Garbage In, Clarity Out: Fixing the Input Problem Before It Reaches Legal

Patent attorneys, agents, and pretty much everyone involved in the IP process all know the phrase: Garbage In, Garbage Out. And in the age of generative AI, it’s easy to assume that the problem has only grown louder. Longer documents, more polished filler, and fewer clear points of novelty.

But what if the issue isn’t the AI itself?

What if the real problem is that we’ve been feeding the same vague, ill-structured invention inputs into tools (and processes) that were never built to handle ambiguity in the first place?

The Illusion of Progress

We’ve seen it ourselves. A sleek, AI-generated invention disclosure filled with jargon, background context, and paragraphs that read like a grant proposal... but nowhere near a claimable invention.

It’s not that AI got it wrong. It’s that the process was never designed to guide the user toward what matters. Technical differentiation, real-world use cases, and the inventive step.

It’s the digital equivalent of enlarging a blurry photo. You don’t gain resolution, you just get a bigger blur.

Rewriting the GIGO Rulebook

At Tangify, we don’t just use AI to generate more content. We use it to structure better inputs, long before the draft ever reaches a patent attorney.

We’ve built a practical and impactful tool that includes:

• Guided workflows that help engineers surface actual points of novelty
• Smart question trees that draw out relevant problem-solution framing
• Internal consistency checks that flag contradictions before they waste anyone’s time
• A narrative layer that’s clear enough for legal, and clean enough for R&D

Tangify’s AI is less about replacing legal strategy, and more about accelerating collaboration between inventors and counsel. But we get it - not everyone understands just how quickly AI is moving, far beyond general use of chatGPT, Gemini, or CoPilot. Drafts created by dropping a loose paragraph into a public LLM often are useless. But that critique rests on several logical shortcuts that don’t apply to specialized, workflow-driven systems like Tangify. Agentic AI actually fixes the root-cause “garbage in” problem instead of amplifying it.

Seven Misconceptions About AI for IP and Patents

1) False Equivalence: “ChatGPT output = all AI output.”

• Why It Misses the Mark: Generic chatbots have no domain schema or legal guardrails.
• How Tangify Addresses It: Tangify uses a domain ontology, novelty prompts, and claim-thinking templates built with IP pros.

2) Straw Man: “Inventor pastes an idea, hits Generate, files application.”

• Why It Misses the Mark: Serious orgs don’t file raw LLM text.
• How Tangify Addresses It:  Agents guide inventors through step-wise interrogation (problem → prior approaches → differentiators) before any narrative appears.

3) Hasty Generalization: “One messy draft proves AI can’t identify novelty.”

• Why It Misses the Mark: Outliers ≠ rule. Most failures come from unstructured input.
• How Tangify Addresses It:  System checks novelty signals against prior-art embeddings and flags gaps for human review.

4) Appeal to Tradition: “Hand-written forms worked for decades, why change?”

• Why It Misses the Mark: Speed, volume, and cross-disciplinary R&D outgrew static forms.
• How Tangify Addresses It:  Agentic AI scales the Q&A loop and timestamps ideation hours after the eureka moment.

5) Black-or-White Thinking: “AI either replaces attorneys or is worthless.”

• Why It Misses the Mark: The value spectrum is broader: assist, augment, triage.
• How Tangify Addresses It:  Tangify frees experts from clerical capture so they can focus on claim strategy and portfolio fit.

6) Slippery Slope: “AI will swamp the PTO with junk apps.”

• Why It Misses the Mark: Quantity ≠ quality when filters exist.
• How Tangify Addresses It:  Tangify embeds quality-gates (completeness, technical detail, internal consistency) that block noise upstream.

7) Illusion of Comprehension: “Longer text feels safer.”

• Why It Misses the Mark: Length often hides missing enablement.
• How Tangify Addresses It:  The platform enforces concise, claim-oriented storytelling and highlights unexplained assertions.

From “Garbage In” to “Guided Insight”

Traditional disclosure forms assume inventors already know how to translate R&D epiphanies into patent-ready language. But the reality is:

1. Engineers think in systems and edge-cases, not §112 enablement.
2. Counsel needs clear novelty hooks, not academic backgrounders.
3. Product teams need business context, not claim jargon.

Agentic AI bridges all three. It sits between technical brains and legal brains, asking targeted follow-ups the moment ambiguity appears.

• “Which step in your algorithm reduces compute by 40%? Please attach benchmark data.”
• “Prior methods relied on optical sensing. List the shortcomings you overcame.”
• “Identify any security counter-measures unique to this deployment path.”

Each answer seeds the next question, raising the resolution of the disclosure before a lawyer opens the file.

The Shift From Document Factory to Discovery Engine

We think of Tangify less like a writing tool, and more like an interpretive engine. It turns vague napkin sketches and hallway rants into structured, searchable, actionable invention records.

Not by generating polished garbage, but by preventing garbage in the first place.

Because in the patent process, clarity is leverage. And no one wins when good ideas get buried under bad inputs.