Ahf, ai-human co-creation enterprise formation system and method thereof

Abstract

This invention provides a Human-Machine Collaborative Enterprise Formation System (AHF system) and method, which can be deployed in a zero-server environment using a software development kit (SDK). The system integrates an AI generator and a human verifier. The AI ​​generator produces draft articles of association or patents, which are then signed and verified by the human verifier. A proof-meta engine captures this collaborative process, generating proof-meta evidence containing a root hash, multiple timestamps (N≧3 TSA / STA), signer information, and a nonce. A formatting compiler compiles this collaborative process into a verifiable packet with a fixed field order. The system enforces a quality gate (QG) and fail-closed mechanism to ensure that only formation events conforming to the RegProof framework are anchored. This packet can be replayed and verified in an isolated environment to ensure the legal validity of the formation process. This agreement can be deployed in SDK mode on any existing server architecture without adding new databases or servers.

Claims

1. A human-machine co-creation enterprise formation system (AHF system), comprising: an AI generator for generating a draft containing articles of association or patent text; a quality gate module (QG) embedding a regulatory audit module (RegProof Module) for mandatory verification of the draft's engine version (engine_version), policy ID (policy_id), and regulatory compliance outline; a human verifier interface for a user to perform hardware security module (HSM) segregated signature to confirm the draft; and an evidence chain engine (Proof.meta). The Engine is coupled to N3 cross-jurisdictional timestamp authorities (TSA_i) and a secure time aggregator (STA) to aggregate trusted times and generate proof metadata (proof_meta) evidence containing a root hash, a Merkle path, a timestamp source, signer information, and a nonce; and a formatter. The system uses a compiler to compile the draft generated by the AI ​​generator and the signature of the human reviewer into a verifiable packet in an immutable, fixed field order. Its key feature is that the system is deployed in a zero-server SDK mode, without adding a persistent server or database. Furthermore, when the quality gate verification fails, the human reviewer refuses to sign, or the STA time aggregation fails, a Fail-Closed mechanism is triggered, and a demotion scepter is returned. All demotion and exception event exception reference records (exception_ref) also include the HSM signature and timestamp and are anchored to the Merkle audit log. The fixed field order, together with all required fields, constitutes the necessary and sufficient condition for replay verification; if any field is missing or its order is changed, the offline four-step verification sequence cannot be completed, and the system will automatically mark it as invalid and trigger Fail-Closed.

2. A method for forming a human-machine co-creation enterprise, comprising: (a) generating a draft by an AI automatic generator; (b) forcibly verifying an engine version (engine_version), policy ID (policy_id), and regulatory compliance outline of the draft by a quality gate module (QG); (c) receiving a hardware-isolated signature to confirm the draft by a human reviewer interface; (d) compiling the draft and the signature into a verifiable packet according to a fixed, unavoidable, and immutable field order that serves as the basis for playback verification; and (e) using an evidence chain engine (Proof.meta... Engine) calls N3 cross-jurisdictional TSA_i and STA to aggregate trusted times, and generates one proof metadata (proof_meta) evidence containing a root hash, Merkle path, timestamp source, signers information, and a nonce; (f) executes a self-check procedure, including determining whether the quality gate verification failed, whether the signature was not provided, or whether the time aggregation failed; (g) based on the self-check result, if it fails, a Fail-Closed mechanism is triggered and a demotion scepter is returned; or if it does not fail, the root hash is anchored to a Merkle audit log; wherein, The fixed field order, together with all required fields, constitutes the necessary and sufficient condition for replay verification; if any field is missing or its order is changed, the offline four-step verification sequence cannot be completed, and the system will automatically mark it as invalid and trigger Fail-Closed.

3. The system as described in Request 1, wherein the packet further includes a Replay Vector (RV), which contains a minimum set of reproducible recipes, the minimum set including at least: an execution environment hash (Engine_Hash), configuration parameters (Config_Params), and input features (Input_Features), for reproducing the generation process of the AI ​​generator in an isolated container to verify the authenticity of the process; if the minimum set is not met, the replay verification is considered infeasible and marked E_REPLAY_INVALID.

4. The system as described in Request 1, wherein the human reviewer interface and the HSM are connected via an authenticated secure channel, and the signature is completed within a hardware-isolated boundary.

5. As in Request Item 2, the verification module saves the credential status history (OCSP / CRL Archive) to perform point-in-time verification after expiration, generates an epitaph when the key is revoked and includes its hash in subsequent Merkle nodes, and offline verification is considered valid only if it passes one of the four offline verification steps: hash chain integrity, hardware signature, time aggregation and revocation tracing.

6. The system as described in Request 1, wherein the packet contains no retrievable original event data, but only its hash value, in order to comply with the principles of data minimization and default privacy.

7. The system as described in Request 1, wherein the engine_version of the AI ​​generator is a model version identifier, and the Replay Vector (RV) additionally contains a hash of the model weight file for post-playback and supervisory review.

8. The method of Request 2, wherein the Secure Time Aggregator (STA) first performs offset detection and calculates the trusted time using an aggregation algorithm; when there are fewer than three time sources (N<3) or when a trusted time cannot be generated, the system can only return the degraded scepter and prohibit anchoring.

9. The system as described in Request 1, wherein zero servers means that no new persistent servers or databases are added, nor are the existing core accounting state machines modified, and all evidence is generated and verified in packet form at the network boundary.

10. The system as described in Request 1, wherein the anchoring service aggregates packet hashes using a Merkle tree and periodically submits the Merkle Root to an external notary log or an equivalent immutable notary system; and all degradation events, ICL version drift events, and exception events are recorded as exception_ref and anchored to the Merkle audit log, wherein the exception_ref itself also contains an HSM signature and timestamp, and is available for auditing and judicial proceedings.

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