# 🏛️ UNITED STATES PATENT & TRADEMARK OFFICE (USPTO)
## FORMAL PATENT SPECIFICATION
### CONTINUATION-IN-PART APPLICATION UNDER 35 U.S.C. § 120

```
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INVENTION TITLE:       SYSTEMS AND METHODS FOR MACHINE-ENFORCED DECISION ADMISSIBILITY
                       IN AUTONOMOUS AND DISTRIBUTED COMPUTING ENVIRONMENTS
APPLICANT / ASSIGNEE:  PREDATOR.RUN // SOVEREIGN CONTEXT CUBE GOVERNANCE CONSORTIUM
ATTORNEY DOCKET NO:    PRED-TASK-2026-CIP-SPEC-ADMISSIBILITY
PARENT APPLICATION:    PRED-TASK-2026-OMNIBUS (DECISION PROVENANCE INFRASTRUCTURE)
CANONICAL MARKS:       TASK™ • Machine-Enforced Admissibility™ • Every Decision Has a Proof Chain™
══════════════════════════════════════════════════════════════════════════════════════════════════════
```

---

## 1. FIELD OF THE INVENTION

The present disclosure relates to computer-implemented governance systems and, more particularly, to systems and methods for controlling execution of decisions using machine-enforced admissibility criteria derived from provenance, authority, evidence, validation, and historical retention structures.

---

## 2. BACKGROUND OF THE INVENTION

Conventional computing systems generally permit a decision, recommendation, instruction, transaction, or state mutation to proceed directly from generation to execution.

Typical architectures follow the un-gated pattern:

$$\text{Input} \longrightarrow \text{Processing} \longrightarrow \text{Decision} \longrightarrow \text{Execution}$$

In such systems, execution authority derives primarily from identity, permissions, credentials, role-based access controls (RBAC), attribute-based access controls (ABAC), or process-level operating system credentials.

Existing systems generally do not evaluate whether a decision itself satisfies objective admissibility requirements prior to execution. As a result, recommendations lacking evidence, authority, provenance, governance approval, or auditability may nevertheless be executed if submitted by an authenticated identity.

Consequently, there exists an acute technical need in the art for computing architectures that evaluate decisions against machine-verifiable admissibility requirements prior to allowing state mutation.

---

## 3. SUMMARY OF THE INVENTION

The disclosed system solves the aforementioned deficiencies by inserting a machine-enforced admissibility determination layer directly between decision generation and physical execution.

In one embodiment, the execution control lifecycle comprises:

$$\text{Input} \longrightarrow \text{Decision Generation} \longrightarrow \mathbf{\Big[\ Admissibility\ Evaluation\ \Big]} \longrightarrow \mathbf{\Big[\ Execution\ Authorization\ \Big]} \longrightarrow \text{Execution}$$

Execution eligibility is derived from admissibility status. A decision object may exist, occupy memory, and undergo computational evaluation, yet remain strictly non-executable until predefined admissibility requirements are verified.

---

## 4. DEFINITIONS & CANONICAL TERMINOLOGY

* **Decision Object:** A machine-readable data structure representing a recommendation, instruction, capital allocation, transaction, policy action, workflow activity, model output, autonomous action, or state mutation.
* **Admissibility:** A machine-determined execution status indicating whether a decision object satisfies required prerequisite conditions across provenance, authority, evidence, and retention.
* **Proof Chain:** A traversable provenance structure connecting a decision object to one or more supporting objects. In preferred embodiments, a Proof Chain comprises seven canonical structural layers: **Origin**, **Evidence**, **Validation**, **Policy**, **Governance**, **Execution**, and **Outcome**. The chain may be linear, graph-based, hierarchical, or hybrid.
* **Orphan Decision:** A decision object missing one or more required provenance relationships, policy authorities, or evidentiary links.

---

## 5. SYSTEM ARCHITECTURE

```
┌────────────────────────────────────────────────────────┐
│               DECISION GENERATION LAYER                │
│         (Autonomous Agent, Model, or Human UI)         │
└───────────────────────────┬────────────────────────────┘
                            │ (Candidate Decision Object)
                            ▼
┌────────────────────────────────────────────────────────┐
│                  ADMISSIBILITY ENGINE                  │
│       (Evaluates Provenance, Evidence, Authority)      │
└───────────────────────────┬────────────────────────────┘
                            │ (Admissible State Assigned)
                            ▼
┌────────────────────────────────────────────────────────┐
│                 AUTHORIZATION LAYER                    │
│           (Issues Machine-Verifiable Token)            │
└───────────────────────────┬────────────────────────────┘
                            │ (Token Presented)
                            ▼
┌────────────────────────────────────────────────────────┐
│                   EXECUTION LAYER                      │
│     (State Machine Mutation / Blockchain Actuator)     │
└────────────────────────────────────────────────────────┘
```

The admissibility engine determines whether a decision object is eligible for execution. Without an affirmative determination, downstream execution endpoints refuse request processing.

---

## 6. DETAILED DESCRIPTION OF EMBODIMENTS

### A. Admissibility Engine & Evaluation Criteria
The admissibility engine evaluates one or more admissibility requirements including:
1. **Provenance requirements** (unbroken causal path to origin);
2. **Evidence requirements** (source-identified empirical data);
3. **Authorization requirements** (explicit policy mandates and delegated authority);
4. **Governance requirements** (independent multi-agent or human quorum ratification);
5. **Retention requirements** (archival commitments to persistent memory);
6. **Policy requirements** (operating boundary compliance); and
7. **Regulatory requirements** (statutory compliance checks).

The engine may apply Boolean evaluation, weighted scoring, Bayesian inference, rule-based systems, machine learning classifiers, or hybrid combinations thereof.

### B. Finite Admissibility State Machine
In a preferred embodiment, a decision object is managed under a finite state machine comprising:
* `PENDING`: Formulated but undergoing evaluation; non-executable.
* `ADMISSIBLE`: All criteria satisfied; execution authorization unlocked.
* `INADMISSIBLE`: One or more criteria failed; execution blocked.
* `REVOKED`: Previously granted status rescinded due to input invalidation.
* `QUARANTINED`: Structural anomalies or missing dependencies detected; isolated for forensic review.

### C. Traceability Evaluation
Traceability determines whether a decision can be linked to an originating source:

$$\text{Decision Object} \longleftarrow \text{Evidence Object} \longleftarrow \text{Originating Source Object}$$

The path must be discoverable by graph traversal across an attributed property graph. If the lineage path cannot be traversed, the decision is classified as non-admissible.

### D. Accountability Evaluation
Accountability determines whether the decision is associated with a valid authority:
* Human approvers;
* Investment committees;
* Autonomous governance councils;
* Policy engines;
* Cryptographic signers; or
* Delegated authorities.

Execution is blocked when no valid authority relationship exists within the graph.

### E. Substantiation Evaluation
Substantiation determines whether sufficient supporting evidence exists. Evidence may comprise market order books, sensor outputs, physical telemetry, research datasets, economic reports, or smart contract logs. Evidence quality is evaluated against:
$$\text{Freshness},\quad \text{Completeness},\quad \text{Statistical Confidence},\quad \text{Sample Size } (N),\quad \text{Source Credibility}.$$

### F. Knowledge Evaluation & Institutional Memory
Knowledge evaluates whether the decision contributes to durable institutional memory. Decision objects, evidence, approvals, and post-execution outcomes are retained in a persistent hypergraph (e.g. Neo4j/Valkey). Future decisions retrieve historical precedent using sub-graph cosine similarity.

---

## 7. EXECUTION AUTHORIZATION & ORPHAN PREVENTION

### Gated Execution Flow
```
    [ Decision Generated ]
              │
              ▼
    [ Evaluate Admissibility ]
        /                  \
   (TASK PASS)         (TASK FAIL)
      /                      \
     ▼                        ▼
[ Generate Token ]       [ Deny Authorization ]
     │                        │
     ▼                        ▼
[ Execute State ]        [ Block Execution ]
```

### Automated Orphan Prevention
The system continuously detects orphan conditions: missing evidence, missing authority references, missing governance records, or broken graph paths. Upon detection:
1. Mark decision object as `INADMISSIBLE`;
2. Revoke active authorization tokens;
3. Quarantine execution thread; and
4. Emit structured remediation instructions to the initiating system.

---

## 8. EXEMPLARY EMBODIMENTS

### Embodiment 1: Institutional Pension Allocation
A pension allocation recommendation of \$50M is generated by an algorithmic model:
1. **Evidence Ingress:** Ingests $N = 1,247$ empirical observations (liquidity depth, yield curve telemetry).
2. **Policy Verification:** Resolves Investment Policy Statement mandate `IPS-2026-A §4.2` (max single-sector allocation 5%).
3. **Governance Ratification:** Accumulates 5/5 unanimous cryptographic signatures from the Sovereign Investment Council.
4. **Admissibility Assignment:** Transitions state from `PENDING` to `ADMISSIBLE`.
5. **Token Generation:** Issues cryptographic token `TOK-20261012-447`.
6. **Execution Mutation:** Rebalance gateway executes transaction on-chain only upon validating `TOK-20261012-447`.

### Embodiment 2: Autonomous Agent Governance Boundary
An autonomous machine learning agent formulates a proposed state mutation. The agent itself has zero direct execution privileges. The agent emits a candidate `Decision Object` to the `Admissibility Engine`. The engine enforces a machine-verifiable constitutional boundary between cognition and action:

$$\mathbf{Autonomous\ Agent\ (Cognition)} \longrightarrow \mathbf{Decision\ Object} \longrightarrow \mathbf{Admissibility\ Engine} \longrightarrow \mathbf{Execution\ (Action)}$$

---

## 9. TECHNICAL ADVANTAGES OVER PRIOR ART

The disclosed architecture fundamentally transforms computing systems by making execution contingent upon admissibility status:
1. **Decision-Centric Governance:** Evaluates the decision itself rather than merely the credentials of the user or process.
2. **Deterministic State Mutation Gating:** State machines refuse mutation in the absence of valid execution tokens.
3. **Zero Orphan Propagation:** Unsubstantiated or unauthorized actions are quarantined before producing execution events.
4. **Durable Lineage Reconstruction:** Every executed action possesses an immutable proof chain linking origin, evidence, authority, execution, and outcome.