the embodiments described herein. For example, in embodiments such other data may represent individual identity data. For example, should a group of individuals be recognised from the historical gaming data as being particularly highrolling, the system 1 may optimise revenue by allocating entitlements coinciding with visits by the individuals.

[Ill] Further other data may also comprise weather information wherein, for example, a correlation between ambient temperature and spending patterns may be ascertained by the system 1 wherein, for example, gaming entitlements may be reallocated to venues experiencing fair weather as opposed to venue is experiencing coldweather.

[112] As can be seen from figure 1, the historical gaming data 17 is stored within the database 45. In an embodiment, the raw data may be stored within the database 45. However, in other embodiments, calculated statistical derivatives may be stored rather, such as revenue per hour and the like.

[113] Turning again to figure 3, at step 32 of method 30, the system 1 is configured for generating a timeseries revenue predictive model for each of the gaming machines 11 or venues 10.

[114] Specifically, as can be seen from figure 1, the database 11 may comprise the timeseries revenue predictive model 21. The timeseries revenue predictive model is configured for predicting revenue for future timeslots for gaming machines 11 or venues 10.

[115] Specifically, the timeseries revenue predictive model having being generated or trained in accordance with historical gaming data may be able to predict the revenue takings of a particular gaming machine and a future timeslot. For example, for a gaming machine 11 or venue 10, the timeseries revenue predictive model 21 may be able to predict that the revenue taking at 2 PM in the afternoon would be $500.

[116] It should be noted that in embodiments, the system 1 may additionally generate predictive models for other historical data type inputs. For example, the system 1 may additionally generate a usage predictive model so as to be able to optimise revenue in accordance with both predicted revenue and usage. Furthermore, the system may additionally generate a demographic prediction model so as to, for example, predict a percentage of a demographic type at a future time.

[117] Now, in a preferred embodiment, the system 1 utilises a machine learning technique for the purposes of generating the timeseries revenue predictive model. Specifically, as can be seen, the software modules 3 may comprise a revenue optimiser module 5 configured for machine learning using the historical gaming data for the purposes of generating the timeseries revenue predictive model 21.

[118] In one embodiment, the revenue optimiser 5 comprises a machine learning training algorithm 6 configured to train a revenue predicting neural network 7.

[119] Specifically, the system 1 may undergo a training phase wherein historical gaming data is input into the machine learning training algorithm 6. For example, at least one of the historical revenue timeseries data, historical usage timeseries data, historical demographic timeseries data and other historical timeseries data may input into the machine learning training algorithm 6. It should be appreciated that the training may be a once off training or a continual training process.

[120] The machine learning training algorithm 6 may train both the weightings of the neurons of the revenue predicting neural network 7 and the structure thereof. For example, the revenue predicting neural network 7 may comprise input and output neurons interconnected by way of hidden neurons for which the connections, number of hidden neurons and the like is configured by the machine learning training algorithm 6 and the weightings thereof adjusted so as to optimise the revenue predicting ability of the timeseries revenue predictive model 21.

[121] As such, turning again to figure 3, the method 30 may comprise step 33 wherein the system 1 calculates predicted revenue for each gaming machine/venue for a plurality of future timeslots.

[122] Specifically, having trained the timeseries revenue predictive model 21, the revenue predicting neural network 7, by utilising the data from the timeseries revenue predictive model 21 is configured for predicting revenue at future timeslots. For example, for a future 24-hour period, the revenue predicting neural network 7 may be configured for calculating the expected revenue for each hour for each gaming machine/venue. [123] Now, turning again to figure 3, the method 30 may further comprise step 34 wherein the system 1 is configured for calculating revenue optimising gaming machine entitlement allocations in accordance with the calculated predicted revenue for the future timeslots.

[124] For example, and referring to figure 2, the revenue optimising entitlement allocator module 8 may recognise that, since the revenue for venue A peaks at 10 PM in the evening whereas the predicted revenue for venue B is somewhat moderate at this time, there is opportunity to allocate underutilised entitlements from Venue B to Venue A.

[125] Furthermore, at 4 PM in the afternoon, both in the revenue for Venue A and Venue B has a peak but, because at this time period the predicted usage ratio for Venue A is less than that of Venue B, the system 1 may allocate additional entitlements from Venue A to Venue B.

[126] It should be noted that, in embodiments, and as will be described in further detail below, the revenue optimising entitlement allocator 8 may utilise candidate entitlement reallocation data as a feedback into the revenue predicting neural network 7. In this way, candidate reallocations may be tested by the neural network 7 for optimisation. Turning again to figure 3, at step 5 of method 30, the system 1 is configured for reallocating a plurality of gaming machine entitlements across a data network 26 in accordance with the optimised entitlement allocation calculation calculated by the revenue optimising entitlement allocator 8.

[127] As such, as can be seen from figure 1, the software models 3 may comprise an entitlement reallocator 9 configured for dynamically reallocating the gaming entitlements. As alluded to above, in one embodiment, the entitlement reallocated 9 may dynamically transmit the digitally encrypted gaming machine entitlement certificates to each enablement controller 13 across the data network 26. In other embodiments, the entitlement reallocated 9 may update the relation between the entitlements 24 the database 45 and the venues as is recorded within venues/machine table 22. In this manner, each enablement controller 13 is configured for querying the current venue/machine allocation.

[128] As can be appreciated, the ability of the system 1 to optimise revenue in accordance with a potential plurality of data inputs utilising prediction is able to tease out complex revenue optimising strategies that would otherwise be beyond the scope or capabilities of human endeavour alone. For example, in one embodiment, should a gaming Venue B be geographically located adjacent a factory which pays its factory workers fortnightly, the system 1 may dynamically allocate gaming entitlements in advance for a three-hour period post close of business on fortnightly paydays so as to dynamically enable additional gaming machines at the venue proximate the factory optimises revenue. Exemplary functional data schematic

[129] Turning now to figure 4, there is shown an exemplary functional data schematic 44 for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation.

[130] As can be seen, the schematic 44 is roughly divided in columns into data inputs, computing functions and computed data.

[131] As is shown, historical data is fed into the machine learning training algorithm 6. For example, historical gaming data 40 from Venue A and historical gaming data 41 from Venue B may be input into the machine learning training algorithm 6.

[132] The machine learning training algorithm 6 is configured for utilising a machine learning technique, such as a neural network structure and weighting optimisation algorithm for the purposes of generating trained data, being the timeseries revenue predictive model 21. As described above, the timeseries revenue predictive model 21 may be stored within the database 45 of the server 2.

[133] As alluded to above, other timeseries predictive models may be generated for other input variable data types such as wherein the system 1 generates usage and demographic timeseries predictive models for each of the venues.

[134] Thereafter, the trained data/timeseries revenue predictive model 21 is utilised by the revenue predicting neural network 17 to generate future timeseries revenue predictive models for each venue/machine.

[135] As can be seen, the revenue predicting neural network 7 has generated a timeseries revenue predictive model 36 for Venue A and a timeseries revenue predictive model 37 for Venue B.

[136] In embodiments, revenue predicting neural network 7 may take into account live data, being data received from the gaming machines 11 and substantial real time.

[137] For example, should a bus load of Asian males unexpectedly disgorge into a venue, and whereas the system 1 has identified a higher revenue correlation for the Asian male demographic, the revenue predicting neural network 7, receiving such information in such real time (such as wherein the Asian males insert their gaming cards into the gaming machines 11 such that there are demographic is able to be recognised) may react accordingly in substantial real time or for future timeslots, especially where the timeslots have a short time period, such as per minute.

[138] Now, having generated the future timeseries revenue predictive models 36 and 37, the revenue optimising entitlement allocator 8 is configured for generating optimised entitlement allocations 38 accordingly so as to optimise revenue.

[139] As alluded to above, the entitlement reallocation optimisation may comprise candidate reallocation feedback 44 which is fed back into the revenue predicting neural network so as to calculate revenue for each of the candidate reallocations so as to ascertain an optimised candidate reallocation.

[140] For example, various reallocation permutations and combinations may be proposed as candidate reallocations and fed back into the revenue predicting neural network 7 wherein the revenue predicting neural network 7 may test each proposed candidate reallocation in turn or alternatively utilise gradient descent optima finding technique so as to reduce the potential number of candidate reallocation permeation and combinations.

[141] Having settled upon and optimised entitlement reallocation, the entitlement reallocated module 9 is configured for dynamically reallocating the entitlements across the data network 26 accordingly which, in embodiments, may comprise generating entitlement reallocation instruction output data 39 configured for the purposes of updating the relation between the entitlements 24 and the venues 22 within the database 45 or alternatively dynamically issuing the cryptographic gaming machine entitlements across the data network 26.

[142] In embodiments, the entitlement authority server 16 may act as a "middleman" wherein the entitlement reallocation instructions forwarded by the server 2 to the entitlement authority server 16 and wherein the enablement controller symbol 13 rather communicate with entitlement authority server 16 such that the entitlement authority server 16 may maintain the issuance of the cryptographic gaming machine entitlements.

[143] It should be noted that the various software modules 3 and the data stored within the database 45 is provided in accordance with a particular embodiments only and it should be noted that variations may be made to the types and number of software modules 3 and the types and arrangement of data stored within the database 45 within the purposive scope of the embodiments described herein of dynamically reallocating gaming machine entitlements for revenue optimisation.

Dynam ic ga mi ng type rea llocation

[144] In further embodiments, the system 1 may be further configured for further dynamically deploying differing gaming machine game types for revenue optimisation.

[145] Specifically, the memory device of each gaming machine 11 may comprise a plurality of game types. Additionally or alternatively, the database 45 of the server 2 may comprise a computer program code for the plurality of game types which is dynamically transmitted across the data network 26 to each relevant gaming machine 11 accordingly.

[146] As such, in a similar manner as described above for the optimisation of revenue in accordance with the dynamic reallocation of gaming entitlements, the system 1 may furthermore be configured for optimising revenue further in accordance with gaming type reallocations. [147] For example, the historical gaming data may further comprise gaming type data representing the type of game being played on a particular gaming machine. In this regard, when training, the machine learning training algorithm 6 may further identify correlations between demographic and game type for example.

[148] As such, when issuing gaming entitlements to enable or disable gaming machines, the system 1 may further dynamically reallocate available game types accordingly. In this manner, when selectively enabling and disabling gaming machines in 11 a particular venues 10, the system 1 may further configure the types of games presented on the gaming machines 11 to further optimise revenue.

[149] In embodiments, the types of games may further be optimised in accordance with gaming machine location wherein, for example, the system 1 determines a correlation between gaming revenue at a particular time for a particular game type at gaming machines located near the entrance of the gaming venue.

I nterpretation

Wireless:

[150] The invention may be embodied using devices conforming to other network standards and for other applications, including, for example other WLAN standards and other wireless standards. Applications that can be accommodated include IEEE 802.11 wireless LANs and links, and wireless Ethernet.

[151] In the context of this document, the term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. In the context of this document, the term "wired" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a solid medium. The term does not imply that the associated devices are coupled by electrically conductive wires.

Processes:

[152] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "processing", "computing", "calculating", "determining", "analysing" or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

Processor:

[153] In a similar manner, the term "processor" may refer to any device or portion of a device that processes electronic data, e.g., from registers and/or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and/or memory. A "computer" or a "computing device" or a "computing machine" or a "computing platform" may include one or more processors.

[154] The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. The processing system further may include a memory subsystem including main RAM and/or a static RAM, and/or ROM.

Computer-Readable Medium :

[155] Furthermore, a computer-readable carrier medium may form, or be included in a computer program product. A computer program product can be stored on a computer usable carrier medium, the computer program product comprising a computer readable program means for causing a processor to perform a method as described herein.

Networked or Multiple Processors:

[156] In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. The one or more processors may form a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[157] Note that while some diagram(s) only show(s) a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

Additional Embodiments:

[158] Thus, one embodiment of each of the methods described herein is in the form of a computer- readable carrier medium carrying a set of instructions, e.g., a computer program that are for execution on one or more processors. Thus, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium. The computer-readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause a processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

Carrier Medium :

[159] The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an example embodiment to be a single medium, the term "carrier medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "carrier medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media.

Implementation :

[160] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.

Means For Carrying out a Method or Function

[161] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a processor device, computer system, or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

Connected

[162] Similarly, it is to be noticed that the term connected, when used in the claims, should not be interpreted as being limitative to direct connections only. Thus, the scope of the expression a device A connected to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. "Connected" may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

Embodiments:

[163] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments.

[164] Similarly it should be appreciated that in the above description of example embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description of Specific Embodiments are hereby expressly incorporated into this Detailed Description of Specific Embodiments, with each claim standing on its own as a separate embodiment of this invention.

[165] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Different Instances of Objects

[166] As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

Specific Details

[167] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Terminology

[168] In describing the preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. However, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar technical purpose. Terms such as "forward", "rearward", "radially", "peripherally", "upwardly", "downwardly", and the like are used as words of convenience to provide reference points and are not to be construed as limiting terms.

Comprising and Including

[169] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" are used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

[170] Any one of the terms: including or which includes or that includes as used herein is also an open term that also means including at least the elements/features that follow the term, but not excluding others. Thus, including is synonymous with and means comprising.

Scope of Invention

[171] Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as fall within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

[172] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms.

I ndustria l Applicability

[173] It is apparent from the above, that the arrangements described are applicable to the gaming system industries.

Claims

1. A gaming machine entitlement reallocation centralised server implemented method for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation, the number of gaming machines exceeding the number of entitlements, the method comprising:

receiving historical gaming data comprising at least historical gaming machine revenue data from a plurality of gaming machines at a respective plurality of gaming venues;

generating a time series revenue predictive model for the at least one of the plurality of gaming machines and gaming venues using the historical gaming data;

calculating predicted revenue for the at least one of the plurality of gaming machines and gaming venues using the time series revenue predictive model for a plurality of future time slots; calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues using the predicted revenue; and

dynamically reallocating a plurality of gaming machine entitlement across the data network for at least a subset of each the plurality of future time slots so as to selectively enable and disable a corresponding number of a subset of the gaming machines.

2. A method as claimed in claim 1, wherein calculating predicted revenue comprises training a revenue predictive neural network utilising a machine learning training algorithm.

3. A method as claimed in claim 2, wherein training the revenue predictive neural network comprises optimising at least one of a structure of the neural network and weightings of neurons of the neural network.

4. A method as claimed in claim 1, wherein calculating revenue optimising gaming machine entitlement reallocations further comprises receiving substantially real time gaming data from the plurality of gaming machines and calculating the revenue optimising gaming machine entitlement reallocations further in accordance with the real time gaming data.

5. A method as claimed in claim 1, wherein the historical gaming data further comprises historical usage data and wherein the machine learning training algorithm has as input the historical usage data.

6. A method as claimed in claim 5, further comprising generating a timeseries usage predictive model for the at least one of the plurality of gaming machines and gaming venues using the machine learning training algorithm and calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues further using the usage predictive model.

7. A method as claimed in claim 5, wherein the historical usage data comprises time period utilisation ratio data and wherein the machine learning training algorithm has as input the time period utilisation ratio data.

8. A method as claimed in claim 1, wherein the historical gaming data further comprises historical demographic data and wherein the machine learning training algorithm has as input the historical demographic data.

9. A method as claimed in claim 8, further comprising generating a demographic predictive model for the at least one of the plurality of gaming machines and gaming venues using the machine learning training algorithm and calculating revenue optimising gaming machine entitlement reallocations for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues further using the demographic predictive model.

10. A method as claimed in claim 8, wherein the historical demographic data comprises at least one of the race, gender and age demographic data.

11. A method as claimed in claim 8, wherein the demographic data is received from a gaming machine user interface.

12. A method as claimed in claim 11, wherein the gaming machine user interface is configured for receiving the data from a user identification gaming card.

13. A method as claimed in claim 11, wherein the gaming machine user interface comprises a demographic identifying biometric reader.

14. A method as claimed in claim 13, wherein the biometric reader comprises an image capture device configured for identifying at least one of skin colour, hair colour and facial features.

15. A method as claimed in claim 1, wherein calculating revenue optimising gaming machine entitlement allocations comprises feeding back candidate gaming machine entitlement reallocations into the neural network.

16. A method as claimed in claim 15, further comprising generating the candidate gaming machine entitlement reallocations in accordance with a revenue optima finding gradient ascent algorithm.

17. A method as claimed in claim 1, wherein reallocating a plurality of gaming machine entitlements comprises updating a relationship between an entitlements table and a venues table of a database of a server in operable communication with the plurality of gaming machines.

18. A method as claimed in claim 1, wherein reallocating a plurality of gaming machine entitlements comprises transmitting gaming machine entitlement data across a data network to the plurality of gaming machines.

19. A method as claimed in claim 18, wherein the gaming machine entitlement data comprises cryptographically signed gaming machine digital certificates.

20. A method as claimed in claim 1, wherein reallocating a plurality of gaming machine entitlements comprises transmitting entitlement reallocation instruction data to an entitlement authority server.

21. A method as claimed in claim 1, wherein the historical gaming data further comprises historical game configuration data and wherein the method further comprises:

generating the time series revenue predictive model for the at least one of the plurality of gaming machines and gaming venues further using the historical game configuration data;

calculating predicted revenue for the at least one of the plurality of gaming machines and gaming venues using the time series revenue predictive model for a plurality of future time slots; calculating a revenue optimising game configuration for the plurality of future time slots for the at least one of the plurality of gaming machines and gaming venues using the predicted revenue; and

dynamically reconfiguring a plurality of gaming machine game configurations across the data network for at least a subset of each the plurality of future time slots.

22. A gaming machine entitlement reallocation server for dynamically reallocating gaming machine entitlements for a plurality of distributed gaming machines across a data network for gaming machine revenue optimisation, the server comprising:

a processor for processing digital data;

a memory device for storing digital data including computer program code, the memory device being in operable communication with the processor;

a data network interface for communicating with a plurality of gaming machines across a computer network; and

a database comprising a historical gaming data table, wherein the computer program code comprises a plurality of software modules comprising:

a gaming data receiver module configured for receiving gaming data from the plurality of gaming machines across a computer network and populating the historical gaming data table; and a revenue optimising module configured for generating a gaming entitlement reallocation for revenue optimisation in accordance with the historical gaming data from the historical gaming data table; and an entitlement reallocation module configured for reallocating gaming machine entitlements across the data network.

23. A server as claimed in claim 22, wherein the revenue optimising module comprises a revenue predictive neural network.

24. A server as claimed in claim 23, wherein the revenue optimising module further comprises a machine learning training algorithm configured for training the revenue predictive neural network in accordance with the historical gaming data.

25. A server as claimed in claim 24, wherein the machine learning training algorithm is configured for optimising at least one of the structure and neuron weightings of the revenue predictive neural network.