System and method for determining player skill index and providing performance optimization feedback for skill activities

Abstract

A system collects and utilizes activity information of a person, such as skill-based activity information, and utilizes that information to generate (including update) one or more skill indexes of the player. The system may implement a data weighting engine that weights activity data based upon the integrity of the data, such as based upon a measure of how anomalous the data is in relation to other data of the player, and the identifiability of the data.

Description

RELATED APPLICATION DATA

[0001] This application claims priority to U.S. Provisional Application Ser. No. 63 / 747,153, Jan. 20, 2025, which is incorporated by reference herein in its entirety.FIELD OF THE INVENTION

[0002] The present invention relates to skill-based activities, such as games of skill, and performance evaluation of such activities.BACKGROUND OF THE INVENTION

[0003] Many skill-based activities (whether played recreationally or professionally, and whether for wager or amusement) include some measure of skill tracking, such as the Golf Handicap & Information Network (GHIN), various bowling handicapping systems, and video game ranking systems. These systems lack integrity checks for the data that drives the skill determination, allowing for “sandbagging” (pretending to be worse at a skill than actual), “vanity handicaps” (inputting more favorable data than what really occurred), and versions of user fraud including more experienced players acting like less experienced players in order to get advantages.

[0004] Moreover, many of these systems lack cross-platform integration, meaning their data exists in a silo for a specific source of data, rather than accumulating data from as many sources as possible. This limits the ability to truly track a player's skill to the data source, missing crucial data in other systems. For example, the GHIN system requires manual entry of data from real world golf courses, with a minimum of 9 holes. If a golfer plays less than 9 holes, or hits balls on a driving range or golf simulator, that data does not inform their GHIN index.

[0005] Lastly, many skills indexing systems use the final results of an activity, with limited internal metrics, to determine skill. For the example of golf, the number of shots needed to complete a hole may be tracked. Some apps like the GHIN app and the Grint app allow users to manually enter additional information to track trends in performance, like “sand save %” or “greens in regulation”. But the player's skill index is limited by the GHIN's requirement for a certain number of holes to be completed, and limitation wherein only the total score for a hole is measured. Arccos Golf tracks users' individual shot data, but that is not used to update or inform their GHIN index. Moreover, the Arccos data is proprietary to the Arccos system, limiting its usefulness for golfers when playing across varying platforms. For example, Arccos data is not used for locations like Topgolf, Five Iron or the GHIN system, where billions of golf shots are taken, and millions of golf shots are recorded every year.

[0006] There is also no integrated method of taking the skill index and applying it to increase engagement, retention and monetization of the player. The Arccos system and GHIN can be integrated into other systems, but those integrations have to date lacked the efficacy of a positive feedback loop to maximize utilization of the system. The invention allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with AI-recommendations, (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and (c) cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0007] While each of these are being done independently, the lack of a systemic method of combining them, automated and verified, limits their usefulness in real world scenarios, as is evident by the lack of products and services similar to the invention in the market today.

[0008] Related to these issues, skill game players also desire feedback regarding their performance and guidance or input on how to improve their performance. There are existing devices and systems which can provide information to a player of a skill game about their inputs or performance. For example, golf ball trackers can be used by a player during warm-up to provide information to the player about the trajectory of their practice shots (loft; speed; distance). However, the player then needs to try and analyze this information to make determinations about adjustments in their inputs in hopes of improving their play.

[0009] There is thus no integrated method of taking information regarding a player's skill-based activities, including as transformed into a skill index, and applying that information to increase performance, engagement, retention and monetization of the player.

[0010] It is an object of the invention to overcome these and other issues associated with existing systems and methods for tracking and measuring player skill and provide performance guidance.SUMMARY OF THE INVENTION

[0011] Aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.

[0012] In one embodiment, a player skill indexing and guidance system comprises an indexing and guidance system comprising at least one server and a data storage device, the server comprising a communication interface, a processor, a memory and machine readable code stored in said memory and executable by the processor to cause the server to: receive, from a plurality of activity tracking platforms over one or more electronic communication networks, user activity data regarding a user's performance of a skill-based activity; implement a data weighting engine which implements a data identity analyzer and an anomalous data analyzer, the data identity analyzer configured to generate a first weighting value for the received user activity data based upon a level of verifiability of an identity of the user to the user activity data and a second weighting value for the received user activity data based upon a level of anomalousness of the user activity data, the data weighting engine configured to utilize the first and second weighting values to output weighted user activity data; and generate a player skill index utilizing the weighted user activity information. The one or more generated player skill indices may be transmitted, via an electronic communication link, to at least one remote electronic device.

[0013] As one aspect of the invention, unlike “trust-based systems” like Arccos or GHIN, the present invention is able to give additional weight to data which can be verified and decrease the weight of data which is less reliable, like anonymous data (recorded without player identification) or anomalous data (data which deviates significantly from the expected).

[0014] In one embodiment, the invention uses AI-algorithms to weight anonymous and anomalous data by comparing the results of each action to the expected results and weighing each action and associated underlying metric based on the expected results. This discounting adapts over time, to identify and reweight trend, so that if a user's data show changes over any significant period of time or sequence, their past data is re-weighted to accommodate for the change.

[0015] In one embodiment, the invention also allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with AI-recommendations, (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and / or (c) enables cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0016] In one embodiment, a system is configured to receive event condition and player activity information, such as from condition sensors and activity action sensors, and use that information to generate performance guidance for the player. The performance guidance may comprise instructions for a practice or warm-up session, or an event. The system may utilize updated event condition information and / or current player activity information, such as received from the sensors, to update the performance guidance or otherwise provide feedback to the player. The updated performance guidance may comprise, modifications to a practice or warm-up session based upon changes in conditions or current player activity information, or modifications to guidance for an event, such as based upon changing conditions or the player's performance during the event.

[0017] In one embodiment, a skill-based event analysis system may comprise at least one server, a data storage device, a memory, a communication interface, and a processor, said memory storing machine-readable code for execution by said processor to cause the server to: receive skill-based event condition data from one or more condition sensors; receive past skill-based event performance data relating to a player; utilize the event condition and event performance data to generate a first performance guidance output to the player for use in a performance of a first skill-based event action by the player; receive, from the one or more condition sensors, updated event condition data; receive, from at least one activity sensor which is configured to sense an action associated with an input by a player to an element of physical game equipment relative to the first skill-based event action an output of the at least one activity sensor relative to the first skill-based event action; and utilize at least one of the updated event condition data and output of the at least one activity sensor to generate a modification to the first performance guidance.

[0018] Further objects, features, and advantages of the present invention over the prior art will become apparent from the detailed description of the drawings which follows, when considered with the attached figures.DESCRIPTION OF THE DRAWINGS

[0019] In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0020] FIG. 1 illustrates one embodiment of a system of the invention;

[0021] FIG. 2 is a flow diagram which illustrates one method of the invention;

[0022] FIG. 3 schematically illustrates a configuration of aspects of the system illustrated in FIG. 1;

[0023] FIG. 4 is a flow diagram which illustrates another method of the invention; and

[0024] FIG. 5 is a schematic illustrating aspects of another system of the invention.DETAILED DESCRIPTION OF THE INVENTION

[0025] In the following description, numerous specific details are set forth to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.

[0026] Some aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.Skill Event Analysis System

[0027] One embodiment of an exemplary system 20 of the invention is illustrated in FIG. 1. As illustrated, the system 20 may comprise at least one skill event analysis server 22. The skill event analysis server 22 may comprise a processor and a memory. The processor may be configured to execute machine readable code or “software” or otherwise process information, such as obtained from one or more remote devices. In one embodiment, the software is instantiated in non-transitory form in the memory. The software may have various structures, such as comprising a plurality of modules that, when executed by the processor, cause the processor to perform portions of a process. The memory or another data storage device 24 which is accessible by the processor may store data which is usable by the processor. Such data may comprise, for example, one or more player index values as described below. Further, the skill event analysis server 22 preferably comprises one or more communication interfaces, such as to allow the skill event analysis server 22 to communication with one or more networks or other devices via one or more communication links. These communication links may be wired or wireless.

[0028] In a preferred embodiment, the skill event analysis server 22 is configured to receive activity information from a plurality of remote devices or systems. For example, as illustrated in FIG. 1, the skill event analysis server 22 may communicate with a plurality of activity platforms P1-P3 (of course, as indicated herein, the number of platforms or devices, including their configuration, may vary). The platforms P1-P3 may similarly comprise at least one computing device, such as a server, which comprises a processor and a memory which stored machine-readable code which is executable by the processor. In one embodiment, the platform P1-P3 is configured to collect player activity information, such as by direct input to one or more input devices, or from one or more sensors S.

[0029] The input devices may have various forms and may be configured to be directly manipulated by the player, with the input device outputting activity information to the processor of the platform P1-P3. For example, “direct input” devices might comprise buttons, joysticks, hand-held devices or the like.

[0030] On the other hand, various types of sensors S or other devices might be used to sense or detect player activity information, such as in relating to a player's inputs to another device. These may include, but are not limited to, accelerometers, motion detecting devices, velocity measuring devices (radar, dual radar, Doppler radar, LIDAR, etc.), distance measuring devices, force measuring devices (strain gauges, etc.), cameras or other image devices or sensors, identification tags (including but not limited to RFID tags, printed bar codes, etc.), location determining devices (GPS devices, etc.) pressure and / or mass sensors, light (visible or invisible) sensors, sound sensors, and others. For example, at a golf driving range, sensors may be used to track a player's swing of a golf club. Further, RFID tags may be associated with golf balls and those tags may be read, such as to determine a location where a ball is hit and thus the distance that the ball traveled, how close the ball is to the hole, etc. As another example, one or more cameras may track the path of a golf ball to determine the same or similar information. These or other sensors might be used to track the path or movement of a ball, the location of a ball, a hole sensor (such as for detecting a golf ball in the hole) etc., for determining an outcome of the event as to a particular player. Of course, the particular type of sensor or detector may vary, such as by the type of event, and might comprise more than one type of detector or sensor, including combinations of sensors or detectors, including in a mesh. Additional sensors may comprise environment detection sensors. Such sensors might comprise a anemometer, a thermometer, an atmospheric pressure sensor, a hygrometer, a rain sensor, or various other devices for receiving input or detecting one or more conditions associated with a skill event location (golf course, tennis court, bowling alley, etc.)

[0031] Such detectors or sensors might be positioned in various locations. For example, as illustrated in FIG. 1 in relation to a platform P3 which is associated with a golf event at a golf course, the detectors or sensors S might be positioned at the tee box, in tee markers, in trees or bushes, in electrical or sprinkler boxes, at the pin or hole, around the green and / or fairway, on poles, on golf carts or other equipment. In some cases, detectors or sensors might be associated with arial craft such as blimps or drones.

[0032] In some embodiments, the various input devices and detectors or sensors might be configured for wired communications, but most preferably wireless communications. For example, the input devices and detectors or sensors might comprise Wi-Fi enabled devices which sync with one or more Wi-Fi communication hubs for communicating with the platform server, such as via one or more networks N (WANs, LANs, the Internet, cellular network, etc.).

[0033] In some embodiments, the platform might comprise one or more user input devices (such as a mobile device, a kiosk, smart watch or fitness watch, etc.), which are configured to sense or detect player activity information or receive such information from one or more other devices, such as sensors. In some embodiments, such devices may communicate with an event server or an application running on a processor of the user's device. For example, the user device might comprise a tablet, phone, PDA or other device.

[0034] As various non-limiting examples, the platforms P1-P3 may comprise customized platforms or existing platforms or devices. Such existing platforms or devices might comprise a Topgolf™ activity platform, a SkyTrak™ home golf simulator or launch monitor, a Full Swing Golf™ simulator, a Toptracer™ golf range platform, a Swing Suite™ activity platform, a GolfTec™ training / lesson location, a Dryvebox™ mobile golf similar, or others.

[0035] These platforms P1-P3 may also be associated with systems other than the skill event analysis server 22. For example, activity information at a Topgolf™ location may be collected and provided to a back-end hosting system of the Topgolf™ platform. As another example, activity information which is collected by a user's smart watch or fitness tracker may also be provided to one or more specific third-party activity tracking systems. Thus, the skill event analysis server 22 may receive player activity or other information directly from platforms that comprise individual devices, or from one or more components of a multi-component system (such as from sensors at a Topgolf™ bay or a back end Topgolf™ system server).

[0036] Most importantly, in accordance with the present invention, a player's activity information from these various different or disparate platforms is collected by the skill event analysis server 22. In one embodiment, an application program interface (API) may be provided between one or more of the remote platforms P1-P3 and the skill event analysis server 22, such as to allow player activity information from the different platforms to be usable by the skill event analysis server. For example, a first API might be stored in the memory of the skill event analysis server 22 and be executable by the processor in order to process (such as by interpreting or translating) player data received from the first platform P1, such as from a first form or format, to a form or format which is usable by the skill event analysis server 22. Of course, each API may be required to be customized based upon the format of the player information as output by each platform P1-P3 in relation to the desired format at the skill event analysis server 22.

[0037] As illustrated in FIG. 1, the indexing system 20 may include one or more user devices 30. Such devices might comprise, for example, a desktop computer, laptop computer, tablet, mobile communication device or the like. Such devices may comprise a processor, a memory, machine-readable instructions stored in the memory and executable by the processor. Such instructions may be in the form of an application, such as a browser application or dedicated application. In one embodiment, the user device 30 and the skill event analysis server 22 may communicate, such as via one or more communication links (including the Internet, etc.)

[0038] As one example, a user might communicate with the skill event analysis server 22 via a browser executed on their user device 30 as interfacing to a web server implemented by or associated with the skill event analysis server 22. The browser may enable the presentation of information from the skill event analysis server 22 via a display of the user's device 30, and input to the skill event analysis server 22, such as via input to input devices of the user device (such as selection of elements which are graphically displayed on a display of the user device 30 via an associated touch screen).

[0039] In another embodiment, a user might download an application to their user device 30. This application may be stored in a memory of the user device 30 and when executed, implement aspects of the functionality described herein, such as by communication with the skill event analysis server 22. Such an application, when executed, might cause a display of the user's device 30 to display one or more graphical user interfaces and information provided by the skill event analysis server 22.Skill Event Indexing

[0040] Further aspects of the skill event analysis system 20 will be provided with reference to FIGS. 2 and 3. One aspect of the invention is a method of utilizing player activity data to generate and update one or more player skill indexes.

[0041] Referring to FIG. 2, in a first step S1, a player profile may be created. The profile may include information which identifies a player. In some embodiments, the player profile may be identified by a player identifier, such as a unique ID. Relative to the system illustrated in FIG. 1, the player profile may be created by the skill event analysis server 22, wherein information regarding the player profile is maintained in one or more data records, such as in the associated database 24.

[0042] In a step S2, player activity information is generated and received. For example, the player may participate in one or more activities in association with one or more platforms P1-P3 such as illustrated in FIG. 1. In one embodiment, information regarding the activity is transmitted along with information which identifies the player. The information which identifies the player might comprise the player's profile ID, their name, phone number or various other information which is linked to their player profile and which the skill event analysis server 22 may use to determine that the activity information corresponds to the player.

[0043] As noted above, as part of receiving the activity information, the information may be translated or transformed, such as from a format used by the platform P1-P3 to a format native to the skill event analysis server 22 (such as via the above-noted APIs). In one embodiment, the activity information is stored in association with the player's profile, such as in data records in the database 24.

[0044] Most importantly, in accordance with the invention, the player activity information is analyzed and is utilized to generate one or more modified player skill index values. In one embodiment, as illustrated in FIG. 3, this analysis (of activity information / inputs 302) is performed by a data weighting engine 300. This engine 300 may be implemented by software which is stored in the memory of the skill event analysis server 22 and executed by the processor thereof, or by an external processing system.

[0045] In one embodiment, as illustrated in FIG. 2, at a step S3, an identity level L1 is determined for the received activity information. As illustrated in FIG. 3, this step may be performed by a data identity analyzer 304 of the data weighting engine 300. In one embodiment, this step involves determining an identity weight for the activity data. The identity weight is reflective of a measure of the reliability of the source of the data. The weight might be a numerical value, such as on a scale of 0-100, where 0 represents a lowest reliability of the source of the data, to 100 being the greatest. As one example, data which is provided to the engine 300 and which does not include linking player identification information and which was input by a player themselves, may be given a low weight, whereas data which is received from a trustworthy system which independently records player activity information and links it to an identity of a player which has been verified, may be given a high weight. Also, the “identity” may alternatively, or in addition, reflect other aspects of the reliability of the data, such as how old the data is (older data given less weight), the source of the data, including how the data was received or collected (for example, data collected from high precision sensors such as LIDAR and RADAR may be given higher weight than data collected from less reliable sensors or sources, including simple player input).

[0046] In a step S4 (see FIG. 2), the player activity information is also analyzed to determine an anomalous data level L2. This step may be performed, for example, by an anomalous data analyzer 306 of the data weighting engine 300. In one embodiment, this step involves comparing the player activity information to other activity information of the player (and / or other data, such as the data of other players) to determine how anomalous the data is (e.g. the variance of the data from existing data). Again, an anomalous data weight may be on a scale of 0-100, where a low score may be used to indicate that the data does not corroborate to other data of the player, while a high score does. For example, past player information may indicate that a player has hit tee shots of 350, 355, 345 and 360 yards. Newly received data may indicate a tee shot of 415 yards. This data may be deemed to be relatively anomalous and given a low score, such as 20. This process helps ensure that a player's index is not manipulated by a player's input of data to skew their index and to prevent the player's index from being skewed by erroneous data or data associated with anomalous events (for example, a player playing golf in a high wind may generate abnormal data which is then given a lower weight so that it doesn't disproportionately skew the player's skill index).

[0047] In a step S5, the outcomes of the identity level analysis L1 and the anomalous level analysis L3 of the activity information, such as in the form of first and second weighting values, is utilized to generate weighted activity information (such as the weighted data 308 which is output from the data weighting engine 300 in FIG. 3).

[0048] In a step S6, the weighted activity data is utilized to create one or more player skill index values or modify existing ones, such as the modified indexes 310 indicated in FIG. 3, which indexes may be stored in the database 24, and reported, such as by a reporting module 312). For example, for a particular index, the index may be determined by a numerical analysis of the weighted data for that skill. For example, a player's “tee shot” index might be reflected as [(w1×data1)+(w2×data2)+ . . . +(wn×data3)] / Σ(w), where data(n) represents a collected tee shot data point and w(n) reflects the weight assigned to that datapoint in terms of its identity and anomalousness.

[0049] It will be appreciated that when a player's profile is created, one or more default index values may be assigned, and those index values may then be modified based upon the receipt of actual activity information. In other embodiments, initial skill index values may be assigned based upon collected player information. For example, if a player has a GHIN handicap, that information may be utilized by the skill event analysis server 22 to assign one or more initial index values to the player, which values may then be modified based upon the receipt of actual activity information.

[0050] It will be appreciated that different index values may be generated for both different types of activities (bowling vs. golf), but different types of the same activity (in golf, putting vs. driving, etc.). In fact, index values may be generated corresponding to a wide range of activities and sub-activities, including associated characteristics.

[0051] In one embodiment, when activity information is received, it is analyzed for the particular type of activity it pertains to (e.g. golf vs. bowling; sub-activity such as driving vs. putting; activity characteristic information such as indoor vs. outdoor, weather, etc.). The amount of analysis may depend upon the level of detail of the information which is received by the skill event analysis server 22, as the amount of detail of the activity information which is obtained from different platforms P1-P3 may vary.

[0052] Of course, a variety of skill indices may be generated, and they may have different scales. For example, a skill index might be a 0-100 index (such as 0 being the lowest skill level and 100 the highest).

[0053] In one embodiment, a player's skill index (for a particular activity or sub-activity) may be translated to another indicator. For example, relative to golf, a player's skill index might be converted to a course handicap, such as by a translator module 314 of the activity server 24. The translator module 314 may be configured to automatically translate a player's skill level to a course handicap, or might do so in response to input from a player (such as a “provide handicap” selection by a player to an input button displayed by a graphical user interface displayed on a display of their mobile device). The translator may use parameters like course rating or slope rating, or may other data (such as proxy distances when actual course / hole distances are not known), to translate the player's general skill index to a handicap for a particular course.

[0054] In one embodiment, the player's skill index might also be translated, converted or correlated to other indices, such as a ranking. As one example, a player's overall skill or sub-skills may be assigned a ranking (such as represented by colors, e.g., green, blue, black, or symbols, etc.), where the ranking is dependent upon the player's skill index. For example, skill levels from 0-25 might be assigned to the “green” ranking, 26-50 to the “blue” ranking, etc.). In another embodiment, player skill levels may be mapped players may be grouped into rankings based upon their location to the mapping (for example, such a mapping may generally reveal a bell curve or indices and the player's assigned skill level may be assigned based upon where their skill lies on the curve). The player's ranking might be displayed graphically to them, such as on a display of their user device 30 by an application running on their device, via a browser or the like, as described in detail above. The player's ranking may be used for a number of purposes, including linking players of similar ranking for purposes of games or challenges.

[0055] As one aspect of the invention, the system may include one or more features for verifying a player's identity, such as to increase the identity level of received player activity data. As one example, a player identification system may be implemented which enables confirmation of a player's identity through a combination of sensors, including but not limited to facial recognition, object detection, kinetic motion tracking, and swing-print technologies. Of course, other player identity verification methods or systems might be utilized (collection and analysis of video or other images, ID validation, etc.).

[0056] As indicated above, in one embodiment, an engine 300 may be utilized to process / analyze player activity data in the process of generating player index information. In some embodiments, the system and method are model-driven, such as using artificial intelligence (“AI”) and other technology. Specifically, the engine may implement predictive activity data analysis, where the predictive formulation may be provided by a trained, or learned, AI model trained using an advanced machine learning algorithm which analyzes the player activity information to determine a weighting thereof (such as by analyzing the data against past data to determine the consistency (how anomalous) and reliability (how identifiable) the data is.Performance Feedback / Guidance

[0057] Further aspects of the skill event analysis system 20A will be provided with reference to FIGS. 4 and 5. Another aspect of the invention is a method of utilizing player activity and / or environmental data to generate feedback and / or performance guidance.

[0058] As one example, the skill event analysis system 20A may be configured to generate practice or training feedback or performance guidance which is tailored to at least one of: i) event conditions; ii) past performance; and iii) current performance. Details of this aspect of the invention will be appreciated from several examples.

[0059] Relative to FIG. 5, in one configuration, the system 20A might comprise at least one computing device, such as a system server 522, which is configured to implement a data weighting engine 500. This data weighting engine 300 may be like that described above, such as for receiving activity data from one or more sensors 550 (which might be associated with other devices or systems, such as described above and illustrated in FIG. 1). As with the data weighting engine 300 described above, the data weighting engine 300 may be configured to transform received data and store it in a database 524, such as in association with a player, such as identified from player identity information. As with the system 20 described above, the weighted data may be used by an index generator 560 to generate a player skill index, as in the manner described above.

[0060] The system server 522 may also implement (such as in the form of machine-readable code stored in a memory and executable by a processor of the server) a feedback / guidance generator 570. The feedback / guidance generator 570 may utilize player identity information and stored activity data (such as weighted or non-weighted data) stored in the database 524, to generate player feedback or performance guidance, such as described in more detail below.

[0061] As one example, the system 20A might be used to generate feedback or performance guidance to a player who is practicing or warming up, such as practicing golf shots or warming up for a round of golf. In one embodiment, in a step S100, the player may be identified. In some embodiments, this identification may be anonymous, meaning that actual identifying information about the player is not collected, but where the system 20A does associate a unique identifier (which could be an assigned ID, session number or the like). In other embodiments, the player may be identified, such as by the player providing identification information or information which can be used by the system 20A to access the player's identification information, such as a player's account.

[0062] For example, a player might interact with the system 20A via their mobile device or another computing device, such as facilitated by an application, browser or the like. If the identification is anonymous, a player might simply select “start training session” or the like, at which point a request for a session may be transmitted to the system server 522. The system server 522 might generate a session ID and begin associating information with that session ID, such as in one or more files in the database 24. In another variation, a player might create a player account which stores player identification information. The player might provide a player ID, password or other information to the system server 20, thus enabling the server to access the player's account (including identification information and any past performance data, etc., as described in more detail below). In some embodiments, the creation of a session may include input of a duration of the warm-up or training session, or other relevant information. For example the player might input into their mobile device 30 a warm-up or practice session duration (such as 15 min, 30 min, 1 hour, etc.) or such information might be received by the system 20 from another system of device, such as a tee or practice session scheduling system.

[0063] In a step S200, the system 20A receives activity or action data. This activity information or data may comprise at least one of: i) event conditions (event condition data D2) and ii) past activity performance (past player activity data D1).

[0064] The event conditions preferably provide information regarding the elements and / or atmosphere of the event. This information may comprise stored information and / or information which is collected, such as in real-time or near real-time. For example, relative to golf, the information might comprise stored information regarding a golf course. This might comprise information regarding the golf holes and their layouts, general hole yardage information or various other course information collected over time. The information may also comprise real-time information, such as wind speed, wind direction, temperature, humidity and / or other weather information. The information might also comprise information such as the time of day, amount of sunlight, information about the depth of grass (measured, estimated based upon time from last cut), specific hole yardage (such as based upon tee and pin placement) and various other information. As indicated above, this information may be collected by a number of sensors 550, or be provided to the system via other input (for example, a greenskeeper might log data into the system from a user terminal regarding when fairways were cut, etc.). The event condition information might also include information about the equipment used to participate in the event, such as golf clubs being used (such as a designation of different sets of clubs when a player has two or more sets, or of the configuration of a set of clubs, such as which clubs the golfer is using), where the user might input such information, such as via menus, etc. In one embodiment, the sensor data 500 may be provided to the feedback / guidance generator 570, or might be processed and stored by the data weighting engine 500 (where in one embodiment, the data weighting engine does not weight the event condition data).

[0065] Of course, the type of event condition information which is collected and / or utilized may depend upon the event. For example, in the case of bowling, the event condition information might include details regarding the lane oiling. In the case of tennis, it might comprise information regarding the court surface (clay, grass, etc.). Further, it will be appreciated that the event condition information may change over time, such as by being updated in real-time or near real-time, such as based upon the output of sensors.

[0066] In the case of an identified player, such as having a player account, the past activity performance may comprise data regarding the player's past participation in skill events, such as collected in the manner described above. For example, the information might comprise data regarding the player's past performance in skill events, whether they be for practice, competition or other purposes. This information may also comprise the player's generated skill index, as detailed above.

[0067] In a step S300, the activity data is analyzed in order to generate feedback and / or performance guidance. The particular analysis which is performed on the data may depend upon the desired output, such as whether the output is for training purposes, warm-up purposes, performance purposes or the like. The analysis may be performed using a system such that illustrated in FIG. 3 (including where the player's activity information is weighted as part of the analysis process). For example, the system 20A might utilize the information to: 1) generate a warm-up or practice session which is tailed to the player based upon the their past performance and event conditions (and including the amount of time for the session) and / or 2) generate performance guidance for an upcoming event, such as based upon event condition information, past activity information (and, if available, current activity information such as from a warm-up before the event).

[0068] In one example, the generator 570 may use an average of previous data (other options include but are not limited to: weighted average, machine-learning projected next shot dispersion, etc.) to estimate the projected performance in the upcoming event. As one example, the event might be to a play an 18 hole golf course, where the details of the course (such as the configuration of each hole of the course, including layout and distances) is known by the system 20A (such as by being updated and stored, determined from sensor or other collected data, etc.). The system 20A outputs the likely distances needed to minimize the shots necessary to complete the 18 holes based on the player's known data (such as their average hitting distances for different clubs). The system may run a Monte Carlo or other random sampling simulation of the player's projected shot performances. For example, estimate the projected shots needed to complete the course given the player's previous performance and dispersion, run multiple times (e.g. 1000X, 10,000X) with dispersion metrics. The result is an estimated number of target distances, which can be translated for the practice session. For example, a highly skilled player will have tighter shot dispersion, and less distance to the hole as a result. A less skilled player will have more shot dispersion, and longer shots to the hole as a result. Similarly, a longer driver of the golfer ball will have less distance to the hole off the tee than a shorter driver of the golf ball. The result of this analysis is a suggest series of target distances (or golf clubs), with a number of practice shots correlated with the dispersion for each distance (or club). The more the dispersion in the distance (or club), the more practice shots in the guided session.

[0069] For example:Course:Tees:Hole Layouts:Pin Locations:Spanish Trail Country ClubWhitesKnown“C”TABLE 1Player Projected ShotKnown DispersionSuggest PracticeDistancesIndexShots14 × 250yard shotsDispersion Index = 214 shots6 × 200yard shotDispersion Index = 515 shots8 × 150yard shotDispersion Index = 312 shots10 × 100yard shotDispersion Index = 1 5 shotsThe number of shots can be a proportion of the total shots available in the time allocated for the practice session. For example, the number of shots during a 20 minute practice session can be double that in a 10 minute practice session. The number of shots can be increased if the player's analytical performance during the practice falls significantly below expected performance, or might be decreased if their practice shots are performing better than expected.

[0071] In a step S400, the analyzed activity information is transformed into feedback or performance guidance information. This information may be transmitted to the player, such as for presentation on their user device 30. In some embodiments, this information may comprise textural or graphical information, such as an image or video which graphically presents the feedback or performance guidance, as described in more detail below.

[0072] In a step S500, updated activity information, such as updated event condition information and current player activity data D3, may be received by the feedback / guidance generator 570. The current activity performance information or data may comprise information about the player's performance or actions in an event in real or near real-time, and may comprise actions taken in response to the feedback or performance guidance generated and provided to the player in step S400. Such information may be collected in the manner described above, such as in relation to the systems L1, L2 and L3 which are illustrated in FIG. 1. For example, relative to a golf warm-up session, the current activity performance information may comprise information regarding golf clubs which are used and shots taken with those clubs, including ball speed, trajectory, distance and the like.

[0073] In a step S600 this additional data may be analyzed (such as in association with past player activity data) to, in a step S700, generate modified feedback or performance guidance. Such may comprise generating: 1) modified warm-up or practice session information, such as by using a player's current activity information from an on-going warm-up or practice session in order to modify that session and 2) modified performance guidance for use by a player during an event, such as to modify a player's actions during the event based upon collected activity information during the event and / or changed event condition information.

[0074] As illustrated in FIG. 4, current player activity data D3 may also be stored for future use as past player activity data D1, such as after being processed by the data weighting engine 500.

[0075] Additional aspects of the invention will be appreciated from a number of examples.

[0076] As one aspect of the invention, the system 20A may be used to generate customized event guidance to a player. For example, a player who is about to play a particular golf course may be provided with customized performance guidance. First, the system 20A may use event condition information to inform the player of performance guidance. As indicated above, this guidance may be based upon current event conditions (such as weather, including wind) and stored event conditions such as course layout, etc. For example, if at the time of play a 15 mph North wind is detected, when the player prepares to play a hole where the player is teeing off to the East, the system 20A may instruct the player to target their shot at a corrective angle towards the North, to compensate for the wind. This information might be indicated textually, or might be indicated graphically, such as via a graphically generated shot projection that may include course image data, thus allowing the player to visually reference the desired correction indicated by the system 20A to the actual course.

[0077] As indicated, however, the system 20A might also use past and / or current activity information about the player to provide this guidance. As one example, assume that the player is preparing to tee off on a short par-3 hole into the North wind, where the yardage to the green should, based upon past performance data, be reachable by the player hitting a 9 iron. In that instance, the system 20A may provide guidance that, due to the wind and the normal shot distance of the player using a 9 iron, that the player should tee off using an 8 iron.

[0078] Moreover, as also indicated above, the player's existing activity information may be used to generate the guidance. Relative to the above example, as the player plays the course, the activity information of the player may be tracked and utilized relative to the generation of later guidance. As one example, the current activity information may indicate that the player is not sufficiently correcting their shot trajectory when the wind is a cross-wind to their shot. The system may thus, as to later shots, provide guidance to the player which indicates to the player that they should make a larger trajectory correction.

[0079] As another example, a player might obtain guidance, feedback and / or performance information relative to a warm-up session. For example, the system 20A might use event condition information, past activity information and / or current activity information in order to provide guidance relative to a warm-up session. The system 20A might generate a warm-up sequence (such as in which the player is instructed to take golf shots with different clubs in a golf warm-up session) and evaluate the performance of the player (current activity / action information) to determine additional feedback or performance guidance. For example, upon evaluating current activity data which indicates that the player is pulling their golf shots, the system 20A might instruct the player to make designated adjustments to their swing (and after which, the player's subsequent activity information may be re-evaluated). As another example, upon determining that the player is pulling their golf shots but that swing corrections have not corrected the problem, the player might be instructed to make adjustments in the trajectory of their golf shots when they play a golf game after their warm-up. As yet another example, the player's performance during a warm-up session may be compared to their past performance for purposes of generating feedback or performance guidance. For example, the system 20A may determine that during a warm-up session, a player is hitting their driver 250 yards per shot, whereas historically the player has been hitting 275 yards. When the player begins play, such as of a hole that has a water hazard at 250 yards, the system 20A might instruct the player to use a different club, such as a 2 iron, in order to layup short of the hazard, since based upon current activity information, they are not driving their normal 275 yards which would clear the hazard.

[0080] As another example relating to a practice or warm-up session, the system 20A may generate and output information relating to a warm-up or practice session where that output is generated using the above-reference event condition, past performance and current performance information, such as in relation to a designated practice time. As one example, relative to a golf warm-up session, a user may input (such as into the system 20A via an application running on their mobile device) the time available to practice (e.g. 10 minutes) or the system 20A may receive that information automatically (such as from a communicatively linked tee time management system). The system 20A may optimize the clubs to be used in the session and the number of practice shots knowing (i) what clubs the player is likely to use (see below) and (ii) the pace of practice for the user. User 1 may hit one golf ball every 10 seconds, with 20 seconds between clubs, whereas User 2 may hit one golf ball every 20 seconds with 30 seconds between clubs. User 1 would be guided to take more shots in the 10 minutes than User 2 to optimize the time available. The system optimizes the selection of clubs based on the physical reality of how long the player takes to hit balls in a practice session, and how long the player takes to switch clubs, plus what clubs are important to the player's performance (such as to an upcoming event), such as based upon event conditions and past and present activity data (for example, relative to practice for a long course, the system 20 might focus the player on practice shots using drivers and low numbered irons; when the player's past performance or performance during the warm-up session indicates that the player is not driving the ball well, the system 20A may instruct the player to take extra practice shots with their driver, etc.)

[0081] As another example, the system may generate projected performance information and performance guidance information. As detailed herein, the system 20A is capable of generating predicted performance data for a player using event condition information and past (and / or present) performance data for a player. For example, relative to golf, the system 20A may utilize (i) information regarding the golf course the player will be playing (either on a simulator or on green grass) and associated event conditions, and (ii) the past and / or present performance of the player, such as evidenced by the player's Skill Index, to generate predicted performance data. This might comprise a handicap for the player for the particular event (given the actual event conditions in relation to the player's current level of performance) and / or might comprise performance guidance information.

[0082] In one embodiment, the system 20A may implement a predictive generator (such as for executing a Monte Carlo type predictor) which runs a simulation (such as a repeated simulation, such as 1000 or 10,000 simulations) of the shots needed to be taken with the clubs likely to be used by the player for one or more golf holes (such as for a complete round), in relation to the event conditions. This information may be used to generate a real-time handicap for the player for the event (including for the particular course and the course conditions). This information may also be used to generate an optimized warm-up or practice session, and / or to provide feedback or guidance to the player relative to participation in the skill-based event.

[0083] For example, the system 20A may weight the golf clubs the player should use in the warm-up or practice session in order to optimize performance relative to the upcoming event and the practice time available. For example, the system 20A may determine that a player will likely hit 14× drivers, 5× 3-woods, 5× 5-irons, 1× 6-irons, 7× 7-irons, 2× 8-irons, 10× PWs (5× full shots, 5× pitch shots), 11× SWs (2× full shots, 9× chips) over the course of a round, on average. Based on the clubs the player is likely to use, the system 20 can (i) divide the time available by the shots by club and guide the player to practice the same percentage of time on each club as they are projected to use on the course, (ii) knowing the consistency of each club, the system can optimize the number of shots to increase number of shots for clubs which are less consistent (e.g. user is more consistent with the PW and less with their Driver, so the system weights additional shots to the Driver), (iii) the system can adjust to the tempo of the current practice session, increasing or decreasing the number of shots to accommodate for the real-time rate of shots and / or instruct the user to speed up or slow down their shots to normalize their session to their optimal rate of practice shots, (iv) seeing the performance of each club during the practice session, the system can increase or decrease the number of shots until the player is showing they are optimizing performance for that club based on their normal behavior with the club and Skill Index.

[0084] While in the example above, reference is made to generated club-usage instructions, the instructions might be generated or referenced in other manners. For example, the warm-up or practice session information might reference practicing shots of a certain distance, rather than with a particular club, thus allowing the player to utilize the club of their choice to fulfill the target.

[0085] As indicated above, the system 20A may generate performance guidance for a skill-based event, such as based upon event conditions, past performance and current performance data. Relative to a round of golf, the output might comprise a yardage matrix with tendencies for the day, such as below in Tables 2 and 3:

[0086] Custom User Real Golf Shot Recommendations for <<DATE>> playing on <<COURSE>><<GREEN GRASS / SIMULATOR GOLF>>:TABLE 2CLUBDISTANCEAIMDriver250yardsAim 10 yards right3-wood220yardsAim 5 yards right5-iron191yardsAim 10 yards left6-iron177yardsAim 10 yards left7-iron166yardsAim straight8-iron153yardsAim straightPW Full128yardsAim 2 yards leftPW Pitch55yardsAim straightSW Full98yardsAim straightSW Chip25yardsAim 1 yard rightTABLE 3TARGETACTUALAIM260 yards250yardsAim 10 yards right235 yards220yardsAim 5 yards right200 yards191yardsAim 10 yards left185 yards177yardsAim 10 yards left170 yards166yardsAim straight160 yards153yardsAim straight135 yards128yardsAim 2 yards left 50 yards55yardsAim straight100 yards98yardsAim straight 25 yards25yardsAim 1 yard rightThis information can be sent via text message, displayed in-app, printed on a sheet of paper, or available via a connected device (golf cart information screen, range finder, etc.) to guide the player through their round of golf. In simulator golf or where there is ball data for shots, the recommendations can be updated real-time based on the performance on the course.

[0088] In accordance with this aspect of the invention, the system 20A is able to provide a player with a real-time view of their performance or skill level for each action, series of actions, and overall performance or skill level, while performing a practice and / or warm-up session or participating in an actual competition or event. Further, the system 20A utilizes event condition, past player activity and / or present player activity information to generate feedback which informs the player of their readiness, such as to perform the upcoming task or play an upcoming game. In case where a player is under-performing, the player may be informed that they may not perform optimally in those specific areas and be provided with feedback or guidance to address the issue, such as to make physical adjustments (such as to a golf swing or to the intended trajectory), or other adjustments, like adding an extra club in golf. On the other hand, if the player is performing better than expected the system may provide feedback that the player can be more aggressive (such as, in the case of golf, choosing a more direct line to the hole or expecting a longer distance from the clubs).

[0089] In accordance with the invention, by optimizing the player's performance of action (such as each sub-task) in the practice or warm-up session, a person is able to reduce the amount of time which is necessary to identify the performance or skill level, reallocating time to areas which require additional actions based on the feedback, and / or saving total time before the game or performance.

[0090] A particular aspect of the invention is that the system 20A evaluates player action information which is specific to the player, and not generalize to a group. The player thus gets individualized feedback which is tied to their own performance. For example, on a windy day, most players in a group of golf players might see a significant degradation in the accuracy and distance of their golf shots. However, a player of that group that tends to keep their shots lower may not see such a degradation. The system 20A provides feedback to that player which is not based upon the group (which would likely result in feedback indicating that the player needs to make a significant change to their game, such as picking different clubs or the like), but which is base upon their own performance.Advantages

[0091] The invention has numerous advantages over prior art systems and methods.

[0092] As one aspect of the invention, the system of the invention automates the collection of data, removing the hassle and integrity issues with manual data input. Moreover, the system collects and analyzes many data points for each activity, allowing for a more detailed understanding of the player activity than only the result of the activity.

[0093] As another aspect, the player activity data is stored, analyzed and normalized across platforms, allowing for users to interact cross-platforms with a homologated skill level. This is different to the siloed data collection and analysis which dominates the market today.

[0094] One advantage to the invention is the ability to collect player activity data from a plurality of different sources (including different systems). The collection of a greater amount of data permits the generation of more reliable skill indices. Further, skill indices are updated in real time as data is obtained from the different sources. For example, a player's skill index for golf tee shots may change during the play of a single round of golf.

[0095] In some embodiments of the invention, collect player activity data is weighted based on the verifiability and consistency. In one embodiment, player identity may be confirmed, increasing the verifiability of the player activity data. Unlike “trust-based systems” like Arccos or GHIN, the invention is able to give additional weight to data which can be verified from the user and decrease the weight of data which is less reliable, like anonymous data (recorded without Player Identification) or anomalous data (data which deviates significantly from the expected).

[0096] The invention uses an engine, which may implement AI-algorithms, to weight anonymous and anomalous data by comparing the results of each action to the expected results and weighing each action and associated underlying metric based on the expected results. This discounting adapts over time, to identify and reweight trend, so that if a user's data show changes over any significant period of time or sequence, their past data is re-weighted to accommodate for the change.

[0097] The invention allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with recommendations (which maybe AI-generated or selected), (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and / or (c) enable cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0098] While each of these are being done independently, the lack of a systemic method of combining them, automated and verified, limits their usefulness in real world scenarios, as is evident by the lack of products and services similar to the invention in the market today.

[0099] Users are able to get a real-time view of their skill level for each action, series of actions, and overall skill. This is used to optimize engagement through training, rewards and cash prizing.

[0100] Their data is automatically collected, removing the need for manual input and / or manual verification. In the case of competitions, their relative skill level is used to ensure fair results independent of skill level. And their skill level is applicable and visible across all integrated platforms of gameplay. In the case of golf, their skill level will adjust for each driving range, launch monitor, and golf course, and for each shot, series of shots (skill game, hole or holes, etc.), and represent a fair and accurate overall skill level.

[0101] Game and sport companies can create a better user experience by rewarding increased engagement through skill-normalized activities. This leverages what is popular in places like on-course handicapped golf tournaments and extends it to every time a user is taking a golf shot.

[0102] Game and sport companies can now activate reward systems and cash prizing. Game and sport companies can also target training advice using a holistic understanding of a player's actions, rather than a limited view through siloed data collection. Lastly, the mitigation of anonymous and anomalous data allows for effective re-engagement that lacks the risk of sand bagging, fraud or similar unverified skill level measurement methods.

[0103] Currently, no accurate data-driven technology exists for predicting the performance of a player in a skill-based event. Currently, performance prediction is loosely based upon a review of a player's past performance. For example, the prediction of a golfer's performance may be based upon a handicap, where currently that handicap is a generic value which is generated from scores of past rounds of golf. The system of the invention enables the accurate generation of a projected performance of a player in a skill-based event based upon collected past performance data, collected practice or warm-up data, and / or event condition data. The system is configured to use this data to predict the performance of a player, such as in an upcoming event. This may comprise, for example, a prediction of the player's performance in an upcoming round of golf in relation to course data and course conditions (e.g. event condition data), based upon specific player performance data (data reflecting the specific past performance activities of the player, such as relating to driving distance, putting performance, etc.), such as to provide an accurate handicap for the player for that event (and thus where the performance prediction would vary based upon differences in conditions, such as weather, and for different courses, as well as if the player's warm-up performance varies from their past performance data).

[0104] Currently, there is no system which is capable of generating feedback or performance guidance in substantially real-time between a player's practice or warm-up performance and an upcoming event. In accordance with the present invention, the system is capable of analyzing a player's warm-up performance in relation to event condition data and the player's past performance data, to generate feedback or performance guidance for the player that links their current performance status to the upcoming event.

[0105] Similarly, there is currently no technology which links a player's past performance to a practice or warm-up session in substantially real-time, such as to generate feedback or performance guidance for the warm-up or practice session. As indicated above, the system is capable of: 1) generating feedback or performance guidance (such as a warm-up session guidance) which is based upon the current event conditions in relation to the player's past performance (for example, by generating a warm-up session that is tailored to most accurately prepare the player for an upcoming event based upon the event condition when considering the known performance of the player) and 2) modify the practice or warm-up session when the player's performance deviates from the predicted performance. As one example, if the player is preparing the warm-up for play of a short Par-3 oriented course when past performance shows that the player has been hooking their shots with middle irons, the system may generate and output a warm-up session which focuses on more middle iron warm-up shots with directional correction. If during the session the system detects that the directional correction is insufficient to correct the trajectory of the shots or the player is hitting shots a shorter distance than anticipated, the system might output a guidance to change which middle irons are used and to make a further directional correction.

[0106] It will be understood that the above-described arrangements of apparatus and the method there from are merely illustrative of applications of the principles of this invention and many other embodiments and modifications may be made without departing from the spirit and scope of the invention as defined in the claims.

Examples

Embodiment Construction

[0025]In the following description, numerous specific details are set forth to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.

[0026]Some aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.

Skill Event Analysis System

[0027]One embodiment of an exemplary system 20 of the invention is illustrated in FIG. 1. As illustrated, the system 20 may comprise at least one skill event analysis server 22. The skill event analysis server 22 may comprise a processor an...

RELATED APPLICATION DATA

[0001] This application claims priority to U.S. Provisional Application Ser. No. 63 / 747,153, Jan. 20, 2025, which is incorporated by reference herein in its entirety.FIELD OF THE INVENTION

[0002] The present invention relates to skill-based activities, such as games of skill, and performance evaluation of such activities.BACKGROUND OF THE INVENTION

[0003] Many skill-based activities (whether played recreationally or professionally, and whether for wager or amusement) include some measure of skill tracking, such as the Golf Handicap & Information Network (GHIN), various bowling handicapping systems, and video game ranking systems. These systems lack integrity checks for the data that drives the skill determination, allowing for “sandbagging” (pretending to be worse at a skill than actual), “vanity handicaps” (inputting more favorable data than what really occurred), and versions of user fraud including more experienced players acting like less experienced players in order to get advantages.

[0004] Moreover, many of these systems lack cross-platform integration, meaning their data exists in a silo for a specific source of data, rather than accumulating data from as many sources as possible. This limits the ability to truly track a player's skill to the data source, missing crucial data in other systems. For example, the GHIN system requires manual entry of data from real world golf courses, with a minimum of 9 holes. If a golfer plays less than 9 holes, or hits balls on a driving range or golf simulator, that data does not inform their GHIN index.

[0005] Lastly, many skills indexing systems use the final results of an activity, with limited internal metrics, to determine skill. For the example of golf, the number of shots needed to complete a hole may be tracked. Some apps like the GHIN app and the Grint app allow users to manually enter additional information to track trends in performance, like “sand save %” or “greens in regulation”. But the player's skill index is limited by the GHIN's requirement for a certain number of holes to be completed, and limitation wherein only the total score for a hole is measured. Arccos Golf tracks users' individual shot data, but that is not used to update or inform their GHIN index. Moreover, the Arccos data is proprietary to the Arccos system, limiting its usefulness for golfers when playing across varying platforms. For example, Arccos data is not used for locations like Topgolf, Five Iron or the GHIN system, where billions of golf shots are taken, and millions of golf shots are recorded every year.

[0006] There is also no integrated method of taking the skill index and applying it to increase engagement, retention and monetization of the player. The Arccos system and GHIN can be integrated into other systems, but those integrations have to date lacked the efficacy of a positive feedback loop to maximize utilization of the system. The invention allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with AI-recommendations, (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and (c) cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0007] While each of these are being done independently, the lack of a systemic method of combining them, automated and verified, limits their usefulness in real world scenarios, as is evident by the lack of products and services similar to the invention in the market today.

[0008] Related to these issues, skill game players also desire feedback regarding their performance and guidance or input on how to improve their performance. There are existing devices and systems which can provide information to a player of a skill game about their inputs or performance. For example, golf ball trackers can be used by a player during warm-up to provide information to the player about the trajectory of their practice shots (loft; speed; distance). However, the player then needs to try and analyze this information to make determinations about adjustments in their inputs in hopes of improving their play.

[0009] There is thus no integrated method of taking information regarding a player's skill-based activities, including as transformed into a skill index, and applying that information to increase performance, engagement, retention and monetization of the player.

[0010] It is an object of the invention to overcome these and other issues associated with existing systems and methods for tracking and measuring player skill and provide performance guidance.SUMMARY OF THE INVENTION

[0011] Aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.

[0012] In one embodiment, a player skill indexing and guidance system comprises an indexing and guidance system comprising at least one server and a data storage device, the server comprising a communication interface, a processor, a memory and machine readable code stored in said memory and executable by the processor to cause the server to: receive, from a plurality of activity tracking platforms over one or more electronic communication networks, user activity data regarding a user's performance of a skill-based activity; implement a data weighting engine which implements a data identity analyzer and an anomalous data analyzer, the data identity analyzer configured to generate a first weighting value for the received user activity data based upon a level of verifiability of an identity of the user to the user activity data and a second weighting value for the received user activity data based upon a level of anomalousness of the user activity data, the data weighting engine configured to utilize the first and second weighting values to output weighted user activity data; and generate a player skill index utilizing the weighted user activity information. The one or more generated player skill indices may be transmitted, via an electronic communication link, to at least one remote electronic device.

[0013] As one aspect of the invention, unlike “trust-based systems” like Arccos or GHIN, the present invention is able to give additional weight to data which can be verified and decrease the weight of data which is less reliable, like anonymous data (recorded without player identification) or anomalous data (data which deviates significantly from the expected).

[0014] In one embodiment, the invention uses AI-algorithms to weight anonymous and anomalous data by comparing the results of each action to the expected results and weighing each action and associated underlying metric based on the expected results. This discounting adapts over time, to identify and reweight trend, so that if a user's data show changes over any significant period of time or sequence, their past data is re-weighted to accommodate for the change.

[0015] In one embodiment, the invention also allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with AI-recommendations, (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and / or (c) enables cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0016] In one embodiment, a system is configured to receive event condition and player activity information, such as from condition sensors and activity action sensors, and use that information to generate performance guidance for the player. The performance guidance may comprise instructions for a practice or warm-up session, or an event. The system may utilize updated event condition information and / or current player activity information, such as received from the sensors, to update the performance guidance or otherwise provide feedback to the player. The updated performance guidance may comprise, modifications to a practice or warm-up session based upon changes in conditions or current player activity information, or modifications to guidance for an event, such as based upon changing conditions or the player's performance during the event.

[0017] In one embodiment, a skill-based event analysis system may comprise at least one server, a data storage device, a memory, a communication interface, and a processor, said memory storing machine-readable code for execution by said processor to cause the server to: receive skill-based event condition data from one or more condition sensors; receive past skill-based event performance data relating to a player; utilize the event condition and event performance data to generate a first performance guidance output to the player for use in a performance of a first skill-based event action by the player; receive, from the one or more condition sensors, updated event condition data; receive, from at least one activity sensor which is configured to sense an action associated with an input by a player to an element of physical game equipment relative to the first skill-based event action an output of the at least one activity sensor relative to the first skill-based event action; and utilize at least one of the updated event condition data and output of the at least one activity sensor to generate a modification to the first performance guidance.

[0018] Further objects, features, and advantages of the present invention over the prior art will become apparent from the detailed description of the drawings which follows, when considered with the attached figures.DESCRIPTION OF THE DRAWINGS

[0019] In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0020] FIG. 1 illustrates one embodiment of a system of the invention;

[0021] FIG. 2 is a flow diagram which illustrates one method of the invention;

[0022] FIG. 3 schematically illustrates a configuration of aspects of the system illustrated in FIG. 1;

[0023] FIG. 4 is a flow diagram which illustrates another method of the invention; and

[0024] FIG. 5 is a schematic illustrating aspects of another system of the invention.DETAILED DESCRIPTION OF THE INVENTION

[0025] In the following description, numerous specific details are set forth to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.

[0026] Some aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.Skill Event Analysis System

[0027] One embodiment of an exemplary system 20 of the invention is illustrated in FIG. 1. As illustrated, the system 20 may comprise at least one skill event analysis server 22. The skill event analysis server 22 may comprise a processor and a memory. The processor may be configured to execute machine readable code or “software” or otherwise process information, such as obtained from one or more remote devices. In one embodiment, the software is instantiated in non-transitory form in the memory. The software may have various structures, such as comprising a plurality of modules that, when executed by the processor, cause the processor to perform portions of a process. The memory or another data storage device 24 which is accessible by the processor may store data which is usable by the processor. Such data may comprise, for example, one or more player index values as described below. Further, the skill event analysis server 22 preferably comprises one or more communication interfaces, such as to allow the skill event analysis server 22 to communication with one or more networks or other devices via one or more communication links. These communication links may be wired or wireless.

[0028] In a preferred embodiment, the skill event analysis server 22 is configured to receive activity information from a plurality of remote devices or systems. For example, as illustrated in FIG. 1, the skill event analysis server 22 may communicate with a plurality of activity platforms P1-P3 (of course, as indicated herein, the number of platforms or devices, including their configuration, may vary). The platforms P1-P3 may similarly comprise at least one computing device, such as a server, which comprises a processor and a memory which stored machine-readable code which is executable by the processor. In one embodiment, the platform P1-P3 is configured to collect player activity information, such as by direct input to one or more input devices, or from one or more sensors S.

[0029] The input devices may have various forms and may be configured to be directly manipulated by the player, with the input device outputting activity information to the processor of the platform P1-P3. For example, “direct input” devices might comprise buttons, joysticks, hand-held devices or the like.

[0030] On the other hand, various types of sensors S or other devices might be used to sense or detect player activity information, such as in relating to a player's inputs to another device. These may include, but are not limited to, accelerometers, motion detecting devices, velocity measuring devices (radar, dual radar, Doppler radar, LIDAR, etc.), distance measuring devices, force measuring devices (strain gauges, etc.), cameras or other image devices or sensors, identification tags (including but not limited to RFID tags, printed bar codes, etc.), location determining devices (GPS devices, etc.) pressure and / or mass sensors, light (visible or invisible) sensors, sound sensors, and others. For example, at a golf driving range, sensors may be used to track a player's swing of a golf club. Further, RFID tags may be associated with golf balls and those tags may be read, such as to determine a location where a ball is hit and thus the distance that the ball traveled, how close the ball is to the hole, etc. As another example, one or more cameras may track the path of a golf ball to determine the same or similar information. These or other sensors might be used to track the path or movement of a ball, the location of a ball, a hole sensor (such as for detecting a golf ball in the hole) etc., for determining an outcome of the event as to a particular player. Of course, the particular type of sensor or detector may vary, such as by the type of event, and might comprise more than one type of detector or sensor, including combinations of sensors or detectors, including in a mesh. Additional sensors may comprise environment detection sensors. Such sensors might comprise a anemometer, a thermometer, an atmospheric pressure sensor, a hygrometer, a rain sensor, or various other devices for receiving input or detecting one or more conditions associated with a skill event location (golf course, tennis court, bowling alley, etc.)

[0031] Such detectors or sensors might be positioned in various locations. For example, as illustrated in FIG. 1 in relation to a platform P3 which is associated with a golf event at a golf course, the detectors or sensors S might be positioned at the tee box, in tee markers, in trees or bushes, in electrical or sprinkler boxes, at the pin or hole, around the green and / or fairway, on poles, on golf carts or other equipment. In some cases, detectors or sensors might be associated with arial craft such as blimps or drones.

[0032] In some embodiments, the various input devices and detectors or sensors might be configured for wired communications, but most preferably wireless communications. For example, the input devices and detectors or sensors might comprise Wi-Fi enabled devices which sync with one or more Wi-Fi communication hubs for communicating with the platform server, such as via one or more networks N (WANs, LANs, the Internet, cellular network, etc.).

[0033] In some embodiments, the platform might comprise one or more user input devices (such as a mobile device, a kiosk, smart watch or fitness watch, etc.), which are configured to sense or detect player activity information or receive such information from one or more other devices, such as sensors. In some embodiments, such devices may communicate with an event server or an application running on a processor of the user's device. For example, the user device might comprise a tablet, phone, PDA or other device.

[0034] As various non-limiting examples, the platforms P1-P3 may comprise customized platforms or existing platforms or devices. Such existing platforms or devices might comprise a Topgolf™ activity platform, a SkyTrak™ home golf simulator or launch monitor, a Full Swing Golf™ simulator, a Toptracer™ golf range platform, a Swing Suite™ activity platform, a GolfTec™ training / lesson location, a Dryvebox™ mobile golf similar, or others.

[0035] These platforms P1-P3 may also be associated with systems other than the skill event analysis server 22. For example, activity information at a Topgolf™ location may be collected and provided to a back-end hosting system of the Topgolf™ platform. As another example, activity information which is collected by a user's smart watch or fitness tracker may also be provided to one or more specific third-party activity tracking systems. Thus, the skill event analysis server 22 may receive player activity or other information directly from platforms that comprise individual devices, or from one or more components of a multi-component system (such as from sensors at a Topgolf™ bay or a back end Topgolf™ system server).

[0036] Most importantly, in accordance with the present invention, a player's activity information from these various different or disparate platforms is collected by the skill event analysis server 22. In one embodiment, an application program interface (API) may be provided between one or more of the remote platforms P1-P3 and the skill event analysis server 22, such as to allow player activity information from the different platforms to be usable by the skill event analysis server. For example, a first API might be stored in the memory of the skill event analysis server 22 and be executable by the processor in order to process (such as by interpreting or translating) player data received from the first platform P1, such as from a first form or format, to a form or format which is usable by the skill event analysis server 22. Of course, each API may be required to be customized based upon the format of the player information as output by each platform P1-P3 in relation to the desired format at the skill event analysis server 22.

[0037] As illustrated in FIG. 1, the indexing system 20 may include one or more user devices 30. Such devices might comprise, for example, a desktop computer, laptop computer, tablet, mobile communication device or the like. Such devices may comprise a processor, a memory, machine-readable instructions stored in the memory and executable by the processor. Such instructions may be in the form of an application, such as a browser application or dedicated application. In one embodiment, the user device 30 and the skill event analysis server 22 may communicate, such as via one or more communication links (including the Internet, etc.)

[0038] As one example, a user might communicate with the skill event analysis server 22 via a browser executed on their user device 30 as interfacing to a web server implemented by or associated with the skill event analysis server 22. The browser may enable the presentation of information from the skill event analysis server 22 via a display of the user's device 30, and input to the skill event analysis server 22, such as via input to input devices of the user device (such as selection of elements which are graphically displayed on a display of the user device 30 via an associated touch screen).

[0039] In another embodiment, a user might download an application to their user device 30. This application may be stored in a memory of the user device 30 and when executed, implement aspects of the functionality described herein, such as by communication with the skill event analysis server 22. Such an application, when executed, might cause a display of the user's device 30 to display one or more graphical user interfaces and information provided by the skill event analysis server 22.Skill Event Indexing

[0040] Further aspects of the skill event analysis system 20 will be provided with reference to FIGS. 2 and 3. One aspect of the invention is a method of utilizing player activity data to generate and update one or more player skill indexes.

[0041] Referring to FIG. 2, in a first step S1, a player profile may be created. The profile may include information which identifies a player. In some embodiments, the player profile may be identified by a player identifier, such as a unique ID. Relative to the system illustrated in FIG. 1, the player profile may be created by the skill event analysis server 22, wherein information regarding the player profile is maintained in one or more data records, such as in the associated database 24.

[0042] In a step S2, player activity information is generated and received. For example, the player may participate in one or more activities in association with one or more platforms P1-P3 such as illustrated in FIG. 1. In one embodiment, information regarding the activity is transmitted along with information which identifies the player. The information which identifies the player might comprise the player's profile ID, their name, phone number or various other information which is linked to their player profile and which the skill event analysis server 22 may use to determine that the activity information corresponds to the player.

[0043] As noted above, as part of receiving the activity information, the information may be translated or transformed, such as from a format used by the platform P1-P3 to a format native to the skill event analysis server 22 (such as via the above-noted APIs). In one embodiment, the activity information is stored in association with the player's profile, such as in data records in the database 24.

[0044] Most importantly, in accordance with the invention, the player activity information is analyzed and is utilized to generate one or more modified player skill index values. In one embodiment, as illustrated in FIG. 3, this analysis (of activity information / inputs 302) is performed by a data weighting engine 300. This engine 300 may be implemented by software which is stored in the memory of the skill event analysis server 22 and executed by the processor thereof, or by an external processing system.

[0045] In one embodiment, as illustrated in FIG. 2, at a step S3, an identity level L1 is determined for the received activity information. As illustrated in FIG. 3, this step may be performed by a data identity analyzer 304 of the data weighting engine 300. In one embodiment, this step involves determining an identity weight for the activity data. The identity weight is reflective of a measure of the reliability of the source of the data. The weight might be a numerical value, such as on a scale of 0-100, where 0 represents a lowest reliability of the source of the data, to 100 being the greatest. As one example, data which is provided to the engine 300 and which does not include linking player identification information and which was input by a player themselves, may be given a low weight, whereas data which is received from a trustworthy system which independently records player activity information and links it to an identity of a player which has been verified, may be given a high weight. Also, the “identity” may alternatively, or in addition, reflect other aspects of the reliability of the data, such as how old the data is (older data given less weight), the source of the data, including how the data was received or collected (for example, data collected from high precision sensors such as LIDAR and RADAR may be given higher weight than data collected from less reliable sensors or sources, including simple player input).

[0046] In a step S4 (see FIG. 2), the player activity information is also analyzed to determine an anomalous data level L2. This step may be performed, for example, by an anomalous data analyzer 306 of the data weighting engine 300. In one embodiment, this step involves comparing the player activity information to other activity information of the player (and / or other data, such as the data of other players) to determine how anomalous the data is (e.g. the variance of the data from existing data). Again, an anomalous data weight may be on a scale of 0-100, where a low score may be used to indicate that the data does not corroborate to other data of the player, while a high score does. For example, past player information may indicate that a player has hit tee shots of 350, 355, 345 and 360 yards. Newly received data may indicate a tee shot of 415 yards. This data may be deemed to be relatively anomalous and given a low score, such as 20. This process helps ensure that a player's index is not manipulated by a player's input of data to skew their index and to prevent the player's index from being skewed by erroneous data or data associated with anomalous events (for example, a player playing golf in a high wind may generate abnormal data which is then given a lower weight so that it doesn't disproportionately skew the player's skill index).

[0047] In a step S5, the outcomes of the identity level analysis L1 and the anomalous level analysis L3 of the activity information, such as in the form of first and second weighting values, is utilized to generate weighted activity information (such as the weighted data 308 which is output from the data weighting engine 300 in FIG. 3).

[0048] In a step S6, the weighted activity data is utilized to create one or more player skill index values or modify existing ones, such as the modified indexes 310 indicated in FIG. 3, which indexes may be stored in the database 24, and reported, such as by a reporting module 312). For example, for a particular index, the index may be determined by a numerical analysis of the weighted data for that skill. For example, a player's “tee shot” index might be reflected as [(w1×data1)+(w2×data2)+ . . . +(wn×data3)] / Σ(w), where data(n) represents a collected tee shot data point and w(n) reflects the weight assigned to that datapoint in terms of its identity and anomalousness.

[0049] It will be appreciated that when a player's profile is created, one or more default index values may be assigned, and those index values may then be modified based upon the receipt of actual activity information. In other embodiments, initial skill index values may be assigned based upon collected player information. For example, if a player has a GHIN handicap, that information may be utilized by the skill event analysis server 22 to assign one or more initial index values to the player, which values may then be modified based upon the receipt of actual activity information.

[0050] It will be appreciated that different index values may be generated for both different types of activities (bowling vs. golf), but different types of the same activity (in golf, putting vs. driving, etc.). In fact, index values may be generated corresponding to a wide range of activities and sub-activities, including associated characteristics.

[0051] In one embodiment, when activity information is received, it is analyzed for the particular type of activity it pertains to (e.g. golf vs. bowling; sub-activity such as driving vs. putting; activity characteristic information such as indoor vs. outdoor, weather, etc.). The amount of analysis may depend upon the level of detail of the information which is received by the skill event analysis server 22, as the amount of detail of the activity information which is obtained from different platforms P1-P3 may vary.

[0052] Of course, a variety of skill indices may be generated, and they may have different scales. For example, a skill index might be a 0-100 index (such as 0 being the lowest skill level and 100 the highest).

[0053] In one embodiment, a player's skill index (for a particular activity or sub-activity) may be translated to another indicator. For example, relative to golf, a player's skill index might be converted to a course handicap, such as by a translator module 314 of the activity server 24. The translator module 314 may be configured to automatically translate a player's skill level to a course handicap, or might do so in response to input from a player (such as a “provide handicap” selection by a player to an input button displayed by a graphical user interface displayed on a display of their mobile device). The translator may use parameters like course rating or slope rating, or may other data (such as proxy distances when actual course / hole distances are not known), to translate the player's general skill index to a handicap for a particular course.

[0054] In one embodiment, the player's skill index might also be translated, converted or correlated to other indices, such as a ranking. As one example, a player's overall skill or sub-skills may be assigned a ranking (such as represented by colors, e.g., green, blue, black, or symbols, etc.), where the ranking is dependent upon the player's skill index. For example, skill levels from 0-25 might be assigned to the “green” ranking, 26-50 to the “blue” ranking, etc.). In another embodiment, player skill levels may be mapped players may be grouped into rankings based upon their location to the mapping (for example, such a mapping may generally reveal a bell curve or indices and the player's assigned skill level may be assigned based upon where their skill lies on the curve). The player's ranking might be displayed graphically to them, such as on a display of their user device 30 by an application running on their device, via a browser or the like, as described in detail above. The player's ranking may be used for a number of purposes, including linking players of similar ranking for purposes of games or challenges.

[0055] As one aspect of the invention, the system may include one or more features for verifying a player's identity, such as to increase the identity level of received player activity data. As one example, a player identification system may be implemented which enables confirmation of a player's identity through a combination of sensors, including but not limited to facial recognition, object detection, kinetic motion tracking, and swing-print technologies. Of course, other player identity verification methods or systems might be utilized (collection and analysis of video or other images, ID validation, etc.).

[0056] As indicated above, in one embodiment, an engine 300 may be utilized to process / analyze player activity data in the process of generating player index information. In some embodiments, the system and method are model-driven, such as using artificial intelligence (“AI”) and other technology. Specifically, the engine may implement predictive activity data analysis, where the predictive formulation may be provided by a trained, or learned, AI model trained using an advanced machine learning algorithm which analyzes the player activity information to determine a weighting thereof (such as by analyzing the data against past data to determine the consistency (how anomalous) and reliability (how identifiable) the data is.Performance Feedback / Guidance

[0057] Further aspects of the skill event analysis system 20A will be provided with reference to FIGS. 4 and 5. Another aspect of the invention is a method of utilizing player activity and / or environmental data to generate feedback and / or performance guidance.

[0058] As one example, the skill event analysis system 20A may be configured to generate practice or training feedback or performance guidance which is tailored to at least one of: i) event conditions; ii) past performance; and iii) current performance. Details of this aspect of the invention will be appreciated from several examples.

[0059] Relative to FIG. 5, in one configuration, the system 20A might comprise at least one computing device, such as a system server 522, which is configured to implement a data weighting engine 500. This data weighting engine 300 may be like that described above, such as for receiving activity data from one or more sensors 550 (which might be associated with other devices or systems, such as described above and illustrated in FIG. 1). As with the data weighting engine 300 described above, the data weighting engine 300 may be configured to transform received data and store it in a database 524, such as in association with a player, such as identified from player identity information. As with the system 20 described above, the weighted data may be used by an index generator 560 to generate a player skill index, as in the manner described above.

[0060] The system server 522 may also implement (such as in the form of machine-readable code stored in a memory and executable by a processor of the server) a feedback / guidance generator 570. The feedback / guidance generator 570 may utilize player identity information and stored activity data (such as weighted or non-weighted data) stored in the database 524, to generate player feedback or performance guidance, such as described in more detail below.

[0061] As one example, the system 20A might be used to generate feedback or performance guidance to a player who is practicing or warming up, such as practicing golf shots or warming up for a round of golf. In one embodiment, in a step S100, the player may be identified. In some embodiments, this identification may be anonymous, meaning that actual identifying information about the player is not collected, but where the system 20A does associate a unique identifier (which could be an assigned ID, session number or the like). In other embodiments, the player may be identified, such as by the player providing identification information or information which can be used by the system 20A to access the player's identification information, such as a player's account.

[0062] For example, a player might interact with the system 20A via their mobile device or another computing device, such as facilitated by an application, browser or the like. If the identification is anonymous, a player might simply select “start training session” or the like, at which point a request for a session may be transmitted to the system server 522. The system server 522 might generate a session ID and begin associating information with that session ID, such as in one or more files in the database 24. In another variation, a player might create a player account which stores player identification information. The player might provide a player ID, password or other information to the system server 20, thus enabling the server to access the player's account (including identification information and any past performance data, etc., as described in more detail below). In some embodiments, the creation of a session may include input of a duration of the warm-up or training session, or other relevant information. For example the player might input into their mobile device 30 a warm-up or practice session duration (such as 15 min, 30 min, 1 hour, etc.) or such information might be received by the system 20 from another system of device, such as a tee or practice session scheduling system.

[0063] In a step S200, the system 20A receives activity or action data. This activity information or data may comprise at least one of: i) event conditions (event condition data D2) and ii) past activity performance (past player activity data D1).

[0064] The event conditions preferably provide information regarding the elements and / or atmosphere of the event. This information may comprise stored information and / or information which is collected, such as in real-time or near real-time. For example, relative to golf, the information might comprise stored information regarding a golf course. This might comprise information regarding the golf holes and their layouts, general hole yardage information or various other course information collected over time. The information may also comprise real-time information, such as wind speed, wind direction, temperature, humidity and / or other weather information. The information might also comprise information such as the time of day, amount of sunlight, information about the depth of grass (measured, estimated based upon time from last cut), specific hole yardage (such as based upon tee and pin placement) and various other information. As indicated above, this information may be collected by a number of sensors 550, or be provided to the system via other input (for example, a greenskeeper might log data into the system from a user terminal regarding when fairways were cut, etc.). The event condition information might also include information about the equipment used to participate in the event, such as golf clubs being used (such as a designation of different sets of clubs when a player has two or more sets, or of the configuration of a set of clubs, such as which clubs the golfer is using), where the user might input such information, such as via menus, etc. In one embodiment, the sensor data 500 may be provided to the feedback / guidance generator 570, or might be processed and stored by the data weighting engine 500 (where in one embodiment, the data weighting engine does not weight the event condition data).

[0065] Of course, the type of event condition information which is collected and / or utilized may depend upon the event. For example, in the case of bowling, the event condition information might include details regarding the lane oiling. In the case of tennis, it might comprise information regarding the court surface (clay, grass, etc.). Further, it will be appreciated that the event condition information may change over time, such as by being updated in real-time or near real-time, such as based upon the output of sensors.

[0066] In the case of an identified player, such as having a player account, the past activity performance may comprise data regarding the player's past participation in skill events, such as collected in the manner described above. For example, the information might comprise data regarding the player's past performance in skill events, whether they be for practice, competition or other purposes. This information may also comprise the player's generated skill index, as detailed above.

[0067] In a step S300, the activity data is analyzed in order to generate feedback and / or performance guidance. The particular analysis which is performed on the data may depend upon the desired output, such as whether the output is for training purposes, warm-up purposes, performance purposes or the like. The analysis may be performed using a system such that illustrated in FIG. 3 (including where the player's activity information is weighted as part of the analysis process). For example, the system 20A might utilize the information to: 1) generate a warm-up or practice session which is tailed to the player based upon the their past performance and event conditions (and including the amount of time for the session) and / or 2) generate performance guidance for an upcoming event, such as based upon event condition information, past activity information (and, if available, current activity information such as from a warm-up before the event).

[0068] In one example, the generator 570 may use an average of previous data (other options include but are not limited to: weighted average, machine-learning projected next shot dispersion, etc.) to estimate the projected performance in the upcoming event. As one example, the event might be to a play an 18 hole golf course, where the details of the course (such as the configuration of each hole of the course, including layout and distances) is known by the system 20A (such as by being updated and stored, determined from sensor or other collected data, etc.). The system 20A outputs the likely distances needed to minimize the shots necessary to complete the 18 holes based on the player's known data (such as their average hitting distances for different clubs). The system may run a Monte Carlo or other random sampling simulation of the player's projected shot performances. For example, estimate the projected shots needed to complete the course given the player's previous performance and dispersion, run multiple times (e.g. 1000X, 10,000X) with dispersion metrics. The result is an estimated number of target distances, which can be translated for the practice session. For example, a highly skilled player will have tighter shot dispersion, and less distance to the hole as a result. A less skilled player will have more shot dispersion, and longer shots to the hole as a result. Similarly, a longer driver of the golfer ball will have less distance to the hole off the tee than a shorter driver of the golf ball. The result of this analysis is a suggest series of target distances (or golf clubs), with a number of practice shots correlated with the dispersion for each distance (or club). The more the dispersion in the distance (or club), the more practice shots in the guided session.

[0069] For example:Course:Tees:Hole Layouts:Pin Locations:Spanish Trail Country ClubWhitesKnown“C”TABLE 1Player Projected ShotKnown DispersionSuggest PracticeDistancesIndexShots14 × 250yard shotsDispersion Index = 214 shots6 × 200yard shotDispersion Index = 515 shots8 × 150yard shotDispersion Index = 312 shots10 × 100yard shotDispersion Index = 1 5 shotsThe number of shots can be a proportion of the total shots available in the time allocated for the practice session. For example, the number of shots during a 20 minute practice session can be double that in a 10 minute practice session. The number of shots can be increased if the player's analytical performance during the practice falls significantly below expected performance, or might be decreased if their practice shots are performing better than expected.

[0071] In a step S400, the analyzed activity information is transformed into feedback or performance guidance information. This information may be transmitted to the player, such as for presentation on their user device 30. In some embodiments, this information may comprise textural or graphical information, such as an image or video which graphically presents the feedback or performance guidance, as described in more detail below.

[0072] In a step S500, updated activity information, such as updated event condition information and current player activity data D3, may be received by the feedback / guidance generator 570. The current activity performance information or data may comprise information about the player's performance or actions in an event in real or near real-time, and may comprise actions taken in response to the feedback or performance guidance generated and provided to the player in step S400. Such information may be collected in the manner described above, such as in relation to the systems L1, L2 and L3 which are illustrated in FIG. 1. For example, relative to a golf warm-up session, the current activity performance information may comprise information regarding golf clubs which are used and shots taken with those clubs, including ball speed, trajectory, distance and the like.

[0073] In a step S600 this additional data may be analyzed (such as in association with past player activity data) to, in a step S700, generate modified feedback or performance guidance. Such may comprise generating: 1) modified warm-up or practice session information, such as by using a player's current activity information from an on-going warm-up or practice session in order to modify that session and 2) modified performance guidance for use by a player during an event, such as to modify a player's actions during the event based upon collected activity information during the event and / or changed event condition information.

[0074] As illustrated in FIG. 4, current player activity data D3 may also be stored for future use as past player activity data D1, such as after being processed by the data weighting engine 500.

[0075] Additional aspects of the invention will be appreciated from a number of examples.

[0076] As one aspect of the invention, the system 20A may be used to generate customized event guidance to a player. For example, a player who is about to play a particular golf course may be provided with customized performance guidance. First, the system 20A may use event condition information to inform the player of performance guidance. As indicated above, this guidance may be based upon current event conditions (such as weather, including wind) and stored event conditions such as course layout, etc. For example, if at the time of play a 15 mph North wind is detected, when the player prepares to play a hole where the player is teeing off to the East, the system 20A may instruct the player to target their shot at a corrective angle towards the North, to compensate for the wind. This information might be indicated textually, or might be indicated graphically, such as via a graphically generated shot projection that may include course image data, thus allowing the player to visually reference the desired correction indicated by the system 20A to the actual course.

[0077] As indicated, however, the system 20A might also use past and / or current activity information about the player to provide this guidance. As one example, assume that the player is preparing to tee off on a short par-3 hole into the North wind, where the yardage to the green should, based upon past performance data, be reachable by the player hitting a 9 iron. In that instance, the system 20A may provide guidance that, due to the wind and the normal shot distance of the player using a 9 iron, that the player should tee off using an 8 iron.

[0078] Moreover, as also indicated above, the player's existing activity information may be used to generate the guidance. Relative to the above example, as the player plays the course, the activity information of the player may be tracked and utilized relative to the generation of later guidance. As one example, the current activity information may indicate that the player is not sufficiently correcting their shot trajectory when the wind is a cross-wind to their shot. The system may thus, as to later shots, provide guidance to the player which indicates to the player that they should make a larger trajectory correction.

[0079] As another example, a player might obtain guidance, feedback and / or performance information relative to a warm-up session. For example, the system 20A might use event condition information, past activity information and / or current activity information in order to provide guidance relative to a warm-up session. The system 20A might generate a warm-up sequence (such as in which the player is instructed to take golf shots with different clubs in a golf warm-up session) and evaluate the performance of the player (current activity / action information) to determine additional feedback or performance guidance. For example, upon evaluating current activity data which indicates that the player is pulling their golf shots, the system 20A might instruct the player to make designated adjustments to their swing (and after which, the player's subsequent activity information may be re-evaluated). As another example, upon determining that the player is pulling their golf shots but that swing corrections have not corrected the problem, the player might be instructed to make adjustments in the trajectory of their golf shots when they play a golf game after their warm-up. As yet another example, the player's performance during a warm-up session may be compared to their past performance for purposes of generating feedback or performance guidance. For example, the system 20A may determine that during a warm-up session, a player is hitting their driver 250 yards per shot, whereas historically the player has been hitting 275 yards. When the player begins play, such as of a hole that has a water hazard at 250 yards, the system 20A might instruct the player to use a different club, such as a 2 iron, in order to layup short of the hazard, since based upon current activity information, they are not driving their normal 275 yards which would clear the hazard.

[0080] As another example relating to a practice or warm-up session, the system 20A may generate and output information relating to a warm-up or practice session where that output is generated using the above-reference event condition, past performance and current performance information, such as in relation to a designated practice time. As one example, relative to a golf warm-up session, a user may input (such as into the system 20A via an application running on their mobile device) the time available to practice (e.g. 10 minutes) or the system 20A may receive that information automatically (such as from a communicatively linked tee time management system). The system 20A may optimize the clubs to be used in the session and the number of practice shots knowing (i) what clubs the player is likely to use (see below) and (ii) the pace of practice for the user. User 1 may hit one golf ball every 10 seconds, with 20 seconds between clubs, whereas User 2 may hit one golf ball every 20 seconds with 30 seconds between clubs. User 1 would be guided to take more shots in the 10 minutes than User 2 to optimize the time available. The system optimizes the selection of clubs based on the physical reality of how long the player takes to hit balls in a practice session, and how long the player takes to switch clubs, plus what clubs are important to the player's performance (such as to an upcoming event), such as based upon event conditions and past and present activity data (for example, relative to practice for a long course, the system 20 might focus the player on practice shots using drivers and low numbered irons; when the player's past performance or performance during the warm-up session indicates that the player is not driving the ball well, the system 20A may instruct the player to take extra practice shots with their driver, etc.)

[0081] As another example, the system may generate projected performance information and performance guidance information. As detailed herein, the system 20A is capable of generating predicted performance data for a player using event condition information and past (and / or present) performance data for a player. For example, relative to golf, the system 20A may utilize (i) information regarding the golf course the player will be playing (either on a simulator or on green grass) and associated event conditions, and (ii) the past and / or present performance of the player, such as evidenced by the player's Skill Index, to generate predicted performance data. This might comprise a handicap for the player for the particular event (given the actual event conditions in relation to the player's current level of performance) and / or might comprise performance guidance information.

[0082] In one embodiment, the system 20A may implement a predictive generator (such as for executing a Monte Carlo type predictor) which runs a simulation (such as a repeated simulation, such as 1000 or 10,000 simulations) of the shots needed to be taken with the clubs likely to be used by the player for one or more golf holes (such as for a complete round), in relation to the event conditions. This information may be used to generate a real-time handicap for the player for the event (including for the particular course and the course conditions). This information may also be used to generate an optimized warm-up or practice session, and / or to provide feedback or guidance to the player relative to participation in the skill-based event.

[0083] For example, the system 20A may weight the golf clubs the player should use in the warm-up or practice session in order to optimize performance relative to the upcoming event and the practice time available. For example, the system 20A may determine that a player will likely hit 14× drivers, 5× 3-woods, 5× 5-irons, 1× 6-irons, 7× 7-irons, 2× 8-irons, 10× PWs (5× full shots, 5× pitch shots), 11× SWs (2× full shots, 9× chips) over the course of a round, on average. Based on the clubs the player is likely to use, the system 20 can (i) divide the time available by the shots by club and guide the player to practice the same percentage of time on each club as they are projected to use on the course, (ii) knowing the consistency of each club, the system can optimize the number of shots to increase number of shots for clubs which are less consistent (e.g. user is more consistent with the PW and less with their Driver, so the system weights additional shots to the Driver), (iii) the system can adjust to the tempo of the current practice session, increasing or decreasing the number of shots to accommodate for the real-time rate of shots and / or instruct the user to speed up or slow down their shots to normalize their session to their optimal rate of practice shots, (iv) seeing the performance of each club during the practice session, the system can increase or decrease the number of shots until the player is showing they are optimizing performance for that club based on their normal behavior with the club and Skill Index.

[0084] While in the example above, reference is made to generated club-usage instructions, the instructions might be generated or referenced in other manners. For example, the warm-up or practice session information might reference practicing shots of a certain distance, rather than with a particular club, thus allowing the player to utilize the club of their choice to fulfill the target.

[0085] As indicated above, the system 20A may generate performance guidance for a skill-based event, such as based upon event conditions, past performance and current performance data. Relative to a round of golf, the output might comprise a yardage matrix with tendencies for the day, such as below in Tables 2 and 3:

[0086] Custom User Real Golf Shot Recommendations for <<DATE>> playing on <<COURSE>><<GREEN GRASS / SIMULATOR GOLF>>:TABLE 2CLUBDISTANCEAIMDriver250yardsAim 10 yards right3-wood220yardsAim 5 yards right5-iron191yardsAim 10 yards left6-iron177yardsAim 10 yards left7-iron166yardsAim straight8-iron153yardsAim straightPW Full128yardsAim 2 yards leftPW Pitch55yardsAim straightSW Full98yardsAim straightSW Chip25yardsAim 1 yard rightTABLE 3TARGETACTUALAIM260 yards250yardsAim 10 yards right235 yards220yardsAim 5 yards right200 yards191yardsAim 10 yards left185 yards177yardsAim 10 yards left170 yards166yardsAim straight160 yards153yardsAim straight135 yards128yardsAim 2 yards left 50 yards55yardsAim straight100 yards98yardsAim straight 25 yards25yardsAim 1 yard rightThis information can be sent via text message, displayed in-app, printed on a sheet of paper, or available via a connected device (golf cart information screen, range finder, etc.) to guide the player through their round of golf. In simulator golf or where there is ball data for shots, the recommendations can be updated real-time based on the performance on the course.

[0088] In accordance with this aspect of the invention, the system 20A is able to provide a player with a real-time view of their performance or skill level for each action, series of actions, and overall performance or skill level, while performing a practice and / or warm-up session or participating in an actual competition or event. Further, the system 20A utilizes event condition, past player activity and / or present player activity information to generate feedback which informs the player of their readiness, such as to perform the upcoming task or play an upcoming game. In case where a player is under-performing, the player may be informed that they may not perform optimally in those specific areas and be provided with feedback or guidance to address the issue, such as to make physical adjustments (such as to a golf swing or to the intended trajectory), or other adjustments, like adding an extra club in golf. On the other hand, if the player is performing better than expected the system may provide feedback that the player can be more aggressive (such as, in the case of golf, choosing a more direct line to the hole or expecting a longer distance from the clubs).

[0089] In accordance with the invention, by optimizing the player's performance of action (such as each sub-task) in the practice or warm-up session, a person is able to reduce the amount of time which is necessary to identify the performance or skill level, reallocating time to areas which require additional actions based on the feedback, and / or saving total time before the game or performance.

[0090] A particular aspect of the invention is that the system 20A evaluates player action information which is specific to the player, and not generalize to a group. The player thus gets individualized feedback which is tied to their own performance. For example, on a windy day, most players in a group of golf players might see a significant degradation in the accuracy and distance of their golf shots. However, a player of that group that tends to keep their shots lower may not see such a degradation. The system 20A provides feedback to that player which is not based upon the group (which would likely result in feedback indicating that the player needs to make a significant change to their game, such as picking different clubs or the like), but which is base upon their own performance.Advantages

[0091] The invention has numerous advantages over prior art systems and methods.

[0092] As one aspect of the invention, the system of the invention automates the collection of data, removing the hassle and integrity issues with manual data input. Moreover, the system collects and analyzes many data points for each activity, allowing for a more detailed understanding of the player activity than only the result of the activity.

[0093] As another aspect, the player activity data is stored, analyzed and normalized across platforms, allowing for users to interact cross-platforms with a homologated skill level. This is different to the siloed data collection and analysis which dominates the market today.

[0094] One advantage to the invention is the ability to collect player activity data from a plurality of different sources (including different systems). The collection of a greater amount of data permits the generation of more reliable skill indices. Further, skill indices are updated in real time as data is obtained from the different sources. For example, a player's skill index for golf tee shots may change during the play of a single round of golf.

[0095] In some embodiments of the invention, collect player activity data is weighted based on the verifiability and consistency. In one embodiment, player identity may be confirmed, increasing the verifiability of the player activity data. Unlike “trust-based systems” like Arccos or GHIN, the invention is able to give additional weight to data which can be verified from the user and decrease the weight of data which is less reliable, like anonymous data (recorded without Player Identification) or anomalous data (data which deviates significantly from the expected).

[0096] The invention uses an engine, which may implement AI-algorithms, to weight anonymous and anomalous data by comparing the results of each action to the expected results and weighing each action and associated underlying metric based on the expected results. This discounting adapts over time, to identify and reweight trend, so that if a user's data show changes over any significant period of time or sequence, their past data is re-weighted to accommodate for the change.

[0097] The invention allows users to (a) improve skill through feedback of their activities, including but not limited to video replays with recommendations (which maybe AI-generated or selected), (b) engage more through points-based rewards systems that allow users to earn points for each activity, the results of each activity, using established methods of rewards (tier ranking, leaderboards, redemption of points), and / or (c) enable cash based prizing that activates the basic human tendency to want to win money and be rewarded financially for doing something well.

[0098] While each of these are being done independently, the lack of a systemic method of combining them, automated and verified, limits their usefulness in real world scenarios, as is evident by the lack of products and services similar to the invention in the market today.

[0099] Users are able to get a real-time view of their skill level for each action, series of actions, and overall skill. This is used to optimize engagement through training, rewards and cash prizing.

[0100] Their data is automatically collected, removing the need for manual input and / or manual verification. In the case of competitions, their relative skill level is used to ensure fair results independent of skill level. And their skill level is applicable and visible across all integrated platforms of gameplay. In the case of golf, their skill level will adjust for each driving range, launch monitor, and golf course, and for each shot, series of shots (skill game, hole or holes, etc.), and represent a fair and accurate overall skill level.

[0101] Game and sport companies can create a better user experience by rewarding increased engagement through skill-normalized activities. This leverages what is popular in places like on-course handicapped golf tournaments and extends it to every time a user is taking a golf shot.

[0102] Game and sport companies can now activate reward systems and cash prizing. Game and sport companies can also target training advice using a holistic understanding of a player's actions, rather than a limited view through siloed data collection. Lastly, the mitigation of anonymous and anomalous data allows for effective re-engagement that lacks the risk of sand bagging, fraud or similar unverified skill level measurement methods.

[0103] Currently, no accurate data-driven technology exists for predicting the performance of a player in a skill-based event. Currently, performance prediction is loosely based upon a review of a player's past performance. For example, the prediction of a golfer's performance may be based upon a handicap, where currently that handicap is a generic value which is generated from scores of past rounds of golf. The system of the invention enables the accurate generation of a projected performance of a player in a skill-based event based upon collected past performance data, collected practice or warm-up data, and / or event condition data. The system is configured to use this data to predict the performance of a player, such as in an upcoming event. This may comprise, for example, a prediction of the player's performance in an upcoming round of golf in relation to course data and course conditions (e.g. event condition data), based upon specific player performance data (data reflecting the specific past performance activities of the player, such as relating to driving distance, putting performance, etc.), such as to provide an accurate handicap for the player for that event (and thus where the performance prediction would vary based upon differences in conditions, such as weather, and for different courses, as well as if the player's warm-up performance varies from their past performance data).

[0104] Currently, there is no system which is capable of generating feedback or performance guidance in substantially real-time between a player's practice or warm-up performance and an upcoming event. In accordance with the present invention, the system is capable of analyzing a player's warm-up performance in relation to event condition data and the player's past performance data, to generate feedback or performance guidance for the player that links their current performance status to the upcoming event.

[0105] Similarly, there is currently no technology which links a player's past performance to a practice or warm-up session in substantially real-time, such as to generate feedback or performance guidance for the warm-up or practice session. As indicated above, the system is capable of: 1) generating feedback or performance guidance (such as a warm-up session guidance) which is based upon the current event conditions in relation to the player's past performance (for example, by generating a warm-up session that is tailored to most accurately prepare the player for an upcoming event based upon the event condition when considering the known performance of the player) and 2) modify the practice or warm-up session when the player's performance deviates from the predicted performance. As one example, if the player is preparing the warm-up for play of a short Par-3 oriented course when past performance shows that the player has been hooking their shots with middle irons, the system may generate and output a warm-up session which focuses on more middle iron warm-up shots with directional correction. If during the session the system detects that the directional correction is insufficient to correct the trajectory of the shots or the player is hitting shots a shorter distance than anticipated, the system might output a guidance to change which middle irons are used and to make a further directional correction.

[0106] It will be understood that the above-described arrangements of apparatus and the method there from are merely illustrative of applications of the principles of this invention and many other embodiments and modifications may be made without departing from the spirit and scope of the invention as defined in the claims.

Examples

Embodiment Construction

[0025]In the following description, numerous specific details are set forth to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.

[0026]Some aspects of the invention comprise systems and methods for collecting and utilizing activity information of a person, such as skill-based activity information, and utilizing that information to generate (including update) one or more skill indexes of the player and / or to provide performance-enhancing feedback, guidance or instructions.

Skill Event Analysis System

[0027]One embodiment of an exemplary system 20 of the invention is illustrated in FIG. 1. As illustrated, the system 20 may comprise at least one skill event analysis server 22. The skill event analysis server 22 may comprise a processor an...

Claims

1. A player skill indexing system comprising:an indexing system comprising at least one server and a data storage device, said server comprising a communication interface, a processor, a memory and machine-readable code stored in said memory and executable by said processor to cause said server to:receive, from a plurality of activity tracking platforms over one or more electronic communication networks, user activity data regarding a user's performance of a skill-based activity;implement a data weighting engine which implements a data identity analyzer and an anomalous data analyzer, said data identity analyzer configured to generate a first weighting value for said received user activity data based upon a level of verifiability of an identity of the user to the user activity data and a second weighting value for said received user activity data based upon a level of anomalousness of the user activity data, said data weighting engine configured to utilize said first and second weighting values to output weighted user activity data;generate a player skill index utilizing said weighted user activity information; andtransmit, via an electronic communication link; said player skill index to at least one remote electronic device.

2. The player skill indexing system in accordance with claim 1, wherein said skill-based activity comprises golf.

3. The player skill indexing system in accordance with claim 1, wherein said machine-readable code stored in said memory is executable by said processor to cause said server to transform said skill index into a skill level.

4. The player skill indexing system in accordance with claim 1, wherein said machine-readable code stored in said memory is executable by said processor to cause said server to transform said skill index into a handicap.

5. The player skill indexing system in accordance with claim 1, wherein said user activity data is received from at least one first activity platform in a first format and from a second activity platform in a second format, and wherein said system is configured to transform said activity data in said first and second formats into a common third format.

6. The player skill indexing system in accordance with claim 1, wherein said activity tracking platform comprises at least one golf simulation platform.

7. The player skill indexing system in accordance with claim 1, wherein said activity data comprises golf practice activity data and said player skill index comprises a player's golf skill index calculated from said golf practice activity data.

8. The player skill indexing system in accordance with claim 1, wherein said at least one activity tracking platform comprises at least one sensor which is configured to sense an action associated with an input by a player to a golf club.

9. A system for generating real-time skill-based event performance guidance comprising:a skill-based event analysis system comprising at least one server, a data storage device, a memory, a communication interface, and a processor, said memory storing machine-readable code for execution by said processor to cause said server to:receive skill-based event condition data from one or more condition sensors;receive past skill-based event performance data relating to a player;utilize said event condition and event performance data to generate a first performance guidance output to said player for use in a performance of a first skill-based event action by said player;receive, from said one or more condition sensors, updated event condition data;receive, from at least one activity sensor which is configured to sense an action associated with an input by a player to an element of physical game equipment relative to said first skill-based event action an output of said at least one activity sensor relative to said first skill-based event action;utilize at least one of said updated event condition data and output of said at least one activity sensor to generate a modification to said first performance guidance.

10. The system in accordance with claim 9, wherein at least one of said condition sensors comprises an environmental condition sensor.

11. The system in accordance with claim 10, wherein said environmental condition sensor comprise a temperature, humidity, or wind speed sensor.

12. The system in accordance with claim 9, wherein said skill-based event performance data is received from a data weighting engine.

13. The system in accordance with claim 9, wherein said skill-based event comprises a golf event.

14. The system in accordance with claim 9, wherein said activity sensor comprises at least one of LIDAR, RADAR, an accelerometer, a speed measuring device, and an image capture device.

15. The system in accordance with claim 9, wherein said first performance guidance output comprises instructions for a warm-up session.

16. The system in accordance with claim 15, wherein said modification comprises a change to said warm-up session.

17. The system in accordance with claim 9, wherein said server is further configured to receive information identifying said player.

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