High-Quality Drafting for Engineers: Invention Disclosure Form Example for Engineers and What ‘Good’ Looks Like

Step 1 – Define ‘Good’ and Align with Internal Review

When engineers draft an invention disclosure form (IDF), “good” means the document can be read once by someone who did not build the system and still understand (1) what is new, (2) why it matters relative to known solutions, and (3) how to implement it with enough detail that a competent engineer could reproduce it. A company-ready IDF balances technical precision with organizational needs: legal review for patentability, portfolio managers looking for strategic fit, and technical reviewers validating enablement. The goal is not literary style; it is structured clarity that aligns with the decision gates of internal review.

Three quality pillars guide this alignment:

• Clarity: Every key term is defined on first use; acronyms are expanded; architecture and data flows are described without relying on internal tribal knowledge. Clarity means a reviewer can identify the inventive concept in under two minutes and the full mechanism in under ten.
• Completeness: The IDF covers all sections to a reviewable depth: the technical field that frames the domain, the problem with measurable constraints, the summary that isolates the novelty, the detailed description that enables implementation, and keywords that make the disclosure findable in internal systems and external prior art searches.
• Novelty focus: Reviewers look for a clear inventive step over known techniques. A good IDF signals novelty by contrasting prior approaches and explaining the specific mechanism or configuration change that achieves a non-obvious improvement, not merely reporting performance outcomes.

From an internal reviewer’s perspective, three questions dominate:

1. Is it patentable? They scan for novelty, non-obviousness, and utility. This requires explicit contrast statements (e.g., how your scheduler differs from standard greedy or heuristic schemes) and a mechanism-level explanation that goes beyond “we got better results.”
2. Is it enabled? They look for sufficient technical detail to let someone skilled in the art implement the invention without undue experimentation. This includes inputs, transformations, control flow, parameter regimes, and interfaces. Diagrams are not always required in the IDF, but text must specify components and their interactions.
3. Is it clean? They screen for risky language: claiming prior art as your own, speculative marketing promises, disclosures of confidential business partnerships, or implementation details that could prejudice later claim scope. Clean drafting is precise, avoids puffery, and states facts about the mechanism.

Finally, reviewers need to index and retrieve disclosures efficiently. Your IDF must be searchable: use standard domain terms and synonyms so that internal search tools and external patent analysts can discover related art. In short, a “good” IDF is a crisp technical artifact that makes it easy to say yes (patent file), no (not novel), or iterate (clarify enablement), with minimal back-and-forth.

Step 2 – Section-by-Section Deep Dive with Targeted Templates

Each section of the IDF serves a distinct purpose. Use the following heuristics, do’s/don’ts, and sentence patterns to accelerate drafting while maintaining quality.

Technical Field

Purpose: Place the invention in the correct domain so prior art and reviewers are aligned from the start. Keep it broad enough to cover variations but specific enough to avoid misclassification.

• Do: Name the technology area and sub-area; include relevant standards, architectures, or device classes.
• Don’t: Insert claims of novelty, performance numbers, or marketing language.
• Sentence starters:
  • “The present disclosure relates to [domain], and in particular to [sub-domain/technique].”
  • “It concerns systems and methods for [high-level function] in [context/equipment].”

Domain-tuned patterns:

• Semiconductors: “This disclosure relates to integrated circuit design, specifically to [clock distribution/retention flip-flops/EDA placement] for [process node/voltage regime].”
• ML hardware: “This disclosure relates to accelerator architectures, particularly to [on-chip interconnect/topology-aware scheduling/quantization schemes] for [tensor operations/inference/training].”

Background / Problem

Purpose: Define the engineering problem with concrete constraints and the limitations of known approaches. This sets up novelty without oversharing proprietary data.

• Do: State the operational challenge, constraints (latency, power, area, cost, bandwidth), and the failure modes of common solutions. Cite general categories of prior art without admitting specific internal use.
• Don’t: Confess that your team publicly used a prior method or imply long-term commercial deployment that could affect priority. Avoid disparaging language.
• Sentence starters:
  • “Existing approaches to [task] typically rely on [technique], which [limitation] under [conditions].”
  • “In [context], constraints such as [X, Y] lead to [failure mode/performance bottleneck].”

Heuristic: Aim for two to three paragraphs that a patent searcher can map to keywords and that a portfolio manager can recognize as strategically relevant.

Summary of the Invention

Purpose: Isolate the core inventive concept in practical terms. Explain the mechanism, not just the result. One to two paragraphs are ideal, avoiding implementation minutiae that belong in the detailed description.

• Do: State the key mechanism, inputs/outputs, and the differentiator versus known techniques. If applicable, list two or three key configurations or modes.
• Don’t: Over-claim (“all,” “always,” “the only”). Don’t present extensive performance data here.
• Sentence starters:
  • “In one aspect, the disclosure provides a [system/method] that [core action] by [specific mechanism].”
  • “Unlike [general prior approach], the disclosed technique [distinctive step/configuration].”

Pattern for mechanism clarity: “[Component A] determines [signal/state] using [criterion/model]; [Component B] adjusts [resource/control] accordingly, yielding [effect] under [conditions].”

Detailed Description

Purpose: Enable implementation. Provide enough architecture, control flow, data formats, parameter ranges, and integration notes that a skilled person can reproduce without undue experimentation. This is where you earn enablement.

• Do: Describe the system in components and interactions. Include sequences (e.g., pipeline stages), key equations or selection rules in words, parameter ranges (approximate is fine), and variations/alternatives. Mention where data is stored, how it is updated, and how errors are handled.
• Don’t: Reveal confidential third-party relationships, proprietary datasets, or internal codenames if not necessary for enablement. Don’t include speculative future features not yet reduced to practice.
• Sentence patterns:
  • “The system includes [modules]; [module X] receives [inputs] and produces [outputs] based on [rule/algorithm].”
  • “In an embodiment, [control loop/finite-state machine] updates [state] when [condition] is met, using [threshold/heuristic].”
  • “Parameters such as [alpha, buffer depth] are selected within [range], balancing [trade-off] under [operating conditions].”

Domain-tuned patterns:

• Semiconductors: “A clock gating cell coupled to [pipeline stage] disables [net] when [activity detector] indicates [state]; duty cycle distortion is bounded by [tolerance] via [retiming/keeper]. Layout constraints are met by [placement rule].”
• ML hardware: “A scheduler partitions tensors across [N] compute tiles based on [cost function combining data locality and contention]. The interconnect arbiter prioritizes [traffic class] according to [token bucket/latency budget], reducing [head-of-line blocking] under [burst patterns].”

Keywords

Purpose: Make the disclosure searchable internally and externally. Think like a patent examiner and a colleague six months later.

• Do: Include core terms and synonyms, acronyms expanded, component names, and problem descriptors. Include standards or frameworks (e.g., “HBM3,” “RISC-V,” “NoC arbitration,” “quantization-aware training”).
• Don’t: Use only internal codenames or vague terms like “optimization.”
• Pattern: “[Primary term]; [synonym/variant]; [key component]; [constraint/performance metric]; [protocol/standard]; [algorithm category].”

Step 3 – Worked ‘Invention Disclosure Form Example for Engineers’

Title: Resource-Aware Tile Scheduler for Mixed-Precision Tensor Operations on Mesh-Connected Accelerators

Technical Field

The present disclosure relates to accelerator architectures for machine learning workloads, and in particular to scheduling methods for mesh-connected compute arrays executing mixed-precision tensor operations.

Background/Problem

Existing schedulers for array-based accelerators typically assign tensor tiles using greedy heuristics or static striping. Under mixed-precision workloads, these methods suffer from resource imbalance: tiles requiring low-precision units (e.g., INT8) saturate local memory bandwidth while compute units for higher precision (e.g., FP16) remain underutilized. In multi-hop mesh interconnects, naive placement increases east-west traffic and induces head-of-line blocking at contention hot spots, degrading tail latency for latency-sensitive inference.

In workloads with bursty arrival patterns and layer-wise precision changes, two constraints dominate: on-chip memory bandwidth per tile and interconnect hop count to the target activation buffer. Known approaches either ignore per-tile bandwidth budgets or treat tile placement and routing priority as independent decisions, leading to oscillation between bandwidth oversubscription and idle compute.

Summary of the Invention

In one aspect, the disclosure provides a scheduling method that jointly assigns tensor tiles to compute tiles and sets routing priorities based on a per-tile resource profile. Each tile’s profile estimates required memory bandwidth, arithmetic unit type, and acceptable latency budget. The scheduler places tiles to minimize a composite cost combining predicted interconnect contention with bandwidth headroom, and assigns per-flow priorities to preserve latency budgets for sensitive tiles.

Unlike static or purely greedy placement, the method computes a local congestion score from recent interconnect queue depths and uses it to bias tile placement toward regions with sufficient bandwidth headroom for the required precision. Routing priorities are derived from the same resource profile, so placement and traffic shaping remain consistent across bursts and precision changes.

Detailed Description

System Architecture. The accelerator comprises an M×N mesh of compute tiles, on-chip SRAM banks adjacent to each tile, and a credit-based mesh interconnect with per-link queues. Each compute tile supports INT8 and FP16 arithmetic units with distinct peak throughputs and memory access patterns. A central scheduler runs each scheduling interval T_s, receiving a batch of tensor tiles from the runtime. For each tile, the runtime provides dimensions, precision requirement, and a latency budget derived from the calling layer.

Resource Profiling. For each incoming tile, the scheduler computes a resource profile R = {b_req, p_type, L_max}, where b_req estimates sustained memory bandwidth required during execution, p_type identifies the needed arithmetic unit class, and L_max is the end-to-end latency budget for the tile. The estimate b_req is derived from tile dimensions and known reuse factors of the kernel, adjusted by recent cache residency statistics. Profiles are updated if execution history deviates from estimates by more than a configurable error threshold.

Cost Function. For each candidate placement location (i, j), the scheduler computes a cost C(i, j | R) = w1·H(i, j) + w2·B(i, j, p_type) + w3·D(i, j), where H is a predicted hop-induced contention score computed from moving averages of per-link queue depths around (i, j); B is a bandwidth headroom penalty that increases when local SRAM-to-tile bandwidth minus b_req is negative or near zero, adjusted for p_type; and D is a distance term to the target activation buffer to limit latency. Weights w1–w3 are selected from a bounded range configured at deployment and may be re-tuned per model class.

Placement Procedure. Tiles are sorted by decreasing criticality, defined as a function of L_max and p_type. For each tile, the scheduler evaluates C over a neighborhood window and chooses the minimum-cost location that also satisfies an integer constraint on available arithmetic unit cycles per interval. If no location satisfies the constraint, the scheduler defers low-criticality tiles or splits the tile along its minor dimension, updating R for the fragments.

Routing Priority Assignment. For each placed tile, the scheduler sets an interconnect priority tag P based on L_max and the local congestion score, reserving tokens for high-priority flows through a token-bucket regulator at each egress. The regulator ensures that high-priority tiles maintain bounded queueing delay while preventing starvation of best-effort flows via minimum token allocations.

Feedback and Adaptation. At the end of each interval, the scheduler collects (a) measured bandwidth consumption per tile, (b) queueing delay per hop along each path, and (c) arithmetic unit utilization. If observed metrics diverge from predictions beyond the error threshold, the scheduler adjusts b_req estimates and w1–w3 within configured bounds. To avoid oscillation, updates use an exponential moving average with a damping factor chosen to keep the closed-loop system stable under bursty workloads.

Variations. In deployments with hierarchical meshes, the cost function includes a tier-crossing penalty. For chips using HBM-attached memory, B incorporates page locality to reduce row misses. When quantization-aware training shifts precision mid-epoch, the scheduler raises p_type-criticality weight to preserve latency for layers with sensitivity to precision transitions.

Interfaces and Implementation Notes. The scheduler exposes an API to the runtime: submit_batch(tiles), update_stats(stats), and configure(weights, thresholds). All state required for decisions is held in a bounded memory region accessible over the control network. The method can be implemented in firmware on a management core or as a hardware state machine with microcoded operators for cost evaluation.

Keywords

Mesh interconnect; tile scheduler; mixed-precision; INT8; FP16; on-chip bandwidth; congestion-aware placement; token bucket; latency budget; tensor partitioning; accelerator architecture; NoC arbitration; SRAM bandwidth; cost function.

Why this example is “good.” It frames the field correctly, states a concrete problem with measurable constraints, articulates a mechanism-level novelty (joint placement and priority assignment using a cost function tied to resource profiles), provides enablement detail (components, inputs, control flow, parameter ranges, feedback), and includes searchable keywords and synonyms.

Step 4 – Rapid Self-Review Checklist and Pitfall Correction

Use this compact checklist before submitting your IDF:

• Technical Field: Is the domain and sub-domain unambiguous? Would a reviewer route it to the right specialist? Remove novelty claims and marketing words.
• Background/Problem: Did you name the operational constraints, typical approaches, and their specific limitations without admitting internal prior deployments? Are failure modes clear?
• Summary: Have you stated the core mechanism and how it differs from known approaches in two concise paragraphs or fewer? Avoided over-claims and vague outcomes?
• Detailed Description: Could a peer implement it from your text? Did you include components, data/command flow, selection criteria, parameter ranges, and variations? Did you avoid confidential third-party details and unnecessary codenames?
• Keywords: Did you include domain terms, acronyms expanded, and synonyms that examiners and colleagues will search? Are you avoiding generic terms like “optimization” alone?
• Novelty Signals: Did you explicitly contrast with known categories of solutions? Is the differentiator a mechanism, not just a result?
• Enablement Signals: Are inputs, outputs, and control logic specified? Have you identified thresholds, ranges, or heuristics with rationale? Is error handling or adaptation described if relevant?
• Clarity: Are acronyms defined on first use? Are sentences short and active? Did you remove ambiguity (e.g., replace “it” with the named component)?
• Scope Hygiene: Did you avoid statements implying prior public use or sale? Did you avoid naming customers or confidential partners? Are speculative features clearly labeled as variations only if reduced to practice?
• Searchability: Are standards, protocols, and architecture terms included? Are synonyms for key ideas present (e.g., “NoC,” “interconnect,” “fabric”)?

Common pitfalls and how to fix them:

• Marketing language (“breakthrough,” “industry-leading”): Replace with mechanism-focused statements (“computes placement using congestion score and bandwidth headroom”).
• Ambiguity (“better performance”): Specify metric and condition (“reduces tail latency under bursty traffic by prioritizing latency-sensitive flows”).
• Missing enablement (“uses a novel algorithm”): State inputs, decision rule, and output. Provide parameter bounds or selection criteria.
• Accidental prior art admissions (“we have always used X”): Rephrase as field context (“known approaches include X, which exhibits Y”). Avoid claims of your organization’s prior use unless legally vetted.
• Unsearchable keywords (“optimization,” “system”): Add technical anchors (“mesh interconnect,” “tile scheduler,” “token bucket regulator,” “INT8/FP16”).

Rewrite tactics for rapid improvement:

• Define every new term once with a parenthetical (e.g., “token bucket (rate-based regulator)”).
• Use a consistent component naming scheme; avoid pronouns for critical interactions.
• Convert results-only sentences into mechanism statements using the pattern: “By [mechanism], the system achieves [effect] under [condition].”
• Add a two-sentence enablement capsule at the end of the Summary that names inputs, processing, and outputs.
• Insert a short variations paragraph to broaden scope without diluting clarity.

By systematically aligning to reviewer questions, using section-specific templates, and validating with the checklist, engineers can produce company-ready IDFs that are clear, complete, novelty-focused, and searchable. This approach reduces review cycles, increases patent filing quality, and creates a reusable drafting rhythm across technical domains such as semiconductors and ML hardware.