Systems and methods for monitoring and improving the health and well-being of individuals

Abstract

Provided herein are systems and methods for monitoring and improving the health and well-being of individuals, including individuals in the video game (e.g., professional video game) industry. Also disclosed herein are systems and methods for predicting gaming performance in individuals, either in a social gaming or professional gaming setting. Additionally, disclosed herein are systems and methods rating and ranking individuals and teams in the video game industry, including the professional esports scene.

Description

WSGR Docket No.60152-701.601 SYSTEMS AND METHODS FOR MONITORING AND IMPROVING THE HEALTH AND WELL-BEING OF INDIVIDUALS

[0001] This application claims the benefit of U.S. Provisional Application No.63 / 616,226, filed December 29, 2023, which application is incorporated herein by reference in its entirety. BACKGROUND

[0002] In 2022, the video game industry market was estimated to be USD 217.06 billion, and it is expected to continue to grow each year for at least the next 10 years. The professional (e.g., esports) video game scene has seen similar growth and interest in the recent years. In comparison to other professional sports leagues, the professional esports scene is dominated by younger athletes. Similarly, due to the demanding nature of being an esports athlete, athletes are also retiring at a much earlier age as compared to other professional sports leagues. The demanding practice schedules and lack of physical activity has led to many esports athletes to retire by the age of 24, where retiring athletes report burnout manifested by physical and mental disease. Therefore, there exists a need for systems and methods to be put in place help individuals such as esports athletes and other gaming or traditional athletes find balance in their life to not only improve their health and well-being, but also their gaming or athletic performance. In addition, due to the expanding esports scene, there also exists a need for a standard ratings and rankings system to rank individual esports athletes and teams in their performance. SUMMARY

[0003] Recognized herein is a need for systems and methods for monitoring and improving the health and well-being of individuals, including individuals in the video game (e.g., professional video game) industry. Also recognized herein is a need for predicting gaming performance in individuals, either in the social or professional setting. Additionally, recognized herein is a need for systems and methods for rating and ranking individuals and teams in the video game industry, including the professional esports scene.

[0004] In one aspect, the present disclosure provides for a method for monitoring and improving the health and well-being of a gamer, the method comprising: (a) receiving data from one or more data sources associated with the gamer; (b) classifying the received data as being associated with one or more categories of health and well-being; (c) initializing one or more artificial intelligence (AI) models / algorithms of health and well-being, wherein the model comprises data received from at least a reference population or a historical gamer database; and (d) applying the one or more AIWSGR Docket No.60152-701.601 models / algorithms to: (i) determine a score indicative of the gamer’s health and well-being; and (ii) generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s health and well-being.

[0005] In another aspect, the present disclosure provides for a method for predicting a gamer’s gaming performance, the method comprising: (a) receiving data from one or more data sources associated with the gamer; (b) classifying the received data as being associated with one or more categories of gaming performance; (c) receiving a score of the gamer indicative of the gamer’s health and well-being; (d) initializing one or more artificial intelligence (AI) models / algorithms of predicting gaming performance, wherein the model comprises data received from at least a reference population or a historical gamer database; and (e) applying the one or more AI models / algorithms to determine a score indicative of a predicted gaming performance of the gamer.

[0006] In another aspect, the present disclosure provides for a method for rating a gamer’s gaming performance, the method comprising: (a) receiving data from one or more data sources associated with the gamer; (b) classifying the received data as being associated with one or more categories of gaming performance; (c) receiving a score of the gamer indicative of the gamer’s health and well-being; (d) initializing one or more artificial intelligence (AI) models / algorithms of rating individual gaming performance, wherein the model comprises data received from at least a reference population or a historical gamer database; and (e) applying the one or more AI models / algorithms to rate the gamer based at least in part on the gamer’s gaming performance and the score indicative of the gamer’s health and well-being. In another aspect, the present disclosure provides for a method for ranking a gaming team comprised of a plurality of gamers, the method comprising: (a) receiving data from one or more data sources associated with the gaming team; (b) classifying the received data as being associated with one or more categories of team gaming performance; (c) initializing one or more artificial intelligence (AI) models / algorithms of ranking team gaming performance, wherein the model comprises data received from at least a reference population or a historical gaming team database; and (d) applying the one or more AI models / algorithms to rank the gaming team based at least in part on the team’s gaming performance.

[0008] In some embodiments, the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. In some embodiments, the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof. In some embodiments, the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood,WSGR Docket No.60152-701.601 attention, quality of life, demoralization, or a combination thereof, of the gamer. In some embodiments, the mental condition data further comprises data relating to the gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). In some embodiments, the nutrition data comprises data relating to: how much water the gamer is drinking, how much caffeine the gamer is ingesting, how much sugar the gamer is ingesting, or what kinds of food the gamer is eating (e.g., health, fast food, snacks, etc.). In some embodiments, the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a gamer’s routine before sleep. In some embodiments, the lifestyle data comprises data relating to the gamers hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof). In some embodiments, the categories of gaming performance (e.g., team gaming performance) comprise: a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof. In some embodiments, the gamer rating or team ranking is identified at a plurality of time points throughout a season (e.g., a professional gaming league season). In some embodiments, the gamer rating comprises an overall gaming performance score for the gamer. In some embodiments, the team ranking comprises an overall gaming performance of the gaming team. In some embodiments, the gamer plays for an organized club. In some embodiments, the gamer plays in an organized academy system. In some embodiments, the gamer plays video games competitively. In some embodiments, the gamer is a member of a professional gaming (e.g., esports) league or team. In some embodiments, the gamer is a member of a foreign national esports federation. In some embodiments, the gamer is a member of a collegiate gaming league or team. In some embodiments, the gamer is a member of a high school gaming league or team. In some embodiments, the gamer plays video games for their occupation. In some embodiments, the gamer creates content for one or more video games. In some embodiments, the gamer is a casual gamer. In some embodiments, the casual gamer is not a member of a professional, collegiate, or high school gaming league or team. In some embodiments, the casual gamer enjoys playing video games leisurely. In some embodiments, the gamer plays one or more of: Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, Dota2, The Finals, or any combination thereof. In some embodiments, the gamer is above the age of 13. In some embodiments, the gamer is below the age of 13. In some embodiments, the gamer is aged 10-13. In some embodiments, the gamer is aged 13-18. In some embodiments, the gamer is aboveWSGR Docket No.60152-701.601 the age of 18. In some embodiments, improving the health and well-being of the gamer comprises improving the gamer’s cognitive performance. In some embodiments, improving the health and well-being of the gamer comprises improving the gamer’s performance in one or more video games. In some embodiments, the method comprises improving the health and well-being of the gamer by a target (e.g., pre-determined) amount. In some embodiments, the method comprises improving the health and well-being of the gamer by a target (e.g., pre-determined) future time (e.g., a match or competition). In some embodiments, the improving the health and well-being is quantified by a score (e.g., the determined score) indicative of the gamer’s health and well-being. In some embodiments, the method comprises improving the health and well-being of a gaming team (e.g., professional, collegiate, high school team) comprised of multiple individual gamers. In some embodiments, the reference population data, the historical gamer data, or a combination thereof, are gathered over a time period. In some embodiments, the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). In some embodiments, wherein the reference population data, the historical user database, or a combination thereof, are received from a third party library. In some embodiments, the third party comprises an esports data analytics company. In some embodiments, the reference population data comprises data gathered from a plurality of gamers over a period of time. In some embodiments, the data sources comprise: gamer input, third party input, sensor / device input, or a combination thereof. In some embodiments, the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device). In some embodiments, the gamer input is input by the gamer themselves. In some embodiments, the gamer input comprises qualitative input, quantitative input, or a combination thereof. In some embodiments, the method further comprises administering one or more surveys or questionnaires to the gamer to gather the gamer input. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) daily to the user. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a week to the user. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a month to the user. In some embodiments, the one or more surveys or questionnaires are configured to gather physical maintenance, mental conditioning,WSGR Docket No.60152-701.601 nutrition, sleep, or lifestyle data from the gamer. In some embodiments, the gamer input comprises unprompted gamer input. In some embodiments, the unprompted gamer input comprises gamer journal entries. In some embodiments, the journal entries relate to the gamer’s well-being or gaming performance (e.g., self-perceived well-being or gaming performance). In some embodiments, the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer. In some embodiments, the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof. In some embodiments, the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance. In some embodiments, the method further comprises transmitting data gathered from the gamer to the gamer’s gaming coach, and wherein the third party input comprises the gaming coaches’ feedback or recommendations to the gamer based on the transmitted data. In some embodiments, the third party input comprises qualitative or quantitative data from the gamer’s parent(s) relating to the gamer’s well-being. In some embodiments, the third party data comprises gaming performance data received from one or more games the gamer is currently playing. In some embodiments, the gaming performance data comprises a kill / death ratio, a win / loss ratio, a score per minute, or other data gathered by the one or more games while the gamer is playing the one or more games. In some embodiments, the sensor / device input is received from one or more gamer sensors / devices. In some embodiments, the sensors / devices are configured to gather biometric data of the gamer. In some embodiments, the sensors / devices are configured to gather pupillometry data of the gamer. In some embodiments, the sensors / devices comprise: one or more personal computing devices (e.g., a mobile device, tablet, or other personal computing device), or one or more applications associated with the one or more personal computing devices (e.g., social media applications (e.g., Instagram, Facebook, Twitter, TikTok, Calendar, Microphone, Online Purchasing apps, etc.). In some embodiments, the sensor / devices comprise wearable sensors / devices (e.g., wearables). In some embodiments, the wearables comprise one or more smart devices (e.g., smart watch) configured to measure physiological (e.g., heart rate, breathing rate), neurological, psychological, metabolic, or biological data of the gamer. In some embodiments, the wearables comprise Fitbit, Apple Watch, or Oura Ring. In some embodiments, the sensor / device comprises Tobii Eye Tracker products. In some embodiments, the sensors / devices comprise sensors / devices configured to receive voice data (e.g., a microphone, a gaming headset, etc.) from the gamer. In some embodiments, the voice data is gathered while the gamer is playing video games. In some embodiments, the method further comprises classifying the received data as originating from: gamer provided input, third party input, or sensor / deviceWSGR Docket No.60152-701.601 input. In some embodiments, classifying the data is performed using one or more data processing algorithms. In some embodiments, the one or more data processing algorithms comprises one or more feature extraction algorithms, one or more machine learning algorithms, one or more artificial intelligence algorithms, one or more Bayesian algorithms (e.g., Bayesian assimilation), one or more statistical analysis algorithms, or a combination thereof. In some embodiments, the one or more data processing algorithms receive data from: (i) the one or more data sources, (ii) a database, or a combination thereof. In some embodiments, the database comprises stored reference population data, stored historical gamer specific data, or a combination thereof. In some embodiments, the one or more data processing algorithms comprise a natural language processing model configured to extract qualitative data from the one or more data sources, the historical gamer database, a reference population, or a combination thereof. In some embodiments, the one or more AI models / algorithms comprise one or more machine learning models. In some embodiments, the one or more AI models / algorithms comprise a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forward neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof. In some embodiments, the one or more AI models / algorithms are trained. In some embodiments, the method further comprises training the one or more AI models / algorithms. In some embodiments, the method further comprises updating the one or more AI models / algorithms as they are continually trained. In some embodiments, the one or more AI models / algorithms comprise supervised, unsupervised, or semi-supervised models / algorithms. In some embodiments, the method further comprises generating one or more recommendations to improve the gamer’s gaming performance. In some embodiments, the score is calculated for a time period. In some embodiments, the time period is 1 day. In some embodiments, the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the score is determined based at least in part on a comparison of the received data to the reference population data, the historical gamer data, or a combination thereof. In some embodiments, the score is determined in real-time, near real-time, or in a dynamic nature. In some embodiments, the score is a qualitative score, a quantitative score, or a combination thereof. In some embodiments, the score is positive, negative, or neutral. In some embodiments, the positive score is extremely positive, very positive, moderately positive, or slightly positive. In some embodiments, the negative score is extremely negative, very negative, moderately negative, or slightly negative. In some embodiments, the score is a number on a scaleWSGR Docket No.60152-701.601 of from 0-100. In some embodiments, the number score is matched with a qualitative score. In some embodiments, a score above 75 is matched with “Looking Strong!”. In some embodiments, a score between 51 to 75 is matched with “Good Work!”. In some embodiments, a score between 26 and 50 is matched with “You Got This!”. In some embodiments, a score between 1 to 25 is matched with “Keep It Going!”. In some embodiments, a score of 0 is matched with “Let’s Get Started!”. In some embodiments, the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) amount. In some embodiments, the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) future time (e.g., a match or competition). In some embodiments, the one or more recommendations are targeted to one or more categories of the categories of health and well-being. In some embodiments, the one or more recommendations comprises a personalized training regimen configured to improve the gamer’s health and well-being, their gaming performance, or a combination thereof. In some embodiments, the method further comprises simulating a plurality of training regimens in the gamer to identify a most effective training regimen. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the method further comprises predicting an efficacy associated with the training regimen. In some embodiments, the training regimen is recommended only if it meets a threshold level of predicted efficacy. In some embodiments, the training regimen identifies the categories of health and well-being the gamer should focus on to most improve health and well-being, gaming performance, or a combination thereof. In some embodiments, the method further comprises receiving input from the gamer, or a third party, relating to the completion of the one or more recommendations. In some embodiments, the one or more AI models / algorithms uses historical completion data in determining the score or generating the one or more recommendations to the gamer. In some embodiments, the method further comprises applying the one or more AI models / algorithms to identify correlations between a gamer’s, or gaming team’s, performance with their health and well-being. In some embodiments, the method further comprises encouraging the gamer to improve their health and well-being, gaming performance, or a combination thereof by displaying a list of one or more badges for the gamer to earn by completing actions associated with the one or more recommendations. In some embodiments, the method further comprises displaying a completion percentage of the one or more badges to the gamer or a third party. In some embodiments, the method further comprises notifying and displaying to the gamer or third party the completion of the one or more badges. In some embodiments, the method further comprises transmitting the score, the one or moreWSGR Docket No.60152-701.601 recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party. In some embodiments, the method further comprises displaying the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party on a graphical user-interface (GUI). In some embodiments, the gamer’s personalized score, one or more recommendations, ratings, rankings, or a combination thereof are displayed on the GUI of a gamer’s personal device. In some embodiments, the method further comprises displaying a plurality of gamers’ (e.g., members on a gaming team) scores, one or more recommendations, ratings, or rankings, or a combination thereof to a third party (e.g., coach). In some embodiments, the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the determined score. In some embodiments, the gamer or the third party can select a time period (e.g., a past time period) for the score to be determined for. In some embodiments, the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the one or more recommendations. In some embodiments, the gamer or the third party can select a future time for the one or more recommendations to be configured for helping the gamer improve their health and well-being or gaming performance by. In some embodiments, the method further comprises ranking the gamer based at least in part on the gamer’s score (health and well-being, gaming performance score, or a combination thereof). In some embodiments, the rating or ranking is performed on a location (e.g., regional, national, or global) basis. In some embodiments, the rating or ranking is performed on a league (e.g., esports, collegiate, high school gaming league) basis. In some embodiments, the method further comprises displaying the rating or ranking to the gamer on the GUI. In some embodiments, the method further comprises generating and displaying a leaderboard of ratings or rankings. In some embodiments, the method further comprises displaying rating or ranking to a third party (e.g., coach) on the GUI. In some embodiments, the score indicative of the gamer’s health and well-being comprises a profile score. In some embodiments, the score indicative of a predicted gaming performance of the gamer comprises a profile score. In some embodiments, the profile score is a number on a scale of from 0-2.

[0009] Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.

[0010] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.WSGR Docket No.60152-701.601 INCORPORATION BY REFERENCE

[0011] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] A better understanding of the features and advantages of the present subject matter will be obtained by reference to the following detailed description that sets forth illustrative embodiments and the accompanying drawings of which:

[0013] FIG. 1 schematically illustrates a system for performing the methods and embodiments disclosed herein.

[0014] FIGs. 2A – 2G illustrate non-limiting examples of graphical user interfaces that may be utilized by the systems and methods disclosed herein. FIG.2A illustrates GUIs which the systems disclosed herein may utilize to display content, leaderboard, achievement, and scoring information to a user, in accordance with embodiments disclosed herein. FIG. 2B illustrates GUIs which the systems disclosed herein may utilize to display scoring information and enable a user to integrate devices, in accordance with embodiments disclosed herein. FIG. 2C illustrates GUIs which the systems disclosed herein may utilize to display to and gather health and well-being information from a user, in accordance with embodiments disclosed herein. FIG. 2D illustrates GUIs which the systems disclosed herein may utilize to display to and gather health and well-being information from a user, in accordance with embodiments disclosed herein. FIG. 2E illustrates GUIs which the systems disclosed herein may utilize to display to and gather gaming performance metrics from a user, in accordance with embodiments disclosed herein. FIG. 2F illustrates GUIs which the systems disclosed herein may utilize to display health and well-being content to a user, in accordance with embodiments disclosed herein. FIG. 2G illustrates GUIs which the systems disclosed herein may utilize to display health and well-being trends to a user, in accordance with embodiments disclosed herein.

[0015] FIGs. 3A – 3I illustrate non-limiting examples of graphical user interfaces that may be utilized by the systems and methods disclosed herein. FIG. 3A illustrates GUIs allowing a party the ability to create a team dashboard, in accordance with embodiments disclosed herein. FIG.3B illustrates how a party can create a new team dashboard, in accordance with embodimentsWSGR Docket No.60152-701.601 disclosed herein. FIG. 3C illustrates a GUI for displaying health and well-being scores and gaming readiness and performance scores of individuals, in accordance with embodiments disclosed herein. FIG. 3D illustrates a GUI displaying summarized score data for individuals, in accordance with embodiments disclosed herein. FIG.3E illustrates a GUI displaying information for a specifically selected individual, in accordance with embodiments disclosed herein. FIG.3F illustrates how a GUI may be customized, in accordance with embodiments disclosed herein. FIG. 3G illustrates how a GUI may be customized, in accordance with embodiments disclosed herein. FIG. 3H illustrate the functionalities for exporting data for the individuals, in accordance with embodiments disclosed herein. FIG. 3I illustrates how a party can provide recommendations to an individual, in accordance with embodiments disclosed herein.

[0016] FIG.4 illustrates a non-limiting example of a model for team ranking, in accordance with embodiments disclosed herein.

[0017] FIG. 5 shows a non-limiting example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface, in accordance with embodiments disclosed herein.

[0018] FIG. 6 shows a non-limiting example of a web / mobile application provision system; in this case, a system providing browser-based and / or native mobile user interfaces, in accordance with embodiments disclosed herein.

[0019] FIG.7 shows a non-limiting example of a cloud-based web / mobile application provision system; in this case, a system comprising an elastically load balanced, auto-scaling web server and application server resources as well as synchronously replicated databases, in accordance with embodiments disclosed here.

[0020] FIG. 8 depicts an exemplary method for monitoring and improving the health and well- being of a gamer.

[0021] FIG. 9 depicts an exemplary method for predicting one or more aspects of a gamer or rating a gaming performance.

[0022] FIG.10 depicts an exemplary method for ranking a gaming team comprised of a plurality of gamers. DETAILED DESCRIPTION

[0023] Provided in some embodiments herein is a system for performing a method (e.g., computer-implemented method) for monitoring and improving the health and well-being of an individual (e.g., a gamer). In some embodiments, the method comprises: (a) receiving data from one or more data sources associated with the individual; (b) classifying the received data as being associated with one or more categories of health and well-being; (c) initializing one or moreWSGR Docket No.60152-701.601 models or algorithms of health and well-being; and (d) applying the one or more models or algorithms to: (i) determine a score indicative of the individual’s health and well-being; and (ii) generate one or more recommendations to the individual based at least in part on the score to improve the individual’s health and well-being. In some embodiments, the individual is a gamer (e.g., as described herein). In some embodiments, the one or more models or algorithms comprise one or more Artificial Intelligence (AI) models or algorithms. In some embodiments, classifying the received data comprises classifying at least a subset of the received data as being associated with one or more categories of health and well-being. In some embodiments, the model or algorithm comprises data received from at least a reference population or a historical individual database. In some embodiments, the system comprises: (a) one or more processors; and (b) a memory comprising executable instructions which, when executed by the one or more processors, causes the system to perform the steps disclosed herein. In some embodiments, a non-transitory, computer-readable medium comprises executable instructions, wherein when a processor, when executing the executable instructions, performs the methods disclosed herein.

[0024] Provided in some embodiments herein is a system for performing a method (e.g., computer-implemented method) for predicting a gamer’s gaming performance. In some embodiments, the method comprises: (a) receiving data from one or more data sources associated with the gamer; (b) classifying the received data as being associated with one or more categories of health and well-being; (c) receiving a score of the gamer indicative of the gamer’s health and well-being; (d) initializing one or more models or algorithms of predicting gaming performance; and (e) applying the one or more AI models / algorithms to determine a score indicative of a predicted gaming performance of the gamer. In some embodiments, the one or more models or algorithms comprise one or more Artificial Intelligence (AI) models or algorithms. In some embodiments, the model or algorithm comprises data received from at least a reference population or a historical gamer database. In some embodiments, the system comprises: (a) one or more processors; and (b) a memory comprising executable instructions which, when executed by the one or more processors, causes the system to perform the steps disclosed herein. In some embodiments, a non-transitory, computer-readable medium comprises executable instructions, wherein when a processor, when executing the executable instructions, performs the methods disclosed herein.

[0025] Provided in some embodiments herein is a system for performing a method (e.g., computer-implemented method) for rating a gamer’s gaming performance. In some embodiments, the method comprises: (a) receiving data from one or more data sources associated with the gamer; (b) classifying the received data as being associated with one or more categories of gaming performance; (c) receiving a score of the gamer indicative of the gamer’s health and well-being;WSGR Docket No.60152-701.601 (d) initializing one or more models or algorithms of rating individual gaming performance; and (e) applying the one or more AI models / algorithms to rate the gamer. In some embodiments, the one or more models or algorithms comprise one or more Artificial Intelligence (AI) models or algorithms. In some embodiments, the model or algorithm comprises data received from at least a reference population or a historical gamer database. In some embodiments, the system comprises: (a) one or more processors; and (b) a memory comprising executable instructions which, when executed by the one or more processors, causes the system to perform the steps disclosed herein. In some embodiments, a non-transitory, computer-readable medium comprises executable instructions, wherein when a processor, when executing the executable instructions, performs the methods disclosed herein.

[0026] Provided in some embodiments herein is a system for performing a method (e.g., computer-implemented method) for ranking a gaming team. In some embodiments, the method comprises: (a) receiving data from one or more data sources associated with the gaming team; (b) classifying the received data as being associated with one or more categories of team gaming performance; (c) initializing one or more models or algorithms of rating individual gaming performance; and (e) applying the one or more models or algorithms to rank the gaming team. In some embodiments, the one or more models or algorithms comprise one or more Artificial Intelligence (AI) models or algorithms. In some embodiments, the model or algorithm comprises data received from at least a reference population or a historical gaming team database. In some embodiments, the system comprises: (a) one or more processors; and (b) a memory comprising executable instructions which, when executed by the one or more processors, causes the system to perform the steps disclosed herein. In some embodiments, a non-transitory, computer-readable medium comprises executable instructions, wherein when a processor, when executing the executable instructions, performs the methods disclosed herein.

[0027] FIG. 1 schematically illustrates a system 100 for performing the above methods, and the other methods disclosed herein, in accordance with the embodiments of the present disclosure. The system 100 may have a data source(s) 110, a data retrieval module 120, a database 130, a data processing module 140, and a network 170. The system can perform the method of any one of the embodiments described herein.

[0028] The system 100 can receive data from one or more data sources 110. In some embodiments, the one or more data sources 110 can include individual input, gamer input, third party input, sensor / device input, or a combination thereof. In some embodiments, the individual or gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, or other personal computing device). In some embodiments, the individual or gamer input comprises qualitative input, quantitative input, or a combination thereof. For example, qualitativeWSGR Docket No.60152-701.601 input can include written input, or other text data describing the health and well-being, the gaming performance of the individual or gamer, or a team gaming performance of a gaming team. As an example, the quantitative input can include number data including information related to the individual’s or gamer’s health and well-being, the gaming performance of the individual or gamer, or a team gaming performance of a gaming team. In some embodiments, the quantitative data comprises a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof, or any other category of gaming performance known to those of skill in the art. In some embodiments, the data includes individual or gamer responses to one or more questionnaires administered to the individual or gamer. In some embodiments, the third party input comprises data from the individual’s or gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, or teachers, or any combination thereof. In some embodiments, the third party data can include gaming performance data received from one or more games the gamer is currently playing. In some embodiments, the sensor or device input is received from one or more individual or gamer sensors or devices.

[0029] The system 100 can receive the data from the one or more data sources 110 using the data retrieval module 120. The data retrieval module 120 can also be used to classify the incoming data as belonging to one or more categories of data. As an example, the data retrieval module can be used to classify the data as belonging to one or more categories of health and well-being. In some embodiments, the data retrieval module can also be used to identify the source of the data (e.g., individual input, sensor / device input, third party input, etc.).

[0030] The system 100 can store the data from the one or more data sources in the database 130. The database may include, for example, raw data collected and received from the one or more data sources. The system 100 can store the user input 122, third party input 124, device / sensor input 126, and / or score input 128 in the user data 134 portion of the database. The system can compile user data 134 in the database 130 over time to serve as a historical user database. The user data 134 can also be acquired from a third party library. The historical user database can be used in embodiments described herein for initializing models and for data comparison for generating outputs associated with the individual’s or gamer’s health and well-being, a gamer’s gaming performance, a gamer’s rating, or a gaming teams ranking, or any combination thereof. The database 130 can also store population data 132. The population data 132 can be data from a reference population. The reference population data can be acquired using the data retrievalWSGR Docket No.60152-701.601 module for a plurality of individuals or gamers of the system over time. The reference population can include data from people in a similar situation as the individual or gamer. For example, the individual may be a gamer on a professional gaming team, and the reference population data can include data from other people on professional gaming teams.

[0031] The data processing module 140 can comprise one or more data processing algorithms described herein for processing the data received from the one or more data sources 110. The data processing algorithms can comprise, as a non-limiting example, feature extraction algorithms, machine learning algorithms, artificial intelligence algorithms, Bayesian algorithms (e.g., Bayesian assimilation, Bayesian estimation), and / or statistical analysis algorithms. The data processing module can process data in real-time, near real-time, or in a dynamic nature. The system 100 can be used to initiate one or more models as described herein. For example, the one or more models can include pre-programmed models, artificial intelligence models, machine learning models, or a combination thereof. The data processing module 140 can be used for generating an output (e.g., a score or one or more recommendations) to a user of the system that is associated with monitoring or improving an individual’s or gamer’s health and well-being, a gamer’s gaming performance, a gamer’s rating, or a gaming teams ranking. The data processing module 140 can output the one or more recommendations to a user using a graphical user interface on a user’s device. The recommendation can be presented as a visual representation or a textual representation, or a combination of the two. The network 170 can ensure that the components of system 100 are in communication with one another. The components of system 100 can be implemented on a local hard drive. The components of system 100 can be implemented on the cloud. The components of system 100 can be implemented on a combination of local hard drives and the cloud. The system 100 can be operatively coupled to the network 170 with the aid of a communication interface. The network 170 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 170 in some cases is a telecommunication and / or data network. The network 170 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 170, in some cases with the aid of the system 100, can implement a peer-to-peer network, which may enable devices coupled to the system 100 to behave as a client or a server.

[0032] The network 170 can ensure that the components of system 100 are in communication with one another. The components of system 100 can be implemented on a local hard drive. The components of system 100 can be implemented on the cloud. The components of system 100 can be implemented on a combination of local hard drives and the cloud. The system 100 can be operatively coupled to the network 170 with the aid of a communication interface. The networkWSGR Docket No.60152-701.601 170 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 170 in some cases is a telecommunication and / or data network. The network 170 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 170, in some cases with the aid of the system 100, can implement a peer-to-peer network, which may enable devices coupled to the system 100 to behave as a client or a server.

[0033] The system 100 can communicate with one or more remote computer systems through the network 170. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the system 100 via the network 170.

[0034] The remote computer system may include a display. The remote computer system may include one or more wearable devices, one or more implantable devices, or a combination thereof, as described herein. The display may be a screen. The display may or may not be a touchscreen. The display may be a light-emitting diode (LED) screen, OLED screen, liquid crystal display (LCD) screen, plasma screen, or any other type of screen. The display may be configured to show a user interface (UI) or a graphical user interface (GUI) rendered through an application (e.g., via an application programming interface (API) executed on the user device). The GUI may show graphical elements that permit a user to monitor collected sensor data, generated scores, view a notification or report regarding the user’s well-being state, and / or view one or more recommendations to affect or improve the user’s well-being.

[0035] The system 100 of FIG. 1 and its components can be implemented on one or more computing devices. The computing devices can be servers, desktop or laptop computers, electronic tablets, mobile devices, or the like. The computing devices can be located in one or more locations. The computing devices can have general-purpose processors, graphics processing units (GPU), application-specific integrated circuits (ASIC), field-programmable gate-arrays (FPGA), or the like. The computing devices can additionally have memory, e.g., dynamic or static random-access memory, read-only memory, flash memory, hard drives, or the like. The memory can be configured to store instructions that, upon execution, cause the computing devices to implement the functionality of the subsystems. The computing devices can additionally have network communication devices. The network communication devices can enable the computing devices to communicate with each other and with any number of user devices, over a network. The network can be a wired or wireless network. For example, the network can be a fiber optic network, Ethernet® network, a satellite network, a cellular network, a Wi-Fi® network, a Bluetooth® network, or the like. In other implementations, the computing devices can be severalWSGR Docket No.60152-701.601 distributed computing devices that are accessible through the Internet. Such computing devices may be considered cloud computing devices. Individual Health And Well-Being

[0036] The systems described herein can be used to performs methods for monitoring and improving the health and well-being of an individual.

[0037] In any of the embodiments described herein, the systems may be implemented in a mobile application or web application, or both, and the mobile and web application may utilize a graphical user interface(s) to display information to a user (e.g., individual or gamer) of the application, as well as receive data or input from the user of the application. For example, in any of the embodiments herein, a GUI can be utilized to display or retrieve any of the discussed data, scores, ratings, rankings, or predicted performances discussed herein. In some embodiments, the models and algorithms discussed here can utilize the data received from the web or mobile application in order to perform their intended functions (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.). FIGs. 2A-2G and FIGs. 3A-3I illustrate non-limiting examples of the graphical user interfaces that may be utilized by the systems and methods disclosed herein.

[0038] In some embodiments, the individual may be a gamer. In some embodiments, a gamer may be an individual who play video games for their occupation (e.g., at a professional or competitive level). In some embodiments, the individual may play one or more of Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, or any other video game played by an individual at a professional or competitive level. In some embodiments, the gamer is a member of a professional gaming (e.g., esports) league or team. For example, the gaming league or team may be a league or team that professionally play and competes with other leagues or teams in any of the video games mentioned above, or as described elsewhere herein. In some embodiments, the gamer is a member of an esports federation. In some embodiments, the federation may be the National Esports Collegiate Conference, International Esports Federation, Global Esports Federation, Asian Esports Federation, World Esports Federation, Eastern College Athletic Conference, or any combination thereof. In some embodiments, the gamer is a member of a foreign nation esports federation. In some embodiments, the gamer is a member of a collegiate gaming league or team. In some embodiments, the gamer is a member of a high school gaming league or team. In some embodiments, the gamer creates content for one or more video games (e.g., a streamer or content creator). In some embodiments, the gamer streams (e.g., live streams) themselves playing video games on one or more platforms (e.g., YouTube or Twitch). In someWSGR Docket No.60152-701.601 embodiments, the gamer creates videos or other content about video games (e.g., news about newly announced video games, updates to current video games, a playing performance of a current video game, the performance of one or more items used in the game (e.g., a weapon), their review of a video game, etc.) and posts the content to one or more platforms (e.g., YouTube). In some embodiments, the gamer is a casual gamer. In some embodiments, a casual gamer may be someone who is not a member of a professional, esports, collegiate, or high school gaming league or team. In some embodiments, a casual gamer may be a gamer who enjoys playing video games leisurely (e.g., in their free time). In some embodiments, the gamer is above the age of 13. In some embodiments, the gamer is below the age of 13. In some embodiments, the gamer is aged 10-13. In some embodiments, the gamer is aged 13-18. In some embodiments, the gamer is above the age of 18. In some embodiments, the gamer is aged 16-24. In some embodiments, the gamer is aged 10-40, 15-35, 15-25, 15-20, 20-25, or 20-30.

[0039] In some embodiments, the system is able to receive data associated with one or more categories of health and well-being. For example, the categories of health and well-being can include: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. In some embodiments, health and well-being data can be used to assess metrics of an individual’s health and well-being. In some embodiments, metrics of an individual’s health and well-being are predictive metrics. Metrics of an individual’s health and well-being include but are not limited to: performance readiness, growth efficiency, mental fortitude, stamina, consistency, and lifestyle balance. In some embodiments, the data is indicative of an individual’s or gamer’s present status with the one or more categories of health and well-being. In some embodiments, the data is indicative of an individual’s or gamer’s past or historical status with the one or more categories of health and well-being. In some embodiments, the system can use the data gathered to identify one or more trends of the individual or gamer relating to the one or more categories. In some embodiments, the trends can be used when generating a score or one or more recommendations to the individual or gamer, as will be discussed herein. As shown in FIGs.2C-2D, the system may utilize graphical user interfaces to record data from an individual or gamer useful for input into the models and algorithms discussed herein when performing their intended functions (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.).

[0040] In some embodiments, the physical maintenance data may include data relating to the individual’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), or a combination thereof. In some embodiments, the data includes a frequency of movement. In some embodiments, the frequency of movement data can account for an amount of movement by the individual for every hour of being seated. In some embodiments, the type of physical exercise can include aerobics, running, weightlifting, yoga, orWSGR Docket No.60152-701.601 any other type of physical exercise. In some embodiments, the data can relate to an amount of time or distance run, a number of repetitions in a certain weightlifting exercise, and amount of time doing yoga, or any other data quantifying an amount of physical exercise being performed. In some embodiments, the physical state can be self-identified by the individual or gamer. In some embodiments, the physical state can be identified by the system using any one of the methods described herein. For example, the models or algorithms disclosed herein can be used to identify a physical state of the individual based at least in part on other data (e.g., physical exercise, diet, mental condition) received from the one or more data sources. FIG. 2C illustrates non-limiting examples of metrics the system may use when analyzing an individual’s or gamer’s physical maintenance.

[0041] In some embodiments, the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of the individual or gamer. In some embodiments, the mental condition data further comprises data relating to the individual’s or gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). For example, the data can relate to a type of breathing exercise the individual or gamer has recently performed, a number of times the breathing exercise has been performed, a type of meditation exercise that has been performed, or an amount of times a meditation exercise has been performed. In some embodiments, the meditation and / or breathwork exercises can include mindfulness meditation, loving kindness meditation, mantra repetition meditation, yoga nidra, body scan, walking meditation, alternate side breathing, bellow breath, 4-7-8 breathing, energizing breath, belly breath, or any combination thereof. FIG. 2D illustrate non-limiting metrics that the system can utilize when analyzing an individual’s or gamer’s mental condition.

[0042] In some embodiments, the nutrition data can include data relating to generally the amount of food the individual or gamer is eating, types of food the individual or gamer is eating, a time between meals, a number of calories per meal, or other aspects relating to the individual’s or gamer’s diet. For example, the nutrition can include data relating to: how much water the individual or gamer is drinking, how much caffeine the individual or gamer is ingesting, how much sugar the individual or gamer is ingesting, or what kinds of food the individual or gamer is eating (e.g., health, fast food, snacks, etc.). The data can be used by the system to identify trends in the individual’s or gamer’s diet, which can be used to help generate one or more recommendations to the individual or gamer, as will be discussed herein. FIGs. 2C and 2D illustrate non-limiting metrics that the system can utilize when analyzing an individual’s or gamer’s nutrition. In some embodiments, the nutrition data can help to track healthy and non- healthy food intake. For example, the data can include a number of times an individual eats non-WSGR Docket No.60152-701.601 healthy snack or fast food. The user may also be able to input the type of food eaten and identify the food as healthy versus non-healthy.

[0043] In some embodiments, the sleep data comprises data relating to a quality of sleep, a duration of sleep, or an individual’s or gamer’s routine before sleep. For example, the data can include self-reported data from the individual or gamer as to how long they slept the previous night or nights. It may also include self-reported data from the individual or gamer identifying how they felt the quality of their sleep was the previous night or nights. In some embodiments, the system can integrate with one or more wearable devices of the individual or gamer, and the sleep data can include data gathered from the one or more wearable devices as to the individual’s or gamer’s sleep. The routine before sleep can be self-reported by the individual or gamer. In some embodiments, the individual or gamer can input data indicating they have followed a recommended routine before sleep. FIG. 2C illustrates non-limiting examples of metrics the system may use when analyzing an individual’s or gamer’s sleep.

[0044] In some embodiments, the lifestyle data comprises data relating to the gamers’ hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof). For example, the data can include an amount of time spent outdoors, types of physical activities performed outdoors, an amount of time spent with family and / or friends, a numberof new friends being met on a given day or within a given time period, an amount of time spent on vacation, or other time taken not related to the individual’s or gamer’s profession. FIG. 2D illustrates non-limiting examples of metrics the system may use when analyzing an individual’s or gamer’s lifestyle. In some embodiments, an individual’s lifestyle can be measured (e.g., by the computer-implemented methods disclosed herein) by identifying a willingness or ability to try new things. For example, input can include an individual’s self- identified interest in trying a new hobby, new food, new gaming tactic, or other relevant activity related to the categories of health and well-being. In some embodiments, an individual’s lifestyle can be measured (e.g., by the computer-implemented methods disclosed herein) by identifying a willingness or ability of the individual to challenge themselves. For example, the application may send daily, weekly, monthly, or yearly challenges to the gamer to complete, and the systems discussed herein can take into account a completion percentage of these challenges when calculating scores or making recommendations.

[0045] In some embodiments, improving the health and well-being of the individual or gamer includes improving the gamer’s cognitive performance. In some embodiments, cognitive performance can be measured by an individual’s grade point average (GPA) in academics or their performance in video games. In some embodiments, the models and algorithms disclosed herein can identify trends between an individual academic performance (e.g., via their GPA) toWSGR Docket No.60152-701.601 their in game performance. In some embodiments, improving the health and well-being of the individual or gamer includes improving the gamer’s performance in one or more video games. For example, a gamer may be preparing for an individual or team competition coming up, and the system can be implemented to improve the gamer’s performance before the competition takes place. In some embodiments, the system can be tuned to improve a specific aspect of a gamer’s performance. For example, a gamer may wish to tune the system to improve their hand eye coordination, reaction time, kill to death ratio, score per minute, or other gaming performance indicator as disclosed herein. In some embodiments, the categories of gaming performance (e.g., in-game categories) may include a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof.

[0046] An individual or a gamer’s cognitive performance can be assessed through games integrated into the system. In some embodiments, a game integrated into the system can analyze an individual or a gamer’s behavior to assess the individual or gamer’s cognitive performance. A system provided herein can assess an individual’s cognitive skills, e.g., via a game integrated into the system. Examples of cognitive skills that can be assessed include but are not limited to: problem solving (e.g., logical reasoning and decision-making); spatial cognition (e.g., understanding of spatial relationships and navigation); perception (e.g., reaction time and awareness); multitasking / task-switching (e.g., efficiency in managing multiple objectives); inhibition (e.g., self-control and impulse regulation); and top-down attention (e.g., focus and prioritization.) In some embodiments, the scores determined using the methods herein are determined based on one or more assessments of the cognitive skills.

[0047] An individual or gamer’s career skills (e.g., real-world professional competencies) can be measured by the system. An individual’s career skills can be indicative of the individual’s potential for success in business-oriented environments. In some embodiments, an individual’s career skills are measured through AI simulation. In some embodiments, an individual’s career skills are measured through AI simulations that evaluate an individual on various business- related tasks. Examples of business-related tasks include but are not limited to: marketing, sales, operations, general management, and business strategy. In some embodiments, the scores determined using the methods herein are determined based on one or more assessments of the cognitive skills.

[0048] In some embodiments, the system can improve the health and well-being of the individual or gamer by a target (e.g., pre-determined) amount (e.g., by improving the score of aWSGR Docket No.60152-701.601 gamer). As will be discussed herein, the individual’s or gamer’s health and well-being can be quantified by a score (e.g., a score indicative of the individual’s or gamer’s health and well- being), and improving the health and well-being can include increasing the gamer’s score by a pre-determined amount (e.g., by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, or 70 or more points). In some embodiments, the system can improve the health and well- being of the individual or gamer by a target (e.g., pre-determined) future time (e.g., a match or competition). For example, an individual or gamer can identify a target future date they would like to increase their score by, and the system can be configured to improve the health and well- being of the individual or gamer by the target date and by the target amount. In some embodiments, the system can perform the method to improve the health and well-being of a plurality of individuals or gamers that are on a team (e.g., professional, collegiate, high school team).

[0049] The system can receive data from one or more data sources associated with the individual or gamer. In some embodiments, the data is received from at least a reference population or a historical gamer database. In some embodiments, the reference population data, the historical gamer data, or a combination thereof, include data gathered over a time period. In some embodiments, the data may be data of a specific individual or gamer, or it may be data from a population of individuals or gamers. In some embodiments, the time period comprises at least about 1 day (e.g., about 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 or more days. In some embodiments, comprises at least about 1 week (e.g., about 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 or more weeks. In some embodiments, the time period comprises at least about 1 month (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the time period comprises at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 or more months. In some embodiments, the time period comprises at least about 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). In some embodiments, the time period comprises at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 or more years.

[0050] In some embodiments, the reference population data, the historical user database, or a combination thereof, are received from a third party library (e.g., a gaming publisher or esports data analytics partner). For example, a gamer may professionally play one or more specific video games, and the system can receive data from the publisher of those one or more videoWSGR Docket No.60152-701.601 games. The data may include data related to the gamer’s gaming performance, trends of the gamer within the one or more video games, or any other metrics as discussed herein. In some embodiments, the system can integrate with the gaming publisher to visualize a score or one or more recommendations to the gamer based on the data received from the gaming publisher. For example, the system can display a score or one or more recommendations in a pop up window in the video game while the gamer is playing the video game. In some embodiments, the reference population data comprises data gathered from a plurality of gamers over a period of time (e.g., such as the time periods discussed above).

[0051] In some embodiments, receiving the data includes administering one or more surveys or questionnaires to the individual or gamer to gather the data. Examples or surveys or questionnaires that may be administered to an individual or gamer are includes in FIGs.2A-2G. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) daily to the user. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, ten times, eleven times, twelve times, thirteen times, fourteen times, or fifteen times or more) a week to the user. In some embodiments, the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, ten times, eleven times, twelve times, thirteen times, fourteen times, fifteen times, sixteen times, seventeen times, eighteen times, nineteen times, twenty times, twenty-one times, twenty-two times, twenty-three times, twenty-four times, twenty-five times, twenty-six times, twenty-seven times, twenty-eight times, twenty-nine times, or thirty times or more) a month to the user. In some embodiments, the one or more surveys or questionnaires are configured to gather the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data from the individual or gamer that the system needs in order to score or provide recommendations to the individual or gamer.

[0052] In some embodiments, the received data includes unprompted individual or gamer input. For example, an individual or gamer who is a user of the systems discussed herein can provide written or other information relating to their health and well-being or gaming performance using a number of devices. As will be discussed herein, the system can be comprised in a mobile or web application, and the user (e.g., individual or gamer) can provide input using the web or mobile application. For example, the devices may include a mobile device, a tablet, or other personal computing device. In some embodiments, the unprompted input can be gathered using individual or gamer journal entries that the individual or gamer inputs into the mobile or web application. For example, the journal entries may relate to the individual’s or gamer’s well-beingWSGR Docket No.60152-701.601 or gaming performance. FIG.2B illustrates examples of journal entries that an individual or gamer may input into the system. The journal entries may include notes from the individual or gamer that provide useful information for the system to process when performing the desired function (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.). As shown, the journal feature may include a team journal feature, where a group or team of individuals or gamers can provide journal notes relevant to the entire team. In some embodiments, the team journal feature can allow for third party input to be entered into the team journal. In some embodiments, the unprompted input relates to the one or more categories of health and well-being (e.g., physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data).

[0053] In some embodiments, receiving the data comprises receiving data of the individual or gamer from a third party (e.g., someone else other than the individual or gamer). FIGs.3A-3I illustrate non-limiting examples of user interfaces which may be utilized by the system to collect and display data to a third party. These figures also illustrate features of the system that will allow for a third party (e.g., a coach, such as an esports director) to create and manage a plurality of individuals or gamers. In some embodiments, the system includes a mobile application for use by a user (e.g., individual or gamer), as well as a web application for use by a third party (e.g., a coach). In some embodiments, both applications may be mobile applications or web applications. In some embodiments, the system utilizes data received from both the user via the mobile application, and the third party via the web application when performing the intended function (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.). In some embodiments, when a coach is assigned to an individual or gamer or to a team comprising a plurality of individuals or gamers, a user interface displays that the coach has been assigned. In some embodiments, when a coach is assigned, a user interface displays data and metrics relevant to the coach to the individuals or teams to which the coach is assigned.

[0054] As shown in FIG.3A, the graphical user interface of the application (e.g., a web or mobile application) can allow a third party (e.g., a coach, such as an esports director, or a recruiter) the ability to create a team dashboard. As shown, the team dashboard can include a name, username, email, date joined, status, and role for each member of a team. In some embodiments, the team is a team who plays video games, such as a professional or other team as discussed herein. As shown, the graphical user interface may provide the option for a coach to invite other members to the team. In some embodiments, a third party may be able to view this information on the web or mobile application, but the individual team members included in the team dashboard may not have access to the information. This can limit the opportunity for some players to see data related to other players on the team. FIG.3B illustrates how a third party canWSGR Docket No.60152-701.601 create a new team dashboard. In some embodiments, the third party is a coach, such as an esports director, and the coach can manage multiple teams that play multiple different video games professionally. In some embodiments, the coach may manage several teams (e.g., an A team, a B team, a C team, etc.) for a single video game. In some embodiments, the coach may manage several teams (e.g., an A team, a B team, a C team, etc.) for each of a plurality of video games. In some embodiments, the video games may be those as discussed herein.

[0055] In some embodiments, the third party includes coaches (e.g., gaming or fitness coaches), teammates, recruiters, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, teachers, professors or any other source having information on the individual’s or gamer’s health and wellbeing or gaming performance, or a combination thereof. In some embodiments, the third party input comprises qualitative or quantitative data from the individual’s or gamer’s coach relating to the individual’s or gamer’s well-being or gaming performance. For example, a coach may include a write up summarizing a gamer’s performance in practice for a time period, or a number identifying a number of times the individual or gamer has performed physical exercise in a given time period. In some embodiments, the system can transmit received data of the individual or gamer to the individual’s or gamer’s coach (e.g., or other third party associated with the individual or gamer), and the third party input can include the third party’s feedback based on the data transmitted to them. For example, a coach may be coaching a gamer, or gaming team, who has an upcoming match. The data transmitted to the coach may include data indicative of the gamer’s recent health and well-being. Based on this data, the coach can input one or more recommendations to the gamer that the coach thinks will help prepare the gamer for the upcoming match. In some embodiments, a plurality of coaches or third parties can input a plurality of recommendations to the gamer. In some embodiments, the third party can include the individual’s or gamer’s parents or family. For example, the parents or family of the individual or gamer can have access to the mobile or web application and provide quantitative or qualitative data including information they have observed of the individual or gamer. In some embodiments, the system may enable multiple third parties to communicate with one another to coordinate and finalize recommendations to be provided to the individual or gamer. For example, a coach and parent of the individual or gamer can discuss recent health and well-being data of the individual or gamer and they can together come up with one or more recommendations to help the individual or gamer improve their health and well-being or gaming performance.

[0056] FIGs.3C-3G illustrate different data and metrics the system can display to a third party. In some embodiments, the data and metrics are useful for a coach to receive and analyze when managing an individual gamer, or a team comprised of a plurality of gamers. In someWSGR Docket No.60152-701.601 embodiments, the data and metrics are useful for a recruiter to receive and analyze when deciding whether to recruit an individual gamer or a team comprised of a plurality of gamers. FIG.3C shows a graphical user interface that can display health and well-being score and gaming readiness and performance scores of each individual the coach is coaching. As shown, the data can be displayed as line graphs, or circle charts. Each individual line can be color coded to correspond to a specific individual the coach has responsibility for. The circle chart may also be color coded to show a number of individual that fall into a given category. For example, the circle chart can show gamers who are ready for gaming (game on), moderately ready, subpar ready, not ready, or for whom no gaming readiness data exists yet. The circle chart can also visualize a number of gamers who are in peak form, pretty solid, not ideal, danger zone, or for whom no gaming performance data exists yet.

[0057] FIG.3D illustrates a graphical user interface displaying summarized score data for each individual a third party may have responsibility for. For example, the third party may be a coach, and the graphical user interface can display the score data for each individual gamer included on a team, or teams, that the coach is responsible for. As shown, the score data can include a health and well-being score (the far right score), a daily score, which can be a health and well-being score for the individual on that given day, a gaming readiness score, and a gaming performance score. The figures also displays that the graphical user interface can indicate to a coach a percentage of data input for each individual. For example, the system can look at a pre- determined number of prior days and calculate a percentage of days the gamer input date (e.g., into a mobile application) for the system to receive and analyze. This information may allow a coach to identify individuals who are not keeping up with recommended action, or individuals who need additional assistance in improving their health and well-being, gaming readiness, and gaming performance scores.

[0058] FIG.3E illustrates a graphical user interface displaying information to a third party for a specifically selected individual. For example, the third party may be a coach, and the coach can select a specific gamer who is a member of at least one team the coach is responsible for, and the system can display the information shown in FIG.3E. In some embodiments, a gamer may be associated with one or more categories of health and well-being (also referred to herein as “pillars.”) As shown, the system can display trend information for the individual at the top left of the screen, which may include pillar score (e.g., for a particular pillar or for a plurality of pillars,) health and well-being score, game readiness score, and game performance score. The coach may select one of these categories, and an expanded view can be shown. As shown in the figure, an expanded view of the weekly pillar (e.g., each pillar can correspond to each of the categories of health and well-being) scores are shown. The coach is able to see in whichWSGR Docket No.60152-701.601 categories the individual is performing well in, and which categories the individual may need additional assistance. The coach can take this information and use it to provide recommendations to the individual for improving in certain categories of health and well-being. This information may also give the coach an opportunity to identify a root cause of an individual’s low gaming performance or readiness scores. As shown, the coach may be able to select certain time periods for which the data is to be shown. In some embodiments, the time period may be daily. In some embodiments, the time period may be hourly, daily, weekly, monthly, or yearly. In some embodiments, the data may be displayed after receiving user input indicating that the data should be displayed. In some embodiments, the data may include commentary regarding one or more pillars for a user or a plurality of users. In some embodiments, the commentary may be provided daily.

[0059] FIG.3F is similar to FIG.3E, but FIG.3F illustrates how a third party can select a given pillar (e.g., category of health and well-being), to see additional information. As shown, the coach has selected the nutrition pillar from a drop down menu, and the far right of the graphical user interface is displaying the specific nutrition information derived for that individual for a given day or time period. Similarly, FIG.3G shows that the coach has chosen the sleep category from the drop down menu, and the far right of the graphical user interface is displaying the sleep information input by the individual for a given day.

[0060] FIG.3H illustrates the functionalities provided to a third party for exporting data for the individuals they are responsible for. For example, the third party may be a coach, and the coach may desire to view a report for each of the gamers or individuals they are coaching. The coach can choose which format the report is to be exported as (e.g., excel, word, pdf, csv, etc.), can choose to de-identify data, and choose a duration for the report to provide data for. In some embodiments, a coach may wish to de-identify data to comply with research requirements for research studies.

[0061] Once a third party has had time to review the information for a given individual, or team of individuals, the third party can provide recommendations to the individual, as shown in FIG. 3I. The recommendations can include words of affirmation or encouragement, or specific ideas the third party has in mind that can help an individual improve their health and well-being, game readiness, or game performance score. In some embodiments, the models and algorithms disclosed herein can receive the third party recommendations as input when performing their intended purpose (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.).

[0062] In some embodiments, a user interface comprising one or more seats may be displayed. In some embodiments, the one or more seats may be filled or associated with one or more userWSGR Docket No.60152-701.601 assignments (e.g., filling a seat with a user name.) In some embodiments, the one or more seats may additionally be filled by one or more third parties (e.g., one or more coaches of users.) In some embodiments, user input indicating which user and / or third party may be assigned to the one or more seats.

[0063] As has been discussed herein, in some embodiments, the third party data includes gaming performance data received from one or more games the gamer is currently playing. In some embodiments, the gaming performance data can include a kill / death ratio, a win / loss ratio, a score per minute, or other data gathered by the one or more games while the gamer is playing the one or more games.

[0064] The system can receive data from one or more sensors or devices associated with the individual or gamer. In some embodiments, the sensors or devices can be configured to gather biometric data of the individual or gamer. For example, the sensors or devices can gather fingerprint, facial, voice, pupil, iris (e.g. or other eye), DNA, hand, or other biometric data associated with the individual or gamer. In some embodiments, the sensors or devices are configured to gather pupillometry data of the gamer. In some embodiments, the sensors or devices may include one or more personal computing devices (e.g., a mobile device, tablet, or other personal computing device), or one or more applications associated with the one or more personal computing devices (e.g., social media applications (e.g., Instagram, Facebook, Twitter, TikTok, Calendar, Microphone, Online Purchasing apps, etc.).

[0065] In some embodiments, the sensors or devices comprise wearable sensors or devices. For example, the wearable sensors or devices can be sensors or devices worn by the individual or gamer. The system can identify data gathered from the wearable at all times, including times of practice, exercise, a match or performance, or other event associated with the individual’s or gamer’s health and well-being or gaming performance. In some embodiments, the wearables comprise one or more smart devices (e.g., smart watch or a heartrate sensor) configured to measure physiological (e.g., heart rate, breathing rate), neurological, psychological, metabolic, or biological data of the gamer. In some embodiments, the wearables can include Fitbit, Apple Watch, Oura, Garmin, or Polar wearables. In some embodiments, the sensor or device can be a camera. For example, the camera can be a Tobii Eye Tracker product affixed to a computer monitor of the individual or gamer. As shown in FIG.2B, graphical user interface may display to a user the wearables that are currently integrated with the system and can provide a user the opportunity to integrate additional wearables with the system. In some embodiments, integrating a wearable into the system comprises integrating data retrieved from software that supports the wearable into the system. In some embodiments, data from FitBit, Apple Health, Oura, Polar Access API, or Garmin Connect software can be integrated into the system. In someWSGR Docket No.60152-701.601 embodiments, the sensors or devices include sensors or devices configured to receive voice data (e.g., a microphone, a gaming headset, etc.) from the gamer. In some embodiments, the voice data is gathered while a gamer is playing a video game (e.g., during practice or during a match).

[0066] In some embodiments, the system can provide a recommendation to an individual or gamer based on data received from a device. In some embodiments, the system provides a recommendation to an individual after data received from a device is reviewed by a third party (e.g., a coach of a user.) In some embodiments, the system provides a recommendation to an individual based on data from a device that has not been reviewed by a third party (e.g., a coach of a user.) In some embodiments, a recommendation provided by the system based at least in part on data received form a device is a recommendation that an individual perform a certain activity or review certain content. For example, if data from a device (e.g., an Apple Watch) indicates that an individual’s number of standing hours is low, the system may recommend a series of exercises performed while standing.

[0067] In some embodiments, the system can process (e.g., classify) the received data. In some embodiments, processing can include classifying the data as belonging to one or more categories of health and well-being. In some embodiments, the method includes classifying the received data as originating from: gamer provided input, third party input, or sensor / device input. In some embodiments, classifying the data is performed using one or more data processing algorithms. In some embodiments, the one or more data processing algorithms comprises one or more feature extraction algorithms, one or more machine learning algorithms, one or more artificial intelligence algorithms, one or more Bayesian algorithms (e.g., Bayesian assimilation), one or more statistical analysis algorithms, or a combination thereof. In some embodiments, the one or more data processing algorithms receive data from: (i) the one or more data sources, (ii) a database, or a combination thereof. For example, the system can use data from the data sources, and from the database (e.g., such as a historical individual or gamer database) as input data when performing the initializing a model or algorithm to perform an intended function (e.g., determine a score, generate recommendations, rate a gamer, rank a gaming team, etc.). In some embodiments, the database comprises stored reference population data, stored historical gamer specific data, or a combination thereof. In some embodiments, the one or more data processing algorithms comprise a natural language processing model configured to extract qualitative data from the one or more data sources, the historical gamer database, a reference population, or a combination thereof.

[0068] In some embodiments, classifying the data is performed in a real-time, near real-time, or in a dynamic nature. In some embodiments, classifying the data further comprises labeling the reference population or the historical gamer data. In some embodiments, labeling the dataWSGR Docket No.60152-701.601 identifies a data source of the data. In some embodiments, labeling the data comprises identifying the data as being associated with one or more categories of health and well-being or gaming performance. In some embodiments, classifying the data further comprises identifying arbitrary data, data outliers (e.g., missing data or data falling outside a trend), or a combination thereof. In some embodiments, classifying the data further comprises filling in missing data using one or more data interpolation methods.

[0069] In any of the embodiments provided herein, processing the data is performed using one or more data processing algorithms. In any of the embodiments provided herein, the one or more data processing algorithms comprises one or more feature extraction algorithms, one or more machine learning algorithms, one or more artificial intelligence algorithms, one or more Bayesian algorithms (e.g., Bayesian assimilation), one or more statistical analysis algorithms, or a combination thereof. In any of the embodiments provided herein, processing the data is performed in a real-time, near real-time, or dynamic nature. In any of the embodiments provided herein, processing the data comprises batch processing. In any of the embodiments provided herein, the one or more data processing algorithms receive data from: (i) the one or more data sources, (ii) a database, or a combination thereof. In any of the embodiments provided herein, the database comprises stored reference population data, stored historical user specific data, or a combination thereof. In any of the embodiments provided herein, the one or more data processing algorithms comprise a natural language processing model configured to extract qualitative data from the one or more data sources, the historical user database, a reference population, or a combination thereof. In any of the embodiments provided herein, processing the received data further comprises processing the data from the historical user database, the reference population, or a combination thereof. In any of the embodiments provided herein, processing the data further comprises generating or extracting one or more labels from the reference population data. In any of the embodiments provided herein, processing the data further comprises identifying arbitrary data, data outliers (e.g., missing data or data falling outside a trend), or a combination thereof. In any of the embodiments provided herein, the processing further comprises filling in missing data using one or more data interpolation methods. In any of the embodiments provided herein, updating the one or more models comprises updating the one or more labels based at least in part on the newly received data. In any of the embodiments provided herein, the one or more user specific parameters comprises data associated with: health and well-being, the one or more categories of health and well-being, gaming readiness, gaming performance, gamer rating, or team gaming ranking. In any of the embodiments provided herein, the one or more labels identifies data in reference population associated with: health and well-being, the one or more categories of health and well-being,WSGR Docket No.60152-701.601 gaming readiness, gaming performance, gamer rating or ranking, or team gaming ranking or rating.

[0070] The system can initialize one or more models or algorithms for use in determining a score indicative of an individual’s health and well-being and generate one or more recommendations to the individual to improve their score. In some embodiments, the recommendations are based at least in part on the determined score.

[0071] In some embodiments, initializing the model or algorithm of health and well-being can include receiving data from the reference population or from the one or more data sources discussed herein. For example, the initial model can be built on a historical reference population database, a historical individual or gamer database, or data gathered from an individual or gamer on a given day. The historical database can include a reference population of individuals similar to the user of the system. For example, the user can be a professional esports gamer, and the model or algorithm can be initialized using reference population data of other professional esports gamers.

[0072] A model provided herein can provide a recommendation to an individual or user (e.g., through one or more pages of a user interface.) In some embodiments, a recommendation provided to an individual or user is based on data or metrics received for the individual or user. In some embodiments, the model allows for human review of the data and metrics received for the individual or gamer. In some embodiments, once the human reviews the data and metrics, they can provide their own score or recommendation to the individual or gamer. In some embodiments, a recommendation is provided to the user by the model without human review of data or metrics from the individual or gamer. In some embodiments, a recommendation is provided in a user interface of the system (e.g., on a “Recommendation Page” of the user interface.) In some embodiments, the recommendation page may display one or more indicators (e.g., one or more thumbnails or titles) of recommended videos. In some embodiments, the recommendation can be to watch a certain video or read certain content that can be displayed to the individual or gamer on the mobile or web application. In some embodiments, a recommendation is to watch a video or read content on health and well-being. In some embodiments, user input may be provided indicating a topic that a gamer wishes to watch a video on. In some embodiments, the user input may be text, voice, or other input indicating the video topic. In some embodiments, the user interface may display one or more topics, and the user input may be to one or more of the displayed one or more topics. Examples of topics of content include but are not limited to meditation, nutrition, exercise (e.g., workouts), physical pain, lifestyle hobbies, and breathing exercises. For example, a user may enter "Stressed" in their self-reflection journal, and they may be offered a meditation video recommendation in theWSGR Docket No.60152-701.601 content vault, such as “breath awareness." As an additional example, if someone enters, "sad" in their self-reflection, they may be offered a meditation video recommendation such as an "inner smile meditation" video. If someone enters "Meh" in their self-reflection they may be recommended a basic workout from the Content Vault, such as "Baseline Workout #1."

[0073] In some embodiments, the system can generate individual specific parameters relating to the individual’s health and well-being. For example, based on data received about the individual, the model or algorithm can generate parameters for that specific user. In some embodiments, the parameters may correspond to the one or more categories of health and well-being.

[0074] In some embodiments, the model or algorithm may simulate the individual’s health and well-being based on the generated parameters and the data used to initialize the model. For example, the model or algorithm may be able to simulate the individual’s health and well-being out to a target future date. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 11, 12, 13, or 14 or more days in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more weeks in the future or in the past.. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months in the future or in the past.. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more years in the future or in the past.

[0075] In some embodiments, the model or algorithm may be updated based on newly received data from the individual or from the reference population database. For example, the model or algorithm can continue to grow and evolve as more and more data is received from the individual regarding the individual’s health and well-being. In some embodiments, the determined score or generated recommendations may be based at least in part of the updated model or algorithm.

[0076] In any of the embodiments provided herein, the one or more models comprises one or more pre-programmed models. In any of the embodiments provided herein, the one or more models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more neuromorphic computing models. In any of the embodiments provided herein, the one or more neuromorphic computing models comprises a neural network (e.g., a spiking neural network). In any of the embodiments provided herein, the one or more models comprises one or more machine learning models. In any of the embodiments provided herein, the one or more models comprises one or more natural language processing algorithms. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificialWSGR Docket No.60152-701.601 intelligence models comprises a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forward neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof. In any of the embodiments provided herein, the one or more artificial intelligence models comprises trained artificial intelligence models. In any of the embodiments provided herein, the one or more machine learning models comprises supervised machine learning models, unsupervised machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the historical user database, the reference population data, or a combination thereof. In any of the embodiments provided herein, the one or more models comprises, one or more pre-programmed models, one or more artificial intelligence models, one or more machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models generates an association between the user and the reference population data based at least in part on the one or more user specific parameters and the one or more labels. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the reference population data using at least the association generated between the user and the reference population. In any of the embodiments provided herein, the output (e.g., score or recommendation) is generated based at least in part on the comparison of the data received from the one or more data sources to the reference population data. In any of the embodiments provided herein, the output is generated based at least in part on the comparison of the data received from the one or more data sources to historical user data stored on the database. In any of the embodiments provided herein, the output is generated in real-time, near real-time, or in a dynamic nature.

[0077] In some embodiments, the system can train the models or algorithms discussed herein. In some embodiments, training may comprise: developing a catalog of data captured from external data source(s) or database, initializing and deploying one or more initial AI models or algorithms of the one or more models or algorithms using the catalog of data; using the one or more initial AI models or algorithms to determine a score or generate one or more recommendations; validating the score and recommendations; and / or generating one or more new AI models or algorithms. In some embodiments, the training may further comprise: developing a database of the scores and recommendations determined and generated by the one or more new AI models or algorithms; validating the scores and recommendations determined and generated by the one or more new AI models or algorithms; and / or updating the one or more new AI models or algorithmsWSGR Docket No.60152-701.601 to increase an accuracy of the scores and recommendations determined and generated by the one or more new AI models or algorithms, or any combination thereof. In some embodiments, the active training loop can be continuously (e.g., periodically) performed using the one or more new AI models or algorithms as input to continuously (e.g., periodically) dynamically update and generate new AI models or algorithms.

[0078] In some embodiments, the catalog of data can be compiled using any one of the data sources or databases discussed herein. In some embodiments, the catalog of data can be stored in database 130 as population data 132 or user data 134. In some embodiments, the catalog of data can be supplemented with newly received data from the initial or one or more new models or algorithms as the models or algorithms are continuously trained and updated.

[0079] In some embodiments, developing the catalog comprises labeling the data. In some embodiments, the catalog of data can be reviewed by humans and labeled to identify the data as being associated with the categories of health and well-being.

[0080] In some embodiments, labeling the data comprises extracting features from the data. In some embodiments, the features can be supervised, semi-supervised, or unsupervised features. In some cases, the extracted features may be used for training the models and algorithms for determining a score, generating recommendations, rating a gamer, ranking a gaming team, or other actions discussed herein.

[0081] In some embodiments, the scores or recommendations identified by the initial model or algorithm can be validated. In some embodiments, validating comprises reviewing the scores or recommendations for accuracy. For example, a human reviewer (e.g., such as a third party) can review the score and recommendations to determine an accuracy of the score and a likelihood that the recommendations can help with improving an individual’s health and well-being. In some embodiments, validating the scores and recommendations comprises identifying: (i) true positives, and (ii) false positives for further labeling and classification. In some embodiments, the further labeling and classification can comprise adjusting the labels in the catalog of data or received data identified as a false positive and integrating the newly labeled data back into the catalog.

[0082] In some embodiments, the initial model or algorithm can be trained for a given period of time before it moves on to determining a score and generating recommendations. In some embodiments, the active training loop trains the initial AI model or algorithm for at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours or more before a new AI model or algorithm is generated. In some embodiments, the active training loop trains the initial AI model or algorithm for about 6 to 24 hours, 7 to 23 hours, 8 to 22 hours, 9 to 21 hours, 10 to 20 hours, 11 to 19 hours, 12 to 18 hours, or 13 to 17 hours before a new AI model or algorithm is generated.WSGR Docket No.60152-701.601

[0083] In some embodiments, the models and algorithms are validated for their performance in determining scores and generating recommendations. In some embodiments, validating comprises using the newly generated models or algorithms from the active training loop to determine scores and generate recommendations for an individual from base validation data (e.g., a golden data set) not used in the active training loop. In some embodiments, validating the performance comprises using the one or more new models or algorithms to determine scores and generate recommendations for an individual whose data was not used in the active training loop of the one or more new models or algorithms. By using data not included in the active training loop, the models or algorithms can be tested for their performance in new and fresh data before being implemented in the system.

[0084] The system can apply the one or more models or algorithms to determine a score indicative of an individual’s health and well-being. In some embodiments, the score is calculated for a past or future time period. In some embodiments, the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). For example, the system can determine a score based on the individual’s or gamer’s performance in relation to the categories of health and well-being for the past identified time period. The system can also determine a future score the individual or gamer can achieve based on their current performance with the categories of health and well-being. In some embodiments, a score provided herein comprises one or more subscores calculated based on a duration of time an individual interacts with a system provided herein ((e.g., a score indicating an amount of days in a row that a gamer has interacted with the system, also referred to herein as a “streak score.”)

[0085] A score (e.g., the streak score) indicative of an individual’s health and well-being can be based, in part, on the individual’s interaction with a system provided herein. For example, a score can be reduced if an individual fails to interact with the system or does not interact sufficiently with the system (e.g., fails to record data in the system) for a duration of time, e.g., if an individual “skips” a day. In some embodiments, a score is reduced if an individual fails to interact or does not interact with the system for at least half a day (e.g., 1, 2, 3, 4, 5, 6, 7 or more days.) In some embodiments, a score is reduced if an individual fails to interact or does not interact with the system for at least a week (e.g., 2, 3, 4, or more weeks.) In some embodiments, a score is reduced if an individual fails to interact or does not interact with the system for at least a month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more months.) In some embodiments, a score is reduced if an individual fails to interact or does not interact with the system for at least a yearWSGR Docket No.60152-701.601 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more years.) The change in the score can be proportional to the duration of time an individual has failed interact sufficiently with the system. For example, a score can be reduced more if an individual does not interact sufficiently with the system of one day than it is reduced if an individual fails to interact sufficiently with the system for several weeks. In some embodiments, a score is increased in an individual interacts with the systems for a duration of time, e.g., is on a “streak”. In some embodiments, a score is increased if an individual interacts with the system for at least half a day (e.g., 1, 2, 3, 4, 5, 6, 7 or more days.) In some embodiments, a score is increased if an individual interacts with the system for at least a week (e.g., 2, 3, 4, or more weeks.) In some embodiments, a score is increased if an individual interacts with the system for at least a month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more months.) In some embodiments, a score is increased if an individual interacts with the system for at least a year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more years.)

[0086] In some embodiments, the system can use the models or algorithms disclosed herein for identifying correlations between a gamer’s gaming performance, a gamer’s rating, or a team’s ranking based on their performance in the one or more categories of health and well-being. For example, different categories of health and well-being may affect an individual or gamer in different ways, and the system can identify important categories for an individual that best helps them exceed in performance, rating, or ranking.

[0087] In some embodiments, the score is determined based at least in part on a comparison of the received data to the reference population data, the historical gamer data, or a combination thereof. In some embodiments, the score is determined in real-time, near real-time, or in a dynamic nature. In some embodiments, the score is a qualitative score, a quantitative score, or a combination thereof. In some embodiments, the score is positive, negative, or neutral. In some embodiments, the score is extremely positive, very positive, moderately positive, or slightly positive. In some embodiments, the negative score is extremely negative, very negative, moderately negative, or slightly negative. In some embodiments, the score is a number on a scale of from 1-100. In some embodiments, the score is ready for gaming (game on), moderately ready, subpar ready, not ready, or gaming readiness data does not exist yet. In some embodiments, the score is peak form, pretty solid, not ideal, danger zone, or no gaming performance data exists yet.

[0088] FIG.2B illustrates GUIs for displaying a score (e.g., a game readiness score or game performance score) to an individual. FIG.2G illustrates GUIs which can be displayed to an individual to help the individual track their scores, identify trends in their scores, and track their metrics. For example, the system can identify trends and other aspects of a specific individual that can help that specific individual improve their health and well-being in the best wayWSGR Docket No.60152-701.601 possible. Since each individual may be different, the models or algorithms can be tuned to each specific individual to specifically help them in the best way.

[0089] The system can apply the one or more models or algorithms to generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s health and well-being. In some embodiments, the one or more recommendations are configured to improve the health and well-being of the individual, or the gaming performance of the individual, by a target (e.g., pre-determined) amount. For example, an individual may wish to improve their score by a certain amount or percentage by a target future date. In some embodiments, the one or more recommendations are configured to improve the health and well- being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) future time (e.g., a match or competition). In some embodiments, the one or more recommendations are targeted to one or more categories of the categories of health and well- being. For example, the system can identify that an individual is not performing well in one or more certain categories of health and well-being or gaming performance. The system can narrow the recommendations to be specific to those categories.

[0090] In some embodiments, the one or more recommendations comprises a personalized training regimen configured to improve the gamer’s health and well-being, their gaming performance, or a combination thereof. For example, the models or algorithms can identify certain actions to be taken associated with either the categories of health and well-being, or gaming performance metrics, in order to help the individual improve their health and well-being or gaming performance.

[0091] In some embodiments, the models or algorithms can simulate the recommendations in the specific individual in an effort to identify a most effective training regimen. For example, as has been mentioned, the models or algorithms can be tuned to specific individuals over time, and the models or algorithms may be able to simulate a project score increase, or performance increase, that can occur based on the one or more recommendations. In some embodiments, an efficacy associated with the training regimen. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the system can predict an efficacy associated with the training regimens. In this way, recommendations, or training regimens, can only be recommended if they meet a threshold level of predicted efficacy or predicted improvement in score or performance.

[0092] FIGs.2A and 2F illustrate GUIs that can be utilized to display information related to the one or more recommendations to an individual. For example, the one or more recommendations can suggest an individual to view a video or read a post in the content library shown in FIG.2AWSGR Docket No.60152-701.601 that can help them improve in one or more categories of health and well-being. The system can track a completion of these recommendations to determine if an individual is being cooperative. Completion of tasks can help an individual improve their scores. In some embodiments, the recommendations include watching a daily video from the daily programming section of the application. As shown in FIG.2F, the recommendations can lead an individual to view or read content that is specifically categorized as being related to the one or more categories of health and well-being.

[0093] In some embodiments, the improvements to an individual’s or gamer’s health and well- being can be quantified and tracked using biometric data. For example, the improvements can be quantified using pupil data. The quantification can be done by comparing pupil data of an individual before and after an identified improvement in health and well-being as indicated by an improved score. Predicting Gaming Performance

[0094] The system can predict an individual’s performance. For example, the individual may be a gamer and the system can predict the gamer’s gaming performance. The system may do so at a present or future time. The gamer may be any of the gamers as discussed herein. The system can receive data from one or more data sources associated with the individual or gamer. In some embodiments, the data can be retrieved from any of the sources discussed above herein (e.g., data sources, reference population, database, etc.).

[0095] The system can process the received data, which can include classifying the received data as being associated with one or more categories of gaming performance. In some embodiments, the categories of gaming performance can include hand eye coordination, reaction time, kill to death ratio, score per minute, or other gaming performance indicator as disclosed herein. In some embodiments, the categories of gaming performance may include a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof. In some embodiments, the data processing techniques and algorithms and models may be any of the technique, models, or algorithms discussed above herein for data processing. In some embodiments, the system can receive data that is related to the individual’s health and well-being. In this manner, the system can base a predicted gaming performance for an individual based on both current or recent health and well-being and gaming performance data. In some embodiments, the data retrieved can be for a previous at least aboutWSGR Docket No.60152-701.601 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 11, 12, 13, or 14 or more days. In some embodiments, the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more weeks. In some embodiments the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months. In some embodiments, the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more years.

[0096] The system can initialize one or more models or algorithms for use in determining a score indicative of an individual’s predicted gaming performance. In some embodiments, the system may generate one or more recommendations to the individual aimed at helping them to improve their score. In some embodiments, the recommendations are based at least in part on the determined score.

[0097] In some embodiments, initializing the model or algorithm of predicting gaming performance can include receiving data from the reference population or from the one or more data sources discussed herein. For example, the initial model can be built on a historical reference population database, a historical individual or gamer database, or data gathered from an individual or gamer on a given day. The historical database can include a reference population of individuals similar to the user of the system. For example, the user can be a professional esports gamer, and the model or algorithm can be initialized using reference population data of other professional esports gamers. The databases can include past gaming performance metrics from the individual or gamer, or from a reference population of individual or gamers. For example, the gamer may be a professional esports gamer who plays League of Legends, and the reference population database can include data from other professional League of Legend players. The data can include information related to gaming performance and health and well-being, as health and well-being can have an influence on the gamer’s performance.

[0098] In some embodiments, the system can generate individual specific parameters relating to the individual’s gaming performance. For example, based on data received about the individual, the model or algorithm can generate parameters for that specific user. In some embodiments, the parameters may correspond to the one or more metrics of gaming performance as discussed herein.

[0099] In some embodiments, the model or algorithm may simulate the individual’s gaming performance based on the generated parameters and the data used to initialize the model. For example, the model or algorithm may be able to simulate the individual’s gaming performance for a target future date. In some embodiments, the target future date may be the current date. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 11, 12, 13, or 14 or more days in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more weeks in the future or in the past. InWSGR Docket No.60152-701.601 some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more years in the future or in the past.

[0100] In some embodiments, the model or algorithm may be updated based on newly received data from the individual or from the reference population database. For example, the model or algorithm can continue to grow and evolve as more and more data is received from the individual regarding the individual’s health and well-being and gaming performance. In some embodiments, the determined score or generated recommendations may be based at least in part of the updated model or algorithm.

[0101] In any of the embodiments provided herein, the one or more models comprises one or more pre-programmed models. In any of the embodiments provided herein, the one or more models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more neuromorphic computing models. In any of the embodiments provided herein, the one or more neuromorphic computing models comprises a neural network (e.g., a spiking neural network). In any of the embodiments provided herein, the one or more models comprises one or more machine learning models. In any of the embodiments provided herein, the one or more models comprises one or more natural language processing algorithms. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificial intelligence models comprises a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forward neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof. In any of the embodiments provided herein, the one or more artificial intelligence models comprises trained artificial intelligence models. In any of the embodiments provided herein, the one or more machine learning models comprises supervised machine learning models, unsupervised machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the historical user database, the reference population data, or a combination thereof. In any of the embodiments provided herein, the one or more models comprises, one or more pre-programmed models, one or more artificial intelligence models, one or more machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models generates an association between the user and the reference population data based at least in part on the oneWSGR Docket No.60152-701.601 or more user specific parameters and the one or more labels. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the reference population data using at least the association generated between the user and the reference population. In any of the embodiments provided herein, the output (e.g., score or recommendation) is generated based at least in part on the comparison of the data received from the one or more data sources to the reference population data. In any of the embodiments provided herein, the output is generated based at least in part on the comparison of the data received from the one or more data sources to historical user data stored on the database. In any of the embodiments provided herein, the output is generated in real-time, near real-time, or in a dynamic nature.

[0102] In some embodiments, the system can train the models or algorithms discussed herein. In some embodiments, training may comprise: developing a catalog of data captured from external data source(s) or database, initializing and deploying one or more initial AI models or algorithms of the one or more models or algorithms using the catalog of data; using the one or more initial AI models or algorithms to determine a score indicative of a predicted gaming performance or generate one or more recommendations; validating the score and recommendations; and / or generating one or more new AI models or algorithms. In some embodiments, the training may further comprise: developing a database of the scores and recommendations determined and generated by the one or more new AI models or algorithms; validating the scores and recommendations determined and generated by the one or more new AI models or algorithms; and / or updating the one or more new AI models or algorithms to increase an accuracy of the scores and recommendations determined and generated by the one or more new AI models or algorithms, or any combination thereof. In some embodiments, the active training loop can be continuously (e.g., periodically) performed using the one or more new AI models or algorithms as input to continuously (e.g., periodically) dynamically update and generate new AI models or algorithms.

[0103] In some embodiments, the catalog of data can be compiled using any one of the data sources or databases discussed herein. In some embodiments, the catalog of data can be stored in database 130 as population data 132 or user data 134. In some embodiments, the catalog of data can be supplemented with newly received data from the initial or one or more new models or algorithms as the models or algorithms are continuously trained and updated.

[0104] In some embodiments, developing the catalog comprises labeling the data. In some embodiments, the catalog of data can be reviewed by humans and labeled to identify the data as being associated with the categories of health and well-being or gaming performance metrics, as discussed herein.WSGR Docket No.60152-701.601

[0105] In some embodiments, labeling the data comprises extracting features from the data. In some embodiments, the features can be supervised, semi-supervised, or unsupervised features. In some cases, the extracted features may be used for training the models and algorithms for determining a score, generating recommendations, rating a gamer, ranking a gaming team, or other actions discussed herein.

[0106] In some embodiments, the scores or recommendations identified by the initial model or algorithm can be validated. In some embodiments, validating comprises reviewing the scores or recommendations for accuracy. For example, a human reviewer (e.g., such as a third party) can review the score and recommendations to determine an accuracy of the score. In some embodiments, the human reviewer can compare the predicted gaming performance score and compare it to an actual gaming performance score to validate the accuracy. In some embodiments, the validation can be performed using a computer-implemented method. In some embodiments, validating the scores and recommendations comprises identifying: (i) true positives, and (ii) false positives for further labeling and classification. In some embodiments, the further labeling and classification can comprise adjusting the labels in the catalog of data or received data identified as a false positive and integrating the newly labeled data back into the catalog.

[0107] In some embodiments, the initial model or algorithm can be trained for a given period of time before it moves on to determining a predicted gaming performance score and generating recommendations. In some embodiments, the active training loop trains the initial AI model or algorithm for at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours or more before a new AI model or algorithm is generated. In some embodiments, the active training loop trains the initial AI model or algorithm for about 6 to 24 hours, 7 to 23 hours, 8 to 22 hours, 9 to 21 hours, 10 to 20 hours, 11 to 19 hours, 12 to 18 hours, or 13 to 17 hours before a new AI model or algorithm is generated.

[0108] In some embodiments, the models and algorithms are validated for their performance in determining predicted gaming performance scores and generating recommendations. In some embodiments, validating comprises using the newly generated models or algorithms from the active training loop to determine predicted gaming performance scores and generate recommendations for an individual from base validation data (e.g., a golden data set) not used in the active training loop. In some embodiments, an actual gaming performance for the individual in the base validation may be known, and the validation can comprise comparing the models predicted score to the known score. In some embodiments, validating the performance comprises using the one or more new models or algorithms to determine scores and generate recommendations for an individual whose data was not used in the active training loop of the one or more new models or algorithms. By using data not included in the active training loop, theWSGR Docket No.60152-701.601 models or algorithms can be tested for their performance in new and fresh data before being implemented in the system.

[0109] The system can apply the one or more models or algorithms to determine a score indicative of an individual’s predicted gaming performance. In some embodiments, the score is calculated for a past or future time period. In some embodiments, the time period comprises the present day. In some embodiments, the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). For example, the system can determine a score for an individual for an upcoming match or competition. Based on that score, the individual can take certain action to better prepare for the match or competition.

[0110] In some embodiments, the system can determine a profile score for an individual. A profile score can comprise sub-scores. In some embodiments, a profile score can comprise one or more sub-scores based at least in part on an assessment (e.g., an assessment performed by the system provided herein) of a player’s in-game, health and wellness, cognitive, and career capabilities. In some embodiments, a sub-score is a number on a scale from 0-100. A profile score can comprise an overall score, such as a composite rating. In some embodiments, the overall score is a number on a scale from 0 to 2.0. In some embodiments, a profile score can be predictive of an overall health and wellness of a gamer. In some embodiments, a sub-score can be predictive of an overall health and wellness of a gamer or of an aspect of the health and wellness of a gamer. In some embodiments a profile score or sub-score can provide one or more features useful to a third party’s assessment of a player or to a player’s self assessment, the features including but not limited to: standardization (e.g., providing a unified comparison metric); clarity (e.g., allowing third-party assessment of a gamer’s strengths across multiple dimensions); comparability (e.g., providing a large-scale view of performance while preserving granularity); and comprehensiveness (e.g., allowing third parties to gain a holisitic understanding of each gamer.)

[0111] In some embodiments, the system can determine a score for an individual that is used by a recruiter. A recruiter can recruit a gamer to join a team based in part on the score determine by the system. A score determined by the system can comprise sub-scores. In some embodiments, sub-scores are metrics of an individual’s performance. In some embodiments, sub-scores provide insight relevant to an individual’s team dynamics. Examples of sub-scores include, but are not limited to, impact (e.g., an individual’s contribution to team success),WSGR Docket No.60152-701.601 teamwork (e.g., an individual’s collaborative effectiveness), and clutch factor (e.g., an individual’s performance under high-pressure situations.)

[0112] In some embodiments, the system can use the models or algorithms disclosed herein for identifying correlations between a gamer’s gaming performance, a gamer’s rating, or a team’s ranking based on their performance in the one or more categories of health and well- being. For example, different categories of health and well-being may affect an individual or gamer in different ways, and the system can identify important categories for an individual that best helps them exceed in performance, rating, or ranking.

[0113] In some embodiments, the score is determined based at least in part on a comparison of the received data to the reference population data, the historical gamer data, or a combination thereof. In some embodiments, the score is determined in real-time, near real-time, or in a dynamic nature. In some embodiments, the score is a qualitative score, a quantitative score, or a combination thereof. In some embodiments, the score is positive, negative, or neutral. In some embodiments, the score is extremely positive, very positive, moderately positive, or slightly positive. In some embodiments, the negative score is extremely negative, very negative, moderately negative, or slightly negative. In some embodiments, the score is a number on a scale of from 1-100. In some embodiments, the score is ready for gaming (game on), moderately ready, subpar ready, not ready, or gaming readiness data does not exist yet. In some embodiments, the score is peak form, pretty solid, not ideal, danger zone, or no gaming performance data exists yet.

[0114] FIG.2B illustrates GUIs for displaying a score (e.g., a game readiness score or game performance score) to an individual. FIG.2G illustrates GUIs which can be displayed to an individual to help the individual track their scores, identify trends in their scores, and track their metrics. For example, the system can identify trends and other aspects of a specific individual that can help that specific individual improve their gaming performance in the best way possible. Since each individual may be different, the models or algorithms can be tuned to each specific individual to specifically help them in the best way.

[0115] The system can apply the one or more models or algorithms to generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s predicted gaming performance. In some embodiments, the one or more recommendations are configured to improve the health and well-being of the individual, or the gaming performance of the individual, by a target (e.g., pre-determined) amount. For example, an individual may wish to improve their score by a certain amount or percentage by a target future date. In some embodiments, the one or more recommendations are configured to improve the health and well- being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined)WSGR Docket No.60152-701.601 future time (e.g., a match or competition). In some embodiments, the one or more recommendations are targeted to one or more categories of the categories of health and well- being. For example, the system can identify that an individual is not performing well in one or more certain categories of health and well-being or gaming performance. The system can narrow the recommendations to be specific to those categories.

[0116] In some embodiments, the one or more recommendations comprises a personalized training regimen configured to improve the gamer’s health and well-being, their gaming performance, or a combination thereof. For example, the models or algorithms can identify certain actions to be taken associated with either the categories of health and well-being, or gaming performance metrics, in order to help the individual improve their health and well- being or gaming performance.

[0117] In some embodiments, the models or algorithms can simulate the recommendations in the specific individual in an effort to identify a most effective training regimen. For example, as has been mentioned, the models or algorithms can be tuned to specific individuals over time, and the models or algorithms may be able to simulate a project score increase, or performance increase, that can occur based on the one or more recommendations. In some embodiments, an efficacy associated with the training regimen. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the system can predict an efficacy associated with the training regimens. In this way, recommendations, or training regimens, can only be recommended if they meet a threshold level of predicted efficacy or predicted improvement in score or performance.

[0118] FIGs.2A and 2F illustrate GUIs that can be utilized to display information related to the one or more recommendations to an individual. For example, the one or more recommendations can suggest an individual to view a video or read a post in the content library shown in FIG.2A that can help them improve in one or more categories of health and well- being. The system can track a completion of these recommendations to determine if an individual is being cooperative. Completion of tasks can help an individual improve their scores. In some embodiments, the recommendations include watching a daily video from the daily programming section of the application. As shown in FIG.2F, the recommendations can lead an individual to view or read content that is specifically categorized as being related to the one or more categories of health and well-being. Rating Individual Performance and Ranking TeamsWSGR Docket No.60152-701.601

[0119] The system can rate a gamer’s individual gaming performance. The system can also rank a gaming team based on their collective gaming performance. For example, the individual gamer, or the team of gamers, may be a part of a league or federation in which an individual rating or team ranking may be important. As is done in other sporting leagues (e.g., American College Football), an individual rating or team ranking can be used in identifying top individuals or teams within a league. It can also be used to identify matchups in championship or tournament matches. The ratings and rankings can help to identify and crown a single individual or team as a champion at the end of a gaming season. In some embodiments, the gamer rating or team ranking is identified at a plurality of time points throughout a season (e.g., a professional gaming league season). In some embodiments, the ratings and rankings can be used by an entity (e.g., a collegiate, school, or other professional or competitive gaming team) for recruiting new players. In some embodiments, the ratings and rankings can be used as the basis for generating odds associated with competitive or professional matches that can be used for gambling purposes.

[0120] The system can receive data from one or more data sources associated with the individual or team. In some embodiments, the data can be retrieved from any of the sources discussed above herein (e.g., data sources, reference population, database, etc.). In some embodiments, the data can be retrieved for the individual or team throughout a single season of a professional gaming league. In some embodiments, the data can be retrieved throughout a plurality of seasons (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more seasons). In some embodiments, the data can be retrieved for the individual throughout their entire professional career or for the team throughout the teams existence in a league or federation. In some embodiments, the system may receive gaming performance data of the individual or team to generate the rating or ranking. In some embodiments, the system may also receive health and well-being data of the individual or each individual on a team to generate the rating or ranking. In this way, both gaming performance and a health and well-being score can be used as a basis to generate the rating or ranking. In some embodiments, the data (e.g., gaming performance and a health and well-being score data) retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 11, 12, 13, or 14 or more days. In some embodiments, the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more weeks. In some embodiments the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months. In some embodiments, the data retrieved can be for a previous at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more years. In some embodiments, the data received can relate to an individual’s biometric data. For example, the system can receive pupillometry data, and data from any of the one or more wearables discussed herein (e.g., to gather other data such as heart rate, breathing rate,WSGR Docket No.60152-701.601 etc.). In this fashion, a rating or ranking can also be based on this biometric data. In some embodiments, the data received relates to voice data gathered from individuals during practice or gaming matches. For example, the system can use the voice data to recognize verbiage used, leadership styles shown, in-game sentiment shown, and other aspects recognized from an individual’s voice. In this fashion, a rating or ranking may also be based on the voice data gathered and received by the system.

[0121] In some embodiments, the data received can be displayed to a third party, such as a coach, such as discussed above when referencing FIGs.3A-3I. This can allow a third party to track individual and team performance for the individuals and teams they are responsible for and help to provide coaching and recommendations to help improve performance and rating and ranking.

[0122] The system can process the received data, which can include classifying the received data as being associated with one or more categories of individual or team gaming performance. In some embodiments, the categories of gaming performance can include hand eye coordination, reaction time, kill to death ratio, score per minute, or other gaming performance indicator as disclosed herein. In some embodiments, the categories of gaming performance may include a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof. In some embodiments, the categories of gaming performance can include come from behind team wins, an individual being able to overcome difficult odds to win for their team (e.g., in a round based game, the individual may be a last member surviving on their team and can eliminate all opposing players on the opposing team to secure a victory), a margin of victory or defeat (e.g., a large, moderate, or small margin of victory or defeat), a quality of a win or loss (e.g., if a win or loss comes against a highly ranked team, it can hold more weight than a victory or loss against a lower ranked team), an average individual rating of gamers on a team, common opponent results, or any other relevant metric in a professional gaming league. This can enable the system to take into account a quality of wins or losses, compare how opponents in the league have fared against similar opponents, and identify how individuals rate and teams ranks amongst other individuals and teams in the league. In some embodiments, the data processing techniques and algorithms and models may be any of the technique, models, or algorithms discussed above herein for data processing.

[0123] The system can initialize one or more models or algorithms for use in determining an individual rating or a team ranking. In some embodiments, the system mayWSGR Docket No.60152-701.601 generate one or more recommendations to the individual or team aimed at helping them to improve their rating or ranking. In some embodiments, the recommendations are based at least in part on the determined ratings or rankings.

[0124] In some embodiments, the model or algorithm may be as shown in FIG.4. For example, team ranking may be based on wins / losses, quality of wins, quality of losses, head-to- head results, average peak player rank, timing of the season, common opponent results, round differential, achievements, and roster availability.

[0125] In some embodiments, initializing the model or algorithm of rating individual performance, or ranking team performance, can include receiving data from the reference population or from the one or more data sources discussed herein. For example, the initial model can be built on a historical reference population database, a historical individual or gamer database, or data gathered from an individual or gamer on a given day. The historical database can include a reference population of individuals similar to the individual or team (e.g., past season data for the individual or team, or similarly situated individuals or teams) in the league. For example, the user can be a professional esports gamer, and the model or algorithm can be initialized using reference population data of other professional esports gamers. The databases can include past gaming performance metrics from the individual or gamer, or from a reference population of individual or gamer’s. For example, the gamer may be a professional esports gamer who plays League of Legends, and the reference population database can include data from other professional League of Legend players. The data can include information related to gaming performance and health and well-being, as health and well-being can have an influence on the gamer’s performance.

[0126] In some embodiments, the system can generate individual specific parameters relating to the individual’s or team’s gaming performance. For example, based on data received about the individual or team, the model or algorithm can generate parameters for that specific individual or team. In some embodiments, the parameters may correspond to the one or more metrics of gaming performance as discussed herein, or the one or more categories of health and well-being discussed herein.

[0127] In some embodiments, the model or algorithm may simulate the individual’s or team’s present or future gaming performance based on the generated parameters and the data used to initialize the model. For example, the model or algorithm may be able to simulate the individual’s or team’s gaming performance for a target future date. In some embodiments, the target future date may be the current date. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 11, 12, 13, or 14 or more days in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 orWSGR Docket No.60152-701.601 more weeks in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months in the future or in the past. In some embodiments, the target future date may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more years in the future or in the past.

[0128] In some embodiments, the model or algorithm may be updated based on newly received data from the individual or team or from the reference population database. For example, the model or algorithm can continue to grow and evolve as more and more data is received from the individual or team regarding the individual’s or teams health and well-being and gaming performance data. In some embodiments, the individual rating or team ranking may be based at least in part of the updated model or algorithm.

[0129] In any of the embodiments provided herein, the one or more models comprises one or more pre-programmed models. In any of the embodiments provided herein, the one or more models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more neuromorphic computing models. In any of the embodiments provided herein, the one or more neuromorphic computing models comprises a neural network (e.g., a spiking neural network). In any of the embodiments provided herein, the one or more models comprises one or more machine learning models. In any of the embodiments provided herein, the one or more models comprises one or more natural language processing algorithms. In any of the embodiments provided herein, the one or more artificial intelligence models comprises one or more artificial intelligence models. In any of the embodiments provided herein, the one or more artificial intelligence models comprises a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forward neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof. In any of the embodiments provided herein, the one or more artificial intelligence models comprises trained artificial intelligence models. In any of the embodiments provided herein, the one or more machine learning models comprises supervised machine learning models, unsupervised machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the historical user database, the reference population data, or a combination thereof. In any of the embodiments provided herein, the one or more models comprises, one or more pre-programmed models, one or more artificial intelligence models, one or more machine learning models, or a combination thereof. In any of the embodiments provided herein, the one or more machine learning models generates anWSGR Docket No.60152-701.601 association between the user and the reference population data based at least in part on the one or more user specific parameters and the one or more labels. In any of the embodiments provided herein, the one or more machine learning models compares the data received from the one or more data sources to the reference population data using at least the association generated between the user and the reference population. In any of the embodiments provided herein, the output (e.g., score or recommendation) is generated based at least in part on the comparison of the data received from the one or more data sources to the reference population data. In any of the embodiments provided herein, the output is generated based at least in part on the comparison of the data received from the one or more data sources to historical user data stored on the database. In any of the embodiments provided herein, the output is generated in real-time, near real-time, or in a dynamic nature.

[0130] In some embodiments, the system can train the models or algorithms discussed herein. In some embodiments, training may comprise: developing a catalog of data captured from external data source(s) or database, initializing and deploying one or more initial AI models or algorithms of the one or more models or algorithms using the catalog of data; using the one or more initial AI models or algorithms to generate individual rating or team ranking, or generate one or more recommendations for an individual or team to improve their rating or ranking; validating the individual rating or team ranking and recommendations; and / or generating one or more new AI models or algorithms. In some embodiments, the training may further comprise: developing a database of the individual rating or team ranking and recommendations determined and generated by the one or more new AI models or algorithms; validating the individual rating or team ranking and recommendations determined and generated by the one or more new AI models or algorithms; and / or updating the one or more new AI models or algorithms to increase an accuracy of the individual rating or team ranking and recommendations determined and generated by the one or more new AI models or algorithms, or any combination thereof. In some embodiments, the active training loop can be continuously (e.g., periodically) performed using the one or more new AI models or algorithms as input to continuously (e.g., periodically) dynamically update and generate new AI models or algorithms.

[0131] In some embodiments, the catalog of data can be compiled using any one of the data sources or databases discussed herein. In some embodiments, the catalog of data can be stored in database 130 as population data 132 or user data 134. In some embodiments, the catalog of data can be supplemented with newly received data from the initial or one or more new models or algorithms as the models or algorithms are continuously trained and updated.

[0132] In some embodiments, developing the catalog comprises labeling the data. In some embodiments, the catalog of data can be reviewed by humans and labeled to identify the data asWSGR Docket No.60152-701.601 being associated with the categories of health and well-being or gaming performance (e.g., either individual or team), metrics as discussed herein.

[0133] In some embodiments, labeling the data comprises extracting features from the data. In some embodiments, the features can be supervised, semi-supervised, or unsupervised features. In some cases, the extracted features may be used for training the models and algorithms for determining an individual rating or team ranking, generating recommendations, or other actions discussed herein.

[0134] In some embodiments, the individual rating or team ranking or recommendations identified by the initial model or algorithm can be validated. In some embodiments, validating comprises reviewing the individual rating or team ranking or recommendations for accuracy. For example, a human reviewer (e.g., such as a third party) can review the individual rating or team ranking to determine an accuracy of the individual rating or team ranking. In some embodiments, the human reviewer can compare the individual rating or team ranking to an actual individual rating or team ranking to validate the accuracy. In some embodiments, the validation can be performed using a computer-implemented method. In some embodiments, validating comprises identifying: (i) true positives, and (ii) false positives for further labeling and classification. In some embodiments, the further labeling and classification can comprise adjusting the labels in the catalog of data or received data identified as a false positive and integrating the newly labeled data back into the catalog.

[0135] In some embodiments, the initial model or algorithm can be trained for a given period of time before it moves on to determining a predicted gaming performance score and generating recommendations. In some embodiments, the active training loop trains the initial AI model or algorithm for at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours or more before a new AI model or algorithm is generated. In some embodiments, the active training loop trains the initial AI model or algorithm for about 6 to 24 hours, 7 to 23 hours, 8 to 22 hours, 9 to 21 hours, 10 to 20 hours, 11 to 19 hours, 12 to 18 hours, or 13 to 17 hours before a new AI model or algorithm is generated.

[0136] In some embodiments, the models and algorithms are validated for their performance in determining individual ratings or team rankings and generating recommendations. In some embodiments, validating comprises using the newly generated models or algorithms from the active training loop to determine individual ratings or team rankings and generate recommendations for base validation data set (e.g., a golden data set) not used in the active training loop. For example, the base validation data set can include actual individual ratings and team ranking from past individual or team performance in a professional gaming league. In some embodiments, an actual rating or ranking for individuals or teams in the base validation may thusWSGR Docket No.60152-701.601 be known, and the validation can comprise comparing the models predicted rating and rankings to the known ratings and ranking. In some embodiments, validating the performance comprises using the one or more new models or algorithms to determine scores and generate recommendations for an individual whose data was not used in the active training loop of the one or more new models or algorithms. By using data not included in the active training loop, the models or algorithms can be tested for their performance in new and fresh data before being implemented in the system.

[0137] The system can apply the one or more models or algorithms to rate an individual gamer or rank a gaming team. In some embodiments, the individual rating or team ranking is calculated for a past or future time period. In some embodiments, the time period comprises the present day. In some embodiments, the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). In some embodiments, the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). In some embodiments, the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). In some embodiments, the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). For example, the system can determine an individual rating or team ranking for an individual or team before an upcoming match or competition, or at a plurality of timepoints throughout a season (e.g., after each match).

[0138] In some embodiments, the system can use the models or algorithms disclosed herein for identifying correlations between an individual rating or team ranking, and scores for health and well-being. For example, different categories of health and well-being may affect an individuals or gamers in different ways, and the system can identify important categories for an individual that best helps them exceed in performance, rating, or ranking.

[0139] The system can apply the one or more models or algorithms to generate one or more recommendations to the gamer or gaming team based at least in part on the individual rating or team ranking to improve the individual rating or team ranking. In some embodiments, the one or more recommendations are configured to improve the health and well-being of the individual or team, or the gaming performance of the individual or team, by a target (e.g., pre- determined) amount. In some embodiments, the one or more recommendations comprises a personalized training regimen configured to improve the individuals (or each individual on a team) health and well-being, their gaming performance, or a combination thereof. For example, the models or algorithms can identify certain actions to be taken associated with either the categories of health and well-being, or gaming performance metrics, in order to help the individual improve their health and well-being or gaming performance.

[0140] In some embodiments, the models or algorithms can simulate the recommendations in the specific individual or team in an effort to identify a most effectiveWSGR Docket No.60152-701.601 training regimen. For example, as has been mentioned, the models or algorithms can be tuned to specific individuals or teams over time, and the models or algorithms may be able to simulate a projected score increase, ratings or rankings increase, or performance increase, that can occur based on the one or more recommendations. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the system can predict an efficacy associated with the training regimens. In this way, recommendations, or training regimens, can only be recommended if they meet a threshold level of predicted efficacy or predicted improvement in score or performance.

[0141] In any of the embodiments provided herein, the system can encourage the gamer to improve their health and well-being, gaming performance, rating, ranking, or a combination thereof by displaying a list of one or more badges or goals for the gamer to earn or complete by completing actions associated with the one or more recommendations. FIG.2A illustrates non- limiting examples of badges or goals that may be displayed to a user of the system (e.g., user of a mobile application associated with the system). In any of the embodiments provided herein, the system can display a completion percentage of the one or more badges or goals to the individual or a third party so that the individual or third party can track progress. In some embodiments, the system can notify and display to the individual or third party the completion of the one or more badges or goals.

[0142] In any of the embodiments provided herein, the system can transmit the score, the one or more recommendations, the gamer readiness score, the rating, or ranking, or a combination thereof to the individual or a third party. In any of the embodiments provided herein, this may include displaying the score, the one or more recommendations, the gamer readiness score, the rating, or ranking, or a combination thereof to the individual or a third party on a graphical user-interface (GUI), such as a GUI as discussed herein. In any of the embodiments provided herein, an individual’s personalized score, one or more recommendations, the gamer readiness score, the rating, or ranking, or a combination thereof are displayed on the GUI of a gamer’s personal device. In any of the embodiments provided herein, a plurality of individuals’ scores, one or more recommendations, the gamer readiness score, the rating, or ranking, or a combination thereof are displayed on the GUI of a third party for their reference.

[0143] In any of the embodiments provided herein, as has been described herein, the systems can receive individual or third party input to customize the scores, rating, or rankings discussed herein. For example, the input can customize the scores, rating, or rankings to be determined for a specific time period (e.g., either past, present, or future). In any of theWSGR Docket No.60152-701.601 embodiments provided herein, the systems can receive individual or third party input to customize the one or more recommendations. For example, the input can be to select a future time for the one or more recommendations to be configured for helping the gamer improve their health and well-being or gaming performance by.

[0144] In any of the embodiments provided herein, the system can rate a gamer or rank a gaming team based at least in part on the gamer’s score or gaming performance, or the team’s score or performance. In any of the embodiments provided herein, the ranking or rating is performed on a location (e.g., regional, national, or global) basis. For example, an individual gamer can receive a regional, national, or global rating. Additionally, a gaming teams can receive a regional, national, or global ranking. In any of the embodiments provided herein, the system can display the gamer’s rating to an individual or third party using any one of the GUIs discussed herein. FIG.2A illustrates a global leaderboard that an individual can reference to see how they compare to other gamers in their region, nation, or globally. This may also include displaying the individual’s ranking to a third party, such as the individual’s coach. In some embodiments, the system may update the global leaderboard based on one or more rankings which are determined as described herein. In some embodiments, the global leaderboard may be updated daily. In some embodiments, the global leaderboard may be updated hourly, daily, weekly, monthly, or yearly. Example Methods for Analyzing or Predicting One or More Aspects of a Gamer

[0145] FIG.8 shows an exemplary method 800 for monitoring and improving the health and well-being of a gamer. In some embodiments, the steps of the method may be performed with one or more processing devices (e.g., as described with respect to FIGs.1-7.)

[0146] The method begins at step 802 by receiving data from one or more data sources associated with a gamer. In some embodiments, the data sources may comprise data for a reference population of gamers. In some embodiments, the data sources may be public data sources. In some embodiments, the data sources may comprise data regarding one or more gamers health or wellbeing. In some embodiments, the data may comprise gamer input, third party input, sensor / device input, or a combination thereof. In some embodiments, the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device). In some embodiments, the gamer input comprises qualitative input, quantitative input, or a combination thereof. In some embodiments, the method further comprises administering one or more surveys or questionnaires in order to gather the gamer input. In some embodiments, the one or more surveys or questionnaires may be administered as described with respect to FIGs.1-7. In some embodiments, the gamer input comprises unprompted gamer input or gamer journal entries. InWSGR Docket No.60152-701.601 some embodiments, the journal entries relate to the gamer’s well-being or gaming performance (e.g., self-perceived well-being or gaming performance). In some embodiments, the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer. In some embodiments, the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof. In some embodiments, the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance. In some embodiments, the method further comprises transmitting the gamer’s data to the third party, and receiving the third party input in response to the transmitted gamer data. In some embodiments, the sensor / device input is received from one or more gamer sensors / devices as described above. In some embodiments, the received data is classified as gamer provided input, third party input, or sensor / device input.

[0147] At step 804, the received data is classified as being associated with one or more categories. In some embodiments, the one or more categories are associated with health and wellbeing. In some embodiments, the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. In some embodiments, the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof. In some embodiments, the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of the gamer. In some embodiments, the mental condition data further comprises data relating to the gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). In some embodiments, the nutrition data comprises data relating to: how much water the gamer is drinking, how much caffeine the gamer is ingesting, how much sugar the gamer is ingesting, or what kinds of food the gamer is eating (e.g., health, fast food, snacks, etc.). In some embodiments, the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a gamer’s routine before sleep. In some embodiments, the lifestyle data comprises data relating to the gamers hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof).

[0148] At step 806, one or more artificial intelligence models or algorithms are initialized, wherein the one or more AI models or algorithms comprise comprises data received from at least a reference population or a historical gamer database.WSGR Docket No.60152-701.601

[0149] At step 808, the one or more artificial intelligence models are applied to determine a score indicative of the gamer’s health and well-being and generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s health and well-being. In some embodiments, the score may comprise one or more scores as described with respect to FIGs.1-7 (e.g., a health and wellbeing score or a profile score.) In some embodiments, the score indicative of the gamer’s health and well-being and / or the one or more recommendations may be provided to the gamer or a third-party. In some embodiments, the third-party may a coach of the gamer or a recruiter. In some embodiments, the score is calculated for a time period (e.g., covering a gamer’s health and wellbeing or gaming performance for that time period) as described above. In some embodiments, the score is determine dynamically or in real-time. In some embodiments, the score is a qualitative score, a quantitative score (e.g., a score of 0 to 2 or 0 to 100), or both. In some embodiments, the score is positive, negative, or neutral. In some embodiments, the positive score is extremely positive, very positive, moderately positive, or slightly positive. In some embodiments, the negative score is extremely negative, very negative, moderately negative, or slightly negative. In some embodiments, the qualitative score is “Looking Strong!”, “Good Work!”, “You Got This!”, “Keep it Going!”, or “Let’s Get Started”. In some embodiments, “Looking Strong!” may be shown for quantitative scores of 75-100, “Good Work!” may be shown for quantitative scores of 51-74, “You Got This!” may be shown for quantitative scores of 26-50, “Keep it Going!” may be shown for quantitative scores of 1-25, or “Let’s Get Started” may be shown for quantitative scores of 0. In some embodiments, the one or more recommendations are targeted towards the one or more categories of health and wellbeing. In some embodiments, the one or more recommendations are targeted towards one or more categories of gaming performance.

[0150] In some embodiments, improving the health and well-being of the gamer comprises improving the gamer’s cognitive performance, improving the gamer’s performance in one or more video games, improving the health and well-being of the gamer by a target (e.g., pre-determined) amount (e.g., with respect to raising or lowering one or more scores as described herein, such as stat scores or profile scores), improving the health and well-being of the gamer by a target time (e.g., an upcoming match or competition). In some embodiments, the method further comprises improving the health and wellbeing of a gaming team that the gamer is a part of.

[0151] In some embodiments, the data of the reference population of gamers may be referred to as reference data. In some embodiments, the reference data may be collected over a period of time as described above. In some embodiments, the reference data may be receivedWSGR Docket No.60152-701.601 from a third party library. In some embodiments, the third party library comprises an esports data analytics library.

[0152] In some embodiments, the gamer may play for an organized club or an organized academy system. In some embodiments, the gamer may play video games competitively or casually. In some embodiments, the gamer is a member of a professional gaming league or team. In some embodiments, the gamer is a member of a foreign national esports federation, a collegiate gaming league or team, or a high school gaming league or team. In some embodiments, the gamer plays video games for their occupation or creates content for one or more video games. In some embodiments, the gamer may play one or more of: Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, Dota2, The Finals, or any combination thereof, and may be evaluated for any of the listed games. In some embodiments, the gamer may be of an age or age range as described above.

[0153] In some embodiments, the method further comprises simulating a plurality of training regimens in the gamer to identify a most effective training regimen as described above. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the method further comprises predicting an efficacy associated with the training regiment. In some embodiments, the training regimen is recommended only if it meets a threshold level of predicted efficacy. In some embodiments, the training regimen identifies the categories of health and well-being the gamer should focus on to most improve health and well- being, gaming performance, or a combination thereof.

[0154] In some embodiments, the method further comprises receiving input from the gamer, or a third party, relating to the completion of the one or more recommendations.

[0155] In some embodiments, the method further comprises applying the one or more AI models or algorithms to identify correlations between a gamer’s, or gaming team’s, performance with their health and well-being. In some embodiments, the method further comprises providing one or more indications encouraging the gamer to improve their health and well-being, gaming performance, or a combination thereof by displaying a list of one or more badges for the gamer to earn by completing actions associated with the one or more recommendations, as described above. In some embodiments, the method further comprises transmitting the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party or displaying the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party on a graphical user-interface (GUI), asWSGR Docket No.60152-701.601 described herein. In some embodiments, the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the determined score.

[0156] FIG.9 shows an exemplary method 900 for predicting a gamer’s gaming performance. In some embodiments, the steps of the method may be performed with one or more processing devices (e.g., as described with respect to FIGs.1-7.)

[0157] The method begins at step 902 by receiving data from one or more data sources associated with a gamer. In some embodiments, the data sources may comprise data for a reference population of gamers. In some embodiments, the data sources may be public data sources. In some embodiments, the data sources may comprise data regarding one or more gamers health or wellbeing. In some embodiments, the data may comprise gamer input, third party input, sensor / device input, or a combination thereof. In some embodiments, the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device). In some embodiments, the gamer input comprises qualitative input, quantitative input, or a combination thereof. In some embodiments, the method further comprises administering one or more surveys or questionnaires in order to gather the gamer input. In some embodiments, the one or more surveys or questionnaires may be administered as described with respect to FIGs.1-7. In some embodiments, the gamer input comprises unprompted gamer input or gamer journal entries. In some embodiments, the journal entries relate to the gamer’s well-being or gaming performance (e.g., self-perceived well-being or gaming performance). In some embodiments, the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer. In some embodiments, the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof. In some embodiments, the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance. In some embodiments, the method further comprises transmitting the gamer’s data to the third party, and receiving the third party input in response to the transmitted gamer data. In some embodiments, the sensor / device input is received from one or more gamer sensors / devices as described above. In some embodiments, the received data is classified as gamer provided input, third party input, or sensor / device input.

[0158] At step 904, the received data is classified as being associated with one or more categories. In some embodiments, the one or more categories are associated with gaming performance. In some embodiments, the one or more categories comprise a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality ofWSGR Docket No.60152-701.601 team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof.

[0159] In some embodiments, the received data the one or more categories are associated with health and wellbeing. In some embodiments, the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. In some embodiments, the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof. In some embodiments, the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of the gamer. In some embodiments, the mental condition data further comprises data relating to the gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). In some embodiments, the nutrition data comprises data relating to: how much water the gamer is drinking, how much caffeine the gamer is ingesting, how much sugar the gamer is ingesting, or what kinds of food the gamer is eating (e.g., health, fast food, snacks, etc.). In some embodiments, the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a gamer’s routine before sleep. In some embodiments, the lifestyle data comprises data relating to the gamers hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof).

[0160] At step 906, a score of the gamer indicative of the gamer’s health and wellbeing is received. In some embodiments, the score of the gamer indicative of the gamer’s health and wellbeing is generated based on the received data that is associated with health and wellbeing.

[0161] At step 908, one or more artificial intelligence models or algorithms are initialized, wherein the one or more AI models or algorithms comprise comprises data received from at least a reference population or a historical gamer database.

[0162] At step 910, the one or more artificial intelligence models are applied to determine a score indicative of a predicted gaming performance of the gamer or to rate the gamer based at least in part on the gamer’s gaming performance and the score indicative of the gamer’s health and well-being.

[0163] In some embodiments, the score may comprise one or more scores as described with respect to FIGs.1-7 (e.g., a health and wellbeing score or a profile score.) In some embodiments, the score indicative of the gamer’s health and well-being and / or the one or moreWSGR Docket No.60152-701.601 recommendations may be provided to the gamer or a third-party. In some embodiments, the third-party may a coach of the gamer or a recruiter. In some embodiments, the score is calculated for a time period (e.g., covering a gamer’s health and wellbeing or gaming performance for that time period) as described above. In some embodiments, the score is determine dynamically or in real-time. In some embodiments, the score is a qualitative score, a quantitative score (e.g., a score of 0 to 2 or 0 to 100), or both. In some embodiments, the score is positive, negative, or neutral. In some embodiments, the positive score is extremely positive, very positive, moderately positive, or slightly positive. In some embodiments, the negative score is extremely negative, very negative, moderately negative, or slightly negative. In some embodiments, the qualitative score is “Looking Strong!”, “Good Work!”, “You Got This!”, “Keep it Going!”, or “Let’s Get Started”. In some embodiments, “Looking Strong!” may be shown for quantitative scores of 75-100, “Good Work!” may be shown for quantitative scores of 51-74, “You Got This!” may be shown for quantitative scores of 26-50, “Keep it Going!” may be shown for quantitative scores of 1-25, or “Let’s Get Started” may be shown for quantitative scores of 0. In some embodiments, the one or more recommendations are targeted towards the one or more categories of health and wellbeing. In some embodiments, the one or more recommendations are targeted towards one or more categories of gaming performance.

[0164] In some embodiments, improving the health and well-being of the gamer comprises improving the gamer’s cognitive performance, improving the gamer’s performance in one or more video games, improving the health and well-being of the gamer by a target (e.g., pre-determined) amount (e.g., with respect to raising or lowering one or more scores as described herein, such as stat scores or profile scores), improving the health and well-being of the gamer by a target time (e.g., an upcoming match or competition). In some embodiments, the method further comprises improving the health and wellbeing of a gaming team that the gamer is a part of.

[0165] In some embodiments, the data of the reference population of gamers may be referred to as reference data. In some embodiments, the reference data may be collected over a period of time as described above. In some embodiments, the reference data may be received from a third party library. In some embodiments, the third party library comprises an esports data analytics library.

[0166] In some embodiments, the gamer may play for an organized club or an organized academy system. In some embodiments, the gamer may play video games competitively or casually. In some embodiments, the gamer is a member of a professional gaming league or team. In some embodiments, the gamer is a member of a foreign national esports federation, a collegiate gaming league or team, or a high school gaming league or team. In someWSGR Docket No.60152-701.601 embodiments, the gamer plays video games for their occupation or creates content for one or more video games. In some embodiments, the gamer may play one or more of: Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, Dota2, The Finals, or any combination thereof, and may be evaluated for any of the listed games. In some embodiments, the gamer may be of an age or age range as described above.

[0167] In some embodiments, the method further comprises simulating a plurality of training regimens in the gamer to identify a most effective training regimen as described above. In some embodiments, the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. In some embodiments, the method further comprises predicting an efficacy associated with the training regiment. In some embodiments, the training regimen is recommended only if it meets a threshold level of predicted efficacy. In some embodiments, the training regimen identifies the categories of health and well-being the gamer should focus on to most improve health and well- being, gaming performance, or a combination thereof.

[0168] In some embodiments, the method further comprises receiving input from the gamer, or a third party, relating to the completion of the one or more recommendations.

[0169] In some embodiments, the method further comprises applying the one or more AI models or algorithms to identify correlations between a gamer’s, or gaming team’s, performance with their health and well-being. In some embodiments, the method further comprises providing one or more indications encouraging the gamer to improve their health and well-being, gaming performance, or a combination thereof by displaying a list of one or more badges for the gamer to earn by completing actions associated with the one or more recommendations, as described above. In some embodiments, the method further comprises transmitting the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party or displaying the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party on a graphical user-interface (GUI), as described herein. In some embodiments, the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the determined score.

[0170] FIG.10 shows an exemplary method 1000 for ranking a gaming team comprised of a plurality of gamers. In some embodiments, the steps of the method may be performed with one or more processing devices (e.g., as described with respect to FIGs.1-7.)

[0171] The method begins at step 1002, by receiving data from one or more data sources associated with the gaming team. In some embodiments, the data sources may comprise data for a reference population of gamers and / or gaming teams. In some embodiments, the data sourcesWSGR Docket No.60152-701.601 may be public data sources. In some embodiments, the data sources may comprise data regarding one or more gamers health or wellbeing. In some embodiments, the data may comprise gamer input, third party input, sensor / device input, or a combination thereof. In some embodiments, the gamer input may be gaming team input. In some embodiments, the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device). In some embodiments, the gamer input comprises qualitative input, quantitative input, or a combination thereof. In some embodiments, the method further comprises administering one or more surveys or questionnaires in order to gather the gamer input. In some embodiments, the one or more surveys or questionnaires may be administered as described with respect to FIGs.1-7. In some embodiments, the gamer input comprises unprompted gamer input or gamer journal entries. In some embodiments, the journal entries relate to the gamer’s well-being or gaming performance (e.g., self-perceived well-being or gaming performance). In some embodiments, the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer. In some embodiments, the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof. In some embodiments, the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance. In some embodiments, the method further comprises transmitting the gamer’s data to the third party, and receiving the third party input in response to the transmitted gamer data. In some embodiments, the sensor / device input is received from one or more gamer sensors / devices as described above. In some embodiments, the received data is classified as gamer provided input, third party input, or sensor / device input.

[0172] At step 1004, the received data is classified as being associated with one or more categories. In some embodiments, the one or more categories are associated with gaming performance. In some embodiments, the one or more categories comprise a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof. In some embodiments, the one or more categories are associated with gaming performance are one or more categories associated with gaming performance of a gaming team.WSGR Docket No.60152-701.601

[0173] In some embodiments, the received data the one or more categories are associated with health and wellbeing of one or more gamers of the gaming team, or of the gaming team itself. In some embodiments, the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. In some embodiments, the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof. In some embodiments, the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of one or more gamers of the gaming team. In some embodiments, the mental condition data further comprises data relating to the one or more gamers of the gaming team’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). In some embodiments, the nutrition data comprises data relating to: how much water the one or more gamers of the gaming team is drinking, how much caffeine the one or more gamers of the gaming team is ingesting, how much sugar the one or more gamers of the gaming team is ingesting, or what kinds of food the one or more gamers of the gaming team is eating (e.g., health, fast food, snacks, etc.). In some embodiments, the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a one or more gamers of the gaming team’s routine before sleep. In some embodiments, the lifestyle data comprises data relating to the one or more gamers of the gaming team hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof).

[0174] At step 1006, one or more artificial intelligence models or algorithms are initialized, wherein the one or more AI models or algorithms comprises data received from at least a reference population or a historical gaming team database.

[0175] At step 1008, the one or more artificial intelligence models are applied to rank the gaming team based at least in part on the team’s gaming performance, as described above. Computer Systems

[0176] Provided herein are computer systems configured to implement methods for determining a score, generating recommendations, rating a gamer, ranking a gaming team, or other actions discussed herein.

[0177] In some embodiments, the system comprises a computing device comprising at least one processor; an operating system configured to perform executable instructions; and a memory. In some embodiments, the system further comprises a computer program including instructions executable by the computing device to create an application. In some embodiments, the application comprises one or more software modules. In some embodiments, the applicationWSGR Docket No.60152-701.601 comprises a software module configure to observing and preserving the security of a physical space in accordance with the embodiments disclosed herein.

[0178] Referring to FIG.5, a block diagram is shown depicting an exemplary machine that includes a computer system 500 (e.g., a processing or computing system) within which a set of instructions can execute for causing a device to perform or execute any one or more of the aspects and / or methodologies for static code scheduling of the present disclosure. The components in FIG.5 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments.

[0179] Computer system 500 may include one or more processors 501, a memory 503, and a storage 508 that communicate with each other, and with other components, via a bus 540. The bus 540 may also link a display 532, one or more input devices 533 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 534, one or more storage devices 535, and various tangible storage media 536. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 540. For instance, the various tangible storage media 536 can interface with the bus 540 via storage medium interface 526. Computer system 500 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[0180] Computer system 500 includes one or more processor(s) 501 (e.g., central processing units (CPUs), general purpose graphics processing units (GPGPUs), or quantum processing units (QPUs)) that carry out functions. Processor(s) 501 optionally contains a cache memory unit 502 for temporary local storage of instructions, data, or computer addresses. Processor(s) 501 are configured to assist in execution of computer readable instructions. Computer system 500 may provide functionality for the components depicted in Fig.5 as a result of the processor(s) 501 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 503, storage 508, storage devices 535, and / or storage medium 536. The computer-readable media may store software that implements particular embodiments, and processor(s) 501 may execute the software. Memory 503 may read the software from one or more other computer-readable media (such as mass storage device(s) 535, 536) or from one or more other sources through a suitable interface, such as network interface 520. The software may cause processor(s) 501 to carry out one or more processes or one or more steps of one or more processes described orWSGR Docket No.60152-701.601 illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 503 and modifying the data structures as directed by the software.

[0181] The memory 503 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 504) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 505), and any combinations thereof. ROM 505 may act to communicate data and instructions unidirectionally to processor(s) 501, and RAM 504 may act to communicate data and instructions bidirectionally with processor(s) 501. ROM 505 and RAM 504 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 506 (BIOS), including basic routines that help to transfer information between elements within computer system 500, such as during start-up, may be stored in the memory 503.

[0182] Fixed storage 508 is connected bidirectionally to processor(s) 501, optionally through storage control unit 507. Fixed storage 508 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 508 may be used to store operating system 509, executable(s) 510, data 511, applications 512 (application programs), and the like. Storage 508 can also include an optical disk drive, a solid- state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 508 may, in appropriate cases, be incorporated as virtual memory in memory 503.

[0183] In one example, storage device(s) 535 may be removably interfaced with computer system 500 (e.g., via an external port connector (not shown)) via a storage device interface 525. Particularly, storage device(s) 535 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 500. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 535. In another example, software may reside, completely or partially, within processor(s) 501.

[0184] Bus 540 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 540 may be any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a MicroWSGR Docket No.60152-701.601 Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[0185] Computer system 500 may also include an input device 533. In one example, a user of computer system 500 may enter commands and / or other information into computer system 500 via input device(s) 533. Examples of an input device(s) 533 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is a Kinect, Leap Motion, or the like. Input device(s) 533 may be interfaced to bus 540 via any of a variety of input interfaces 523 (e.g., input interface 523) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0186] In particular embodiments, when computer system 500 is connected to network 530, computer system 500 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 530. Communications to and from computer system 500 may be sent through network interface 520. For example, network interface 520 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 530, and computer system 500 may store the incoming communications in memory 503 for processing. Computer system 500 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 503 and communicated to network 530 from network interface 520. Processor(s) 501 may access these communication packets stored in memory 503 for processing.

[0187] Examples of the network interface 520 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 530 or network segment 530 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such asWSGR Docket No.60152-701.601 network 530, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used.

[0188] Information and data can be displayed through a display 532. Examples of a display 532 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 532 can interface to the processor(s) 501, memory 503, and fixed storage 508, as well as other devices, such as input device(s) 533, via the bus 540. The display 532 is linked to the bus 540 via a video interface 522, and transport of data between the display 532 and the bus 540 can be controlled via the graphics control 521. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0189] In addition to a display 532, computer system 500 may include one or more other peripheral output devices 534 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 540 via an output interface 524. Examples of an output interface 524 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0190] In addition or as an alternative, computer system 500 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0191] Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrativeWSGR Docket No.60152-701.601 components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0192] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0193] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0194] In accordance with the description herein, suitable computing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers, in various embodiments, include those with booklet, slate, and convertible configurations, known to those of skill in the art.

[0195] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services forWSGR Docket No.60152-701.601 execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple®Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell®NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft®Windows®, Apple®Mac OS X®, UNIX®, and UNIX- like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia®Symbian®OS, Apple®iOS®, Research In Motion®BlackBerry OS®, Google®Android®, Microsoft®Windows Phone®OS, Microsoft®Windows Mobile®OS, Linux®, and Palm®WebOS®. Those of skill in the art will also recognize that suitable media streaming device operating systems include, by way of non-limiting examples, Apple TV®, Roku®, Boxee®, Google TV®, Google Chromecast®, Amazon Fire®, and Samsung®HomeSync®. Those of skill in the art will also recognize that suitable video game console operating systems include, by way of non-limiting examples, Sony®PS3®, Sony®PS4®, Sony®PS5®, Microsoft®Xbox 360®, Microsoft®Xbox One, Microsoft®Xbox Series X, Microsoft®Xbox Series S, Nintendo®Wii®, Nintendo®Wii U®, Nintendo®SwitchTM, and Ouya®.

[0196] Another aspect of the disclosure herein describes a non-transitory, computer- readable medium comprising executable instructions, wherein when a processor, when executing the executable instructions, performs a method as described herein. Web application

[0197] In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft®.NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, XML, and document oriented database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft®SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application isWSGR Docket No.60152-701.601 written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or eXtensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous JavaScript and XML (AJAX), Flash®ActionScript, JavaScript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM®Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe®Flash®, HTML 5, Apple®QuickTime®, Microsoft®Silverlight®, Java™, and Unity®.

[0198] In some embodiments, a front end of the system may include a react ecosystem, using React, React Router, Redux, or Redux Saga platforms. In some embodiments, a front end of the system may include a styling and UI component, using Chakra UI. In some embodiments, a front end of the system may include a data handling component, using Apollo Client or GraphQL. In some embodiments, a front end of the system may include a charting and visualization component, using ApexCharts or React-Quill. In some embodiments, a back end of the system may use programs such as Node.js, Express, GraphQL, Apollo Server, Prisma, PostgresSQL, JSON Web Token, Keycloak Connect, Sentry, Amazon Web Services, Stripe, or various Google APIs.

[0199] Referring to FIG.6, in a particular embodiment, an application provision system comprises one or more databases 600 accessed by a relational database management system (RDBMS) 610. Suitable RDBMSs include Firebird, MySQL, PostgreSQL, SQLite, Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, Teradata, and the like. In this embodiment, the application provision system further comprises one or more application severs 620 (such as Java servers, .NET servers, PHP servers, and the like) and one or more web servers 630 (such as Apache, IIS, GWS and the like). The web server(s) optionally expose one or more web services via app application programming interfaces (APIs) 640. Via a network, such as the Internet, the system provides browser-based and / or mobile native user interfaces.

[0200] Referring to FIG.7, in a particular embodiment, an application provision system alternatively has a distributed, cloud-based architecture 700 and comprises elastically loadWSGR Docket No.60152-701.601 balanced, auto-scaling web server resources 710 and application server resources 720 as well synchronously replicated databases 730. Mobile application

[0201] In some embodiments, a computer program includes a mobile application provided to a mobile computing device. In some embodiments, the mobile application is provided to a mobile computing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile computing device via the computer network described herein.

[0202] In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non- limiting examples, C, C++, C#, Objective-C, Java™, JavaScript, Pascal, Object Pascal, Python™, Ruby, Rails, VB.NET, WML, and XHTML / HTML with or without CSS, or combinations thereof.

[0203] Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry®SDK, BREW SDK, Palm®OS SDK, Symbian SDK, webOS SDK, and Windows®Mobile SDK.

[0204] Those of skill in the art will recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple®App Store, Google®Play, Chrome WebStore, BlackBerry®App World, App Store for Palm devices, App Catalog for webOS, Windows®Marketplace for Mobile, Ovi Store for Nokia®devices, Samsung®Apps, and Nintendo®DSi Shop. Standalone application

[0205] In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written inWSGR Docket No.60152-701.601 a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications. Web browser plug-in

[0206] In some embodiments, the computer program includes a web browser plug-in (e.g., extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe®Flash®Player, Microsoft®Silverlight®, and Apple®QuickTime®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add- ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands.

[0207] In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

[0208] Web browsers (also called Internet browsers) are software applications, designed for use with network-connected computing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non- limiting examples, Microsoft®Internet Explorer®, Mozilla®Firefox®, Google®Chrome, Apple®Safari®, Opera Software®Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called microbrowsers, mini-browsers, and wireless browsers) are designed for use on mobile computing devices including, by way of non- limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google®Android®browser, RIM BlackBerry®Browser, Apple®Safari®, Palm®Blazer, Palm®WSGR Docket No.60152-701.601 WebOS®Browser, Mozilla®Firefox®for mobile, Microsoft®Internet Explorer®Mobile, Amazon®Kindle®Basic Web, Nokia®Browser, Opera Software®Opera®Mobile, and Sony®PSP™ browser. Software modules

[0209] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non- limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location. Databases

[0210] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of individual or team data relating to health and well-being, gaming performance, or individual or team ratings or rankings, or any combination thereof. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, XML databases, document oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, andWSGR Docket No.60152-701.601 MongoDB. In some embodiments, a database is Internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices. Data transmission

[0211] The subject matter described herein, including methods and systems as described herein and may be configured to be performed in one or more facilities at one or more locations. Facility locations are not limited by country and include any country or territory. In some instances, one or more steps are performed in a different country than another step of the method. In some embodiments, one or more method steps involving a computer system are performed in a different country than another step of the methods provided herein. In some embodiments, data processing and storage are performed in a different country or location than one or more steps of the methods described herein. In some embodiments, one or more products or data are transferred from one or more of the facilities to one or more different facilities for analysis or further analysis. Data includes, but is not limited to, information regarding the stratification of a subject, and any data produced by the methods disclosed herein. In some embodiments of the methods and systems described herein, the subject information is compiled, and a subsequent data transmission step will transmit or store the subject information.

[0212] In some embodiments, any step of any method described herein is performed by a software program or module on a computer. In additional or further embodiments, data from any step of any method described herein is transferred to and from facilities located within the same or different countries, including analysis performed in one facility in a particular location and the data shipped to another location or directly to an individual in the same or a different country. In additional or further embodiments, data from any step of any method described herein is transferred to and / or received from a facility located within the same or different countries, including analysis of a data input, such as queries, objects, properties, types, filters, tables, or any combination thereof, performed in one facility in a particular location and corresponding data transmitted to another location. Business methods utilizing a computer

[0213] The methods described herein may utilize one or more computers. The computer may be used for managing customer and subject information. The computer may include a monitor or other user interface for displaying data, results, billing information, marketing information (e.g. demographics), customer information, or sample information. The computer may also include means for data or information input. The computer may include a processingWSGR Docket No.60152-701.601 unit and fixed or removable media or a combination thereof. The computer may be accessed by a user in physical proximity to the computer, for example via a keyboard and / or mouse, or by a user that does not necessarily have access to the physical computer through a communication medium such as a modem, an internet connection, a telephone connection, or a wired or wireless communication signal carrier wave. In some cases, the computer may be connected to a server or other communication device for relaying information from a user to the computer or from the computer to a user. In some cases, the user may store data or information obtained from the computer through a communication medium on media, such as removable media. It is envisioned that data relating to the methods can be transmitted over such networks or connections for reception and / or review by a party.

[0214] The entity entering or reviewing information into a database for the purpose of one or more of the following: inventory tracking, order tracking, customer management, customer service, billing, and sales. Sample information may include, but is not limited to: customer name, unique customer identification, or any information suitable for storage in a database.

[0215] The database may be accessible by a user. Database access may take the form of electronic communication such as a computer or telephone. The database may be accessed through an intermediary such as a customer service representative, business representative, or consultant. The availability or degree of database access may change upon payment of a fee for products and services rendered or to be rendered. A user may submit a coupon as part of the fee for products and services rendered or to be rendered. EXEMPLARY EMBODIMENTS

[0216] Among the exemplary embodiments are: 1. A method for monitoring and improving the health and well-being of a gamer, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of health and well-being; c. initializing one or more artificial intelligence (AI) models / algorithms of health and well-being, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; and d. applying the one or more AI models / algorithms to: i. determine a score indicative of the gamer’s health and well-being; and ii. generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s health and well-being.WSGR Docket No.60152-701.601 A method for predicting a gamer’s gaming performance, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of gaming performance; c. receiving a score of the gamer indicative of the gamer’s health and well-being; d. initializing one or more artificial intelligence (AI) models / algorithms of predicting gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; and e. applying the one or more AI models / algorithms to determine a score indicative of a predicted gaming performance of the gamer. A method of rating a gamer’s gaming performance, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of gaming performance; c. receiving a score of the gamer indicative of the gamer’s health and well-being; d. initializing one or more artificial intelligence (AI) models / algorithms of rating individual gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; and e. applying the one or more AI models / algorithms to rate the gamer based at least in part on the gamer’s gaming performance and the score indicative of the gamer’s health and well-being. A method of ranking a gaming team comprised of a plurality of gamers, the method comprising: a. receiving data from one or more data sources associated with the gaming team; b. classifying the received data as being associated with one or more categories of team gaming performance; c. initializing one or more artificial intelligence (AI) models / algorithms of ranking team gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gaming team database; and d. applying the one or more AI models / algorithms to rank the gaming team based at least in part on the team’s gaming performance.WSGR Docket No.60152-701.601 5. The method of embodiment 1, wherein the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle. 6. The method of embodiment 5, wherein the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof. 7. The method of any one of embodiments 5 or 6, wherein the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of the gamer. 8. The method of embodiment 7, wherein the mental condition data further comprises data relating to the gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed). 9. The method of any one of embodiments 5-8, wherein the nutrition data comprises data relating to: how much water the gamer is drinking, how much caffeine the gamer is ingesting, how much sugar the gamer is ingesting, or what kinds of food the gamer is eating (e.g., health, fast food, snacks, etc.). 10. The method of any one of embodiments 5-9, wherein the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a gamer’s routine before sleep. 11. The method of any one of embodiments 5-10, wherein the lifestyle data comprises data relating to the gamers hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof). 12. The method of any one of embodiments 2-4, wherein the categories of gaming performance (e.g., team gaming performance) comprise: a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof. 13. The method of any one of embodiments 3-4, wherein the gamer rating or team ranking is identified at a plurality of time points throughout a season (e.g., a professional gaming league season). 14. The method of any one of embodiments 12 or 13, wherein the gamer rating comprises an overall gaming performance score for the gamer. 15. The method of any one of embodiments 12 or 13, wherein the team ranking comprises an overall gaming performance of the gaming team.WSGR Docket No.60152-701.601 16. The method of any one of the previous embodiments, wherein the gamer plays for an organized club. 17. The method of any one of the previous embodiments, wherein the gamer plays in an organized academy system. 18. The method of any one of the previous embodiments, wherein the gamer plays video games competitively. 19. The method of any one of the previous embodiments, wherein the gamer is a member of a professional gaming (e.g., esports) league or team. 20. The method of any one of the previous embodiments, wherein the gamer is a member of a foreign national esports federation. 21. The method of any one of the previous embodiments, wherein the gamer is a member of a collegiate gaming league or team. 22. The method of any one of the previous embodiments, wherein the gamer is a member of a high school gaming league or team. 23. The method of any one of the previous embodiments, wherein the gamer plays video games for their occupation. 24. The method of any one of the previous embodiments, wherein the gamer creates content for one or more video games. 25. The method of any one of the previous embodiments, wherein the gamer is a casual gamer. 26. The method of embodiment 25, wherein the casual gamer is not a member of a professional, collegiate, or high school gaming league or team. 27. The method of embodiment 25 or 26, wherein the casual gamer enjoys playing video games leisurely. 28. The method of any one of the previous embodiments, wherein the gamer plays one or more of: Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, Dota2, The Finals, or any combination thereof. 29. The method of any one of the previous embodiments, wherein the gamer is above the age of 13. 30. The method of any one of the previous embodiments, wherein the gamer is below the age of 13. 31. The method of any one of the previous embodiments, wherein the gamer is aged 10-13. 32. The method of any one of the previous embodiments, wherein the gamer is aged 13-18.WSGR Docket No.60152-701.601 33. The method of any one of the previous embodiments, wherein the gamer is above the age of 18. 34. The method of embodiment 1, wherein improving the health and well-being of the gamer comprises improving the gamer’s cognitive performance. 35. The method of embodiment 1, wherein improving the health and well-being of the gamer comprises improving the gamer’s performance in one or more video games. 36. The method of embodiment 1, wherein the method comprises improving the health and well-being of the gamer by a target (e.g., pre-determined) amount. 37. The method of embodiment 1, wherein the method comprises improving the health and well-being of the gamer by a target (e.g., pre-determined) future time (e.g., a match or competition). 38. The method of embodiment 1, wherein the improving the health and well-being is quantified by a score (e.g., the determined score) indicative of the gamer’s health and well-being. 39. The method of embodiment 1, wherein the method comprises improving the health and well-being of a gaming team (e.g., professional, collegiate, high school team) comprised of multiple individual gamers. 40. The method of any one of the previous embodiments, wherein the reference population data, the historical gamer data, or a combination thereof, are gathered over a time period. 41. The method of embodiment 40, wherein the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). 42. The method of embodiment 40, wherein the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). 43. The method of embodiment 40, wherein the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). 44. The method of embodiment 40, wherein the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years). 45. The method of any one of embodiments 40 to 44, wherein the reference population data, the historical user database, or a combination thereof, are received from a third party library. 46. The method of embodiment 45, wherein the third party comprises an esports data analytics company. 47. The method of any one of embodiments 40 to 46, wherein the reference population data comprises data gathered from a plurality of gamers over a period of time.WSGR Docket No.60152-701.601 48. The method of any one of the previous embodiments, wherein the data sources comprise: gamer input, third party input, sensor / device input, or a combination thereof. 49. The method of embodiment 48, wherein the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device). 50. The method of embodiment 49, wherein the gamer input is input by the gamer themselves. 51. The method of any one of embodiments 48 to 50, wherein the gamer input comprises qualitative input, quantitative input, or a combination thereof. 52. The method of any one of embodiments 48 to 51, wherein the method further comprises administering one or more surveys or questionnaires to the gamer to gather the gamer input. 53. The method of embodiment 52, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) daily to the user. 54. The method of any one of embodiments 52 to 53, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a week to the user. 55. The method of any one of embodiments 52 to 54, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a month to the user. 56. The method of any one of embodiments 52 to 55, wherein the one or more surveys or questionnaires are configured to gather physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data from the gamer. 57. The method of any one of embodiments 48 to 56, wherein the gamer input comprises unprompted gamer input. 58. The method of embodiment 57, wherein the unprompted gamer input comprises gamer journal entries. 59. The method of embodiment 58, wherein the journal entries relate to the gamer’s well- being or gaming performance (e.g., self-perceived well-being or gaming performance). 60. The method of any one of embodiments 57 to 59, wherein the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer. 61. The method of any one of embodiments 48 to 60, wherein the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g.,WSGR Docket No.60152-701.601 parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof. 62. The method of embodiment 61, wherein the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance. 63. The method of embodiment 62, wherein the method further comprises transmitting data gathered from the gamer to the gamer’s gaming coach, and wherein the third party input comprises the gaming coaches’ feedback or recommendations to the gamer based on the transmitted data. 64. The method of any one of embodiments 61 to 63, wherein the third party input comprises qualitative or quantitative data from the gamer’s parent(s) relating to the gamer’s well- being. 65. The method of any one of embodiments 48 to 64, wherein the third party data comprises gaming performance data received from one or more games the gamer is currently playing. 66. The method of embodiment 65, wherein the gaming performance data comprises a kill / death ratio, a win / loss ratio, a score per minute, or other data gathered by the one or more games while the gamer is playing the one or more games. 67. The method of any one of embodiments 48 to 66, wherein the sensor / device input is received from one or more gamer sensors / devices. 68. The method of embodiment 67, wherein the sensors / devices are configured to gather biometric data of the gamer. 69. The method of any one of embodiments 67 to 68, wherein the sensors / devices are configured to gather pupillometry data of the gamer. 70. The method of any one of embodiments 67 to 69, wherein the sensors / devices comprise: one or more personal computing devices (e.g., a mobile device, tablet, or other personal computing device), or one or more applications associated with the one or more personal computing devices (e.g., social media applications (e.g., Instagram, Facebook, Twitter, TikTok, Calendar, Microphone, Online Purchasing apps, etc.). 71. The method of any one of embodiments 67 to 70, wherein the sensor / devices comprise wearable sensors / devices (e.g., wearables). 72. The method of embodiment 71, wherein the wearables comprise one or more smart devices (e.g., smart watch) configured to measure physiological (e.g., heart rate, breathing rate), neurological, psychological, metabolic, or biological data of the gamer.WSGR Docket No.60152-701.601 73. The method of any one of embodiments 71 to 72, wherein the wearables comprise Fitbit, Apple Watch, or Oura Ring. 74. The method of embodiment 67, wherein the sensor / device comprises Tobii Eye Tracker products. 75. The method of embodiment 67, wherein the sensors / devices comprise sensors / devices configured to receive voice data (e.g., a microphone, a gaming headset, etc.) from the gamer. 76. The method of embodiment 75, wherein the voice data is gathered while the gamer is playing video games. 77. The method of any one of the previous embodiments, wherein the method further comprises classifying the received data as originating from: gamer provided input, third party input, or sensor / device input. 78. The method of any one of the previous embodiments, wherein classifying the data is performed using one or more data processing algorithms. 79. The method of embodiment 78, wherein the one or more data processing algorithms comprises one or more feature extraction algorithms, one or more machine learning algorithms, one or more artificial intelligence algorithms, one or more Bayesian algorithms (e.g., Bayesian assimilation), one or more statistical analysis algorithms, or a combination thereof. 80. The method of any one of embodiments 78 to 79, wherein the one or more data processing algorithms receive data from: (i) the one or more data sources, (ii) a database, or a combination thereof. 81. The method of embodiment 80, wherein the database comprises stored reference population data, stored historical gamer specific data, or a combination thereof. 82. The method of any one of embodiments 78 to 81, wherein the one or more data processing algorithms comprise a natural language processing model configured to extract qualitative data from the one or more data sources, the historical gamer database, a reference population, or a combination thereof. 83. The method of any one of the previous embodiments, wherein the one or more AI models / algorithms comprise one or more machine learning models. 84. The method of any one of the previous embodiments, wherein the one or more AI models / algorithms comprise a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forwardWSGR Docket No.60152-701.601 neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof. 85. The method of any one of the previous embodiments, wherein the one or more AI models / algorithms are trained. 86. The method of embodiment 85, wherein the method further comprises training the one or more AI models / algorithms. 87. The method of embodiment 86, wherein the method further comprises updating the one or more AI models / algorithms as they are continually trained. 88. The method of any one of the previous embodiments, wherein the one or more AI models / algorithms comprise supervised, unsupervised, or semi-supervised models / algorithms. 89. The method of embodiment 2, further comprising generating one or more recommendations to improve the gamer’s gaming performance. 90. The method of any one of embodiments 1 to 2, wherein the score is calculated for a time period. 91. The method of embodiment 90, wherein the time period is 1 day. 92. The method of embodiment 90, wherein the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days). 93. The method of embodiment 90, wherein the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks). 94. The method of embodiment 90, wherein the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months). 95. The method of any one of embodiments 90 to 94, wherein the score is determined based at least in part on a comparison of the received data to the reference population data, the historical gamer data, or a combination thereof. 96. The method of any one of embodiments 90 to 95, wherein the score is determined in real-time, near real-time, or in a dynamic nature. 97. The method of any one of embodiments 90 to 96, wherein the score is a qualitative score, a quantitative score, or a combination thereof. 98. The method of any one of embodiments 90 to 97, wherein the score is positive, negative, or neutral. 99. The method of embodiment 98, wherein the positive score is extremely positive, very positive, moderately positive, or slightly positive. 100. The method of embodiment 98, wherein the negative score is extremely negative, very negative, moderately negative, or slightly negative.WSGR Docket No.60152-701.601 101. The method of any one of embodiments 90 to 97, wherein score is a number on a scale of from 0-100. 102. The method of embodiment 101, wherein the number score is matched with a qualitative score. 103. The method of embodiment 102, wherein a score above 75 is matched with “Looking Strong!”. 104. The method of embodiment 102, wherein a score between 51 to 75 is matched with “Good Work!”. 105. The method of embodiment 102, wherein a score between 26 and 50 is matched with “You Got This!”. 106. The method of embodiment 102, wherein a score between 1 to 25 is matched with “Keep It Going!”. 107. The method of embodiment 102, wherein a score of 0 is matched with “Let’s Get Started!”. 108. The method of any one of embodiments 90 to 107, wherein the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) amount. 109. The method of any one of embodiments 90 to 108, wherein the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) future time (e.g., a match or competition). 110. The method of any one of embodiments 90 to 109, wherein the one or more recommendations are targeted to one or more categories of the categories of health and well-being. 111. The method of any one of embodiments 90 to 110, wherein the one or more recommendations comprises a personalized training regimen configured to improve the gamer’s health and well-being, their gaming performance, or a combination thereof. 112. The method of embodiment 111, wherein the method further comprises simulating a plurality of training regimens in the gamer to identify a most effective training regimen. 113. The method of embodiment 112, wherein the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period. 114. The method of any one of embodiments 111 to 113, wherein the method further comprises predicting an efficacy associated with the training regimen.WSGR Docket No.60152-701.601 115. The method of embodiment 114, wherein the training regimen is recommended only if it meets a threshold level of predicted efficacy. 116. The method of any one of embodiments 111 to 114, wherein the training regimen identifies the categories of health and well-being the gamer should focus on to most improve health and well-being, gaming performance, or a combination thereof. 117. The method of any one of embodiments 90 to 116, wherein the method further comprises receiving input from the gamer, or a third party, relating to the completion of the one or more recommendations. 118. The method of embodiment 117, wherein the one or more AI models / algorithms uses historical completion data in determining the score or generating the one or more recommendations to the gamer. 119. The method of any one of the previous embodiments, further comprising applying the one or more AI models / algorithms to identify correlations between a gamer’s, or gaming team’s, performance with their health and well-being. 120. The method of any one of the previous embodiments, wherein the method further comprises encouraging the gamer to improve their health and well-being, gaming performance, or a combination thereof by displaying a list of one or more badges for the gamer to earn by completing actions associated with the one or more recommendations. 121. The method of embodiment 120, wherein the method further comprises displaying a completion percentage of the one or more badges to the gamer or a third party. 122. The method of any one of embodiments 120 to 121, wherein the method further comprises notifying and displaying to the gamer or third party the completion of the one or more badges. 123. The method of any one of the previous embodiments, wherein the method further comprises transmitting the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party. 124. The method of any one of the previous embodiments, wherein the method further comprises displaying the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party on a graphical user- interface (GUI). 125. The method of embodiment 124, wherein the gamer’s personalized score, one or more recommendations, ratings, rankings, or a combination thereof are displayed on the GUI of a gamer’s personal device. 126. The method of embodiment 124, wherein the method further comprises displaying a plurality of gamers’ (e.g., members on a gaming team) scores, one or moreWSGR Docket No.60152-701.601 recommendations, ratings, or rankings, or a combination thereof to a third party (e.g., coach). 127. The method of any one of embodiments 1 or 2, wherein the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the determined score. 128. The method of embodiment 127, the gamer or the third party can select a time period (e.g., a past time period) for the score to be determined for. 129. The method of any one of embodiments 1 to 2 or 127 to 128, wherein the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the one or more recommendations. 130. The method of embodiment 129, wherein the gamer or the third party can select a future time for the one or more recommendations to be configured for helping the gamer improve their health and well-being or gaming performance by. 131. The method of embodiment 3, wherein the method further comprises ranking the gamer based at least in part on the gamer’s score (health and well-being, gaming performance score, or a combination thereof). 132. The method of embodiment 131, wherein the rating or ranking is performed on a location (e.g., regional, national, or global) basis. 133. The method of any one of embodiments 131 to 132, wherein the rating or ranking is performed on a league (e.g., esports, collegiate, high school gaming league) basis. 134. The method of any one of embodiments 131 to 133, wherein the method further comprises displaying the rating or ranking to the gamer on the GUI. 135. The method of embodiment 134, wherein the method further comprises generating and displaying a leaderboard of ratings or rankings. 136. The method of any one of embodiments 131 to 135, wherein the method further comprises displaying rating or ranking to a third party (e.g., coach) on the GUI. 137. The method of any one of embodiments 1, 3, 5 to 88, or 90 to 136, wherein the score indicative of the gamer’s health and well-being comprises a profile score. 138. The method of any one of embodiments 2 or 89 to 136, wherein the score indicative of a predicted gaming performance of the gamer comprises a profile score. 139. The method of any one of embodiments 137 or 138, wherein the profile score is a number on a scale of from 0-2. 140. A computer-implemented system comprising at least one processor and a memory comprising executable instructions, wherein, when the at least one processor executes theWSGR Docket No.60152-701.601 executable instructions, the at least one processor causes the system to perform the method of any one of embodiments 1 to 139. 141. A non-transitory computer-readable medium comprising executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of embodiments 1 to 139. Definitions

[0217] Unless defined otherwise, all terms of art, notations and other technical and scientific terms or terminology used herein are intended to have the same meaning as is commonly understood by one of ordinary skill in the art to which the claimed subject matter pertains. In some cases, terms with commonly understood meanings are defined herein for clarity and / or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a substantial difference over what is generally understood in the art.

[0218] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0219] Reference throughout this specification to “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in further embodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0220] As utilized herein, terms “component,” “system,” “interface,” “unit” and the like are intended to refer to a computer-related entity, hardware, software (e.g., in execution), and / or firmware. For example, a component can be a processor, a process running on a processor, an object, an executable, a program, a storage device, and / or a computer. By way of illustration, an application running on a server and the server can be a component. One or more components can reside within a process, and a component can be localized on one computer and / or distributed between two or more computers.

[0221] Further, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system,WSGR Docket No.60152-701.601 and / or across a network, e.g., the Internet, a local area network, a wide area network, etc. with other systems via the signal).

[0222] As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry; the electric or electronic circuitry can be operated by a software application or a firmware application executed by one or more processors; the one or more processors can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts; the electronic components can include one or more processors therein to execute software and / or firmware that confer(s), at least in part, the functionality of the electronic components. In some cases, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system.

[0223] Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

[0224] The term “real-time,” or “real time,” as used herein, generally refers to a response time of less than 1 second, tenth of a second, hundredth of a second, a millisecond, or less, such as by a computer processor. Real-time can also refer to a simultaneous or substantially simultaneous occurrence of a first event with respect to occurrence of a second event. One or more operations in the present disclosure can be performed in real-time, near real-time, or substantially real-time.

[0225] While preferred embodiments of the present subject matter have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the present subject matter. It should be understood that various alternatives to the embodiments of the present subject matter described herein may be employed in practicing the present subject matter.

Claims

WSGR Docket No.60152-701.601 CLAIMS WHAT IS CLAIMED IS:

1. A method for monitoring and improving the health and well-being of a gamer, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of health and well-being; c. initializing one or more artificial intelligence (AI) models / algorithms of health and well-being, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; and d. applying the one or more AI models / algorithms to: i. determine a score indicative of the gamer’s health and well-being; and ii. generate one or more recommendations to the gamer based at least in part on the score to improve the gamer’s health and well-being.

2. A method for predicting a gamer’s gaming performance, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of gaming performance; c. receiving a score of the gamer indicative of the gamer’s health and well-being; d. initializing one or more artificial intelligence (AI) models / algorithms of predicting gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; and e. applying the one or more AI models / algorithms to determine a score indicative of a predicted gaming performance of the gamer.

3. A method of rating a gamer’s gaming performance, the method comprising: a. receiving data from one or more data sources associated with the gamer; b. classifying the received data as being associated with one or more categories of gaming performance; c. receiving a score of the gamer indicative of the gamer’s health and well-being; d. initializing one or more artificial intelligence (AI) models / algorithms of rating individual gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gamer database; andWSGR Docket No.60152-701.601 e. applying the one or more AI models / algorithms to rate the gamer based at least in part on the gamer’s gaming performance and the score indicative of the gamer’s health and well-being.

4. A method of ranking a gaming team comprised of a plurality of gamers, the method comprising: a. receiving data from one or more data sources associated with the gaming team; b. classifying the received data as being associated with one or more categories of team gaming performance; c. initializing one or more artificial intelligence (AI) models / algorithms of ranking team gaming performance, wherein the one or more AI models / algorithms comprises data received from at least a reference population or a historical gaming team database; and d. applying the one or more AI models / algorithms to rank the gaming team based at least in part on the team’s gaming performance.

5. The method of claim 1, wherein the categories of health and well-being comprise: physical maintenance, mental conditioning, nutrition, sleep, and lifestyle.

6. The method of claim 5, wherein the physical maintenance data comprises data relating to the gamer’s physical exercise (e.g., amount and / or type of physical exercise), physical state (e.g., amount of pain or discomfort), a frequency of movement, or a combination thereof.

7. The method of any one of claims 5 or 6, wherein the mental condition data comprises data relating to happiness, depression, anxiety, stress, encouragement, coping, mood, attention, quality of life, demoralization, or a combination thereof, of the gamer.

8. The method of claim 7, wherein the mental condition data further comprises data relating to the gamer’s meditation (e.g., an amount or type thereof) and breathwork (e.g., amount or kinds of breathing exercises being performed).

9. The method of any one of claims 5-8, wherein the nutrition data comprises data relating to: how much water the gamer is drinking, how much caffeine the gamer is ingesting, how much sugar the gamer is ingesting, or what kinds of food the gamer is eating (e.g., health, fast food, snacks, etc.).

10. The method of any one of claims 5-9, wherein the sleep data comprises data relating to a quality of sleep, a duration of sleep, or a gamer’s routine before sleep.

11. The method of any one of claims 5-10, wherein the lifestyle data comprises data relating to the gamers hobbies (e.g., new or old hobbies, amount of time spent on hobbies), outdoor time, or social interaction (e.g., a quality or a quantity thereof).WSGR Docket No.60152-701.601 12. The method of any one of claims 2-4, wherein the categories of gaming performance (e.g., team gaming performance) comprise: a win / loss ratio, a quality of individual wins, a quality of team wins, a quality of individual losses, a quality of team losses, individual player rating, team ranking, head to head results (e.g., wins or losses by an individual gamer or gaming team against other individual gamers or gaming teams), opponent results, round differential, achievements, roster variability, kill / death ratio, score per minute, or a combination thereof.

13. The method of any one of claims 3-4, wherein the gamer rating or team ranking is identified at a plurality of time points throughout a season (e.g., a professional gaming league season).

14. The method of any one of claims 12 or 13, wherein the gamer rating comprises an overall gaming performance score for the gamer.

15. The method of any one of claims 12 or 13, wherein the team ranking comprises an overall gaming performance of the gaming team.

16. The method of any one of the previous claims, wherein the gamer plays for an organized club.

17. The method of any one of the previous claims, wherein the gamer plays in an organized academy system.

18. The method of any one of the previous claims, wherein the gamer plays video games competitively.

19. The method of any one of the previous claims, wherein the gamer is a member of a professional gaming (e.g., esports) league or team.

20. The method of any one of the previous claims, wherein the gamer is a member of a foreign national esports federation.

21. The method of any one of the previous claims, wherein the gamer is a member of a collegiate gaming league or team.

22. The method of any one of the previous claims, wherein the gamer is a member of a high school gaming league or team.

23. The method of any one of the previous claims, wherein the gamer plays video games for their occupation.

24. The method of any one of the previous claims, wherein the gamer creates content for one or more video games.

25. The method of any one of the previous claims, wherein the gamer is a casual gamer.

26. The method of claim 25, wherein the casual gamer is not a member of a professional, collegiate, or high school gaming league or team.WSGR Docket No.60152-701.601 27. The method of claim 25 or 26, wherein the casual gamer enjoys playing video games leisurely.

28. The method of any one of the previous claims, wherein the gamer plays one or more of: Counter Strike, League of Legends, Valorant, Overwatch, Super Smash Bros, Rocket League, PUBG, PUBG Mobile, Mobile Legends Bang Bang, Call of Duty, Call of Duty Mobile, Fortnite, EAFC24, Dota2, The Finals, or any combination thereof.

29. The method of any one of the previous claims, wherein the gamer is above the age of 13.

30. The method of any one of the previous claims, wherein the gamer is below the age of 13.

31. The method of any one of the previous claims, wherein the gamer is aged 10-13.

32. The method of any one of the previous claims, wherein the gamer is aged 13-18.

33. The method of any one of the previous claims, wherein the gamer is above the age of 18.

34. The method of claim 1, wherein improving the health and well-being of the gamer comprises improving the gamer’s cognitive performance.

35. The method of claim 1, wherein improving the health and well-being of the gamer comprises improving the gamer’s performance in one or more video games.

36. The method of claim 1, wherein the method comprises improving the health and well- being of the gamer by a target (e.g., pre-determined) amount.

37. The method of claim 1, wherein the method comprises improving the health and well- being of the gamer by a target (e.g., pre-determined) future time (e.g., a match or competition).

38. The method of claim 1, wherein the improving the health and well-being is quantified by a score (e.g., the determined score) indicative of the gamer’s health and well-being.

39. The method of claim 1, wherein the method comprises improving the health and well- being of a gaming team (e.g., professional, collegiate, high school team) comprised of multiple individual gamers.

40. The method of any one of the previous claims, wherein the reference population data, the historical gamer data, or a combination thereof, are gathered over a time period.

41. The method of claim 40, wherein the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days).

42. The method of claim 40, wherein the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks).

43. The method of claim 40, wherein the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months).

44. The method of claim 40, wherein the time period comprises at least 1 year (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more years).WSGR Docket No.60152-701.601 45. The method of any one of claims 40 to 44, wherein the reference population data, the historical user database, or a combination thereof, are received from a third party library.

46. The method of claim 45, wherein the third party comprises an esports data analytics company.

47. The method of any one of claims 40 to 46, wherein the reference population data comprises data gathered from a plurality of gamers over a period of time.

48. The method of any one of the previous claims, wherein the data sources comprise: gamer input, third party input, sensor / device input, or a combination thereof.

49. The method of claim 48, wherein the gamer input comprises data received from one or more gamer devices (e.g., a mobile device, tablet, a computer, a gaming console, or other personal computing device).

50. The method of claim 49, wherein the gamer input is input by the gamer themselves.

51. The method of any one of claims 48 to 50, wherein the gamer input comprises qualitative input, quantitative input, or a combination thereof.

52. The method of any one of claims 48 to 51, wherein the method further comprises administering one or more surveys or questionnaires to the gamer to gather the gamer input.

53. The method of claim 52, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) daily to the user.

54. The method of any one of claims 52 to 53, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a week to the user.

55. The method of any one of claims 52 to 54, wherein the one or more surveys or questionnaires are administered at least once (e.g., twice, three times, four times, five times, six times, seven times, eight times, nine times, or ten times) a month to the user.

56. The method of any one of claims 52 to 55, wherein the one or more surveys or questionnaires are configured to gather physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data from the gamer.

57. The method of any one of claims 48 to 56, wherein the gamer input comprises unprompted gamer input.

58. The method of claim 57, wherein the unprompted gamer input comprises gamer journal entries.

59. The method of claim 58, wherein the journal entries relate to the gamer’s well-being or gaming performance (e.g., self-perceived well-being or gaming performance).WSGR Docket No.60152-701.601 60. The method of any one of claims 57 to 59, wherein the unprompted gamer input relates to the physical maintenance, mental conditioning, nutrition, sleep, or lifestyle data of the gamer.

61. The method of any one of claims 48 to 60, wherein the third party input comprises data from the gamer’s coaches (e.g., gaming or fitness coaches), teammates, family (e.g., parents, siblings, etc.) doctors, therapists, counselors, professors, or teachers, or any combinations thereof.

62. The method of claim 61, wherein the third party input comprises qualitative or quantitative data from the gamer’s coach relating to the gamer’s well-being or gaming performance.

63. The method of claim 62, wherein the method further comprises transmitting data gathered from the gamer to the gamer’s gaming coach, and wherein the third party input comprises the gaming coaches’ feedback or recommendations to the gamer based on the transmitted data.

64. The method of any one of claims 61 to 63, wherein the third party input comprises qualitative or quantitative data from the gamer’s parent(s) relating to the gamer’s well- being.

65. The method of any one of claims 48 to 64, wherein the third party data comprises gaming performance data received from one or more games the gamer is currently playing.

66. The method of claim 65, wherein the gaming performance data comprises a kill / death ratio, a win / loss ratio, a score per minute, or other data gathered by the one or more games while the gamer is playing the one or more games.

67. The method of any one of claims 48 to 66, wherein the sensor / device input is received from one or more gamer sensors / devices.

68. The method of claim 67, wherein the sensors / devices are configured to gather biometric data of the gamer.

69. The method of any one of claims 67 to 68, wherein the sensors / devices are configured to gather pupillometry data of the gamer.

70. The method of any one of claims 67 to 69, wherein the sensors / devices comprise: one or more personal computing devices (e.g., a mobile device, tablet, or other personal computing device), or one or more applications associated with the one or more personal computing devices (e.g., social media applications (e.g., Instagram, Facebook, Twitter, TikTok, Calendar, Microphone, Online Purchasing apps, etc.).WSGR Docket No.60152-701.601 71. The method of any one of claims 67 to 70, wherein the sensor / devices comprise wearable sensors / devices (e.g., wearables).

72. The method of claim 71, wherein the wearables comprise one or more smart devices (e.g., smart watch) configured to measure physiological (e.g., heart rate, breathing rate), neurological, psychological, metabolic, or biological data of the gamer.

73. The method of any one of claims 71 to 72, wherein the wearables comprise Fitbit, Apple Watch, or Oura Ring.

74. The method of claim 67, wherein the sensor / device comprises Tobii Eye Tracker products.

75. The method of claim 67, wherein the sensors / devices comprise sensors / devices configured to receive voice data (e.g., a microphone, a gaming headset, etc.) from the gamer.

76. The method of claim 75, wherein the voice data is gathered while the gamer is playing video games.

77. The method of any one of the previous claims, wherein the method further comprises classifying the received data as originating from: gamer provided input, third party input, or sensor / device input.

78. The method of any one of the previous claims, wherein classifying the data is performed using one or more data processing algorithms.

79. The method of claim 78, wherein the one or more data processing algorithms comprises one or more feature extraction algorithms, one or more machine learning algorithms, one or more artificial intelligence algorithms, one or more Bayesian algorithms (e.g., Bayesian assimilation), one or more statistical analysis algorithms, or a combination thereof.

80. The method of any one of claims 78 to 79, wherein the one or more data processing algorithms receive data from: (i) the one or more data sources, (ii) a database, or a combination thereof.

81. The method of claim 80, wherein the database comprises stored reference population data, stored historical gamer specific data, or a combination thereof.

82. The method of any one of claims 78 to 81, wherein the one or more data processing algorithms comprise a natural language processing model configured to extract qualitative data from the one or more data sources, the historical gamer database, a reference population, or a combination thereof.

83. The method of any one of the previous claims, wherein the one or more AI models / algorithms comprise one or more machine learning models.WSGR Docket No.60152-701.601 84. The method of any one of the previous claims, wherein the one or more AI models / algorithms comprise a neural network (e.g., a spiking neural network, a deep neural network, a dynamic neural network, or a convolutional neural network), a regression-based learning algorithm, a linear or non-linear algorithm, a feed-forward neural network, a generative adversarial network (GAN), deep residual networks, a genetic algorithm, or any combination thereof.

85. The method of any one of the previous claims, wherein the one or more AI models / algorithms are trained.

86. The method of claim 85, wherein the method further comprises training the one or more AI models / algorithms.

87. The method of claim 86, wherein the method further comprises updating the one or more AI models / algorithms as they are continually trained.

88. The method of any one of the previous claims, wherein the one or more AI models / algorithms comprise supervised, unsupervised, or semi-supervised models / algorithms.

89. The method of claim 2, further comprising generating one or more recommendations to improve the gamer’s gaming performance.

90. The method of any one of claims 1 to 2, wherein the score is calculated for a time period.

91. The method of claim 90, wherein the time period is 1 day.

92. The method of claim 90, wherein the time period comprises at least 1 day (e.g., 2, 3, 4, 5, 6, or 7 or more days).

93. The method of claim 90, wherein the time period comprises at least 1 week (e.g., 2, 3, or 4 or more weeks).

94. The method of claim 90, wherein the time period comprises at least 1 month (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more months).

95. The method of any one of claims 90 to 94, wherein the score is determined based at least in part on a comparison of the received data to the reference population data, the historical gamer data, or a combination thereof.

96. The method of any one of claims 90 to 95, wherein the score is determined in real-time, near real-time, or in a dynamic nature.

97. The method of any one of claims 90 to 96, wherein the score is a qualitative score, a quantitative score, or a combination thereof.

98. The method of any one of claims 90 to 97, wherein the score is positive, negative, or neutral.WSGR Docket No.60152-701.601 99. The method of claim 98, wherein the positive score is extremely positive, very positive, moderately positive, or slightly positive.

100. The method of claim 98, wherein the negative score is extremely negative, very negative, moderately negative, or slightly negative.

101. The method of any one of claims 90 to 97, wherein score is a number on a scale of from 0-100.

102. The method of claim 101, wherein the number score is matched with a qualitative score.

103. The method of claim 102, wherein a score above 75 is matched with “Looking Strong!”.

104. The method of claim 102, wherein a score between 51 to 75 is matched with “Good Work!”.

105. The method of claim 102, wherein a score between 26 and 50 is matched with “You Got This!”.

106. The method of claim 102, wherein a score between 1 to 25 is matched with “Keep It Going!”.

107. The method of claim 102, wherein a score of 0 is matched with “Let’s Get Started!”.

108. The method of any one of claims 90 to 107, wherein the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) amount.

109. The method of any one of claims 90 to 108, wherein the one or more recommendations are configured to improve the health and well-being of the gamer, or the gaming performance of the gamer, by a target (e.g., pre-determined) future time (e.g., a match or competition).

110. The method of any one of claims 90 to 109, wherein the one or more recommendations are targeted to one or more categories of the categories of health and well-being.

111. The method of any one of claims 90 to 110, wherein the one or more recommendations comprises a personalized training regimen configured to improve the gamer’s health and well-being, their gaming performance, or a combination thereof.

112. The method of claim 111, wherein the method further comprises simulating a plurality of training regimens in the gamer to identify a most effective training regimen.

113. The method of claim 112, wherein the simulation predicts how the training regimen will improve the gamer’s health and well-being, or gaming performance, over a time period.

114. The method of any one of claims 111 to 113, wherein the method further comprises predicting an efficacy associated with the training regimen.

115. The method of claim 114, wherein the training regimen is recommended only if it meets a threshold level of predicted efficacy.WSGR Docket No.60152-701.601 116. The method of any one of claims 111 to 114, wherein the training regimen identifies the categories of health and well-being the gamer should focus on to most improve health and well-being, gaming performance, or a combination thereof.

117. The method of any one of claims 90 to 116, wherein the method further comprises receiving input from the gamer, or a third party, relating to the completion of the one or more recommendations.

118. The method of claim 117, wherein the one or more AI models / algorithms uses historical completion data in determining the score or generating the one or more recommendations to the gamer.

119. The method of any one of the previous claims, further comprising applying the one or more AI models / algorithms to identify correlations between a gamer’s, or gaming team’s, performance with their health and well-being.

120. The method of any one of the previous claims, wherein the method further comprises encouraging the gamer to improve their health and well-being, gaming performance, or a combination thereof by displaying a list of one or more badges for the gamer to earn by completing actions associated with the one or more recommendations.

121. The method of claim 120, wherein the method further comprises displaying a completion percentage of the one or more badges to the gamer or a third party.

122. The method of any one of claims 120 to 121, wherein the method further comprises notifying and displaying to the gamer or third party the completion of the one or more badges.

123. The method of any one of the previous claims, wherein the method further comprises transmitting the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party.

124. The method of any one of the previous claims, wherein the method further comprises displaying the score, the one or more recommendations, the ratings, the rankings, or a combination thereof to the gamer or a third party on a graphical user-interface (GUI).

125. The method of claim 124, wherein the gamer’s personalized score, one or more recommendations, ratings, rankings, or a combination thereof are displayed on the GUI of a gamer’s personal device.

126. The method of claim 124, wherein the method further comprises displaying a plurality of gamers’ (e.g., members on a gaming team) scores, one or more recommendations, ratings, or rankings, or a combination thereof to a third party (e.g., coach).

127. The method of any one of claims 1 or 2, wherein the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the determined score.WSGR Docket No.60152-701.601 128. The method of claim 127, the gamer or the third party can select a time period (e.g., a past time period) for the score to be determined for.

129. The method of any one of claims 1 to 2 or 127 to 128, wherein the method further comprises receiving gamer or third party (e.g., coaches or parents) input to customize the one or more recommendations.

130. The method of claim 129, wherein the gamer or the third party can select a future time for the one or more recommendations to be configured for helping the gamer improve their health and well-being or gaming performance by.

131. The method of claim 3, wherein the method further comprises ranking the gamer based at least in part on the gamer’s score (health and well-being, gaming performance score, or a combination thereof).

132. The method of claim 131, wherein the rating or ranking is performed on a location (e.g., regional, national, or global) basis.

133. The method of any one of claims 131 to 132, wherein the rating or ranking is performed on a league (e.g., esports, collegiate, high school gaming league) basis.

134. The method of any one of claims 131 to 133, wherein the method further comprises displaying the rating or ranking to the gamer on the GUI.

135. The method of claim 134, wherein the method further comprises generating and displaying a leaderboard of ratings or rankings.

136. The method of any one of claims 131 to 135, wherein the method further comprises displaying rating or ranking to a third party (e.g., coach) on the GUI.

137. The method of any one of claims 1, 3, 5 to 88, or 90 to 136, wherein the score indicative of the gamer’s health and well-being comprises a profile score.

138. The method of any one of claims 2 or 89 to 136, wherein the score indicative of a predicted gaming performance of the gamer comprises a profile score.

139. The method of any one of claims 137 or 138, wherein the profile score is a number on a scale of from 0-2.

140. A computer-implemented system comprising at least one processor and a memory comprising executable instructions, wherein, when the at least one processor executes the executable instructions, the at least one processor causes the system to perform the method of any one of claims 1 to 139.

141. A non-transitory computer-readable medium comprising executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 1 to 139.

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