Sanitized case study 06
Quality Control System for a Pear-Fruit Sparkling Water Production Line
Rejection reversed in reexamination
The response separated algorithm words from actual control-system architecture.
FIELD: AI/control systems / food productionSTAGE: SANITIZED TECHNICAL REVIEWRESULT: Rejection reversed in reexamination
Review boundary
AI/control systems / food production
This is a sanitized technical-prosecution note prepared for peer-agency due diligence. Full file histories, claim amendments, cited references, and client documents are shared only after NDA and conflict clearance.
EXAMINER LOGIC
How the rejection framed the case
The examiner combined a dairy contamination detection model, an agricultural multi-source sensing method, and alleged common knowledge such as potential functions and gradient descent, treating the invention as an obvious transfer from detection optimization to beverage-line control.
FIP RECONSTRUCTION
How the response rebuilt the case
We dismantled the false equivalence: D1 outputs risk levels, while the invention outputs actuator action vectors; D1's gradient optimizes model hyperparameters, while the invention's gradient updates control actions; D1's disturbance is algorithm-search disturbance, while the invention's disturbance is an industrial-process disturbance. We also exposed the hindsight pattern: starting from the invention and searching backward for similar words.
OUTCOME
What changed procedurally
Reexamination was filed within 28 days after rejection; the rejection was revoked in about three months and the case proceeded to grant.
Deep technical note
Detailed English-only prosecution analysis.
This section expands the case beyond the homepage summary so foreign counsel can assess the reasoning pattern, not just the outcome.
Diagnostic read
- The examiner combined a dairy contamination model, an agricultural sensing method, and alleged common knowledge such as potential functions and gradient descent.
- The rejection treated shared vocabulary as shared technical teaching.
- The real mismatch was output and control meaning: risk-level prediction is not actuator-vector optimization for an industrial production line.
Response architecture
- Separate the outputs: D1 produced risk levels, while the invention produced control-device action vectors.
- Separate the gradients: model hyperparameter optimization is not the same as updating actuator actions.
- Expose hindsight: the rejection started from the invention and then searched backward for similar words.
Due-diligence takeaways
- AI/control inventions must be defended at the level of variables, outputs, and system effect.
- Common mathematical terminology should not be allowed to erase technical architecture.
- Reexamination can reverse a rejection when the combination depends on vocabulary matching rather than technical teaching.
What a peer firm can test
For a live matter, we normally ask for the relevant patent office or jurisdiction, prosecution stage, core rejection issue, principal cited references, current deadline, and a neutral technical summary. Client names and unpublished full documents can wait until NDA and conflict clearance are complete.
The first review focuses on whether the examiner has mis-modeled the technical problem, overstated a motivation to combine, relied on unsupported common knowledge, or missed an allowance route available through disciplined claim amendment.