Building user profiles by identifying and classifying actions in a virtual environment to provide user-specific content

Abstract

User activity data is ingested from multiple distinct virtual environments and used to develop a profile describing how a user interacts with the virtual environments. For example, the profile can be the user's GamerDNA that describes the user's unique playstyle, skill set, and behavioral patents across multiple games. The user's profile can be used to identify virtual content or tangible rewards to provide to the user.

Description

BUILDING USER PROFILES BY IDENTIFYING AND CLASSIFYING ACTIONS IN A VIRTUAL ENVIRONMENT TO PROVIDE USER-SPECIFIC CONTENTInventor: Jeffrey IvoryCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 707,643, filed October 15, 2024, which is incorporate by reference.BACKGROUND1. TECHNICAL FIELD

[0002] The subject matter described relates generally to online virtual environments and, in particular, to using artificial intelligence (Al) to build user profiles based on actions in online virtual environments to provide user-specific content, such as personalized assets, merchandise, coaching, avatar mimicry, game creation, or the like.2. BACKGROUND INFORMATION

[0003] Billions of people interact with online virtual environments for a diverse range of purposes, such as gaming, social interaction, education, and professional collaboration. Existing virtual environments typically present the same content to all users of the virtual environment. To the extent content customization is available, it is typically limited to a user manually changing one of a predefined set of settings or purchasing virtual content (e.g., skins, avatars, or items) from a static store that offers the same options to all users of the virtual environment. This can lead to virtual environments lacking depth and long-term appeal to all but a few dedicated users. There is thus a need for mechanisms to provide dynamically generated, highly personalized content to users based on their activity in virtual environments.SUMMARY

[0004] The above and other problems may be solved by a computer-based system that ingests data describing use of virtual environments to build profiles for users and analyze the users’ activity in the virtual environments. For example, the user profiles may be dynamically generated based on gameplay in multiple games and thus, in such examples, may be referred to as the user’s “GamerDNA.” Although the following description provides examples where the user profile is GamerDNA and the virtual environments are gaming environments, it should be appreciated that the same or similar techniques may be applied to other virtual environments.

[0005] In various embodiments, the system identifies or generates user-specific content to offer to the user based on their user profile or activities within the virtual environments. The userspecific content may be virtual content that is integrated into one or more of the virtual environments. Additionally or alternatively, the system may integrate with a point-of-sale (POS) system of a real-world merchant to provide access to user-specific content such as discounts, physical items, or services based on user activity or achievements within the virtual environments. The integration with the POS system may use a Decentralized Attestation and Verification Relay (DAVR) to provide user verification in a privacy-preserving manner.

[0006] In some embodiments, a user’s activity in one or more virtual environments may be used to train an Al avatar to mimic the user’s activity (e.g., playstyle and behavior patterns) in the virtual environments. Additionally or alternatively, an Al system may analyze the user’s activity (e.g., gameplay footage) in one or more virtual environments and provide personalized coaching feedback on how to improve the user’s activity in the virtual environment, which can be delivered as an augmented reality (AR) overlay during gameplay or via interactive replays.

[0007] Furthermore, an Al system may be trained to receive natural language input and build a custom virtual environment for a user based on parameters provided in the natural language input and, in some instances, the user’s profile (or a target user’s profile) as generated from the relevant user’s activity in other virtual environments. In one embodiment, a game creation Al system receives natural language input from the user to define game requirements and employs procedural content generation (PCG) techniques to generate a custom virtual environment tailored to the user's GamerDNA. This game creation process can be further enhanced by orchestrating one or more specialized Al agents (e.g., programming, graphics, narrative, and quality assurance agents) that collaboratively build a complete game from user ideas.

[0008] The entire ecosystem can use privacy-preserving mechanisms, such as federated learning, and leverage blockchain for secure, transparent, and immutable record-keeping.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 is a block diagram of a networked computing environment suitable for providing users with access to virtual environments, according to one embodiment.

[0010] FIG. 2 is a block diagram of an application server of FIG. 1, according to one embodiment.

[0011] FIG. 3 is a block diagram of the content integration server of FIG. 1, according to one embodiment.

[0012] FIG. 4 is a block diagram of the DAVR system of FIG. 1, according to one embodiment.

[0013] FIG. 5 illustrates an example architecture for a GamerDNA and Al avatar system, according to one embodiment.

[0014] FIG. 6 illustrates an example architecture for a gamer vision system, according to one embodiment.

[0015] FIG. 7 illustrates an example architecture for a POS integration system, according to one embodiment.

[0016] FIG. 8 illustrates an example architecture for a gameplay-triggered user-specific content system, according to one embodiment.[00171 FIG. 9 illustrates an example architecture for an Al-driven game creation system, according to one embodiment.

[0018] FIG. 10 illustrates an example architecture for an Al-swarm game creation system, according to one embodiment.

[0019] FIG. 11 is a block diagram illustrating an example of a computer suitable for use in the networked computing environment of FIG. 1, according to one embodiment.DETAILED DESCRIPTION

[0020] The figures and the following description describe certain embodiments by way of illustration only. The embodiments described relate primarily to virtual environments that are part of games (i.e., virtual environments in which gameplay takes place) but it should be appreciated that the same or similar techniques may be applied to other types of virtual environments (e.g., social metaverses, educational simulations, or professional collaboration spaces) where user activity and personalization are relevant. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods may be employed without departing from the principles described. Wherever practicable, similar or like reference numbers are used in the figures to indicate similar or like functionality. Where elements share a common numeral followed by a different letter, this indicates the elements are similar or identical. A reference to the numeral alone generally refers to any one or any combination of such elements, unless the context indicates otherwise.OVERVIEW

[0021] Systems that aim to connect virtual engagement with real-world benefits are a promising evolution for the future of digital engagement. However, their practical implementation is hindered by several fundamental challenges. To attribute a real-world purchase to a user interacting within a virtual environment, a link must be made between their virtual identity and their real-world transactional identity. Existing methods like card-linked offers (CLO) require users to register sensitive payment card information with a third party, creating significant privacy concerns and adoption friction.

[0022] To address this (and other problems), a personalized content integration system uses artificial intelligence or other sophisticated algorithms with data analysis to reward users in realtime with personalized content based on their historical activity (e.g., gameplay) data, habits, and in-environment actions and achievements. In various embodiments, the system includes a content integration server connected to network-enabled virtual environment platforms and servers that is configured to analyze user activity and statistics relating to the virtual environments provided by the virtual environment platforms. The content integration server may have an application programming interface (API) that allows participating content providers to create and manage personalized content for users that meet specified criteria. The API also allows content providersto access data and insights about the user community and its preferences, allowing them to create and deliver engaging and immersive content and experiences custom-curated for users.

[0023] To prevent fraud, it is generally desirable to verify that a user genuinely completed an eligible virtual action (“Proof-of-Play”) that entitles them to a particular piece of personalized content (which may be physical or digital), service, or discount. Relying on compute-heavy methods like continuous video analysis is economically unscalable due to exorbitant cloud computing costs at a global scale. Therefore, a need exists for a novel architecture that can verifiably attribute rewards derived from virtual interaction in a privacy-preserving manner, while remaining economically sustainable at scale.

[0024] Various embodiments of the disclosed personalized content integration system provide solutions for several other issues within existing computer systems and virtual environments such as:1) The one-size-fits-all approach present within existing virtual environments, which fails to cater to the unique preferences, interaction styles, skillsets, and behavioral patterns of individual users. This can lead to a sense of detachment and reduced engagement as virtual environments lacking dynamic, evolving content tailored to the user's journey often struggle to maintain user interest over extended periods, leading to chum.2) User achievements and engagement within virtual environments often lack a direct, tangible correlation or reward in the real world, diminishing the perceived value of ingame effort for many users. The digital and physical economies remain largely siloed and disconnected from each other, with no viable bridge to intertwine them cohesively.3) The creation of custom virtual environments, games, or even game components typically requires advanced technical skills (e.g., programming, graphic design, etc.), making it inaccessible to the vast majority of users, thereby stifling creativity and diversity of content.4) Users seeking to improve their performance or strategy in virtual environments often rely on generic tips or manual self-analysis, which are rarely as effective or personalized as needed.5) Existing non-player characters (NPCs) or bot opponents in virtual environments often exhibit generic behaviors, failing to offer a realistic or challenging representation of actual players, or a personalized stand-in for a user.

[0025] These limitations hinder the potential for truly immersive, engaging, and valuable virtual experiences. Therefore, various embodiments of the personalized content integration system can dynamically understand individual users, adapt content and experiences to their unique characteristics, bridge the gap between virtual achievements and real-world benefits, and empower users to participate in content creation without advanced technical expertise, all while ensuring data privacy and security. Specifically, these and other problems can be addressed providing a holistic, Al-driven ecosystem for virtual environments, as described in greater detail below.

[0026] In some embodiments, privacy-preserving attribution can be provided by a DAVR system. The DAVR system is designed as a holistic, integrated ecosystem that spans virtual and real-world interactions, connecting virtual platforms, user devices, and third-party POS systems cohesively together via three novel, interconnected subsystems: Hybrid Proof-of-Play (H-PoP) Protocol, Ephemeral Transaction Tokens (ETTs), and a Low-Latency Pre-Authorization Cache. This can enable real-time redemption with privacy-preserving attribution which integrates with virtual systems, gaming platforms, user devices, and third-party POS systems. This combination can enable secure identity correlation and verifiable reward distribution.EXAMPLE SYSTEMS

[0027] FIG. 1 illustrates one embodiment of a networked computing environment 100 suitable for providing users with access to virtual environments. In the embodiment shown, the networked computing environment 100 includes one or more applications platforms 105, one or more application servers 110, a content integration server 120, one or more third-party provider servers 130, a blockchain 135, and a set of client devices 140, all connected via a network 170. In other embodiments, the networked computing environment 100 includes different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described.

[0028] An applications platform 105 is one or more computing systems that provide users with access to applications. For example, users may access application functionality by downloading applications to execute locally on client devices, executing applications directly in the cloud, or using a hybrid approach in which a downloaded application interacts with one or more cloud services at runtime. In one embodiment, the applications platforms 105 are gaming platforms such as Steam®, Xbox®, or PlayStation® via which players access games. Users haveaccounts with the gaming platforms that include information about the users and how they use the gaming platform. The information about a user may include demographic information (e.g., age, gender, location, etc.) and gameplay history (e.g., games played, playtime for each game, achievements earned, in-game purchases made, virtual items owned, etc.). This data can be used in the generation of GamerDNA or other user profiles.

[0029] An application server 110 is one or more computing systems that provide application functionality to users via their client devices 140. In one embodiment, the application server 110 is a game server that hosts virtual environments in which users participate in multiplayer games. Different application servers 110 may provide a wide range of game genres, such as card games, first-person shooters, real-time strategy games, massive multiplayer online role-playing games (MMORPGs), battle royale games, and sports simulators, etc. An application server 110 may provide an interface (e.g., an application programming interface (API)) with which gameplay information (e.g., videos of gameplay, gameplay statistics, in-game actions, sensor data, user input, etc.) may be exported and user-specific content (e.g., player avatars, skins, custom items, game modifications, etc.) may be imported. Various embodiments of an application server 110 are described in greater detail below, with reference to FIG. 2.

[0030] The content integration server 120 is one or more computing systems that provide various types of custom content and personalized experiences to users of the applications platforms 105 and application servers 110. In one embodiment, the content integration server 120 ingests diverse user activity data for two or more virtual environments from the applications platforms 105, application servers 110, or both. The user activity data describes the user’s actions (and in some cases achievements) in the virtual environments. For example, for gameplay environments, the user activity data may include data such as achievements, gameplay footage, gameplay statistics, in-game actions, sensor data during play, user inputs, etc.

[0031] The content integration server 120 analyzes the ingested data using a set of one or more Al models to build a profile for the user. In one embodiment, the user profile includes attributes of the user’s interactions with multiple distinct virtual environments that can be generalized across the distinct virtual environments. For example, the user profile may be the user’s GamerDNA that describes their gaming history and behavior. GamerDNA quantifies the user’s unique playstyle, skill set, and behavioral patterns across multiple distinct virtual environment platforms. The GamerDNA may also indicate other information about the user, suchas reward type preferences, social media usage, interests outside of gaming, interactions with other players (e.g., friends lists), or any other data that helps provide a rich understanding of the unique nature of the user.

[0032] The content integration server 120 can leverage the GamerDNA to provide a range of user-specific functionalities. For example, the content integration server 120 may select userspecific custom content (e.g., in-game avatars, skins, or virtual items, as well as custom items outside of gameplay such as non-fungible tokens (NFTs) or physical merchandise) based on the user’s GamerDNA and present the user with an opportunity to obtain the selected custom content. Where appropriate, the content integration server 120 may integrate custom content obtained by the user into games played via the application servers 110. The content integration server 120 may provide other customized functionality to the player, such as: (1) Al-driven personalized coaching based on detailed analysis of the user”s gameplay history and identified strategic patterns and player intentions; (2) a personalized Al avatar trained using the GamerDNA to mimic the user’s unique playstyle and behavioral patterns in a virtual environment, which can apply the user’s playstyle to new, different virtual environments through transfer learning; (3) an interface using Al to create a custom virtual environment or entire game based on natural language input provided by the player that is tailored to the user’s GamerDNA, which may involve orchestrating specialized Al agents; and (4) user-specific tangible rewards by transmitting reward data to a third-party point-of-sale (POS) system, causing the POS system to apply the tangible reward to a real-world transaction associated with the user. Various embodiments of the content integration server 120 are described in greater detail below, with reference to FIG. 3.

[0033] A third-party provider server 130 is one or more computing systems via which users can obtain content (either virtual or tangible) based on their activity in the virtual environments provided by the application servers 110. In various embodiments, third parties may make content available to players based on their GamerDNA or directly based on their gameplay behavior meeting one or more requirements. For example, a third party may make a particular content item (e.g., a discount, exclusive merchandise, or service) available to any player who earns a specific achievement in an identified game. In one embodiment, the third-party provider server 130 controls a POS system to automatically provide a player with access to or a discount on earned tangible rewards in a web or physical store. For example, a player who has earned the “Little Rocket Man” achievement in Half-life 2: Episode 2 may be offered a discount on garden gnomesby a merchant, with the discount automatically being applied in the merchant’s point of sale system using a mapping between the player’s account with the merchant (e.g., a rewards program account) and the player’s account with the content integration server 120. In some embodiments, this uses a secure correlation between the player’s account with the merchant (e.g., a rewards program account) and the player’s virtual identity associated with the GamerDNA, potentially via the content integration server 120 and blockchain 135.

[0034] The blockchain 135 is a distributed ledger that can be used by the content integration server 120 to store immutable records securely. In one embodiment, a record of a player’s virtual identity (associated with their GamerDNA) is stored on the blockchain 135, which can provide a trusted verifiable credential of the player. This verifiable credential can enable other systems (e.g., a merchant’s POS system) to securely identify the player and correlate their virtual identity with their real- world identity. Additionally or alternatively, the content integration server 120 may store identifiers of content earned by a player on the blockchain 135, enabling the player’s content to be verified and integrated into games provided by the application servers 110. Similarly, identifiers of earned tangible rewards may be stored on the blockchain 135, ), enabling the reward's authenticity and the player’s ownership to be verified and subsequently applied at a third-party POS system through the execution of smart contracts.

[0035] A client device 140 is a computing device with which a user accesses the functionality provided by the other elements in the networked computing environment 100. Although three client devices 140 are shown (a first client device 140 A, a second client device 1408, and an Nth client device MON, the networked computing environment 110 can include any number of such devices). In one embodiment, a player plays games on their client device 140, with an application server 110 providing a virtual environment and coordination between multiple players’ client devices 140 enabling them to play the game together, either synchronously or asynchronously, depending on the game. A user may also use a client device 140 to purchase or otherwise obtain content items they have earned through interacting with the virtual environments from a webstore (e.g., provided by a third-party provider server 130). In some embodiments, content providers may use client devices 140 to make content available to users and specify the criteria for accessing that content (e.g., particular gameplay objectives, meeting certain target demographic criteria, etc.). Content providers may also view analytics of how users are interacting with previously provided content (e.g., how much players are using a virtual item provided by thecontent provider in gameplay, demographic information about players that are using a custom skin, etc.).

[0036] The network 170 provides the communication channels via which the other elements of the networked computing environment 100 communicate. The network 170 can include any combination of local area networks (LANs)and wide area networks (WANs), using wired or wireless communication systems. In one embodiment, the network 170 uses standard communications technologies and protocols. For example, the network 170 can include communication links using technologies such as Ethernet, 802.11 (Wi-Fi), worldwide interoperability for microwave access (WiMAX), 3G, 4G, 5G, code division multiple access (CDMA), digital subscriber line (DSL), etc. Examples of networking protocols used for communicating via the network 170 include multiprotocol label switching (MPLS), transmission control protocol / intemet protocol (TCP / IP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), and file transfer protocol (FTP). Data exchanged over the network 170 may be represented using any suitable format, such as hypertext markup language (HTML) or extensible markup language (XML). In some embodiments, some or all of the communication links of the network 170 may be encrypted using any suitable technique or techniques.

[0037] FIG. 2 illustrates one embodiment of an application server 110. In the embodiment shown, the application server 110 includes an application 210, an activity extraction module 220, a content import module 230, and a local datastore 240. In other embodiments, the application server 110 includes different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described.

[0038] The application 210 provides a virtual environment with which the user interacts. In one embodiment, the virtual environment is part of a game and the user plays the game in the virtual environment. As described previously, the user may execute an application on their client device 140 that interacts with the application 210 to provide the game at the user’s client device 140. The application 210 may coordinate game events between multiple players, each playing the game on their own client device 140.

[0039] The activity extraction module 220 extracts user activity data describing the activity of users in the virtual environment provided by the application 210 and provides the user activity data to the content integration server 120. The data may be provided to the content integration server 120 using an API. In one embodiment, the data includes real-time or recorded videofootage of gameplay in a game provided by the application 210. Additionally or alternatively, the data may be information describing the configuration of the game and player actions. For example, in a card game, the starting position of every card and a list of game actions taken by the players may be sufficient to reproduce the entire game. Similar techniques may be applied even to significantly more complicated games such as first-person shooters, where a match can be recreated using the starting configuration of the game and a log of all inputs by all players. In some embodiments, the activity extraction module 220 may also provide sensor data collected during gameplay, such as inertial data indicating movement of the player, gaze tracking data indicating where the user was looking during gameplay, direct neural feedback, and the like.

[0040] The content import module 230 provides an interface (e.g., an API) via which userspecific content can be securely imported into the application 210. In one embodiment, a user earns custom rewards (e.g., virtual items, custom skins, etc.) from the content integration server 120 via gameplay and those custom rewards are imported into the application via the content import module 230. For example, if the user earns a custom costume or character skin by earning an achievement in a game, the content import module 230 may make the custom costume or character skin available for selection by the user when playing the game via the application 210. In some embodiments, an indication of what custom content a player has earned is securely stored on the blockchain 135 and content import module 230 verifies what custom content is available for a player by querying the blockchain 135 with an identifier of the player (e.g., a playerlD or a verifiable credential).

[0041] The local datastore 240 is one or more non-transitory computer-readable media that store data generated or used by the other elements of the application server 110. For example, raw data describing gameplay such as video footage may initially be stored in the local datastore 240 before being uploaded to the content integration server 120. In one embodiment, gameplay footage is stored in the local datastore 240 and a player can later review the footage and select some or all of the footage to provide to the content integration server 120. In another embodiment, gameplay footage is stored in the local datastore 240 and analyzed locally by the activity extraction module 220, which provides metrics describing the gameplay, such as a skill level of play determined by a trained Al model, gameplay statistics (e.g., shot accuracy, score, or total moves taken), decisions made during gameplay, and achievements earned during gameplay, etc. to the content integration server 120.

[0042] FIG. 3 illustrates an embodiment of the content integration server 120. As described previously, the content integration server 120 may provide various functionality to users based on the users’ activity in virtual environments provided by the application server 110. In the embodiment shown, the content integration server includes an ingestion module 310, a user profile module 320, an analysis module 330, a personalized content module 340, a real-world integration module 350, a DAVR system 355, an Al avatar module 360, an environment creation module 370, an analytics module 380, and a cloud datastore 390. In other embodiments, the content integration server 120 includes different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described.

[0043] The ingestion module 310 receives user activity data from other elements of the networked computing environment 100. In one embodiment, the ingestion module 310 retrieves demographic and gameplay history data (e.g., games played, time spent playing games, achievements earned, virtual items acquired, etc.) from one or more applications platforms 105. The ingestion module 310 may also receive granular gameplay data (e.g., gameplay footage, gameplay statistics, user inputs during gameplay, sensor data gathered during gameplay, in-game communication patterns, etc.) from one or more application servers 110. Alternatively, gameplay data may be collected by the applications platforms 105 from application servers 110 and provided to the ingestion module 310. The ingestion module 310 may preprocess received data, such as to aggregate gameplay history from multiple distinct virtual environment platforms and normalize data formats for consistent analysis. This ingestion process can provide the basis for comprehensive cross-platform user profiling.

[0044] The user profile module 320 generates a user profile of a user from the ingested data. In various embodiments, the user profile is a comprehensive cross-platform user profile. The user profile module 320 can collect a range of data about the user to generate the user’s GamerDNA profile. This data can include demographic data (e.g., age, gender, location, and gaming platform preference, etc.), gameplay history (e.g., games played, playtime, achievements, skill level, and in-game purchases, etc.), behavioral data (e.g., game mode preferences, playstyle, interaction with other players, in-game communication patterns, and social media activity, etc.), and preference data (e.g., favorite game genres, preferred reward types, and interests outside of gaming, etc.). The user profile module 320 may normalize data from different sources (e.g., different game platforms).

[0045] The user profile module 320 applies a set of one or more Al models, including machine-learning algorithms, to the data about the user to create a detailed understanding of each user’s unique gaming profile, such as identifying strategic patterns in the user’s gameplay as well as classifying the user’s playstyle and skillset / level based on clustering the user’s gameplay with gameplay of other users with known or assigned playstyles and skill levels. The Al models may include deep neural networks for feature extraction from raw gameplay data and unsupervised learning techniques (e.g., t-Distributed Stochastic Neighbor Embedding (t-SNE)) for dimensionality reduction and play-style clustering. Natural Language Processing (NLP) techniques may be used to analyze in-game communication patterns and textual data.

[0046] In one example, the user profile is the user’s GamerDNA that quantifies the user’s unique playstyle, skill set, and behavioral patterns across distinct virtual environment platforms. For example, the user profile module 320 may distinguish between players that prefer sniperstyle builds versus melee builds based on the weapons the players’ characters typically use in game and determine a skill level based on metrics such as hit percentage, number of kills, number of deaths, number of victories, etc. The GamerDNA can provide information about the user’s interests (e.g., game types primarily played), skill set, strategies, and playstyle, etc. The user’s GamerDNA may also indicate other information about the user, such as reward type preferences, social media usage, interests outside of gaming, interactions with other players (e.g., friends lists), or any other data that helps provide a rich understanding of the unique nature of the user.

[0047] To preserve privacy, the user profile module 320 may implement privacy-preserving mechanisms such as federated learning to update GamerDNA profiles without ingesting raw personal data directly from a client device 140. Specifically, a secure aggregation protocol may be used for collaborative learning across multiple users’ data without sharing individual raw data, and differential privacy techniques may be used to add sufficient noise to individual updates to further preserve privacy while maintaining model utility.

[0048] In some embodiments, the analysis module 330 analyzes user activity data, including gameplay footage, to make recommendations on how the user may better interact with the virtual environment and provide coaching to improve their performance in the game. For example, by analyzing gameplay footage (e.g., using a computer vision model), the analysis module 330 may determine that the user tends to miss shots to the right and suggest to the user they adjust their aiming to target slightly to left of their intended target. As another example, the analysis module330 may determine from correlating gameplay footage and gaze tracking data that the user is often surprised by enemies in the game because they do not pay enough attention to an in-game radar or local map and suggest that the user glances at this portion of the game’s user interface more frequently. The analysis module 330 may identify strategic patterns and player intentions. This feedback can be delivered as an augmented reality (AR) overlay during a subsequent gameplay session or via an interactive replay with overlaid analysis. It should be appreciated that the analysis module 330 may apply Al analysis to identify a wide range of ways in which a user’s gameplay may be improved depending on the nature of the game and the particular quirks of the user’s playstyle.

[0049] The personalized content module 340 identifies virtual content to provide to a user based on the user’s profile. In one embodiment, the personalized content module 340 provides personalized content offers to gamers based on the user’s GamerDNA profile that align with their individual preferences and motivations. For example, a player who consistently excels in first- person shooter games might receive rewards related to weapon skins. Conversely, a player who enjoys playing sports-related games might be rewarded with exclusive in-game character skins for sports teams, such as legacy uniforms. The personalized content module 340 can make these personalized content items available to the user directly in the relevant games through integration with the application servers 110. For example, a custom skin may automatically appear in the in- app purchase screen of a mobile game as a free or discounted download. In some embodiments, personalized content may be automatically added to a user’s game when earned. For example, a custom kit for a sports team may just become available to be used in games once the user has met the criteria for earning it.

[0050] The real-world integration module 350 is similar to the personalized content module 340 but it identifies user-specific tangible rewards to users based on their profiles rather than inapplication virtual content. In various embodiments, the real- world integration module 350 identifies tangible items or services that are likely to be of interest to the user based on the user’s GamerDNA profile and gameplay. For example, a user who mostly plays first-person shooters might be offered credit towards physical merchandise associated with a popular game in this genre while a user who mostly plays sports games might be offered a credit towards tickets to a sporting event in their area.

[0051] The real-world integration module 350 can interface with POS systems of relevant entities to directly provide earned tangible rewards to the user. In one embodiment, the real-world integration module 350 does this by securely correlating a virtual identity of the user, associated with the GamerDNA, with a real-world identity of the user, associated with the third-party POS system. This correlation can be achieved using a verifiable credential stored on the blockchain 135. Alternatively, this may be done by storing a mapping (e.g., in the cloud datastore 390) between the user’s GamerDNA profile and the user’s profile with the relevant entities (e.g., a store’s loyalty program or the user’s account with an online merchant). In either case, when the user visits the physical or online store and identifies themselves (e.g., through their correlated real-world identity), the POS system automatically identifies any credits, discounts, or other tangible rewards the user is entitled to from the real-world integration module 350 and processes them accordingly. The reward data transmitted to the POS system is configured to cause the POS system to apply the tangible reward to a real-world transaction associated with the user. This process may involve executing a smart contract on the blockchain 135 to securely validate the user’s ownership of an earned reward and to authorize distribution of the tangible reward at the third-party POS system.

[0052] The DAVR system 355 provides user verification with a verifiable credential in a privacy-preserving manner. Although The DAVR system 355 is shown as part of the content integration server 120, it may be implemented as an independent component and accessed via the network 170 as needed (e.g., as a cloud-based service). FIG. 4 illustrates one embodiment of the DAVR system 355 that includes three novel, interconnected subsystems: (1) a hybrid proof-of- play (H-PoP) protocol 410 to solve the economic viability challenge of verification; (2) an attribution module 420 that uses Ephemeral Transaction Tokens (ETTs) for privacy-preserving attribution; and (3) a real-time redemption module 430 that uses a low-latency pre-authorization cache to enable real-time redemption.

[0053] The H-PoP protocol 410 institutes a two-tier verification process that minimizes computational cost while maximizing fraud deterrence by using on-device signature generation via a lightweight software development kit (SDK) or client application installed on the user’s client device 140 (e.g., PC, console, mobile phone, etc.) to securely monitor virtual actions or ingame events (or both). In one embodiment, on detecting a potentially reward-eligible event (e.g., “Level 10 reached”), the SDK captures a time-series of critical game-state data points (e.g., API calls from the game, memory state checksums, network event logs, etc.). This game data iscombined with anonymized, time-correlated sensor data from the device (e.g., accelerometer / gyroscope patterns, input device cadence, etc.) to create a multi-factor engagement and interactivity signature. This signature is cryptographically hashed and passed on to the next process in the DAVR pipeline. This H-Pop protocol 410 is computationally inexpensive and provides a high-fidelity, difficult-to-forge fingerprint of the live engagement and interactivity event.

[0054] In some embodiments, the content integration server 120 does not perform deep analysis on every event. Instead, it uses a machine learning model to perform stochastic auditing for deep verification of eligible virtual user activity. The vast majority (e.g., 99.9%) of user interactions are validated solely based on the integrity of the engagement and interactivity signature. A small, randomly selected percentage of achievements may be flagged for full “deep verification,” in which the content integration server 120 requests the corresponding user engagement / interaction snippet for Al-powered analysis. The selection probability need not be uniform. For example, if a user’s Engagement and Interactivity Signature is ever flagged as anomalous or inconsistent with their GamerDNA profile, the probability of their future achievements being selected for deep verification can be dynamically increased. This risk- adaptive model makes fraud costly and unsustainable for bad actors while keeping operational costs low for the system. For example, in one embodiment, statistical sampling can provide at least 95% fraud detection at 1% of the cost of deterministically checking every transaction.

[0055] In one embodiment, the H-PoP protocol 410 uses asymmetric computational distribution that enables the processing the majority (e.g., 99.9%) of verifications with a lightweight, Tier-1 validation method via the SDK and only a small amount (e.g., 0.1%) with an expensive Tier-2 deep analysis that uses real-time video analysis. This can enable a lOOOx reduction in average computational cost per transaction relative to conventional approaches.

[0056] In some embodiments, the H-PoP protocol 410 protocol leverages HMAC-SHA256 (symmetric) instead of RSA / ECDSA (asymmetric) cryptography. HMAC-SHA256 operations are 100- lOOOx faster than RS A signature verification, enabling sub-millisecond validation on commodity hardware. By leveraging client-side computational offloading there is zero serverside compute for signature generation, effectively outsourcing computational costs to millions of user devices using the H-Pop protocol (e.g., -157 bytes per verification vs. video analysis (10- 100MB), representing a 60,000x reduction in data transfer and storage costs at scale).

[0057] The use of Ephemeral Transaction Tokens (ETTs) for privacy-preserving attribution by the attribution module 420 helps to solve the attribution and privacy paradox. In one embodiment, the attribution module 420 avoids making or storing persistent links between user profiles and payment credentials. Instead, the attribution module 420 uses single-use, rights- managed tokens. Upon successful H-PoP verification of an eligible action, the content integration server 120 generates a time-limited, single use Ephemeral Transaction Token (ETT). The ETT is a cryptographically signed data object (e.g., a JSON Web Token - JWT) containing non sensitive information: (a) reward id: An identifier for the specific reward (e.g., "20% off at Merchant X"); (b) id: A hashed or pseudonymous identifier for the user, which cannot be resolved to a real identity without access to the secure server-side database; (c) expiry timestamp: A timestamp after which the token is invalid (e.g., 24 hours); (d) signature: a cryptographic signature to ensure the token's authenticity.

[0058] In one embodiment, the redemption module 430 uses a redemption flow that begins with the ETT being pushed to the user’s client device 140 and stored in a digital wallet within the computer-implemented system application. At the partner merchant’s POS, the user presents the ETT (e.g., as a QR code) and the POS system scans the QR code, validates the ETT’s signature and expiry, and reads the reward id. At no point does the POS system receive or require the user’s name, email, or credit card number from the DAVR system 355. The POS applies the reward to the transaction. The POS system can later send a batch report of all redeemed anon user_ids to the DAVR system 355. Only then, within its own secure environment, does the DAVR system 355 resolve the anonymous ID to the user’s GamerDNA profile to complete the attribution loop for analytics and tracking.

[0059] The use of a low-latency authorization cache can enable real-time redemption. In one embodiment, the DAVR system 355 decouples the POS query from the core server logic using a globally distributed cache. A high-speed, distributed in-memory cache (e.g., Redis, AWS DynamoDB with DAX) is deployed at an edge node of a network (e.g., the internet), geographically close to major retail centers. When the content integration server 120 generates an ETT, it writes the token's essential data (anon_user_id, reward_id, expiry) to the preauthorization cache. This write operation is extremely fast. When a POS system scans an ETT, its first action is to make a lightweight query against the nearest pre-authorization cache endpoint. If the token exists in the cache, it is considered “pre-authorized,” and the POS system can immediately apply the discount, providing a sub-second user experience. The POS system thenmakes a secondary, asynchronous call to the main computer-implemented system API to finalize the redemption, which removes the token from the cache and database. This two-step process can ensure that the user-facing interaction is never delayed by core server load or network latency.

[0060] Referring back to FIG. 3, the Al avatar module 360 provides a custom Al avatar to a user that is trained to mimic the user’s behavior in the virtual environment based on the user’s GamerDNA profile and activity within the virtual environment. In one embodiment, the user’s Al avatar is trained to play games mimicking the user’s playstyle and skillset / level. The Al avatar module 360 uses gameplay footage of the user playing one or more games to train one or more Al models (e.g., a behavior model) of the Al avatar to make gameplay decisions similar to those that were made by the user in the gameplay footage when faced with similar situations.Reinforcement learning techniques may be used to expand the scope of the Al avatar to enable the Al avatar to demonstrate adaptive gameplay strategies while remaining consistent with the play-style of the user whose gameplay it is mimicking. Transfer learning may be used to expand the scope of the Al avatar to mimic the user’s likely behavior in different situations in other games, even ones that the player it is mimicking has not previously played. The user may make the Al avatar available to others to play games with (e.g., if the user is unavailable to participate in a weekly gaming session with a group of friends, the user may provide the Al avatar to take their place).

[0061] The environment creation module 370 provides Al routines to a user to create a custom virtual environment. In one embodiment, the environment creation module 370 provides a natural language interface with which a user can create a custom game. The environment creation module 370 uses a NLP model to interpret the natural language input from the user and define a set of desired parameters and game requirements for a new virtual environment. The NLP model used by the environment creation module 370 may be trained on a corpus of game design documents to convert natural language requests into actionable game requirements.

[0062] The environment creation module 370 applies procedural content generation techniques to create a custom, Al-generated game that meets the game requirements identified by the NLP model from the user input. The game can include a procedurally generated virtual environment based on the set of game requirements. The virtual environment may be specifically tailored to the user’s GamerDNA (e.g., by tailoring the virtual environment for the requesting user’s specific playstyle, preferences, and skill level). In some embodiments, generating the newvirtual environment comprises orchestrating a plurality of specialized Al agents, such as a programming agent (generates executable game code), a graphics agent (generates graphical assets), a narrative agent (generates a narrative structure and dialogue), and a quality assurance (QA) agent (identifies issues). The game can include play-and-eam mechanics that enable the user to earn credits toward obtaining custom content (e.g., as provided by the personalized content module 340) by playing the custom game.

[0063] The analytics module 380 provides a portal or other user interface via which content creators and game developers can access analytics regarding how users are interacting with content. In one embodiment, a content creator can access detailed reports on player demographics, preferences, engagement metrics, and performance of content provided into the ecosystem by the content creator. This information may enable content creators to improve the content they provide and improve relationships with users.

[0064] The cloud datastore 390 is one or more non-transitory computer-readable media that store data used by the other components of the content integration server 120. For example, the cloud datastore 390 can store GamerDNA profiles for users along with definitions of custom content along with one or more requirements for a user to gain access to that custom content (e.g., gameplay or demographic requirements). It may also store raw ingested data, processed analytical data, Al models, and generated content.EXAMPLE FUNCTIONALITY

[0065] The following sections describe various detailed architectures for functionalities of the content integration system, as depicted in FIGS. 5-10.

[0066] FIG. 5 illustrates an example architecture for a GamerDNA and Al avatar system, according to one embodiment. The GamerDNA and Al avatar system analyzes a player’s gaming history and behaviors across one or more platforms to create a comprehensive GamerDNA profile. This profile is then used to generate Al-powered avatars that mimic the user’s behavior in games, creating a unique and personalized gaming experience. The GamerDNA and Al avatar system may use blockchain technology for decentralized identity management and homomorphic encryption to provide privacy-preserving calculations.[00671 The GamerDNA and Al avatar system uses APIs to extract data (e.g., raw gaming data) from various platforms, such as Steam®, Xbox®, and Playstation®. In one embodiment,The GamerDNA and Al avatar system can use real-time data streaming and batch processing to provide rapid but scalable updating of GamerDNA profiles. In one embodiment, the GamerDNA and Al avatar system uses the WebSocket protocol for live data streaming, OAuth 2.0 for secure platform authentication, and an event-driven architecture with Apache Kafka for scalable data ingestion.

[0068] The ingested data, including gameplay statistics, in-game actions, and gameplay footage, can be normalized by a data standardization component and temporally aligned for cross-platform correlation. The normalized data feeds into a feature extraction component which, uses one or more Al models (e.g., deep neural networks, t-SNE) to identify key profile features. These features can be used by a behavioral pattern recognition module and a playstyle clustering module to quantify the user's unique playstyle, skill set, and behavioral patterns, forming the comprehensive GamerDNA profile. Natural Language Processing (NLP) techniques may be used to analyze in-game communication patterns.

[0069] The GamerDNA and Al avatar system can use various techniques for efficient analysis of historical data. In various embodiments, the GamerDNA and Al avatar system uses incremental data fetching to minimize API load and a data lake architecture (e.g., Apache Hadoop) for storing large volumes of historical data. The GamerDNA and Al avatar system can use parallel processing with, for example, Apache Spark, for efficient data analysis.

[0070] Federated learning may be used for updating GamerDNA profiles to preserve privacy. Specifically, a secure aggregation protocol may be used for collaborative learning without raw data sharing and differential privacy techniques may be used to add sufficient noise to individual updates to preserve privacy. Further privacy-preserving mechanisms can be integrated into the GamerDNA and Al avatar system, including secure aggregation and differential privacy, especially when federated learning models are used to update GamerDNA profiles without ingesting raw personal data from client devices 140. Homomorphic Encryption may also be used for secure calculations on encrypted data.

[0071] In some embodiment, a GamerDNA profile can be used to train Al models to generate a personalized Al avatar. Reinforcement learning may be used to enable the Al avatar to demonstrate adaptive gameplay strategies while remaining consistent with the play style of the player whose gameplay it is mimicking. Additionally or alternatively, a Generative Adversarial Network (GAN) is used to generate realistic, player-like actions and behavior mimicking.Proximal Policy Optimization (PPO) may be used to train adaptable Al agents and Inverse Reinforcement Learning (IRL) may be used to infer a player's reward function from observed behavior. Transfer learning may be used to enable an Al avatar to apply the learned behaviors and playstyles for the user to new games that are not present in the ingested data. Contextual bandits may be used for exploration-exploitation balancing in new environments.

[0072] A Blockchain Identity component can be used to manage verifiable credentials for users, providing a secure foundation for identity correlation. Secure storage holds sensitive profile data. The GamerDNA and Al avatar system also includes modules for performance analytics and cross-game predictions, driven by the GamerDNA. In some embodiments, the GamerDNA and Al avatar system has a microservices architecture with containerized microservices. A service mesh may be used for inter-service communication and traffic management. An API gateway may be used to provide unified access to the micorservices.

[0073] The GamerDNA profiles generated can drive a range of functionality. In one example, a gaming system can automatically analyze a player’s GamerDNA profile and dynamically adjust game settings (e.g., difficulty settings) to provide an optimal challenge for each player. This may be a game wide difficulty setting or more focused to specific aspects of a game based on the specific player’s skill set. As another example, GamerDNA profiles may be used for cross-game skill transfer by providing tailored tutorials or suggestions when a player starts a new game in a similar genre to ones they have played before.

[0074] A player’s Al avatar can also drive a range of functionality. For example, a player’s Al avatar can act as an intelligent NPC in cooperative game modes, mimicking the playstyle of the original player and providing a unique gaming experience for other players. As another example, professional gaming teams can use the system to create Al-powered opponents that mimic specific professional players, allowing for targeted practice and strategy development.

[0075] FIG. 6 illustrates one embodiment of the architecture of a gamer vision system, which can be part of the analysis module 330 for personalized coaching. The gamer vision system uses computer vision, machine learning, and real-time data processing techniques to analyze gameplay footage and provides personalized coaching to improve player performance. The gamer vision system can work with both real-time gameplay streams and uploaded video clips, offering adaptive feedback and strategies tailored to individual player skills and habits.

[0076] The gamer vision system may receive gameplay video from an application server 110 or client device 140. The gamer visions system can use real-time video capture and streaming capabilities (e.g., low-latency video streaming with WebRTC) and frame extraction to capture gameplay footage. Temporal downsampling or format conversions may be applied for efficient processing. Multiple video streams may be analyzed in parallel for multi-player analysis and to provide additional context for each players’ gameplay.

[0077] In one embodiment, the gamer vision system uses a computer vision analysis engine to analyze gameplay footage. The computer vision analysis engine may apply one or more trained ML models to gameplay video to detect and track objects for game elements. Optical flow analysis may be used to identify movement patterns. Deep learning can be used to aid in scene understanding and optical character recognition (OCR) can be used to extract in-game text (e.g., chat windows, subtitles, or signs displayed in the game). Where it is made available by the application server 110, the gamer vision system may use real-time memory reading for direct access to game state information.

[0078] The gamer vision system may also use Al to recognize and classify player actions. 3D convolutional neural networks can be used for spatio-temporal action recognition and Long Short-Term Memory (LSTM) networks may be used for sequential action analysis. Transfer learning may be used to tailor pre-trained action recognition models to specific games and contexts.

[0079] The gamer vision system may also include a gameplay context interpreter that recognizes and tracks game state. The gameplay context interpreter may use one or more Al models to infer the player’s intention with various actions and assess the strategic situation of the current game state. For example, Monte Carlo tree search may be used for game state evaluation and Graph Neural Networks (GNNs) may be used for analyzing complex game state relationships. Reinforcement learning may be used for adaptive strategy formulation.

[0080] The gamer vision system may analyze gameplay videos to extract one or more performance metrics for the player. Strategic Assessment then identifies areas for performance improvement. This can include calculating a key performance indicator (KPI) for the player’s overall performance, performing a comparison of the player’s performance with benchmark data, and providing trend analysis and tracking to determine how the player’s performance is evolving over time. Based on this analysis, the gamer vision system can use an Al model to generatepersonalized coaching, such as providing personalized strategy recommendations, adaptively modifying difficulty settings to better align with the player’s skillset, or providing real time feedback (e.g., identifying a mistake the player is repeating). Coaching and other feedback can be provided in various ways, including via an augmented reality (AR) overlay for real time feedback, an interactive replay with overlaid analysis, or a voice interface.

[0081] The gamer vision system may be implemented using containerized microservices. A service mesh may be used for inter-service communication and traffic management and an API gateway may be used for unified access to microservices. Apache Kafka may be used for high- throughput, low-latency data streaming, Apache Flink may be implemented for complex event processing and windowing operations. A time-series database may be used for efficient storage and querying of performance metrics.

[0082] Using some or all of these techniques enables the gamer vision system to provide real time player performance optimization. The gamer vision system can analyze the player's actions during live gameplay and provide immediate feedback through AR overlays to optimize decisionmaking and reflexes. The gamer vision system may also receive gameplay footage that is uploaded after the fact and perform a detailed analysis to provide players with personalized training regimens and strategy recommendations based on their performance. Similarly, E-Sports teams can use the gamer vision system to analyze team performances, identify areas for improvement, and develop tailored strategies for upcoming matches. Game developers can also leverage the gamer visions system’s insights to identify balance issues, optimize difficulty curves, and improve overall game design based on large-scale player behavior analysis.

[0083] FIG. 7 illustrates one embodiment of an architecture for a POS integration system. The POS integration system seamlessly connects point-of-sale (POS) systems with gaming platforms. This system enables real-time, bi-directional data flow between retail environments and gaming ecosystems, creating a novel approach to reward distribution and user engagement. As described previously, APIs can be used to integrate with gaming platforms and provide realtime extraction of data and event processing.

[0084] In one embodiment, the POS integration system provides a standardized API for connecting to diverse POS systems. This API uses a secure data exchange protocol. For example, the POS integration system may use end-to-end encryption and multi-factor authentication. Transaction data can also be extracted out of the POS systems and fed back into the POSintegration system for use in the gaming ecosystem (e.g., players may be rewarded with custom content or rewards in game for making purchases via the POS system of an entity partnering with the game). This data extraction can be implemented via a lightweight SDK that uses change data capture (CDC) techniques for real-time transaction monitoring. The POS integration system may use batch processing for efficient historical data synchronization.

[0085] The POS integration system uses an Al-driven algorithm for correlating user identities across platforms. Secure identity management may be provided by using a verifiable credential or other identifying information stored on a blockchain. A federated learning approach may be used for the Al-driven identification algorithm for privacy-preserving identity correlation. Efficient identity matching may be provided by using probabilistic data structures (e.g., Bloom filters). NLP processing may be used for fuzzy matching of user information to further validate user identity in a privacy-preserving manner.

[0086] The POS integration system may use a real-time transaction processing engine with a high-performance streaming architecture that uses a distributed ledger to confirm transaction integrity. As described previously, machine-learning models may be used to provide personalized recommendations for content based on gameplay data, transaction data, or both. These recommendations can be updated in real time based on user behavior and transaction history. A multi-armed bandit algorithm may be used for dynamic content recommendation optimization. Collaborative filtering techniques may be used to further personalize content suggestions and reinforcement learning may be used for continued improvement in content recommendation strategies. In some embodiments, smart contracts on a blockchain are used for automated distribution of personalized content and validation of user identity and ownership of personalized content.

[0087] Using some or all of these techniques, the POS integration system can provide personalized content in both web-based and physical stores in real time. Furthermore, the POS integration system can detect when a player makes a purchase at a physical store and immediately credit their gaming account with relevant in-game items or currency, creating a seamless bridge between real-world transactions and virtual gaming experiences. By integrating POS data with gaming profiles, the POS integration system can create comprehensive loyalty programs that span both digital and physical realms, offering unique rewards based on a user's holistic engagement across multiple platforms. In addition, gaming platforms can dynamically adjustgame difficulty, storylines, or available items to match the player's real-world preferences and behaviors. Finally, the POS integration system's ability to correlate real-world transactions with in-game activities allows for advanced fraud detection, identifying suspicious patterns that may indicate cheating or unauthorized transactions.

[0088] FIG. 8 illustrates one embodiment of an architecture for a gameplay-triggered userspecific content system. The user-specific content system integrates real-time gameplay analysis with dynamic reward distribution. The user-specific content system seamlessly connects in-game actions to tangible, real-world rewards and virtual content through advanced Al algorithms and robust data processing techniques.

[0089] As described previously, an API can be used for integrating with various systems and providing real-time extraction of gameplay data. The user-specific content system applies Al to interpret gameplay actions and applies pattern recognition to identify significant events in gameplay. The user-specific content system can use an event-driven architecture for data extraction to minimize the impact on gameplay. Deep learning models (e.g., LSTM networks) may be used for sequence-based action classification and transfer learning may be used to adapt to new games quickly. In one embodiment, game events are ingested from a game client and game engine via a game integration SDK. An action classifier interprets gameplay actions, and a data extraction module extracts relevant data. These classified actions and extracted data are passed to an event queue, which feeds into a pattern recognition module to identify significant events in gameplay. A context analyzer may be used to process the recognized patterns to understand the action context.

[0090] The user-specific content system applies a reward matching algorithm to dynamically select rewards based on the player’s GamerDNA profile and action context as extracted from the gameplay data. The GamerDNA profiles may be stored in a scalable database and may be supplemented over time using an Al-driven profiling system to dynamically update the profiles for further, up to date personalization of rewards. Machine learning may be used to optimize the selected rewards relevance to the player. In one embodiment, the reward matching algorithm is a multi-armed bandit algorithm that uses contextual information to improve reward relevance. The user-specific content system can use a secure delivery mechanism to make the selected reward available to the player in real time. For example, the user-specific content system can beintegrated into third-party systems using an API to make the reward immediately available via those third-party systems.

[0091] Using some or all of these approaches, the user-specific content system can detect when a player completes a challenging in-game achievement and immediately offer a relevant real-world reward, such as a discount on gaming peripherals or exclusive merchandise. In addition, the user-specific content system can track player engagement across multiple games, offering escalating rewards for consistent participation in the gaming ecosystem. The userspecific content system can also enable content providers to target players who demonstrate high skill levels in specific game genres, offering promotions for products that align with their interests and abilities. Furthermore, content providers can create time-limited challenges within games, with the user-specific content system automatically distributing rewards to players who successfully complete them.

[0092] Reward delivery may be managed through a notification service and a brand partner interface, integrating with third-party systems via an API to make the reward immediately available. A promotion manager can handle promotional aspects. Model updates for the Al models, including the Al model trainer, can be influenced by analytics data from an analytics dashboard and a data lake. A security module can ensure the integrity and privacy of all transactions and user data. The GamerDNA profiles can stored in a scalable database and dynamically updated using an Al-driven profiling system for continuous personalization.

[0093] FIG. 9 illustrates one embodiment of an architecture for an Al-driven game creation system. The Al-driven game creation system leverages natural language processing, machine learning, and procedural content generation techniques to create custom, Al-generated video games based on user inputs. The generated games may incorporate a play-and-eam monetization model, allowing creators to earn revenue from their generated games within the ecosystem.

[0094] The Al-driven game creation system includes an NLP model that interprets natural language input from the user for generating a game. In one embodiment, the NLP model is a transformer-based language model that is fine-tuned on a corpus of game design documents and player discussions. This allows for nuanced understanding of game concepts and mechanics described in natural language and provides context-aware intent recognition to identify game design choices. A semantic parser extracts game elements, mechanics, and rules from the userinput. The semantic parser may use a combination of dependency parsing and game-specific ontologies.

[0095] A game design Al includes one or more ML models for translating concepts extracted from the user input into game design documents. In one embodiment, the ML models include a sequence-to-sequence model trained on a large corpus of game design documents to generate structured game designs from natural language concepts. A GAN can be used to create game assets. For example, a StyleGAN2-ADA model Can be employed to generate high-quality, diverse game assets based on textual descriptions and style inputs. Reinforcement learning can be used for game balancing and difficulty scaling.

[0096] A procedural content generation engine can be used for algorithmic generation of game levels, quests, or narratives, etc., depending on the specific needs of the game. In one embodiment, a wave function collapse algorithm is implemented for generating diverse and coherent game levels based on predefined rules and constraints. This enables physics-based world building and terrain generation. Similar algorithmic techniques can be applied to generate dynamic NPC behavior and dialogue.

[0097] A game engine integration layer provides an API for connecting Al-generated content to game engine primitives. The game engine integration layer enables real-time rendering and optimization for various platforms and provides cross-platform compatibility management. In one embodiment, the game engine integration layer uses a custom XML-based language to describe game elements, mechanics, and rules in a platform-agnostic manner. An adapter layer translates the universal game description into specific game engine constructs and API calls.

[0098] To enable play-and-eam functionality, the Al-driven game creation system can implement a blockchain-based token system for in-game rewards. Smart contracts can be used for automated revenue distribution. The Al-driven game creation may also provide real-time analytics to game designers for gameplay and revenue tracking. Player engagement can be encouraged by providing collaborative filtering for game recommendations. An A / B testing framework may be deployed to test game design iterations. The Al-driven game creation system can also enable players to provide feedback and provide game developers with insights generated using sentiment analysis of the player feedback to enable continuous improvement.

[0099] Using some or all of these techniques, the Al-driven game creation system can enable rapid game prototyping. Game designers can use the Al-driven game creation system to quickly ingenerate and iterate on game concepts, reducing the time and cost of early-stage game development. The Al-driven game creation system can also provide personalized gaming experiences by enabling players to describe their ideal game in natural language and have the AI- driven game creation system generate a custom game tailored to their preferences. Furthermore, teachers and educational institutions can easily create custom learning games by describing educational objectives and desired gameplay mechanics and small game development teams can leverage the system to generate game assets, levels, and even entire game loops, allowing them to focus on unique creative elements.

[0100] FIG. 10 illustrates one embodiment of an Al-swarm game creation system. The AI- swarm game creation system enables community-driven video game creation using Al swarm agents. Traditional video game development is often limited to professional studios with significant resources, resulting in a lack of diversity in game concepts and mechanics. Existing platforms do not typically allow the general public to collaboratively develop video games without advanced technical skills. Furthermore, large gaming companies are not incentivized to provide tools that enable community-driven game creation, as they prefer to control the development process and profit margins. This limitation prevents individuals with innovative ideas but without programming expertise from contributing to game development, thereby hindering the potential for unique and diverse gaming experiences.

[0101] In various embodiments, the Al-swarm game creation system provides a platform that enables users to collaboratively develop video games using Al swarm agents. Users can submit ideas, images, stories, and game mechanics into a community chat or forum. The Al swarm agents, each specialized in different aspects of game development such as programming, graphic design, and quality assurance, process these inputs to generate video game assets and code. Thus, the Al-swarm game creation system allows users without advanced technical skills to participate in game creation, democratizing the development process.

[0100] In some embodiments, the platform uses Al models trained on existing video game data to understand various genres and mechanics. The Al swarm agents work together to build different components of the game, such as characters, environments, and gameplay mechanics. The generated assets can be further refined by the community, creating an iterative development cycle. Once the game is completed, the contributors can share in the profits generated from its sales, incentivizing participation and promotion.

[0101] This approach leverages Al technologies, such as natural language processing and generative models, to translate user-submitted content into executable game code and assets. By utilizing Al swarm agents in a coordinated manner, the system can efficiently handle complex game development tasks that would otherwise require significant human resources. The platform fosters a collaborative environment where creativity is harnessed and transformed into tangible gaming experiences.

[0102] In one embodiment, the Al-swarm game development system provides a web-based platform for idea submission and community interaction that includes visual tools for viewing game progress and assets. The platform also provides collaborative editing tools for refining AI- generated content. An Al-swarm coordinator orchestrates the activities of the specialized Al agents and manages task allocation and prioritization between the agents. This ensures coherent integration of various game elements. Each agent may use Al algorithms to perform its development tasks. Data extraction methods and APIs may be used to extract and insert data into the platform and game and to enable integration with third party systems, such as gaming platforms and servers.

[0103] A game integration layer may be used to translate Al-generated assets and code into engine-specific formats. The game integration layer may support generating code for one or more game engines. A version control system may track and manage iterations and variations of the game elements to facilitate collaborative development. A community feedback loop may collect and analyze feedback on generated content from users and provide information (e.g., an automatically generated summary of) user feedback to one or more of the Al agents as part of an improvement cycle.

[0104] In various embodiments, an NLP model (e.g., GPT-4 or similar) is used to interpret user-submitted ideas and instructions. Named entity recognition can be used to identify key game elements, such as characters, settings, and core mechanics, etc. Sentiment analysis can be applied to feedback to generate an overall sense of the community reception of generated content.

[0105] Image analysis algorithms such as computer vision algorithms may be used to interpret user-submitted concept art or reference images. These algorithms can extract information from submitted images such as color palettes, art styles, and visual themes to inform the graphics agent on the generation of visual assets to include in a game. Similarly, users may submit reference audio files that are analyzed using signal processing techniques to extractinformation such as tempo, pitch, and timbre to guide the sound agent in the generation of audio assets for the game.

[0106] The specialized Al agents can include a programming agent, a graphics agent, a narrative agent, a sound agent, a quality assurance agent, or any other specialized agent trained to perform a specific game development task. All of the specialized agents perform game development tasks in response to requests (e.g., natural language requests) from users that identify ideas for the game being developed.[01071 The programming agent generates and optimizes executable game code. The programming agent may use transformer-based models that are fine-tuned on game code repositories. This enables code generation with context-aware syntax understanding. The programming agent may also incorporate genetic algorithms for code optimization and performance tuning.

[0108] The graphics agent creates visual assets and designs. In one embodiment, the graphics agent employs Generative Adversarial Networks (GANs) for creating textures and 3D models. The graphics agent may use style transfer algorithms to apply consistent visual themes across the assets created for a game. Reinforcement learning may be used to iteratively improve the graphics agent based on community feedback on generated assets.

[0109] The narrative agent develops storyline and dialogues. In one embodiment, the narrative agent uses large language models fine-tuned on video game scripts and storylines to generate storylines and dialogue. Narrative generation algorithms can be used to create branching storylines and dynamic plot adaptation. Sentiment analysis can be used to evaluate narrative consistency of generated storylines and dialogue with the game's overall tone and provide feedback for iterative improvement of the narrative agent’s output.

[0110] The sound agent produces audio effects and music. In one embodiment, the sound agent uses deep learning models for procedural audio generation. The sound agent can use spectral analysis and synthesis techniques for creating diverse sound effects. Adaptive music generation algorithms may be used so that the generated music is responsive to gameplay events.

[0111] The quality assurance agent identifies and reports bugs or inconsistencies in the outputs from the other agents. In one embodiment, the quality assurance agent uses reinforcement learning for automated gameplay testing. The quality assurance agent can use detectionalgorithms to identify visual glitches or performance issues. Furthermore, natural language processing may be used to analyze and categorize bug reports from community testers.

[0112] Using some or all of these techniques, the Al-swarm game development system can provide rapid prototyping of game ideas from high-level concepts and enables fast iteration and testing of various game mechanics. These agents collaboratively process user inputs to generate game components. Their outputs (code, assets, story / dialogue, sounds, issues / bugs) are integrated via the engine integration layer with the version control system tracking iterations and variations. The generated content culminates in a playable game build. Community feedback can then be collected and analyzed, providing insights that iterate back to the Al swarm coordinator for continuous improvement of the generated game components.

[0113] This system democratizes game development, allowing non-technical users to contribute to complex game creation. For example, the Al-swarm game development system may be used by educators to create custom learning games without extensive programming knowledge and rapidly adapt existing educational content into interactive game formats. The Al-swarm game development system may also be used by indie game developers to enable individual creators to bring their game ideas to life with limited resources and access to high-quality assets and code typically beyond the reach of small teams, but may also be used by large game studios to assist in the generation of secondary content (side quests, NPCs, environments, etc.) as well as streamline the creation of localized versions of games for different markets. Furthermore, the Al-swarm game development system may be used in events such as game jams to support rapid game development during time-limited game creation events and provide on-demand asset generation and code snippets to accelerate development.COMPUTING SYSTEM ARCHITECTURE

[0114] FIG. 11 is a block diagram of an example computer 1100 suitable for use as an application server 110, a content integration server 120, a third-party server 130, or a client device 140. The example computer 1100 includes at least one processor 1102 coupled to a chipset 1104. Generally, any reference to a processor should be understood to refer to one or more processors working individually or collaboratively to provide the corresponding functionality. The chipset 1104 includes a memory controller hub 1120 and an input / output (I / O) controller hub 1122. A memory 1106 and a graphics adapter 1112 are coupled to the memory controller hub 1120, and a display 1118 is coupled to the graphics adapter 1112. A storage device1108, keyboard 1110, pointing device 1114, and network adapter 1116 are coupled to the I / O controller hub 1122. Other embodiments of the computer 1100 have different architectures.

[0115] In the embodiment shown in FIG. 11, the storage device 1108 is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD- ROM), DVD, or a solid-state memory device. The memory 1106 holds instructions and data used by the processor 1102. The pointing device 1114 is a mouse, track ball, touchscreen, or other type of pointing device, and may be used in combination with the keyboard 1110 (which may be an on-screen keyboard) to input data into the computer system 1100. The graphics adapter 1112 displays images and other information on the display 1118. The network adapter 1116 couples the computer system 1100 to one or more computer networks, such as network 170.

[0116] The types of computers used by the entities of FIGS. 1 through 4 can vary depending upon the embodiment and the processing power required by the entity. For example, the integration server 120 might include multiple blade servers working together to provide the functionality described. Furthermore, the computers can lack some of the components described above, such as keyboards 1110, graphics adapters 1112, and displays 1118.ADDITIONAL CONSIDERATIONS[01171 Some portions of above description describe the embodiments in terms of algorithmic processes or operations. These algorithmic descriptions and representations are commonly used by those skilled in the computing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs comprising instructions for execution by a processor or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of functional operations as modules, without loss of generality.

[0118] Any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Similarly, use of “a” or “an” preceding an element or component is done merely for convenience.This description should be understood to mean that one or more of the elements or components are present unless it is obvious that it is meant otherwise.

[0119] Where values are described as “approximate” or “substantially” (or their derivatives), such values should be construed as accurate + / - 10% unless another meaning is apparent from the context. For example, “approximately ten” should be understood to mean “in a range from nine to eleven.”

[0120] The terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0121] Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the described subject matter is not limited to the precise construction and components disclosed. The scope of protection should be limited only by the following claims.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method for providing personalized content, the method comprising: ingesting activity data for a user, the activity data describing interactions of the user with a plurality of distinct virtual environments; building a profile of the user using the activity data, the profile including attributes of the user that are generalized across the plurality of distinct virtual environments; identifying a personalized content item for the user based on the profile; and causing provision of the personalized content to the user.

2. The computer-implemented method of claim 1, wherein the profile is the user’s GamerDNA that quantifies the user’s playstyle, skill set, and behavioral patterns across the plurality of distinct virtual environments.

3. The computer-implemented method of claim 1 or 2, wherein the personalized content item is a customized avatar, skin, or virtual item usable in one or more of the distinct virtual environments.

4. The computer-implemented method of claim 1 or 2, wherein the personalized content item is a tangible reward, and causing provision of the personalized content item to the user comprises interfacing with a point of sale system of a merchant to provide access to or a discount on the tangible reward for the user.

5. The computer-implemented method of claim 4, wherein interfacing with the point of sale system comprises using a privacy-preserving verifiable credential to confirm, via a blockchain, that the user is entitled to the tangible reward.

6. The computer-implemented method of claim 4, wherein interfacing with the point of sale system comprises using a mapping between an identifier of the user with the merchant and an identifier of the user in the profile to confirm that the user is entitled to the tangible reward.

7. The computer-implemented method of claim 1 or 2, further comprising verifying that the user is entitled to the personalized content by: applying a hybrid proof of play protocol to verify that the user completed a required task in one of the plurality of distinct virtual environments; generating, based on verifying that the user completed the required task, an ephemeral transaction token that includes a hashed or pseudonymous identifier for the user; and verifying the entitlement of the user to the personalized content using the hashed or pseudonymous identifier.

8. The computer-implemented method of claim 7, wherein the ephemeral transaction token is a cryptographically signed data object comprising: an identifier for the personalized content; the hashed or pseudonymous identifier for the user; a timestamp after which the ephemeral transaction token is invalid; and a cryptographic signature.

9. The computer-implemented method of claim 7, wherein causing provision of the personalized content to the user comprises: writing details of the ephemeral transaction token to a cache at an edge node of a network; in response to a lightweight query from a point of sale system, pre-authorizing provision of the personalized content using the details in the cache in a sub-second timeframe; in response to an asynchronous query from the point of sale system, finalizing redemption of entitlement to the personalized content and removing the details from the cache.

10. The computer-implemented method of claim 1 or 2, further comprising: training, using the profile, an Al avatar to mimic interactions of the user with the plurality of distinct virtual environments; and deploying the Al avatar to interact with at least one of the distinct virtual environments.

11. The computer-implemented method of claim 1 or 2, further comprising:analyzing footage of the user interacting with one of the distinct virtual environments to identify a recommended change in how the user interacts with the distinct virtual environment; and causing display of information, overlaid on a display of the distinct virtual environment, indicating the recommended change.

12. The computer-implemented method of claim 11 wherein the information is overlaid on the display of the distinct virtual environment while the user is interacting with the distinct virtual environment.

13. The computer-implemented method of claim 1 or 2, further comprising: receiving a request from the user to generate a new virtual environment, the request including a natural language prompt; providing data derived from the natural language prompt and the profile an input to one or more generative artificial intelligence models; receiving, as output from the one or more generative artificial intelligence models, content for the new virtual environment that is personalized to the user based on the profile; and providing the new virtual environment in response to the natural language prompt.

14. The computer-implemented method of claim 13, wherein the one or more generative artificial intelligence models comprise at least one of: a programming agent, a graphics agent, a narrative agent, or a quality assurance agent.

15. The computer-implemented method of claim 1 or 2, further comprising receiving additional data derived from one or more of: gameplay footage, sensor data captured during gameplay, or in-game communications, wherein identifying the personalized content item is further based on the additional data.

16. A computer-implemented method of verifying that the user is entitled to personalized content, the method comprising: applying a hybrid proof of play protocol to verify that the user completed a required task in one of a plurality of distinct virtual environments;generating, based on verifying that the user completed the required task, an ephemeral transaction token that includes a hashed or pseudonymous identifier for the user; and verifying the entitlement of the user to the personalized content using the hashed or pseudonymous identifier.

17. The computer-implemented method of claim 16, wherein the ephemeral transaction token is a cryptographically signed data object comprising: an identifier for the personalized content; the hashed or pseudonymous identifier for the user; a timestamp after which the ephemeral transaction token is invalid; and a cryptographic signature.

18. The computer-implemented method of claim 16, wherein causing provision of the personalized content to the user comprises: writing details of the ephemeral transaction token to a cache at an edge node of a network; in response to a lightweight query from a point of sale system, pre-authorizing provision of the personalized content using the details in the cache in a sub-second timeframe; in response to an asynchronous query from the point of sale system, finalizing redemption of entitlement to the personalized content and removing the details from the cache.

19. One or more non-transitory computer-readable media comprising stored instructions that, when executed, cause a computing system to perform the computer- implemented method of any preceding claim.

20. A computing system comprising: one or more processors; and one or more non-transitory computer-readable media comprising stored instructions that, when executed, cause the computing system to perform the computer- implemented method of any one of claims 1-18.

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