Multi-model ai conferencing system with centralized governance layer, task routing, and merged output generation

Abstract

A multi-model artificial intelligence conferencing system is disclosed, comprising a centralized governance layer configured to receive user input, classify tasks, select appropriate AI modules, and route the input through a task-routing engine. Multiple AI modules generate independent outputs that are processed by a response-merging engine including a multi-perspective comparator, conflict detector, and unified clarity output generator to produce a consolidated result. A hardware-neutral display layer renders outputs on robotic terminals, HUD projections, smartphones, tablets, laptops, or kiosk devices. A unified input module enables a single voice or file submission to be disseminated to selected AI modules, while a security layer enforces user authority verification, data routing authorization, and endpoint activation control. The system supports simultaneous multi-model conferencing, comparative reasoning, and governed task execution across text, audio, visual, and robotic modalities.

Description

[0001] TITLE

[0002] MULTI-MODEL Al CONFERENCING SYSTEM WITH CENTRALIZED

[0003] GOVERNANCE LAYER, TASK ROUTING, AND MERGED OUTPUT GENERATION

[0004] CROSS-REFERENCE TO RELATED APPLICATIONS

[0005] This application is related to and builds upon the governance, workflow, and multi-surface Al control architectures disclosed in the following earlier international applications:

[0006] 1.

[0007] PCT / IB2025 / 058471

[0008] 2.

[0009] PCT / IB2025 / 061978

[0010] The disclosures of all referenced applications are hereby incorporated by reference in their entirety to the extent permitted by applicable law.

[0011] The present application extends these prior architectures by introducing a multi-model Al conferencing system, allowing parallel execution of several Al models, automated task routing, conflict detection, and unified result merging through a centralized governance layer.

[0012] TECHNICAL FIELD The invention relates to computer-implemented systems for:

[0013] • multi-model Al collaboration,

[0014] • Al governance and safety frameworks,

[0015] • Al conferencing interfaces,

[0016] • distributed task routing architectures,

[0017] • merged reasoning engines, and

[0018] • hardware-independent display surfaces.

[0019] More specifically, the invention provides an environment in which multiple independent artificial intelligence models may operate simultaneously, under the control of a centralized governance layer, to produce a single, consolidated, clarity-optimized output.

[0020] BACKGROUND OF THE INVENTION

[0021] Conventional artificial intelligence platforms require users to manually switch between different Al models, windows, and applications to compare responses. Users must independently gather outputs, manually assess differences, and resolve conflicts between model perspectives. This leads to:

[0022] • inefficiency,

[0023] • cognitive overload,

[0024] • fragmentation of workflow, and

[0025] • increased error likelihood.

[0026] Additionally, existing Al systems lack:

[0027] • a unified governing layer to authorize, filter, or coordinate responses, • a mechanism to route tasks to the optimal model(s),

[0028] • a method to produce a merged, conflict-resolved output,

[0029] • hardware-neutral rendering across robotic terminals, HUDs, and screens.

[0030] Users — especially those with limited technology experience or disabilities — are disproportionately impacted.

[0031] No existing system provides a multi-model Al conferencing environment in which several Al models operate simultaneously, under a single governance authority, with automated routing, merging, and display.

[0032] The present invention addresses these deficiencies.

[0033] SUMMARY OF THE INVENTION

[0034] The invention introduces a centralized governance layer configured to orchestrate interactions among multiple Al modules. This includes:

[0035] 1 . Governance Layer (100)

[0036] Receives input, classifies tasks, selects appropriate Al modules, enforces permissions, and governs execution.

[0037] 2. Multi-Model Conferencing Interface (200)

[0038] Displays multiple Al model panels (210-240) for simultaneous or selective interaction.

[0039] 3. Task Routing Engine (300)

[0040] Automatically classifies tasks (310), matches model strengths (320), and routes tasks (330) using defined trigger conditions (340).

[0041] 4. Response Merging Engine (400)

[0042] Compares outputs (410), detects conflicts (420), and generates a unified clarity output (430) for rendering (440).

[0043] 5. Hardware-Neutral Display Layer (500)

[0044] Enables rendering on robotic terminals, HUD projections, smartphones, tablets, laptops, and kiosk terminals.

[0045] 6. Unified Input Module (600)

[0046] Accepts a single voice or file submission for dissemination to all relevant models.

[0047] 7. Logging Repository (700)

[0048] Stores model-specific logs (710), merged output logs (720), and session indexes (730).

[0049] 8. Security Layer (800)

[0050] Ensures only the user may authorize model activation and data routing.

[0051] The result is a safe, governed, multi-model Al collaboration environment capable of text, audio, visual, and robotic modalities.

[0052] BRIEF DESCRIPTION OF THE DRAWINGS

[0053] FIG. 1 illustrates the governance layer (100) including logic core (110), policy engine (120), safety filter (130), and merge-logic controller (140).

[0054] FIG. 2 illustrates the multi- Al conferencing interface (200) including model panels (210, 220, 230) and optional additional panels (240).

[0055] FIG. 3 illustrates the task routing engine (300) including intent recognition (310), capability mapping (320), routing logic (330), and trigger conditions (340).

[0056] FIG. 4 illustrates the response merging engine (400) including comparator (410), conflict detector (420), unified clarity output generator (430), and visual render layer (440).

[0057] FIG. 5 illustrates the hardware-neutral display embodiments (500) including robotic terminal (510), HUD projection (520), smartphone interface (530), tablet interface (540), laptop / PC interface (550), and public kiosk terminal (560).

[0058] FIG. 6 illustrates the unified input module (600) including voice capture (610), file upload handler (620), and multi-model dissemination unit (630).

[0059] FIG. 7 illustrates the logging repository (700) including model logs (710), merged output logs (720), and user session index (730). FIG. 8 illustrates the security layer (800) including user verification (810), data routing authorization (820), and endpoint activation control (830).

[0060] DETAILED DESCRIPTION OF THE INVENTION

[0061] 1. Central Governance Layer (100)

[0062] Receives all user input, classifies tasks, authorizes execution, and directs workflow to appropriate Al modules.

[0063] Subcomponents include:

[0064] • 110 - Governance Logic Core

[0065] • 120 - Policy and Permission Engine

[0066] • 130 - Safety and Compliance Filter

[0067] • 140 - Merge-Logic Controller

[0068] 2. Multi- Al Conferencing Interface (200)

[0069] A visual interface enabling multiple Al models to respond simultaneously.

[0070] Panels (210-240) correspond to individual models.

[0071] The user may select:

[0072] • all models respond,

[0073] • specific models respond, or

[0074] • automatic selection based on routing logic.

[0075] 3. Task Routing Engine (300)

[0076] Classifies the input and determines which Al models are appropriate. • 310 - Intent Recognition

[0077] • 320 - Capability Mapping Database

[0078] • 330 - Routing Logic Matrix

[0079] • 340 - Trigger Conditions for model selection.

[0080] 4. Response Merging Engine (400)

[0081] Consolidates outputs from selected models.

[0082] • compares multiple model outputs (410),

[0083] • detects divergences (420),

[0084] • produces a unified clarity output (430),

[0085] • and renders it visually (440).

[0086] 5. Hardware-Neutral Display Layer (500)

[0087] Allows system output to be rendered on:

[0088] • robotic terminals (510),

[0089] • HUD projections (520),

[0090] • smartphone UI (530),

[0091] • tablets (540),

[0092] • PCs / laptops (550),

[0093] • kiosks (560). 6. Input Module (600)

[0094] A single user input — voice or file — is disseminated to model endpoints via unit (630).

[0095] 7. Logging Repository (700)

[0096] Stores:

[0097] • model-specific logs (710),

[0098] • merged outputs (720),

[0099] • user sessions (730).

[0100] 8. Security Layer (800)

[0101] Ensures user authority and safe execution.

[0102] • 810 - identity verification

[0103] • 820 - routing authorization

[0104] • 830 - endpoint activation control

[0105] EXAMPLE SCENARIO

[0106] In one example scenario, a user provides the input: “Create a business plan for my new product.”

[0107] Although the underlying components used within the system — input capture devices, artificial intelligence models, graphical user interfaces, and logging databases — are themselves known in the art and form no part of the invention individually, the prior art does not provide any mechanism for obtaining multiple Al perspectives simultaneously or orchestrating them through a unified decision-support layer. The novelty of the present invention lies in the architecture that enables these existing components to work together in a governed, synchronized way.

[0108] Upon receiving the user’s request, the governance layer (100) first processes the input to confirm user authority, apply any policy rules, and ensure compliance with safety constraints. The task classification engine (300) then performs intent recognition (310) to determine that the input pertains to business planning. The capability mapping database (320) identifies that certain Al models are better suited for long-form analysis, others for financial modeling, and others for competitive landscape generation.

[0109] Based on this information, the routing logic matrix (330) and trigger conditions (340) automatically select Models A, B, and C to respond in parallel.

[0110] Model A generates an operational strategy, Model B prepares a financial forecast, and Model C provides market positioning insights. Each model operates independently in its respective panel (210-240) within the multi-AI conferencing interface (200).

[0111] Their outputs are then transmitted to the response merging engine (400), where the multi-perspective comparator (410) analyzes differences and overlapping content. Any inconsistencies or contradictions are flagged by the conflict detector (420). The unified clarity output generator (430) consolidates the responses into a single, optimized result and formats the output through the render layer (440) for display.

[0112] The merged result is presented through the hardware-neutral display embodiment layer (500), regardless of whether the user is interacting through a robotic terminal (510), projected HUD (520), smartphone interface (530), tablet interface (540), laptop interface (550), or public kiosk (560).

[0113] This example demonstrates that the invention does not attempt to create new Al models, computer hardware, or processing components. Instead, it architects a central governance layer and routing / merging framework that enables multiple existing Al models — regardless of provider, type, or modality — to be invoked simultaneously, analyzed comparatively, and unified into a single clarity-enhanced output.

[0114] Prior systems require the user to select and compare models manually, whereas the disclosed system provides governed multi-model orchestration, comparative reasoning, and unified result generation across text, audio, visual, and robotic modalities.

Claims

CLAIMSINDEPENDENT CLAIM 11. A multi-model artificial intelligence conferencing system comprising: a centralized governance layer (100) configured to receive user input, classify a task, select one or more Al modules, route the user input to the selected Al modules through a task routing engine (300), and merge outputs from the selected Al modules using a response merging engine (400).DEPENDENT CLAIMSGovernance Layer2. The system of claim 1, wherein the centralized governance layer (100) comprises a governance logic core (110) configured to enforce user authority, activation permissions, and safety policies defined within a policy engine (120).

3. The system of claim 1, wherein the centralized governance layer (100) further comprises a safety and compliance filter (130) configured to evaluate user input prior to routing.

4. The system of claim 1, wherein the centralized governance layer (100) includes a merge-logic controller (140) configured to manage merging priority rules, conflict thresholds, and clarity- weighting parameters.Task Routing Engine5. The system of claim 1, wherein the task routing engine (300) comprises an intent recognition module (310) configured to classify the semantic category of user input.

6. The system of claim 1, wherein the task routing engine (300) further comprises a capability mapping database (320) associating Al module strengths with task categories.

7. The system of claim 1, wherein the task routing engine (300) comprises a routing logic matrix (330) configured to determine which Al modules should respond to the user input.

8. The system of claim 1, wherein the task routing engine (300) further comprises trigger conditions (340) enabling either:(i) all available Al modules to respond; or (ii) a subset of Al modules selected automatically based on task characteristics.Multi-AI Conferencing Interface9. The system of claim 1 , wherein the conferencing interface (200) comprises multiple Al panels (210-240), each corresponding to a distinct Al module endpoint.

10. The system of claim 9, wherein activation of each Al panel (210-240) is controlled by the governance layer (100) according to user instruction or system-generated routing logic.

11. The system of claim 9, wherein the Al panels (210-240) dynamically resize or reconfigure based on the number of active Al modules.Response Merging Engine12. The system of claim 1 , wherein the response merging engine (400) comprises a multi-perspective comparator (410) configured to compare output differences across multiple Al models.

13. The system of claim 1 , wherein the response merging engine (400) further comprises a conflict detector (420) configured to identify divergence between Al outputs.

14. The system of claim 1 , wherein the response merging engine (400) comprises a unified clarity output generator (430) configured to consolidate multiple Al outputs into a single enhanced output.

15. The system of claim 14, wherein the unified clarity output generator (430) formats merged responses through a visual render layer (440) for display on hardware-neutral embodiments.Hardware-Neutral Display Layer16. The system of claim 1 , wherein the conferencing system operates on a hardware-neutral display embodiment layer (500) including at least one of: robotic terminal (510), projected HUD (520), smartphone interface (530), tablet interface (540), laptop or PC interface (550), and public kiosk terminal (560).

17. The system of claim 16, wherein the display embodiment layer (500) dynamically adapts layout, panel configuration, or resolution settings based on available hardware.Input Module18. The system of claim 1, wherein the unified input module (600) comprises a voice capture unit (610) configured to receive speech-based input.

19. The system of claim 1 , wherein the unified input module (600) further comprises a file upload handler (620) configured to ingest images, documents, videos, or other digital files.

20. The system of claim 1 , wherein the unified input module (600) further comprises a dissemination unit (630) configured to distribute a single user input to all or selected Al modules.Logging Repository21. The system of claim 1, further comprising a transcript and interaction repository (700) storing: model-specific logs (710), merged output logs (720), and a user session index (730).

22. The system of claim 21, wherein the logging repository (700) is configured to generate an audit trail comprising routing decisions, module activations, and merged outputs.Security Layer23. The system of claim 1, wherein the permission and control layer (800) comprises user authority verification (810), data routing authorization (820), and endpoint activation control (830).

24. The system of claim 23, wherein cross-model data sharing occurs only when authorized by the permission and control layer (800), preventing direct AI-to-AI communication without governance approval.System Behavior25. The system of claim 1 , wherein each Al module executes only when activated under conditions authorized by the governance layer.

26. The system of claim 1, wherein the governance layer (100) automatically identifies a primary Al module based on task classification and delegates subtasks to additional modules based on capability mapping.

27. The system of claim 1 , wherein the system supports routing audiovisual content, including images, videos, and documents, to selected Al modules according to their specialized capabilities.

28. The system of claim 1 , wherein the system supports asynchronous or real-time multi-model response generation.

29. The system of claim 1 , wherein user invisibility mode prevents disclosure of user identity, video, or personal data to any Al module, while preserving full control over system functions.Merged Reasoning + Interface Behavior30. The system of claim 1 , wherein the response merging engine (400) generates a clarity-enhanced output free of redundancies, conflicts, and contradictions.

31. The system of claim 1, wherein the merged output is displayed simultaneously alongside individual Al model outputs for comparative review.Method Claims32. A method of generating a multi-model Al conference output comprising: receiving user input; classifying the task using a task routing engine (300); selecting Al modules based on capability mapping (320); routing the input to selected Al modules; collecting outputs; comparing outputs; merging outputs; and displaying a unified clarity result.

33. The method of claim 32, wherein input is captured through a voice capture unit (610).

34. The method of claim 32, wherein the unified output is generated through a unified clarity output generator (430).

35. The method of claim 32, wherein responses from all activated Al modules are stored within a repository (700).Computer-Readable Medium36. A non-transitory computer-readable medium storing instructions that, when executed, perform the method of claims 32-35.Extended Orchestration Claims37. The system of claim 1, wherein the governance layer (100) is configured to coordinate simultaneous interactions between multiple Al modules, enabling concurrent multi-model conferencing.

38. The system of claim 1, wherein the governance layer (100) routes tasks through intent recognition (310), capability mapping (320), and routing logic (330) prior to activating any Al module.

39. The system of claim 1, wherein the multi- Al panels (210-240) dynamically activate, deactivate, resize, or reshape according to active module count and task complexity.

40. The system of claim 1 , wherein the system is configured to generate comparative responses from multiple Al modules, enabling divergence analysis and unified clarity synthesis.

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