WO2019246113A1

WO2019246113A1 - System and method for processing information related to event participants

Info

Publication number: WO2019246113A1
Application number: PCT/US2019/037755
Authority: WO
Inventors: Jacob C. BEAM; Michael M. SALERNO
Original assignee: Ice Holdings LLC
Current assignee: Ice Holdings LLC
Priority date: 2018-06-20
Filing date: 2019-06-18
Publication date: 2019-12-26
Anticipated expiration: 2020-12-20

Abstract

Systems and methods described herein are directed towards processing information related to game participant. An exemplary method may include collecting information related to game participants, collecting information related to a customer, the information of the customer including customer characteristics, preferences, customer location and customer trends, providing a user interface to the customer, the user interface including a first component providing handicap information based on the information of the game participants, venue information related to where the game is occurring and the customer is located, analyzing customer information, game information, and third party information from a plurality of vendors, the game participants and the race, generating a scoring model for a betting strategy based on the analyzed information, suggesting a betting strategy based on the scoring models to the customer and receiving customer betting input via the user interface and automatically placing a wager based on the customer betting input.

Description

SYSTEM AND METHOD FOR PROCESSING INFORMATION RELATED TO

EVENT PARTICIPANTS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present disclosure claims benefit of U.S. Provisional Applications No. 62/687,555 filed on June 20, 2018 and U.S. Provisional Applications No. 62/744,730 filed on October 12, 2018 which are incorporated herein by reference in their entirety.

FIELD OF TECHNOLOGY

[0002] The present disclosure generally relates to the field of monitoring data, and, more specifically, to systems and methods for the real time, rules based, AI and ML optimization, processing and dissemination of information related to event participants.

BACKGROUND

[0003] Sports events are becoming increasingly complex competitions in which many different variables of play are considered. Oftentimes“in the know” participants with advanced knowledge and access to statistical tools are able to create a competitive advantage when predicting an outcome of a competition or event. The competitive advantage is the result of the combination and analysis of multiple event variables made available both prior to, during and at the time of the event, and post event. Alternatively, any event involving multiple participants such as auctions, exhibitions, contests or the like may also be affected by this complexity.

[0004] However, there is a need to attract more intermediate level participants to obscure facts and measures in order to make engaging in and with the event(s) more accessible to casual fans, and thereby expand transparency and visibility as well as the popularity of the event(s). Fans of racing or other sports events often are drawn in to the social and experience- focused aspect of the event. Furthermore, fans tend to go to events, remote and on-site in large groups. For younger fans, digital and social media is a core aspect of their lives. However, venues such as race tracks and the like do not have experience in encouraging new and younger fans to visit, nor do they have experience in engaging these fans digitally to interact with the venue. [0005] Therefore, there is a need in the art for an efficient system and method for the real time, rules based, Artificial Intelligence (“AI”) and Machine Learning (“ML”) optimization and processing of information related to events, including remote participants and other facility, and activity participants. Further, there is a need for advanced real time data gathering and dissemination optimization to improve sport and other event attendance and betting and other engagement.

SUMMARY

[0006] Thus, the present disclosure provides an effective solution for the foregoing problems by providing real time, rules based AI and ML optimization, processing and dissemination of information. Disclosed are example systems, methods and computer program products for performing the processing of information related to race, remote and other facility and multi-event participants described herein.

[0007] In one aspect, an exemplary method comprises collecting real time sensor data from one or more sports or auction activities and participants, aggregating the collected sensor data with generically available data and rules based AI and ML optimization and contextualizing of the aggregate data to further assist fans in preforming one or more predictions as to an outcome of an event based on the contextualized aggregate data, and presenting the contextualized aggregate data to a client device.

[0008] The above simplified summary of example aspects serves to provide a basic understanding of the present disclosure. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects of the present disclosure. Its sole purpose is to present one or more aspects in a simplified form as a prelude to the more detailed description of the disclosure that follows. To the accomplishment of the foregoing, the one or more aspects of the present disclosure include the features described and exemplary pointed out in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate one or more example aspects of the present disclosure and, together with the detailed description, serve to explain their principles and implementations. [0010] Fig. 1 illustrates a block diagram of a system for the rules based and AI and ML optimization processing of real time information related to on-site and remote participants of a competition, event, multi-event or any activity according to an exemplary aspect of the disclosure.

[0011] Fig. 2 illustrates data collected in real time from a participant in accordance with exemplary aspects of the present disclosure.

[0012] Fig. 3 is a block diagram of the data collector in accordance with exemplary aspects of the present disclosure.

[0013] Fig. 4 is a block diagram of the rules based AI and ML optimized context processor in accordance with exemplary aspects of the present disclosure.

[0014] Fig. 5 is an illustration of the scanners and participants in accordance with exemplary aspects of the present disclosure.

[0015] Fig. 6 is a flowchart for a method for the real time, rules based AI and ML optimization processing of information related to event participants in accordance with exemplary aspects of the present disclosure.

[0016] Fig. 7 is a block diagram of a system for providing a real time, rules based AI and ML optimized directed user experience in accordance with exemplary aspects of the present disclosure.

[0017] Fig. 8 illustrates a typical interactive session with the venue, in accordance with exemplary aspects of the present disclosure.

[0018] Fig. 9 is an illustration of a user interface of a personalized interactive application, in accordance with exemplary aspects of the present disclosure.

[0019] Fig. 10 is a flow diagram for a method of improving, including with the usage of AI and ML, a modeling strategy for a customer in accordance with exemplary aspects of the present disclosure.

[0020] Fig. 11 illustrates an example of a general-purpose computer system on which the disclosed systems and method can be implemented.

DETAILED DESCRIPTION

[0021] Exemplary aspects are described herein in the context of a system, method, and computer program product for real time AI and ML optimization processing of information related and of value to on-site and remote participants and spectators of a competition or event, multi event or other activity. Those of ordinary skill in the art will realize that the following description is illustrative only and is not intended to be in any way limiting. Other aspects will readily suggest themselves to those skilled in the art having the benefit of this disclosure. Reference will now be made in detail to implementations of the example aspects as illustrated in the accompanying drawings. The same reference indicators will be used to the extent possible throughout the drawings and the following description to refer to the same or like items.

[0022] Fig. 1 illustrates a block diagram of a system 100 for AI and Ml optimized processing real time information related to on-site and remote event, multi event and activity participants according to an exemplary aspect of the disclosure.

[0023] The system 100 comprises a plurality of data collectors 102, each coupled to a participant or object used in a sporting or other event. In exemplary aspects of the disclosure, the participant may be a horse, a dog, amateur or professional athletes, actors, competitive participants, e-gaming, cars robots or the like. The present disclosure does not limit the nature of the participant. The event may be a sporting event, a multi-event on-site and/or remote venue or other activity, a race, auctions, contests, shows, plays, pageants, parades or the like, not to be limited by the present disclosure. The system 100 further comprises a rules based AI and ML optimized context processor 104, which receives real time and historical data collected from each of the data collectors 102 over a data network 101 as aggregated data, including environmental, contextual and physical data 105. The data collectors 102 may store the aggregated data in storage 106 which is local to the context processor and data collector 102, or alternatively store the data in remote storage 110 or cloud storage 112. The context processor 104 contextualizes the data collected by the data collector and presents this data to end user devices Ul, U2... UN. In some aspects these devices may be coupled to the local network 101, or may access the data of the context processor 104 via the Internet or the like.

[0024] According to one aspect of the disclosure, the system 100 operates for the purpose of supporting a sporting or other event or remote or multi-event facility. Such an event may be a racing event (animal racing such as horse racing, dog racing, or the like), basketball, football, baseball and the like, shows, exhibitions, auctions, competitions, contests and the like. In other aspects, the system 100 may support the Westminster Kennel Club®, show horses, jumpers, visage, rodeo, pageants, automobile auctions and expositions, and the like. Those of ordinary skill in the art will recognize that the monitoring and processing system 100 may be applicable to a number of different types of events, not limited to those events listed above. Accordingly, the system 100 can evolve these various competitive events making them more interactive, transparent competition for experts and non-experts to enjoy and participate in. In one aspect, the data collectors 102 can initiate collection of historical data from data centers 120 over the network 101 directly, or when being scanned by a scanner in the system (as shown in Fig. 5). Alternatively, a user U1-U4 may force a historical data collection, or may request the historical data to be analyzed and optimized using AI and ML to perform rules-based actions on or about a particular participant after scanning the data collector 102 associated with the participant. In exemplary aspects, these actions may include using artificial intelligence to generate simulations of future performance of race participants based on historical performances, given the collected data, including venue, weather and other third party data that provides an entire picture of the performance. Actions may also include using Machine Learning to learn how users interact with the system and to modify the user interface UI elements, or the data presented based on previous user interactions. Specific actions may include learning customer habits and generating customized schedules for customers and/or customized options for placing wagers or the like.

[0025] The flow of data in the system 100 happens in several ways. In one example, a mobile device scans a chip (e.g., the data collector 102) on the parti cipant/object that causes activation of the data collector 102. The data collector 102 then transmits a signal to the data centers 120 to retrieve specific data related to the participant. The data center 120 transmits this data to the data collector 102, and the data collector 102 transmits this data to the mobile device of a user.

[0026] In another example, the mobile device scans the data collector 102 that causes activation of the data collector 102. The data collector 102 sends a signal to the data centers

120, and the data centers 120 send data back to the mobile device.

[0027] In another example, the mobile device scans the data collector 102 that causes activation of the data collector 102. The data collector 102 transmits a signal back to the mobile device indicating the data sought by the data collector 102. The mobile device transmits a request to the data centers 120 and receives the data from the data centers 120.

[0028] In yet another example, the mobile device scans the data collector 102. The data collector 102 stores all participant related data and transmits the data back to the mobile device. [0029] In yet another example, the mobile device scans the data collector 102 that causes activation of the data collector 102. The data collector 102 may subsequently transmit a signal to the mobile device identifying particular information or data requested. The mobile device may either retrieve this data from a data center, or locally house this information and transmit the information to the data collector 102.

[0030] In the example of a thoroughbred horse race, the data collector 102 may be a microchip module affixed externally on or in the equipment as well as on or internally in the body of a horse for collecting various information about each competing horse and the race. In some aspects, the information may be collected via direct probes, or sensors Sl to SN shown in Fig· 2, in addition to static data sources such as configurable data centers that provide historical data. The sensors Sl to SN may be body sensors, accelerometers, geolocation devices such as GPRS, gyroscopes, and the like. The data collector 102 or the sensors Sl to S2 may be embedded into clothing equipment, or as a skin tag of competition participants. In one exemplary aspect, in a race (e.g., a horse race) the sensors or data collector 102 can be placed near the head of an animal to improve accuracy of tracking standings when animals are clustered closely.

[0031] Aspects of the present disclosure empower users (e.g., Ul in Fig. 1) to instantaneously identify a horse via scan and access pertinent live as well as historical information. The chip/module (e.g., data collector 102), affixed on or in the body of a horse, may, in one aspect contain embedded coding to allow user Ul to access to live as well as historical horse specific information via an application on their mobile device. In one aspect, once the data collector 102 is scanned by the mobile device, and the data collector 102 directly provides data to the user Ul. The application on the mobile device may then identify the horse based on an embedded identifier, or other identification information, and the application will access information associated with the horse from one or more data centers and deliver the information to the user. Access to the information associated with the horse may also be initiated by logging directly into the cloud-based data centers.

[0032] According to another aspect, the data collectors 102 directly collect or receive real time and/or historical data from various local and remote data sources, including environmental, physical and contextual. In this aspect, the trigger for collection of historical and/or contextual data may be once the data collector 102 has been scanned by a scanner (such as scanners shown in Fig. 5). Subsequently, historical data may be downloaded to the data collector 102 and stored on the device itself, or transmitted to a mobile device, as described above in paragraphs 19-22.

[0033] In some aspects, the data collected by the data collector 102 may include body data 300, location data 302, motion data 304, biological data 306 and historical data 308 as shown in Fig. 3. In exemplary aspects of the present disclosure, these data are all transmitted to the local data collector 102 attached to the horse, or race participant. In another aspect however, the data captured by these sensors Sl to SN are only logged to storage (e.g. storage 106, 110 or 112) as the participants (e.g., horses, dogs, jockeys, or the like) pass particularly placed scanners or sensing devices which capture the data. The scanners then pass the data along to the context processor 104 and/or the storage 106, 110 and/or 112.

[0034] In one aspect, the body data 300 comprises heart rate, oxygen levels, pulse, medication, body fat, BMI and weight. These data points may be measured of the race or event participant such as an animal (e.g., horse, dog, etc.) in addition to those of the controller (e.g., jockey, driver, trainer, or the like).

[0035] In one aspect, the location data 302 comprises relational location and geographical coordinates of the participant. The relational location may be reported by a geolocation sensor and may transmit data to other similar sensors nearby to establish a relative location of the participant. The relational location information can be useful in determining proximities between participants, indicative of possible collisions, or to establish positioning in term of race or competition results. The geolocation sensor may also provide geographical coordinates which give fixed positional information about the participant. This information can be used to track each participant on a track, for example, and can be compared to the relational location information to improve accuracy of tracking and competition results.

[0036] In another aspect, the motion data 304 comprises acceleration, velocity, limb motion, and other comparative data of the event participant. Limb motion data, for example, can help in prediction of injuries or collisions, or suggest improvements in training for trainers. Comparative data can include motion information for those participants nearby within a particular threshold value to the participant.

[0037] In another aspect, the biological data 306 comprises age, pedigree, workout data and the like. The pedigree of a participant may apply in animal races, or the like. The pedigree data is static and may be retrieved from a database such as storage 106, 110 and 112. The age information may be periodically updated to reflect changes. Similarly, the workout data may be updated by a trainer of the participant in the storage 106, 110 and 112, and periodically transmitted or collected by the data collector 102 in exemplary aspects.

[0038] Historical data comprises data relating to the history of the participant, whether that be ownership information, previous race information, previous training information of the participant and the like. In an exemplary aspect, the historical data 308 comprises past performance, earnings, data relating to race information (e.g., retrieved from one or more data centers), trainer data, driver data, and jockey data (though other data may also be included). Moreover, the trainer data may include trainer notes, the driver data may include driver notes, and the jockey data may include jockey notes.

[0039] Fig. 4 is a block diagram of the context processor 104 in accordance with exemplary aspects of the present disclosure.

[0040] The data collector 102 collects all of the previously discussed data into a data set referred to as collected data 400. The data collector 102 transmits the collected data 400 to the context processor 104. In another aspect, the data collector 102 stores the collected data 400 to storage 106, 110 and/or 112, and sends a message to the context processor 104 that the data is ready to be processed. In either aspect, once the context processor 104 receives the collected data 400, the context processor contextualizes the data into contextualized data 400. Finally, the contextualized data is received by a client application on end user device Ul, for example, which utilizes a rules-based system, optimized via AI and ML to format and display the data, according to user and business preferences, to the user.

[0041] In one aspect, the contextualization of the collected data 400 may comprise comparing the data between event participants which will assist in predicting the outcome of the event, or possible events that can occur during the competition, such as injuries, or upsets, standings changes during the race, environmental, physical and contextual data and the like. Additionally, the contextualization may include historical data that is correlated with the success or failure of the participant during the event and/or the visit or experience Finally, though not limiting the present disclosure, the contextualized data 400 may include data which distinguishes various participants in an event when they are clustered together.

[0042] Fig. 5 is an illustration of the scanners and participants in accordance with exemplary aspects of the present disclosure.

[0043] In this aspect, the event is a horse racing competition with four participants, horses Pl, P2, P3 and P4. Each of these horses are competing to win the race. As each of the horses Pl to P4 run the track 510. In this aspect, each of the horses Pl to P4 is equipped with a data collector 102 and various sensors capturing physical data and static data received from external sources relating to the horse. In this exemplary aspect, as each horse passes a scanner, e.g. scanners 501, 502, 503 and 504, the horse is registered with the context processor 104 indicating that they have passed a particular place in the track. In this manner, end-users who are not at the track, or not viewing the track, can know the status of the race as it occurs the speed and time that each horse has run the specific distance as well as each horse’s biometrics. Alternatively, the scanners 501-504 may record the data collected by data collector 102 and store the data in storage 106, 110 and 112 as each horse Pl to P4 passes a scanner. In another aspect, the end-user client device may display horses passing the scanner in the respective order they have passed a particular quarter pole.

[0044] In this aspect, the scanners 501-504 are located at quarter poles at the track 510, though more numerous scanners or different locations of the scanners are also contemplated by the present disclosure. In one aspect, scanner 505 may be located at a pole 520, where the pole 520 can be relocated throughout the track as a movable gate. In some aspects, the horses Pl- P4 may simply be training, or may be in a live race.

[0045] Fig. 6 is a flowchart for a method 600 for processing information related to event participants in accordance with exemplary aspects of the present disclosure.

[0046] The method 600 may be implemented by the data collector 102 and the context processor 104 of the system 100 and executed by processor 21 of computer system 20 shown in Fig. 8. The method begins at 602 and proceeds to 604.

[0047] At step 604, the data collector 102 may collect data from various sensors located either directly on a race or event participant, or, in the case of an animal race, on an animal, or on a jockey riding an animal, or the like. The data collector 102 may be located on the body of the participant or on the jockey. The data collector 102 gathers all data to be transmitted to the context processor 104 and stored in local or remote storage for later historical review and/or processing for display and prediction.

[0048] At step 606, the data collector 102 transmits the collected data to the context processor 104. The context processor 104 receives data collected from all race participants. In some instances, the context processor 104 analyzes the data relating to, for example, locations of each participants, including environmental and physical information, and contextualizes where each participant is located with respect to other participants. Additionally, the context processor 104 may contextualize motion information or body information of each participant with data relating to similar readings from other participants, and/or with environmental, physical and data.

[0049] In some aspects of the disclosure, the rules based AI and ML optimized context processor 104 may determine, based off of the contextualized data of all participants, predictions of bodily failure of participants, future motion information, sports event success, or the like. The context processor 104 may also be used to monitor current workouts or determine future workouts to improve the participants sports event results so that trainers or jockeys may train the participant appropriately to prepare for future events.

[0050] At step 608, the context processor 104 transmits the contextualized data for display at one or more client devices. For instance, during a horse race, fans may be able to view data relating to all horses, or only those horses they own, or are interested in. The client device is enabled with receiving software which manipulates the contextualized data to present the data in an efficient and navigable view. A user can then view the various predictions based on a dominant fact or facts, or even request predictions related to participant criteria such as past or present biometrics, future winnings, earnings, and the like. The user of the client device may also view historical data about each horse (or participant), and can view which horse (or participant) passes a particular scanner first.

[0051] In another aspect, administrators of the sports event may use the contextualized data to correct results of sporting events, gauge accuracy of officials, or repair/improve their venues (e.g., field or track improvements) when discrepancies are observed between sensor data and visual data.

[0052] The method terminates at 610.

[0053] According to exemplary aspects of the present disclosure, the methods and systems presented herein may have several different applications.

[0054] Handicapping:

[0055] Each participant’s data (e.g., horse data) may be viewed independently or collectively alongside other participants that it may be racing against via a mobile device. The user will have access to more comprehensive and live data than an attendee who chooses to use static data to handicap a race.

[0056] Training: [0057] The methods and systems described herein remove the potential for human error when recording workout times for participants such as horses. For example, an embedded microchip (e.g., data collector 102) may correspond to a sensor (e.g., scanner 505 in Fig. 5) anchored on the starting gate or line and each quarter pole on the track. A succinct record of how fast a horse, dog or other athlete has run will be available for users to view, improving accuracy and transparency.

[0058] Social Benefit:

[0059] The methods and systems described herein allow horsemen and fans alike to identify any horse, dog or other athlete; (e.g. thoroughbred horse whether in the bam, training on a track, in the paddock, or in a race). For example, while fans may come to watch participant workouts in the morning, the fans are frequently heard asking“what horse is that?” Now, with a simple scan of the horse’s embedded chip (e.g., the data collector 102) they are able to identify their favorite race horse as well as access pertinent data on the horse.

[0060] Data Acquisition:

[0061] Further aspects of the methods and systems disclosed herein gather contact information as well as the habits and preferences of participating racing and general sport fans via a mobile application on user’s devices. For example, in one aspect, the system 100 can directly contact prior attendees and racing fans. This will provide multiple marketing opportunities for upcoming events, promotions, and the like.

[0062] Fig. 7 is a block diagram of a system 700 for providing a real time rules based Ai and ML optimized directed user experience in accordance with exemplary aspects of the present disclosure.

[0063] The system 700 comprises an interactive application 701, user information 702, a AI and ML optimized rules engine 720, an advertising engine 140, a user database 730, a modeling engine 750 and the context processor 104. The system 700 allows venues to accurately track remote and on-site user information 702 including user behavior to present users a unique interactive experience via the interactive application 701, in addition to on premises offers and service by the venue and/or various vendors, e.g., vendor 1 and vendor 2. An example interface of the interactive application 701 is shown in Fig. 9, though other exemplary layouts and interfaces maybe contained therein.

[0064] The user, e.g., user with device U3, is presented various promotions, incentives, free gifts, advertisements, handicaps for races and other events, static data, dynamic data, biometric and health information of event participants, location of other users, offers for drinks, services, rentals, and the like via the application 701. This information is presented to the user via the application 701 based on analysis of the user information 702 by the context processor 104. In some aspects, as the user attends a venue, the user is identified and their information may be provided through the network 101 from third parties based on attendance at partner events, or at previous visits to the current venue. The identification may be performed through a user sign-in to the interactive application 701, or may be performed automatically through biometric verification (e.g., eye recognition, facial recognition or the like). Alternatively, a user may be identified by comparing current user behavior and user information with previously stored user information and determining a match.

[0065] User information 702 may comprise user characteristics 704, preferences 706, user location 708, and user trends (and/or behavior) 710. In exemplary aspects, the user information 702 may include further information that may be useful in the context processor 104 providing an comprehensive interactive experience to the user, e.g., users U3 and U4, using the interactive application 701.

[0066] Once the user is identified, the user information 702 is optimized via AI and ML rules based processes by the context processor 104 to provide a personalized interactive application to the user. Specifically, in some aspects the context processor 104 provides the user information 702 to the advertising engine 740 that delivers customized advertisements to the user U3. Additionally, the advertising engine 740 may provide the venue with suggestions regarding services the user may want, or may provide suggestions via the application 701. For example, the advertising engine 740 may determine that generally, a period of time after entering the venue, the user generally orders certain drinks and appetizers. The advertising engine 740 may provide the venue this information so that servers may approach the user with a menu, or the particular drinks and appetizers the user prefers to attempt to complete a sale and create an enhanced positive interactive experience for the user as shown in Figure 9 (e.g., the participants component, the betting component and the venue communication component). In some aspects, the advertising engine 740 provides this information directly to the user in the interactive application 701 so that he or she may order the food or services via the interactive application 701. The position of the user is captured in the user information 702, and therefore his or her services can be delivered directly to their location based on the captured location information in the venue. In some aspects, the position and location information is obtained by GPS of the user’s mobile device, and or other sensors or other methods located throughout a facility/venue.

[0067] The AI and ML optimized modeling engine 750 generates an optimized, personalized and customized model for handicapping race participants (e.g., horses), racing at the venue, the model generated based on the user information 720 along with data collected on the participants by the data collector 102 shown in Fig. 1. The modeling engine 750 may extract user information 702 for information on historical handicapping by the user, particular participants the user focuses on. The modeling engine 750 may provide a general model based on weights assigned by the user in previous interactions with the interactive application 701 or the like. The model is presented to the user in the interactive application 701 for the user to interact with, study and / or modify on the fly. For example, the user may update some parameters in the model via the interactive application 701, and the modeling engine 750 may regenerate the model accordingly.

[0068] In one specific illustrative example, a user may select a horse that ultimately wins a race. The system 100 may store statistical data related to the horse and the race, such as conditions of the race track and may also store how such quantitative data compared with its competitors. When the next race is being handicapped by the winning user, the system 100 can assist the user in looking for the same or similar metrics to select the winner using the interactive application 701. For example, past winning horses may have had an average speed figure over the last two races that was five pts higher than the competition on a muddy track over the past seven months. The system 100 may apply the average speed figure to identify different horses to win the next several races and present these to the user. Over time, the engine 750 leams patterns of winning in light of conditions of the track and characteristics of the horse, as well as betting and selection patterns of the user, changes in betting odds, and generate predicted suggestions and/or automated actions generated via the AI learning and adapting based on combined real time and historical information analysis. The engine 750 may improve the analysis over time by simulating unlimited results and learning from the changing simulation outcomes, and/or based on user corrections and/or selections using machine learning. In addition to this being driven by the users past actions, the system 100 provides predetermined metrics that users may select and adjust to then apply to handicapping and selecting their projected winner.

[0069] In exemplary aspects, the rules based, AI and ML optimized context processor 104 may be provided additional data about the customer, the venue, race participants, or related events and the like, and the interactive application 701 consistently maintains a pipe or connection to the context processor 104 over network 101 that keeps the interactive application 701 updated to display the new data streams or data sources. In some examples, the interactive application 701 may use a rules-based system to allow entertainment facilities such as sports parlors, bars, stadiums, arenas, theaters, tracks, or the like to communicate directly with patrons to provide incentives to engage, place bets, order services or the like, or provide reward programs to optimize or minimize the cost of manual labor at the venue. For example, the interactive application 701 removes the need for labor to manually take an order and deliver the order to the bar. A server then simply has to, for example, deliver a drink. This allows the servers to have more interaction with patrons and also generates more opportunities for obtaining a tip therefore reducing labor cost, increasing patron interactive services and entertainment experiences. The server may also then earn higher wages in a more efficient manner. Additionally, the interactive application 701 may include social media layers, allowing customers to share venue information, incentives, handicap strategies or the like with others, further driving others to attend the venue, while providing those customers that share with other incentives or discounts, and increasing the overall customer interactive entertainment experience.

[0070] Fig. 8 illustrates a customer at a venue 800 in accordance with exemplary aspects of the present invention.

[0071] In one example, a venue 800 comprises several scanners 804-1 to 804-N located in various locations across the venue, across various floors and areas in the venue and the like, to provide maximum coverage in geo-locating a position of a customer 802 at the venue. In exemplary aspects, the customer’s equipment, e.g., mobile device or the like, is scanned upon entering the venue 800 and tracked across the various locations within a venue. As the customer’s equipment passes various scanners, e.g., scanner 804-2, the location of the customer is updated in the user information 702, which may store a historical record of the various scanners that have scanned the customer’s equipment. In other exemplary aspects, other signal transmission or geolocation techniques can be applied to locate a user within a facility or group of facilities, for example users can be tracked using cellular and GPS/GPRS signals or the like.

[0072] The context processor 104 may determine the last scanned location of the customer 802, and if the customer 802 either orders a service through the application 701, or if the advertising engine 130 determines that the user may want or need a service, the context processor 104 may send a service consultant, e.g., a waiter or the like, to either deliver the service to the customer 802 or offer beverages and appetizers or the like, to the customer 802. In another example, a facility owner may be notified by the rules based context processor 104 that a customer is recognized and identified or profiled as a patron with the potential for specific targeted purchases, significant amount of purchases, and / or profit margin as compared to other patrons. In this case, the facility owner may manually direct, or automatically have the rules based AI and ML optimized system 700 configured to direct services to offer or provide special services gifts, incentives and or information, such as a seat in VIP area, free tickets to an upcoming event, buy one get one free promotion, or the like. Often premium services and areas are not full and can be leveraged to drive consumer experience/engagement in addition to providing maximum usage of a facility and the chance for the facility to earn higher on premium services.

[0073] Fig. 10 is a flow diagram for a method 1000 of improving a modeling strategy for a customer in accordance with exemplary aspects of the present disclosure. AI and machine learning will manage instantaneous multiple simulations (e.g., Monte Carlo simulation), as well as monitoring all information activity and results in order to continuously optimize and improve the analysis and/or the strategy

[0074] The method begins at 1000 and proceeds to 1002 where the method provides a user interface to a customer with handicaps, venue information and alternative betting options for games at the venue. Additionally, the user interface may provide gaming, waging and other activity information.

[0075] In one aspect, the method 1000 includes assisting gamblers with configuring or personalizing a handicap, or configuring one or more gambling/scoring models that they deem advantageous to use by creating memorized criteria used when evaluating a particular wager. In some aspects, the scoring/rating model can be modified at any time by user as they deem appropriate. For example with horse racing, an advanced“speed index” model with several additional numeric considerations may be created or modified or Ai and ML optimized as an option for a user to select a horse to wager on.

[0076] The method proceeds to 1004, the method 1000 may analyze user information, race, gaming and engagement information, third party information and the like to generate a AI and ML optimized scoring model(s) for betting strategy. [0077] At 1006, the method provides the customer with an improved betting strategy based upon the analysis at step 1004 using the scoring model(s) generated in 1004.

[0078] The method terminates at 1010.

[0079] Finally, Fig. 11 illustrates an example of a general-purpose computer system (which may be a personal computer or a server) on which the disclosed systems and method can be implemented according to an example aspect. It should be appreciated that the detailed general-purpose computer system can correspond to the portions of the system 100 described above with respect to Figs. 1 and 7. Moreover, the remote computer(s) 49, as described below, can correspond to the remote data storage services discussed above with respect to the exemplary system and method.

[0080] As shown in Fig. 11, the computer system 20 includes a central processing unit 21, a system memory 22 and a system bus 23 connecting the various system components, including the memory associated with the central processing unit 21. The system bus 23 is realized like any bus structure known from the prior art, including in turn a bus memory or bus memory controller, a peripheral bus and a local bus, which is able to interact with any other bus architecture. The system memory includes read only memory (ROM) 24 and random- access memory (RAM) 25. The basic input/output system (BIOS) 26 includes the basic procedures ensuring the transfer of information between elements of the personal computer 20, such as those at the time of loading the operating system with the use of the ROM 24.

[0081] The personal computer 20, in turn, includes a hard disk 27 for reading and writing of data, a magnetic disk drive 28 for reading and writing on removable magnetic disks 29 and an optical drive 30 for reading and writing on removable optical disks 31, such as CD-ROM, DVD-ROM and other optical information media. The hard disk 27, the magnetic disk drive 28, and the optical drive 30 are connected to the system bus 23 across the hard disk interface 32, the magnetic disk interface 33 and the optical drive interface 34, respectively. The drives and the corresponding computer information media are power-independent modules for storage of computer instructions, data structures, program modules and other data of the personal computer 20.

[0082] The present disclosure provides the implementation of a system that uses a hard disk 27, a removable magnetic disk 29 and a removable optical disk 31, but it should be understood that it is possible to employ other types of computer information media 56 which are able to store data in a form readable by a computer (solid state drives, flash memory cards, digital disks, random-access memory (RAM) and so on), which are connected to the system bus 23 via the controller 55.

[0083] The computer 20 has a file system 36, where the recorded operating system 35 is kept, and also additional program applications 37, other program modules 38 and program data 39. The user is able to enter commands and information into the personal computer 20 by using input devices (keyboard 40, mouse 42). Other input devices (not shown) can be used: microphone, joystick, game controller, scanner, and so on. Such input devices usually plug into the computer system 20 through a serial port 46, which in turn is connected to the system bus, but they can be connected in other ways, for example, with the aid of a parallel port, a game port or a universal serial bus (USB). A monitor 47 or other type of display device is also connected to the system bus 23 across an interface, such as a video adapter 48. In addition to the monitor 47, the personal computer can be equipped with other peripheral output devices (not shown), such as loudspeakers, a printer, and so on.

[0084] The personal computer 20 is able to operate within a network environment, using a network connection to one or more remote computers 49. The remote computer (or computers) 49 are also personal computers or servers having the majority or all of the aforementioned elements in describing the nature of a personal computer 20. Other devices can also be present in the computer network, such as routers, network stations, peer devices or other network nodes.

[0085] Network connections can form a local-area computer network (LAN) 50, such as a wired and/or wireless network, and a wide-area computer network (WAN). Such networks are used in corporate computer networks and internal company networks, and they generally have access to the Internet. In LAN or WAN networks, the personal computer 20 is connected to the local-area network 50 across a network adapter or network interface 51. When networks are used, the personal computer 20 can employ a modem 54 or other modules for providing communications with a wide-area computer network such as the Internet. The modem 54, which is an internal or external device, is connected to the system bus 23 by a serial port 46. It should be noted that the network connections are only examples and need not depict the exact configuration of the network, i.e., in reality there are other ways of establishing a connection of one computer to another by technical communication modules, such as Bluetooth.

[0086] In various aspects, the systems and methods described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the methods may be stored as one or more instructions or code on a non-transitory computer- readable medium. Computer-readable medium includes data storage. By way of example, and not limitation, such computer-readable medium can comprise RAM, ROM, EEPROM, CD-ROM, Flash memory or other types of electric, magnetic, or optical storage medium, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a processor of a general purpose computer.

[0087] In the interest of clarity, not all of the routine features of the aspects are disclosed herein. It will be appreciated that in the development of any actual implementation of the present disclosure, numerous implementation-specific decisions must be made in order to achieve the developer’s specific goals, and that these specific goals will vary for different implementations and different developers. It will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking of engineering for those of ordinary skill in the art having the benefit of this disclosure.

[0088] Furthermore, it is to be understood that the phraseology or terminology used herein is for the purpose of description and not of restriction, such that the terminology or phraseology of the present specification is to be interpreted by the skilled in the art in light of the teachings and guidance presented herein, in combination with the knowledge of the skilled in the relevant art(s). Moreover, it is not intended for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such.

[0089] The various aspects disclosed herein encompass present and future known equivalents to the known modules referred to herein by way of illustration. Moreover, while aspects and applications have been shown and described, it would be apparent to those skilled in the art having the benefit of this disclosure that many more modifications than mentioned above are possible without departing from the inventive concepts disclosed herein.

Claims

1. A method of processing information related to game participants comprising:

collecting information related to game participants, the information comprising body data, location data, motion data, biological data and historical data;

collecting information related to a customer, the information of the customer including customer characteristics, preferences, customer location and customer trends;

providing a user interface to the customer, the user interface including a first component providing handicap information based on the information of the game participants, venue information related to where the game is occurring and the customer is located, and a plurality of betting options;

analyzing customer information, game information, and third party information from a plurality of vendors related to the customer, the game participants and the race;

generating a scoring model for a betting strategy based on the analyzed information; suggesting a betting strategy based on the scoring models to the customer; and receiving customer betting input via the user interface and automatically placing a wager based on the customer betting input.

2. The method of claim 1, further comprising:

analyzing historical data related to customer betting, game participants, and race information including race results and conditions;

providing suggestions for selecting game participants as winners of a race based on the analysis;

applying user corrections and selections to improve the analysis process for future suggestions.

3. The method of claim 1, further comprising: analyzing the customer information including the location of the customer; and pre-purchasing services for the customer based on customer purchasing trends, the customer location and customer fund information.

4. The method of claim 1, further comprising:

analyzing the customer information including the location of the customer; and providing directed advertisements to the customer for offers at the venue, related to the venue, related to the game, and/or related to the customer, based on the analyzed customer information.

5. The method of claim 1, wherein the body data of the participants comprises heart rate, oxygen level, pulse, medication, body fat, BMI and weight.

6. The method of claim 5, wherein the motion data of the participants comprises acceleration, velocity, limb motion and comparative data.

7. The method of claim 6, wherein the biological data of the participants comprises age, pedigree and workout information.

8. The method of claim 7, wherein the historical data of the participants comprises past performance, earnings data center data, trainer data and jockey data.

9. The method of claim 1, further comprising:

contextualizing game participant information by providing location information of many game participants to the customer; and

allowing a user to handicap several race participants based on the provided information.

10. A system of processing information related to game participants comprising:

a processor configured to: collect information related to game participants, the information comprising body data, location data, motion data, biological data and historical data;

collect information related to a customer, the information of the customer including customer characteristics, preferences, customer location and customer trends;

provide a user interface to the customer, the user interface including a first component providing handicap information based on the information of the game participants, venue information related to where the game is occurring and the customer is located, and a plurality of betting options;

analyze customer information, game information, and third party information from a plurality of vendors related to the customer, the game participants and the race;

generate a scoring model for a betting strategy based on the analyzed information; suggest a betting strategy based on the scoring models to the customer; and

receive customer betting input via the user interface and automatically placing a wager based on the customer betting input.

11. The system of claim 10, the processor further configured to:

providing suggestions for selecting game participants as winners of a race based on the analysis; and

12. The system of claim 10, the processor further configured to:

analyze the customer information including the location of the customer; and pre-purchase services for the customer based on customer purchasing trends, the customer location and customer fund information.

13. The system of claim 10, the processor further configured to: analyzing the customer information including the location of the customer; and providing directed advertisements to the customer for offers at the venue, related to the venue, related to the game, and/or related to the customer, based on the analyzed customer information.

14. The system of claim 10, wherein the body data of the participants comprises heart rate, oxygen level, pulse, medication, body fat, BMI and weight.

15. The system of claim 14, wherein the motion data of the participants comprises acceleration, velocity, limb motion and comparative data.

16. The system of claim 15, wherein the biological data of the participants comprises age, pedigree and workout information.

17. The system of claim 16, wherein the historical data of the participants comprises past performance, earnings data center data, trainer data and jockey data.

18. The system of claim 10, further comprising:

19. A non-transitory, computer-readable medium storing instructions thereon for processing information related to game participants, the instructions comprising:

collecting information related to a customer, the information of the customer including customer characteristics, preferences, customer location and customer trends; providing a user interface to the customer, the user interface including a first component providing handicap information based on the information of the game participants, venue information related to where the game is occurring and the customer is located, and a plurality of betting options;

20. The medium of claim 19, the instructions further comprising: