MIZ OKI 3.5 Patent Integration Plan

Document Version: 1.0.0 Date: January 14, 2026 Patent Reference: MIZ OKI 3.5 PCT Application (Filed January 13, 2026) Prepared By: Claude Code AI Assistant

Executive Summary

This document provides a comprehensive integration plan for aligning the MIZ OKI platform with the MIZ OKI 3.5 PCT patent application claims. The analysis reveals that ~85% of patent requirements are already implemented, with targeted enhancements needed in four key areas:

1. Enterprise Delegation Framework (Decision Control Plane)
2. Distributed Byzantine Consensus (ADCs)
3. G²-Reasoner Enhancement (Counterfactual Simulation)
4. Real-time Decision Cost Attribution (DCP)

The integration can be completed in 3 phases over 6-8 weeks, resulting in full patent claim coverage.

Table of Contents

1. Patent Claims Mapping
2. Current Implementation Status
3. Gap Analysis
4. Phase 1: Core DCP Enhancements
5. Phase 2: ADC & CSE Enhancements
6. Phase 3: Integration & Validation
7. Technical Specifications
8. File Changes Required
9. Testing Strategy
10. Success Metrics

1. Patent Claims Mapping

1.1 Core Innovation Components (Patent Claims 1-50)

1.2 Key Patent Formulas to Implement

Authorization Score (Claim 14)

Authorization_Score = α(Causal_Confidence) + β(Verification_Agreement) +
                      γ(Simulation_Delta) + δ(Policy_Compliance) + ε(Historical_Performance)

Status: ⚠️ Needs explicit implementation in DCP

Attention Score (Claim 8)

Attention_Score = Impact_Magnitude × Uncertainty_Level × Time_Criticality × Strategic_Alignment

Status: ✅ Implemented in srpvdal_adc.py

Analysis Depth (Claim 9)

Analysis_Depth = (Decision_Value × Uncertainty_Reduction_Potential) / (Time_Constraint × Resource_Cost)

Status: ✅ Implemented in REASON-ADC

Strategy Score (Claim 10)

Strategy_Score = Σ(wi × P(Outcomei) × V(Outcomei) × Ethical_Scorei)

Status: ✅ Implemented in DECIDE-ADC

Disagreement Metric (Claim 22)

Disagreement_Metric = Variance(Agent_Scores) × Confidence_Weighted_Deviation

Status: ✅ Implemented in arbitration_protocol.py

Learning Priority (Claim 12)

Learning_Priority = Prediction_Error × Business_Impact × Knowledge_Gap × Frequency_of_Occurrence

Status: ✅ Implemented in LEARN-ADC

2. Current Implementation Status

2.1 SRPVDAL 7-Stage Pipeline (Claims 8-13)

2.2 Decision Control Plane (Claims 14-19)

2.3 Verification & Arbitration (Claims 20-25)

2.4 Counterfactual Simulation (Claims 26-31)

2.5 TCO-KG (Claims 2-7)

3. Gap Analysis

3.1 Priority 1 - Critical Gaps (Required for Patent Claims)

Gap 1: Enterprise Delegation Framework (DCP)

Patent Reference: Claims 14, 17, 18 Missing: - Authority delegation chains - Role-based decision authority - Delegation tokens with expiry - Cascading approval authority

Impact: Without this, Claim 14's "decision control plane" authorization is incomplete.

Gap 2: Explicit Authorization Score Formula

Patent Reference: Claim 14 Missing: - Explicit implementation of: python Authorization_Score = α(Causal_Confidence) + β(Verification_Agreement) + γ(Simulation_Delta) + δ(Policy_Compliance) + ε(Historical_Performance) - Learned weight adaptation (α, β, γ, δ, ε) - Dynamic threshold based on domain risk profile

Impact: Core patent formula not explicitly implemented.

Gap 3: Distributed Byzantine Consensus (ADC)

Patent Reference: Claims 8-13, 50 Missing: - Cross-ADC consensus protocol - Byzantine fault tolerance - Distributed agreement mechanism

Impact: Multi-ADC coordination lacks formal consensus.

Gap 4: G²-Reasoner Enhancement (CSE)

Patent Reference: Claims 26-31 Missing: - Goal + Knowledge scaffolding - Fresh question detection - Multi-hop counterfactual chains

Impact: CSE reasoning depth limited.

3.2 Priority 2 - Enhancement Gaps

3.3 Priority 3 - Nice-to-Have

4. Phase 1: Core DCP Enhancements

Duration: 2-3 weeks Focus: Decision Control Plane full patent compliance

4.1 New Module: Patent-Compliant Authorization Engine

File: miz-oki-adk-agents/boss/patent_authorization_engine.py

"""
MIZ OKI 3.5 Patent-Compliant Authorization Engine

Implements the Decision Control Plane (DCP) as specified in:
- Claim 1: Decision control plane configured to receive proposed actions
- Claim 14: Multi-factor authorization algorithm
- Claim 15: Dynamic threshold adjustment
- Claims 17-19: Cost/risk constraints, parameter modification, rejection logging

Patent Formula Implementation:
Authorization_Score = α(Causal_Confidence) + β(Verification_Agreement) +
                      γ(Simulation_Delta) + δ(Policy_Compliance) + ε(Historical_Performance)
"""

from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
from enum import Enum
import numpy as np
from datetime import datetime

class AuthorizationDecision(str, Enum):
    """DCP decision outcomes per Claim 1"""
    APPROVE = "approve"           # All thresholds met
    MODIFY = "modify"             # Parameters adjusted (Claim 18)
    DEFER = "defer"               # Pending verification
    REJECT = "reject"             # Failed thresholds (Claim 19)
    ESCALATE = "escalate"         # Human review (Claim 16)

@dataclass
class AuthorizationWeights:
    """
    Learned weights for Authorization_Score formula (Claim 14)
    These adapt based on outcome feedback
    """
    alpha: float = 0.25   # Causal_Confidence weight
    beta: float = 0.20    # Verification_Agreement weight
    gamma: float = 0.20   # Simulation_Delta weight
    delta: float = 0.20   # Policy_Compliance weight
    epsilon: float = 0.15 # Historical_Performance weight

    def as_vector(self) -> np.ndarray:
        return np.array([self.alpha, self.beta, self.gamma, self.delta, self.epsilon])

@dataclass
class AuthorizationInput:
    """Input factors for authorization scoring"""
    proposal_id: str
    agent_id: str
    causal_confidence: float         # 0.0-1.0
    verification_agreement: float    # 0.0-1.0 (consensus score)
    simulation_delta: float          # Expected utility vs alternatives
    policy_compliance: float         # 0.0-1.0
    historical_performance: float    # Agent's track record
    domain: str
    risk_profile: str               # "low", "medium", "high", "critical"
    estimated_cost: float
    estimated_impact: float

@dataclass
class AuthorizationResult:
    """Authorization decision with full audit trail"""
    proposal_id: str
    decision: AuthorizationDecision
    authorization_score: float
    threshold_used: float
    component_scores: Dict[str, float]
    weights_used: AuthorizationWeights
    modifications: Optional[Dict] = None
    rejection_reason: Optional[str] = None
    escalation_reason: Optional[str] = None
    timestamp: datetime = field(default_factory=datetime.utcnow)
    trace_id: str = ""

class PatentAuthorizationEngine:
    """
    Decision Control Plane implementation per MIZ OKI 3.5 Patent Claims 1, 14-19

    Key architectural constraint: Agents CANNOT bypass this engine.
    All proposed actions must flow through calculate_authorization_score().
    """

    def __init__(self, firestore_client, kg_brain, config: Optional[Dict] = None):
        self.db = firestore_client
        self.kg_brain = kg_brain
        self.config = config or {}

        # Initialize weights (will be learned over time)
        self.weights = AuthorizationWeights()

        # Domain-specific thresholds (Claim 15: dynamic adjustment)
        self.domain_thresholds = {
            "marketing": {"low": 0.60, "medium": 0.70, "high": 0.80, "critical": 0.90},
            "finance": {"low": 0.70, "medium": 0.80, "high": 0.90, "critical": 0.95},
            "operations": {"low": 0.55, "medium": 0.65, "high": 0.75, "critical": 0.85},
            "default": {"low": 0.60, "medium": 0.70, "high": 0.80, "critical": 0.90}
        }

        # Escalation thresholds (Claim 16)
        self.escalation_threshold = 0.50

        # Cost/risk guardrails (Claim 17)
        self.guardrails = {
            "max_cost_usd": 10000.0,
            "max_risk_score": 0.80,
            "min_confidence": 0.40
        }

    async def calculate_authorization_score(
        self,
        input_data: AuthorizationInput
    ) -> AuthorizationResult:
        """
        Patent Claim 14: Multi-factor authorization algorithm

        Authorization_Score = α(Causal_Confidence) + β(Verification_Agreement) +
                              γ(Simulation_Delta) + δ(Policy_Compliance) + ε(Historical_Performance)
        """
        # Normalize simulation_delta to 0-1 range
        normalized_simulation = self._normalize_simulation_delta(input_data.simulation_delta)

        # Calculate component scores
        component_scores = {
            "causal_confidence": input_data.causal_confidence,
            "verification_agreement": input_data.verification_agreement,
            "simulation_delta": normalized_simulation,
            "policy_compliance": input_data.policy_compliance,
            "historical_performance": input_data.historical_performance
        }

        # Apply weighted formula (Claim 14)
        score_vector = np.array([
            component_scores["causal_confidence"],
            component_scores["verification_agreement"],
            component_scores["simulation_delta"],
            component_scores["policy_compliance"],
            component_scores["historical_performance"]
        ])

        authorization_score = float(np.dot(self.weights.as_vector(), score_vector))

        # Get dynamic threshold (Claim 15)
        threshold = self._get_dynamic_threshold(input_data.domain, input_data.risk_profile)

        # Determine decision
        decision, modifications, rejection_reason, escalation_reason = await self._make_decision(
            input_data, authorization_score, threshold, component_scores
        )

        result = AuthorizationResult(
            proposal_id=input_data.proposal_id,
            decision=decision,
            authorization_score=authorization_score,
            threshold_used=threshold,
            component_scores=component_scores,
            weights_used=self.weights,
            modifications=modifications,
            rejection_reason=rejection_reason,
            escalation_reason=escalation_reason,
            trace_id=f"auth_{input_data.proposal_id}_{datetime.utcnow().timestamp()}"
        )

        # Persist for audit (Claim 19)
        await self._persist_decision(result)

        return result

    async def _make_decision(
        self,
        input_data: AuthorizationInput,
        score: float,
        threshold: float,
        components: Dict[str, float]
    ) -> Tuple[AuthorizationDecision, Optional[Dict], Optional[str], Optional[str]]:
        """
        Determine authorization decision based on score and constraints
        """
        # Check guardrails first (Claim 17)
        if input_data.estimated_cost > self.guardrails["max_cost_usd"]:
            return AuthorizationDecision.ESCALATE, None, None, f"Cost ${input_data.estimated_cost} exceeds guardrail"

        if components["causal_confidence"] < self.guardrails["min_confidence"]:
            return AuthorizationDecision.REJECT, None, f"Causal confidence {components['causal_confidence']:.2f} below minimum", None

        # Check if score meets threshold
        if score >= threshold:
            return AuthorizationDecision.APPROVE, None, None, None

        # Check if modification can bring within bounds (Claim 18)
        if score >= threshold - 0.10:
            modifications = await self._calculate_modifications(input_data, score, threshold)
            if modifications:
                return AuthorizationDecision.MODIFY, modifications, None, None

        # Check if escalation needed (Claim 16)
        if score >= self.escalation_threshold:
            return AuthorizationDecision.DEFER, None, None, "Score between escalation and approval thresholds"

        # Reject with reason (Claim 19)
        return AuthorizationDecision.REJECT, None, f"Authorization score {score:.3f} below threshold {threshold:.3f}", None

    async def _calculate_modifications(
        self,
        input_data: AuthorizationInput,
        current_score: float,
        threshold: float
    ) -> Optional[Dict]:
        """
        Claim 18: Modify parameters to bring proposal within acceptable bounds
        """
        gap = threshold - current_score

        # Suggest parameter modifications
        modifications = {}

        if input_data.estimated_cost > 0:
            # Suggest cost reduction
            cost_reduction_factor = 1.0 - (gap * 0.5)
            modifications["suggested_cost"] = input_data.estimated_cost * cost_reduction_factor

        if input_data.estimated_impact > 0:
            # Suggest scope reduction
            scope_reduction = gap * 0.3
            modifications["scope_reduction_pct"] = scope_reduction

        return modifications if modifications else None

    def _get_dynamic_threshold(self, domain: str, risk_profile: str) -> float:
        """
        Claim 15: Dynamic threshold adjustment based on domain and risk
        """
        domain_config = self.domain_thresholds.get(domain, self.domain_thresholds["default"])
        return domain_config.get(risk_profile, domain_config["medium"])

    def _normalize_simulation_delta(self, delta: float) -> float:
        """Normalize simulation delta to 0-1 range"""
        # Positive delta means proposed action is better than alternatives
        # Use sigmoid-like transformation
        return 1.0 / (1.0 + np.exp(-delta))

    async def _persist_decision(self, result: AuthorizationResult) -> None:
        """
        Claim 5, 19: Persist decision for audit trail
        """
        doc_ref = self.db.collection("dcp_authorization_decisions").document(result.trace_id)
        await doc_ref.set({
            "proposal_id": result.proposal_id,
            "decision": result.decision.value,
            "authorization_score": result.authorization_score,
            "threshold_used": result.threshold_used,
            "component_scores": result.component_scores,
            "weights_used": {
                "alpha": result.weights_used.alpha,
                "beta": result.weights_used.beta,
                "gamma": result.weights_used.gamma,
                "delta": result.weights_used.delta,
                "epsilon": result.weights_used.epsilon
            },
            "modifications": result.modifications,
            "rejection_reason": result.rejection_reason,
            "escalation_reason": result.escalation_reason,
            "timestamp": result.timestamp.isoformat(),
            "trace_id": result.trace_id
        })

    async def update_weights_from_outcomes(self, outcomes: List[Dict]) -> None:
        """
        Claim 15: Adapt weights based on outcome feedback

        Uses gradient descent to improve weight allocation
        """
        if not outcomes:
            return

        # Calculate which components best predicted success
        for outcome in outcomes:
            if outcome.get("success"):
                # Increase weight of components that were high for successful decisions
                component_scores = outcome.get("component_scores", {})
                # Simple reinforcement: boost weights of high-scoring components
                # In production, use proper gradient descent
                pass  # Implementation details...

4.2 New Module: Enterprise Delegation Framework

File: miz-oki-adk-agents/boss/enterprise_delegation_framework.py

"""
Enterprise Delegation Framework for MIZ OKI 3.5

Implements authority delegation chains, role-based decision authority,
and cascading approval authority per patent requirements.
"""

from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set
from enum import Enum
from datetime import datetime, timedelta
import hashlib

class AuthorityLevel(str, Enum):
    """Decision authority levels"""
    SYSTEM = "system"           # Automated decisions
    OPERATOR = "operator"       # Basic operations
    ANALYST = "analyst"         # Analysis and recommendations
    MANAGER = "manager"         # Budget and campaign decisions
    DIRECTOR = "director"       # Strategic decisions
    EXECUTIVE = "executive"     # High-risk decisions
    BOARD = "board"             # Critical/irreversible decisions

@dataclass
class DelegationToken:
    """
    Delegation token with expiry and scope constraints
    """
    token_id: str
    delegator_id: str
    delegate_id: str
    authority_level: AuthorityLevel
    scope: Dict[str, any]  # Domain, cost limits, action types
    created_at: datetime
    expires_at: datetime
    max_uses: Optional[int] = None
    uses_remaining: Optional[int] = None
    revoked: bool = False

    def is_valid(self) -> bool:
        if self.revoked:
            return False
        if datetime.utcnow() > self.expires_at:
            return False
        if self.max_uses and self.uses_remaining <= 0:
            return False
        return True

@dataclass
class AuthorityChain:
    """
    Represents a chain of delegated authority
    """
    chain_id: str
    links: List[DelegationToken]
    effective_authority: AuthorityLevel
    effective_scope: Dict[str, any]

    def get_approval_path(self) -> List[str]:
        return [link.delegator_id for link in self.links]

class EnterpriseDelegationFramework:
    """
    Manages authority delegation for the Decision Control Plane
    """

    # Authority required for different decision types
    DECISION_AUTHORITY_MAP = {
        "budget_change_small": AuthorityLevel.ANALYST,      # < $1000
        "budget_change_medium": AuthorityLevel.MANAGER,     # $1000-$10000
        "budget_change_large": AuthorityLevel.DIRECTOR,     # $10000-$100000
        "budget_change_critical": AuthorityLevel.EXECUTIVE, # > $100000
        "campaign_create": AuthorityLevel.ANALYST,
        "campaign_pause": AuthorityLevel.ANALYST,
        "campaign_delete": AuthorityLevel.MANAGER,
        "bid_adjustment": AuthorityLevel.OPERATOR,
        "creative_rotation": AuthorityLevel.OPERATOR,
        "audience_expansion": AuthorityLevel.ANALYST,
        "policy_override": AuthorityLevel.DIRECTOR,
        "rollback_action": AuthorityLevel.MANAGER,
        "system_config_change": AuthorityLevel.EXECUTIVE,
    }

    def __init__(self, firestore_client):
        self.db = firestore_client
        self.active_tokens: Dict[str, DelegationToken] = {}
        self.authority_chains: Dict[str, AuthorityChain] = {}

    async def create_delegation(
        self,
        delegator_id: str,
        delegate_id: str,
        authority_level: AuthorityLevel,
        scope: Dict,
        duration_hours: int = 24,
        max_uses: Optional[int] = None
    ) -> DelegationToken:
        """Create a new delegation token"""
        token_id = self._generate_token_id(delegator_id, delegate_id, authority_level)

        token = DelegationToken(
            token_id=token_id,
            delegator_id=delegator_id,
            delegate_id=delegate_id,
            authority_level=authority_level,
            scope=scope,
            created_at=datetime.utcnow(),
            expires_at=datetime.utcnow() + timedelta(hours=duration_hours),
            max_uses=max_uses,
            uses_remaining=max_uses
        )

        self.active_tokens[token_id] = token
        await self._persist_token(token)

        return token

    async def check_authority(
        self,
        agent_id: str,
        decision_type: str,
        context: Dict
    ) -> Tuple[bool, Optional[AuthorityChain], str]:
        """
        Check if an agent has authority to make a decision
        Returns: (authorized, authority_chain, reason)
        """
        required_authority = self._get_required_authority(decision_type, context)

        # Check direct authority
        direct_authority = await self._get_agent_authority(agent_id)
        if self._authority_sufficient(direct_authority, required_authority):
            return True, None, "Direct authority"

        # Check delegated authority
        chain = await self._find_authority_chain(agent_id, required_authority)
        if chain and chain.effective_authority:
            if self._authority_sufficient(chain.effective_authority, required_authority):
                return True, chain, "Delegated authority"

        return False, None, f"Requires {required_authority.value} authority"

    def _get_required_authority(self, decision_type: str, context: Dict) -> AuthorityLevel:
        """Determine required authority level for a decision"""
        base_authority = self.DECISION_AUTHORITY_MAP.get(decision_type, AuthorityLevel.MANAGER)

        # Adjust based on context
        if context.get("estimated_cost", 0) > 100000:
            return AuthorityLevel.EXECUTIVE
        if context.get("risk_level") == "critical":
            return AuthorityLevel.DIRECTOR

        return base_authority

    def _authority_sufficient(self, has: AuthorityLevel, needs: AuthorityLevel) -> bool:
        """Check if one authority level is sufficient for another"""
        authority_order = [
            AuthorityLevel.SYSTEM,
            AuthorityLevel.OPERATOR,
            AuthorityLevel.ANALYST,
            AuthorityLevel.MANAGER,
            AuthorityLevel.DIRECTOR,
            AuthorityLevel.EXECUTIVE,
            AuthorityLevel.BOARD
        ]
        return authority_order.index(has) >= authority_order.index(needs)

    def _generate_token_id(self, delegator: str, delegate: str, level: AuthorityLevel) -> str:
        content = f"{delegator}:{delegate}:{level.value}:{datetime.utcnow().timestamp()}"
        return hashlib.sha256(content.encode()).hexdigest()[:32]

    async def _persist_token(self, token: DelegationToken) -> None:
        doc_ref = self.db.collection("delegation_tokens").document(token.token_id)
        await doc_ref.set({
            "token_id": token.token_id,
            "delegator_id": token.delegator_id,
            "delegate_id": token.delegate_id,
            "authority_level": token.authority_level.value,
            "scope": token.scope,
            "created_at": token.created_at.isoformat(),
            "expires_at": token.expires_at.isoformat(),
            "max_uses": token.max_uses,
            "uses_remaining": token.uses_remaining,
            "revoked": token.revoked
        })

4.3 MCP Tools for DCP

Add to boss_agent_core.py:

# New MCP Tools for Patent-Compliant DCP
DCP_MCP_TOOLS = [
    "dcp_authorize_action",        # Calculate authorization score
    "dcp_check_authority",         # Check delegation authority
    "dcp_create_delegation",       # Create delegation token
    "dcp_revoke_delegation",       # Revoke delegation
    "dcp_get_authorization_history", # Audit trail
    "dcp_update_weights",          # Update learned weights
    "dcp_get_thresholds",          # Get domain thresholds
    "dcp_set_threshold",           # Set custom threshold
    "dcp_status",                  # DCP status
]

5. Phase 2: ADC & CSE Enhancements

Duration: 2-3 weeks Focus: Autonomous Decision Controllers and Counterfactual Simulation

5.1 New Module: Distributed ADC Consensus

File: miz-oki-adk-agents/boss/distributed_adc_consensus.py

"""
Distributed ADC Consensus Protocol for MIZ OKI 3.5

Implements Byzantine fault-tolerant consensus for multi-ADC decisions
per patent Claims 8-13.
"""

from dataclasses import dataclass
from typing import Dict, List, Optional, Set
from enum import Enum
import asyncio
import hashlib

class ConsensusPhase(str, Enum):
    PREPARE = "prepare"
    PROMISE = "promise"
    ACCEPT = "accept"
    COMMIT = "commit"
    ABORT = "abort"

@dataclass
class ConsensusProposal:
    """A proposal being voted on by ADCs"""
    proposal_id: str
    proposer_adc: str
    decision_type: str
    payload: Dict
    round_number: int
    timestamp: float

@dataclass
class ConsensusVote:
    """Vote from an ADC"""
    proposal_id: str
    voter_adc: str
    phase: ConsensusPhase
    vote: bool
    confidence: float
    signature: str

class DistributedADCConsensus:
    """
    Byzantine fault-tolerant consensus for ADC coordination

    Uses simplified Paxos-like protocol:
    1. PREPARE: Leader proposes decision
    2. PROMISE: ADCs promise to follow if majority agrees
    3. ACCEPT: ADCs accept proposal
    4. COMMIT: Decision is committed
    """

    def __init__(self, adc_registry: Dict[str, any], quorum_threshold: float = 0.67):
        self.adc_registry = adc_registry
        self.quorum_threshold = quorum_threshold
        self.active_proposals: Dict[str, ConsensusProposal] = {}
        self.votes: Dict[str, List[ConsensusVote]] = {}

    async def propose(self, proposer: str, decision_type: str, payload: Dict) -> str:
        """Start a consensus round"""
        proposal_id = self._generate_proposal_id(proposer, decision_type)

        proposal = ConsensusProposal(
            proposal_id=proposal_id,
            proposer_adc=proposer,
            decision_type=decision_type,
            payload=payload,
            round_number=1,
            timestamp=asyncio.get_event_loop().time()
        )

        self.active_proposals[proposal_id] = proposal
        self.votes[proposal_id] = []

        # Broadcast PREPARE to all ADCs
        await self._broadcast_prepare(proposal)

        return proposal_id

    async def vote(self, proposal_id: str, voter: str, approve: bool, confidence: float) -> ConsensusVote:
        """Cast a vote on a proposal"""
        vote = ConsensusVote(
            proposal_id=proposal_id,
            voter_adc=voter,
            phase=ConsensusPhase.PROMISE,
            vote=approve,
            confidence=confidence,
            signature=self._sign_vote(proposal_id, voter, approve)
        )

        self.votes[proposal_id].append(vote)

        # Check if quorum reached
        if await self._check_quorum(proposal_id):
            await self._advance_phase(proposal_id)

        return vote

    async def _check_quorum(self, proposal_id: str) -> bool:
        """Check if enough votes received"""
        votes = self.votes.get(proposal_id, [])
        total_adcs = len(self.adc_registry)
        votes_received = len(votes)

        if votes_received / total_adcs >= self.quorum_threshold:
            approvals = sum(1 for v in votes if v.vote)
            return approvals / votes_received >= self.quorum_threshold

        return False

    async def _advance_phase(self, proposal_id: str) -> None:
        """Move proposal to next phase"""
        proposal = self.active_proposals.get(proposal_id)
        if not proposal:
            return

        votes = self.votes.get(proposal_id, [])

        # Calculate weighted consensus
        weighted_approval = sum(v.confidence for v in votes if v.vote)
        weighted_total = sum(v.confidence for v in votes)

        if weighted_approval / weighted_total >= self.quorum_threshold:
            # Commit the decision
            await self._commit_decision(proposal)
        else:
            # Abort
            await self._abort_decision(proposal)

    async def _commit_decision(self, proposal: ConsensusProposal) -> None:
        """Commit a consensus decision"""
        # Emit to all ADCs
        for adc_id in self.adc_registry:
            await self._notify_adc(adc_id, proposal, ConsensusPhase.COMMIT)

    async def _abort_decision(self, proposal: ConsensusProposal) -> None:
        """Abort a failed consensus"""
        for adc_id in self.adc_registry:
            await self._notify_adc(adc_id, proposal, ConsensusPhase.ABORT)

    def _generate_proposal_id(self, proposer: str, decision_type: str) -> str:
        content = f"{proposer}:{decision_type}:{asyncio.get_event_loop().time()}"
        return f"consensus_{hashlib.sha256(content.encode()).hexdigest()[:16]}"

    def _sign_vote(self, proposal_id: str, voter: str, approve: bool) -> str:
        content = f"{proposal_id}:{voter}:{approve}"
        return hashlib.sha256(content.encode()).hexdigest()

5.2 G²-Reasoner Enhancement for CSE

File: miz-oki-adk-agents/boss/g2_reasoner.py

"""
G²-Reasoner: Goal-Guided Reasoning for Counterfactual Simulation

Implements goal + knowledge scaffolding for enhanced counterfactual reasoning
per patent Claims 26-31.
"""

from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from enum import Enum

class ReasoningMode(str, Enum):
    GOAL_DECOMPOSITION = "goal_decomposition"
    KNOWLEDGE_RETRIEVAL = "knowledge_retrieval"
    HYPOTHESIS_GENERATION = "hypothesis_generation"
    COUNTERFACTUAL_SYNTHESIS = "counterfactual_synthesis"
    VALIDATION = "validation"

@dataclass
class ReasoningGoal:
    """A goal to be achieved through reasoning"""
    goal_id: str
    description: str
    success_criteria: Dict[str, any]
    priority: int
    deadline: Optional[float] = None
    parent_goal: Optional[str] = None
    subgoals: List[str] = None

@dataclass
class KnowledgeScaffold:
    """Knowledge context for reasoning"""
    facts: List[Dict]
    causal_relationships: List[Dict]
    constraints: List[Dict]
    historical_outcomes: List[Dict]
    confidence_map: Dict[str, float]

@dataclass
class ReasoningStep:
    """A step in the reasoning chain"""
    step_id: str
    mode: ReasoningMode
    input_state: Dict
    output_state: Dict
    reasoning_trace: str
    confidence: float
    alternatives_considered: List[Dict]

class G2Reasoner:
    """
    Goal-Guided Reasoner for enhanced counterfactual simulation

    Key innovations:
    1. Goal decomposition into sub-goals
    2. Knowledge scaffold construction
    3. Multi-hop counterfactual chains
    4. Fresh question detection
    """

    def __init__(self, kg_brain, cse, llm_client):
        self.kg_brain = kg_brain
        self.cse = cse  # Counterfactual Simulation Engine
        self.llm = llm_client
        self.reasoning_history: List[ReasoningStep] = []

    async def reason(
        self,
        goal: ReasoningGoal,
        context: Dict,
        max_depth: int = 5
    ) -> Tuple[Dict, List[ReasoningStep]]:
        """
        Execute goal-guided reasoning

        Returns: (result, reasoning_chain)
        """
        # Step 1: Decompose goal into sub-goals
        subgoals = await self._decompose_goal(goal)

        # Step 2: Build knowledge scaffold
        scaffold = await self._build_scaffold(goal, context)

        # Step 3: Check for fresh question (novelty detection)
        is_novel, novelty_score = await self._detect_novelty(goal, scaffold)

        # Step 4: Generate hypotheses
        hypotheses = await self._generate_hypotheses(goal, scaffold, is_novel)

        # Step 5: Run counterfactual simulation for each hypothesis
        simulated_outcomes = []
        for hypothesis in hypotheses:
            cf_result = await self.cse.simulate_counterfactual(
                hypothesis=hypothesis,
                scaffold=scaffold,
                depth=max_depth
            )
            simulated_outcomes.append(cf_result)

        # Step 6: Synthesize best outcome
        best_outcome = await self._synthesize_outcome(simulated_outcomes, goal)

        return best_outcome, self.reasoning_history

    async def _decompose_goal(self, goal: ReasoningGoal) -> List[ReasoningGoal]:
        """Decompose high-level goal into sub-goals"""
        # Use LLM to decompose
        prompt = f"""
        Decompose this goal into sub-goals:
        Goal: {goal.description}
        Success Criteria: {goal.success_criteria}

        Return a list of sub-goals that, when achieved, will satisfy the main goal.
        """

        response = await self.llm.generate(prompt)
        subgoals = self._parse_subgoals(response, goal.goal_id)

        self._record_step(ReasoningMode.GOAL_DECOMPOSITION, {"goal": goal}, {"subgoals": subgoals})

        return subgoals

    async def _build_scaffold(self, goal: ReasoningGoal, context: Dict) -> KnowledgeScaffold:
        """Build knowledge scaffold from KG"""
        # Query KG for relevant facts
        facts = await self.kg_brain.query_relevant_facts(goal.description, limit=50)

        # Get causal relationships
        causal_rels = await self.kg_brain.get_causal_relationships(
            entities=[f["entity_id"] for f in facts]
        )

        # Get constraints
        constraints = await self.kg_brain.get_constraints(context.get("domain"))

        # Get historical outcomes for similar goals
        historical = await self.kg_brain.get_similar_outcomes(goal.description, limit=20)

        # Build confidence map
        confidence_map = {
            f["entity_id"]: f.get("confidence", 0.5)
            for f in facts
        }

        scaffold = KnowledgeScaffold(
            facts=facts,
            causal_relationships=causal_rels,
            constraints=constraints,
            historical_outcomes=historical,
            confidence_map=confidence_map
        )

        self._record_step(ReasoningMode.KNOWLEDGE_RETRIEVAL, {"goal": goal}, {"scaffold_size": len(facts)})

        return scaffold

    async def _detect_novelty(
        self,
        goal: ReasoningGoal,
        scaffold: KnowledgeScaffold
    ) -> Tuple[bool, float]:
        """
        Detect if this is a fresh/novel question vs. memorized pattern

        Returns: (is_novel, novelty_score)
        """
        # Check similarity to historical outcomes
        if not scaffold.historical_outcomes:
            return True, 1.0

        # Calculate semantic similarity
        max_similarity = 0.0
        for outcome in scaffold.historical_outcomes:
            similarity = await self._calculate_similarity(goal.description, outcome.get("description", ""))
            max_similarity = max(max_similarity, similarity)

        novelty_score = 1.0 - max_similarity
        is_novel = novelty_score > 0.6  # Threshold for "fresh question"

        return is_novel, novelty_score

    async def _generate_hypotheses(
        self,
        goal: ReasoningGoal,
        scaffold: KnowledgeScaffold,
        is_novel: bool
    ) -> List[Dict]:
        """Generate hypotheses for achieving the goal"""
        # If novel, use more exploratory generation
        temperature = 0.8 if is_novel else 0.3

        prompt = f"""
        Given this goal and knowledge context, generate hypotheses for achieving the goal.

        Goal: {goal.description}

        Relevant Facts:
        {self._format_facts(scaffold.facts[:10])}

        Causal Relationships:
        {self._format_relationships(scaffold.causal_relationships[:10])}

        Generate 3-5 distinct hypotheses with expected outcomes.
        """

        response = await self.llm.generate(prompt, temperature=temperature)
        hypotheses = self._parse_hypotheses(response)

        self._record_step(
            ReasoningMode.HYPOTHESIS_GENERATION,
            {"goal": goal.goal_id, "is_novel": is_novel},
            {"hypothesis_count": len(hypotheses)}
        )

        return hypotheses

    async def _synthesize_outcome(
        self,
        simulated_outcomes: List[Dict],
        goal: ReasoningGoal
    ) -> Dict:
        """Synthesize best outcome from simulations"""
        # Rank by goal alignment
        ranked = sorted(
            simulated_outcomes,
            key=lambda x: self._score_goal_alignment(x, goal),
            reverse=True
        )

        best = ranked[0] if ranked else None

        self._record_step(
            ReasoningMode.COUNTERFACTUAL_SYNTHESIS,
            {"simulations": len(simulated_outcomes)},
            {"best_outcome": best}
        )

        return best

    def _record_step(self, mode: ReasoningMode, input_state: Dict, output_state: Dict):
        """Record a reasoning step"""
        step = ReasoningStep(
            step_id=f"step_{len(self.reasoning_history)}",
            mode=mode,
            input_state=input_state,
            output_state=output_state,
            reasoning_trace="",
            confidence=0.0,
            alternatives_considered=[]
        )
        self.reasoning_history.append(step)

6. Phase 3: Integration & Validation

Duration: 2 weeks Focus: Integration testing, patent claim validation, documentation

6.1 Integration Points

6.2 Patent Claim Validation Checklist

6.3 Documentation Requirements

7. Technical Specifications

7.1 Authorization Score Calculation

def calculate_authorization_score(
    causal_confidence: float,      # From KG causal inference
    verification_agreement: float,  # From arbitration
    simulation_delta: float,        # From CSE comparison
    policy_compliance: float,       # From policy engine
    historical_performance: float,  # From agent track record
    weights: AuthorizationWeights
) -> float:
    """
    Patent Claim 14 implementation
    """
    return (
        weights.alpha * causal_confidence +
        weights.beta * verification_agreement +
        weights.gamma * normalize_sigmoid(simulation_delta) +
        weights.delta * policy_compliance +
        weights.epsilon * historical_performance
    )

7.2 Disagreement Metric Calculation

def calculate_disagreement_metric(
    agent_scores: List[float],
    agent_confidences: List[float]
) -> float:
    """
    Patent Claim 22 implementation
    """
    variance = np.var(agent_scores)

    # Confidence-weighted deviation
    mean_score = np.mean(agent_scores)
    weighted_deviations = [
        conf * abs(score - mean_score)
        for score, conf in zip(agent_scores, agent_confidences)
    ]
    confidence_weighted_deviation = np.mean(weighted_deviations)

    return variance * confidence_weighted_deviation

7.3 Learning Priority Calculation

def calculate_learning_priority(
    prediction_error: float,
    business_impact: float,
    knowledge_gap: float,
    frequency: int
) -> float:
    """
    Patent Claim 12 implementation
    """
    return prediction_error * business_impact * knowledge_gap * np.log1p(frequency)

8. File Changes Required

8.1 New Files to Create

8.2 Files to Modify

8.3 Firestore Collections to Add

9. Testing Strategy

9.1 Unit Tests

9.2 Integration Tests

9.3 Patent Claim Compliance Tests

class PatentComplianceTests:
    """
    Tests validating patent claim implementation
    """

    def test_claim_1_dcp_cannot_be_bypassed(self):
        """Agents cannot execute without DCP authorization"""
        pass

    def test_claim_14_authorization_formula(self):
        """Authorization score uses correct formula"""
        pass

    def test_claim_22_disagreement_metric(self):
        """Disagreement calculated correctly"""
        pass

    def test_claim_30_superior_alternative_blocks(self):
        """Authorization denied when alternatives better"""
        pass

10. Success Metrics

10.1 Patent Compliance Metrics

10.2 Performance Metrics

10.3 Business Metrics

Appendix A: Patent Claim to Code Mapping

Appendix B: Implementation Timeline

Phase 1: Core DCP (Weeks 1-3)

• Week 1: patent_authorization_engine.py, MCP tools
• Week 2: enterprise_delegation_framework.py, integration
• Week 3: Testing, documentation

Phase 2: ADC & CSE (Weeks 4-6)

• Week 4: distributed_adc_consensus.py
• Week 5: g2_reasoner.py, CSE integration
• Week 6: Testing, optimization

Phase 3: Integration (Weeks 7-8)

• Week 7: Full integration testing
• Week 8: Patent compliance validation, documentation

Document End

This plan should be reviewed and approved before implementation begins.