Granted Patent B2

US 12,615,401 · App. 18/980,042 · Granted Apr 28, 2026

Inventor: Michael James Fiorentino (New York, NY)

H04N21/236H04N21/845

Abstract

Claims (202)

1 . A computer-implemented system for provisioning media elements, comprising:

a data mix configured to store, associate, or modify a plurality of media elements, wherein each media element includes metadata that may define default or alternative play configurations;

a decisioning engine that determines media selection and playback based on at least one of:

AI-driven or algorithmic decisioning;

stakeholder-defined inputs;

predefined rules;

programmatic logic; or

external system inputs; and

playback system that executes media elements, including original, modified, compiled versions, or aggregated versions according to the data mix;

wherein a stakeholder comprises at least one of:

an end-user, content producer, technology administrator, brand sponsor, licensing entity, compliance engine, regulatory authority, or autonomous agent.

2 . A computer-implemented system for media playback, comprising:

a dynamic multilayered media structure organized into channels, each channel comprising a plurality of independently modifiable layers,

wherein the plurality of independently modifiable layers comprise audiovisual elements and include metadata, metadata-based overlays, or supplementary content, and wherein said channels alternatively may be represented as tracks, feeds, playlists, or playback groupings;

a media control system enabling users to:

modify, swap, persist, enable, or disable media layers during channel transitions;

execute channel changes or track-level transitions that dynamically restructure the arrangement, sequence, or visibility of media layers based on system rules, stakeholder-defined inputs, programmatic logic, or algorithmic decisioning; and

distribute media layers across single devices, multi-device environments, or distributed computing systems; and

a decisioning engine that determines playback behavior based on at least one of:

AI-driven, programmatic, or algorithmic decisioning;

stakeholder-defined inputs;

predefined system rules; or

external inputs including engagement data, contextual metadata, or environmental conditions.

3 . The computer-implemented system according to claim 2 comprising a computer-implemented application for interacting with a plurality of media layers coordinated by a data mix, comprising:

a user interface configured to enable customization of playback settings, including at least one of:

enabling or disabling specific media layers;

transitioning between media layers based on system rules;

modifying playback configurations; or

selecting playback modes; and

a metadata-driven framework that dynamically adjusts media elements in response to at least one of:

AI-driven, programmatic, or algorithmic decisioning;

real-time system conditions;

stakeholder-defined preferences; or

contextual metadata inputs or external environmental parameters.

4 . A computer-implemented method for provisioning a plurality of media elements, the method comprising:

receiving at least two of:

stakeholder inputs;

AI-driven, programmatic, or algorithmic decisioning;

external system data;

pre-configured playback settings;

engagement analytics;

contextual metadata; or

hardware or sensor triggers;

structuring media elements into dynamically modifiable layers based on a metadata-governed framework;

executing playback based on a data mix, wherein playback includes at least one of:

maintaining an existing configuration,

reconfiguring media associations,

adjusting playback sequencing, or

substituting media elements dynamically; and

distributing and synchronizing the media elements across at least one of:

a single computing device;

multiple interconnected devices; or

a distributed computing environment;

wherein the plurality of media elements may be dynamically merged, transformed, or restructured into a composite layer or maintained as separate layers during playback.

5 . The computer-implemented system of claim 1 wherein the media elements are structured into media layers, compiled into dynamic multilayered media structures, and further organized into channels and dynamic channel lineups based on at least one of:

executing real-time commands, adjusting playback settings, or responding to observed or declared stakeholder preferences issued by at least one of: an AI content producer, technology administrator, decisioning engine, compliance engine, human content producer, brand sponsor, or end-user;

enforcing provisioning directives based on payments, subscriptions, financial transactions, contracted agreements, or licensing terms defined by stakeholders including brands, content producers, or governance entities;

applying automated curation and structuring logic, including AI-driven, programmatic, or algorithmic methods, weighted governance models, or metadata-based frameworks to dynamically arrange media layers;

executing content provisioning using machine learning, rule-based systems, or adaptive refinement techniques to optimize media configurations; and

integrating external systems, including third-party licensing platforms, compliance verification services, or programmatic content distribution networks to manage structured media elements.

6 . The computer-implemented system of claim 1 , wherein the provisioning of media elements includes dynamically modifying media elements during playback based on marketplace-influenced parameters, including at least one of:

dynamic content curation of stakeholder preferences, brand-sponsored content, or financial models;

resource exchange mechanisms including direct payments, subscriptions, sponsored content, or tokenized licensing transactions;

programmatic, algorithmic, or AI-driven decisioning for media selection based on weighted governance models;

payment structures involving content producers, original media element creators, and licensing entities;

brand sponsorship and custom messages integrated within specific layers or across the dynamic multilayered media structure; or

blockchain-based or API-driven monetization frameworks for managing licensing, transactions, and resource exchanges; and

measurement of original, modified, or fractionalized media elements, including compilations or pluralities of media segments, based on past performance, predictive interaction data, or stakeholder engagement signals.

7 . The computer-implemented system of claim 1 , wherein the playback system is executed by selecting, synchronizing, or adapting media elements either dynamically in real time or based on pre-defined scheduling, configuration settings, or programmatic logic, across at least one of:

a single device;

multiple synchronized devices;

a 3D immersive environment; or

a distributed computing environment,

wherein media elements are dynamically merged, transformed, restructured into a composite layer, split into temporally aligned tracks, distributed as discrete components across multiple synchronized devices, or maintained as separate layers during playback across said environments.

8 . The computer-implemented system of claim 1 , wherein the data mix maintains real-time state tracking for media elements and dynamically modifies playback configurations based on at least one of:

playback instructions;

media association mappings;

attribution data;

interaction histories;

user-intention signals comprising at least one of: button presses, voice commands, movement-based gestures, biometric responses, brain-computer interface inputs, typed expressions, system-observed behaviors, inferred goals, emotional indicators, or real-time control interactions;

context-sensitive metadata, including at least one of: geographic identifiers, demographic parameters, licensing metadata, intellectual property attributes, or engagement analytics;

system-driven playback modifications; or

adaptive content structuring.

9 . The computer-implemented system of claim 1 , wherein a 3D immersive environment comprises at least one of:

virtual reality (VR) headsets or environments;

augmented reality (AR) displays or devices;

mixed reality (MR) interfaces;

spatial computing systems;

3D environments accessible through 2D screens,

multi-device configurations creating immersive spaces;

user-navigable virtual spaces where media is presented contextually;

environments where media layers adapt based on user movement or position; or

interconnected IoT devices arranged to create spatial media experiences.

10 . The computer-implemented method of claim 4 , wherein a distributed computing environment includes at least one of:

cloud-based systems;

edge computing networks;

blockchain-based distributed systems;

third-party API-integrated services;

decentralized storage networks;

compute-sharing networks configured for real-time content synchronization and ai-driven, programmatic, or algorithmic decisioning; or

Internet of Things (IoT)-connected devices.

11 . The computer-implemented system of claim 2 , wherein the media layers are distributed, modified, or interacted with playback devices, including at least one of:

a smart TV;

an Internet-of-Things (IoT) device;

a personal computer or laptop;

a smartphone;

a tablet;

a virtual reality (VR) headset, augmented reality (AR) device, or a 3D environment,

a multimedia gaming and entertainment console or set-top box;

a smartwatch;

an Over-The-Top (OTT) streaming device;

a smart display or embedded control surface;

a distributed computing node;

a cloud-connected media environment; or

any computing device capable of executing playback, interaction, or synchronization within a dynamic multilayered media structure.

12 . The computer-implemented application of claim 3 , wherein the data mix provides media layers and playback options using at least one of:

graphical user interfaces or screen-based visualizations for selecting, modifying, or arranging media layers;

timeline-based or spatial controls that depict media sequencing, synchronization, or playback adjustments;

overlay-based interaction elements, including layer selection menus, interactive previews, or metadata displays;

adaptive multimodal feedback mechanisms, including sensory feedback mechanisms, auditory signals, or visual responses based on user interactions, accessibility settings, or device-specific formats; or

gesture-based or motion-tracking inputs, or other interface-recognized user actions, translating interactions into real-time modifications of media attributes or playback settings.

13 . The computer-implemented system of claim 1 , wherein user-defined modifications are stored within

the data mix,

a user account,

an external preference database, or

a governance-linked configuration profile

for persistent playback customization across sessions.

14 . The computer-implemented system of claim 2 , further comprising a control interface configured to provide transparency into

AI-driven, programmatic, or algorithmic systems,

compliance visibility, and

user-adjustable content personalization,

wherein the control interface:

displays algorithmic weighting factors, content ranking parameters, compliance constraints, and monetization criteria influencing media selection and playback;

enables users to override, modify, or adjust recommendation models by altering preference weighting, filtering mechanisms, ranking logic, or governance rules in real time; and

provides structured access to governance interventions, compliance validation records, and decisioning logs based on role-based permissions, regional governance models, collective governance preferences, or community-defined standards.

15 . The computer-implemented method of claim 4 , comprising a collaborative media curation interface enabling at least one of:

multi-user contribution to media layer selection, organization, or remixing;

real-time input from stakeholders for content inclusion, exclusion, or weighting;

version control, iterative forking, and conflict resolution for concurrently edited media tracks;

attribution of individual edits, comments, or approvals within shared content compositions; or

layer-specific access permissions for contributors based on governance roles.

16 . The computer-implemented system of claim 2 , wherein the data mix provisions media elements from at least one of:

the platform content library containing original media elements;

locally saved files accessed directly from computing devices;

external media sources connected through API integrations;

digital physical media including optical discs, flash drives, memory cards, or external storage devices;

analog physical media including vinyl records, magnetic tapes, or other non-digital formats;

real-time inputs captured through audio, visual, or sensor devices, wherein captured content is recognized, matched, or identified and structured into the data mix for playback, remixing, or licensing validation;

media elements edited into condensed form by content producers;

original media elements from licensed companies or original media creators;

media elements generated by AI systems, autonomous agents, or automated remix engines;

media elements sourced through governed marketplaces, tokenized licensing platforms, or structured content exchanges;

valid sources online accessed through search and selection methods; or

user-aggregated content structured into the data mix.

17 . The computer-implemented system of claim 1 , wherein playback modifications are dynamically adjust based on at least one of:

real-time or pre-configured scheduling, configuration settings, or programmatic logic, including engagement analytics processed by the decisioning engine to modify media sequencing and layer configurations;

external system data influencing playback behavior through metadata-driven processing, including at least one of:

compliance updates;

licensing adjustments;

marketplace-based content structuring; or

contextual metadata tags;

regulatory compliance enforcement modifying playback based on at least one of:

copyright policies;

geographic content restrictions; or

content moderation rules;

AI-driven, programmatic, or algorithmic content curation optimizing playback sequences and personalizing media structures based on historical, contextual, or system-defined metadata;

monetization-based adjustments modifying media configurations based on:

dynamic brand integrations or custom sponsorship content;

revenue-sharing logic tied to stakeholder engagement or licensing frameworks;

microtransaction-based content access models; or

personalized promotions or sponsorship alignments using AI-driven, programmatic, or algorithmic decisioning systems;

network bandwidth and latency conditions influencing playback synchronization and quality;

device-specific performance factors, including processing power, available memory, or input/output constraints;

power efficiency considerations including battery level monitoring or adaptive playback modes;

media quality optimization routines, including adaptive bitrate control, dynamic volume leveling, brightness or transparency adaptation, or other playback fidelity mechanisms;

wherein these adjustments are implemented across at least one of:

a single computing device;

multiple interconnected devices; or

a distributed computing environment.

18 . The computer-implemented method of claim 4 , wherein the data mix governs media element organization and playback within a dynamic multilayered media structure by:

defining structural relationships between media layers through metadata associations or tag-based dependencies;

dynamically modifying layer configurations based on AI-driven, programmatic, or algorithmic decisioning, stakeholder inputs, or marketplace criteria;

tracking playback modifications, version history, and content variations; or

synchronizing playback across single devices, multi-device environments, or distributed computing networks.

19 . The system computer-implemented of claim 1 , wherein the data mix provisions media elements based on inputs, configurations, or provisioning actions from at least one of:

the AI Content Producer, Technology Administrator, Decisioning Engine, or Compliance Engine;

human content producers, brands, or user-driven curation processes;

AI-driven, programmatic, or algorithmic curation, weighted governance models, or metadata-based structuring protocols;

autonomous provisioning agents executing machine learning models, adaptive content refinement, or algorithmic decisioning logic; or

external system integrations, including third-party licensing platforms, compliance verification services, or programmatic content distribution networks.

20 . The computer-implemented system of claim 2 , wherein the media control system is further configured to provide a user interface for dynamic media layer management, enabling at least one of:

swapping, enabling, disabling, or modifying audiovisual and metadata overlays in real time or according to preconfigured scheduling, stakeholder-defined rules, or programmatic logic;

adjusting playback sequencing and transition logic within channels based on user interactions, system rules, or external inputs;

triggering interactive engagement actions that modify media presentation based on predefined engagement conditions or user responses;

wherein the system further comprises a metadata-driven playback engine configured to:

dynamically modify media layers based on at least one of:

user inputs;

system-defined governance models;

contextual metadata;

external decisioning engines; or

programmatic or algorithmic logic derived from stakeholder-defined parameters; and

structure playback behavior based on hierarchical rules, weighted decision models, or real-time event-driven triggers.

Continuity (5)

References Cited (20)