撰写专利权利要求解释

Claim construction and explanation of the principal claim

1. Claim type and transitional phrase

• Statutory class: The claim is directed to a method (process) for image denoising.
• Transitional term: The use of “comprising” renders the claim open-ended. Under the broadest reasonable interpretation (BRI) consistent with the specification, the presence of additional, unrecited steps or components in a practiced method does not avoid infringement, provided each recited limitation is performed.

2. Preamble

• “An image denoising method” functions as a statement of intended use and field. Unless the preamble is necessary to give life, meaning, or vitality to the claim, it is generally not limiting beyond identifying the context in which the steps are performed. Here, the body of the claim recites a complete process; the preamble primarily supplies context.

3. Element-by-element construction under BRI For clarity, the limitations are enumerated as [A]–[E]:

[A] Acquiring a noisy target image

• Scope: “Acquiring” encompasses receiving, loading, capturing, or otherwise obtaining a digital image that contains noise. No limitation is imposed as to the source, sensor, file format, or noise model.
• Order: This step logically precedes subsequent processing steps; performing the later steps presupposes access to the noisy image.

[B] Extracting multi-scale features with convolution kernels

• Scope: This requires feature extraction at multiple scales using convolutional operations. Under BRI, “multi-scale” reasonably includes use of different kernel sizes, strides, dilations, pyramid features, or hierarchies that yield representations at more than one spatial scale. The claim does not limit the specific network topology, number of layers, or exact convolutional parameters, only that convolution kernels are used to obtain multi-scale features.
• Exclusions: Purely non-convolutional feature extraction (e.g., a pipeline consisting solely of transformer self-attention without any convolution, or only wavelet transforms) would not meet this limitation as written.

[C] Recalibrating channel weights via an attention module

• Scope: This requires an “attention module” that performs channel-wise recalibration, i.e., it computes and applies weights that modulate feature channels. Under BRI, this encompasses any learned mechanism that adjusts per-channel responses conditioned on the features (for example, channel-attention style operations), without limiting the specific architecture or mathematical form.
• Exclusions: An attention mechanism that operates exclusively in the spatial domain without adjusting channel weights would not satisfy this limitation. A mechanism that includes both spatial and channel attention would satisfy the clause insofar as it performs channel-weight recalibration.

[D] Reconstructing a denoised image through deconvolution based on the recalibrated features

• Scope: “Deconvolution” in the convolutional neural network context is reasonably construed to include transposed convolution (also known as fractionally strided convolution) or learned deconvolution operations used for upsampling/decoding. The reconstruction must be “based on the recalibrated features,” i.e., downstream of the channel-weight attention operation.
• Potential ambiguity: If “deconvolution” is intended in the classical signal-processing sense of inverse filtering, the specification should clarify. Under BRI in the CNN art, “deconvolution” commonly denotes transposed convolution. If reconstruction uses only non-deconvolutional upsampling (e.g., bilinear upsampling followed by standard convolution), that may fall outside this limitation.

[E] Wherein parameters of the attention module are obtained by training and remain fixed during inference

• Scope: This is a limiting “wherein” clause. It requires that the attention module’s parameters are learned during a separate training process and are not updated during the claimed inference-time denoising method. The clause does not preclude the attention module from producing different per-input attention outputs at inference; it requires only that the underlying learned parameters are fixed during inference.
• Extent: The clause speaks specifically to the parameters of the attention module; it does not expressly impose the same constraint on other modules, though in ordinary practice inference uses fixed parameters throughout. It also does not require that the training process itself be part of the claimed method.

4. Step order

• Although the claim does not expressly impose an order, the logical and grammatical structure implies the following sequence: acquire the noisy image [A]; extract multi-scale features [B]; apply channel-attention recalibration [C]; and reconstruct the denoised image via deconvolution [D]. Under established principles, an order of steps is required when the claim language, logic, or grammar so dictates. Here, later steps depend on the outputs of earlier steps, supporting an ordered performance.

5. Scope implications and boundaries

• Open-ended nature: Because the claim “comprises” the listed steps, additional operations (e.g., normalization, residual connections, skip connections, non-linearities, loss computations during training, pre- or post-processing such as clipping or color space conversion) may be present without departing from the claim.
• Required technical features:
  • Convolution-based multi-scale feature extraction is mandatory.
  • Channel-wise attention-based recalibration is mandatory.
  • Deconvolution-based reconstruction is mandatory.
  • The attention module’s parameters must be learned and fixed at inference.
• Potential noninfringing variations:
  • Entirely non-convolutional feature extractors (e.g., pure transformer blocks) for the multi-scale features.
  • Attention that does not recalibrate channel weights.
  • Reconstruction that omits deconvolution in favor of other upsampling-only schemes.
  • Methods that update the attention module’s parameters during inference (e.g., test-time training or online adaptation of those parameters).

6. Plain-language synthesis

• In substance, the claim covers an inference-time image-denoising pipeline in which: a noisy image is obtained; multi-scale features are produced using convolutional layers; these features are subjected to a channel-attention recalibration whose weights are governed by parameters learned offline and kept fixed during inference; and the denoised image is generated from the recalibrated features using a deconvolution-based decoder.

以下为对该专利主要权利要求（独立权利要求）的法律式解读与构造性解释，旨在明确其技术特征、限定方式与保护范围边界。

一、权利要求的类别与总体定位

• 标的物类别：装置权利要求（“一种无人机避障系统”），属于设备/系统型权利要求。
• 权利要求结构：采用“包括”用语，原则上为开放式表述，允许系统中含有未列举的其他组件，但不得缺少权利要求中列明的必要技术特征。

二、必要技术特征的逐项解析 该权利要求至少包含以下必要技术特征与功能限定：

A. 硬件组成

1. 前向深度相机：限定为“前向”安装方向且具备获取深度信息的能力。未限定具体成像原理（可涵盖结构光、TOF、双目等实现），但其功能必须能够输出用于生成深度图的数据。
2. 处理器：为实现后述算法与控制逻辑的执行主体，以“用于…”进行功能性限定，构成对处理器的功能配置限制。
3. 姿态传感器：作为系统构件被明确列举。虽权利要求未具体限定其在算法链路中的使用方式，但其作为组成部分为必要特征。
4. 制动控制模块：用于执行“减速”控制的执行类模块。未限定具体执行机构形式（如桨距控制、推力限幅等），但需具备可触发减速的控制能力。

B. 处理器的功能性限定（核心算法与控制逻辑） 处理器被限定为在特定条件下执行以下步骤与策略：

1. 触发条件：当无人机“飞行速度超过阈值”时，启动后续避障流程。该“阈值”为变量，后述与海拔相关的自适应机制将对其进行调整。
2. 感知与建图：基于深度图生成“占用栅格”。此处限定输出表征空间占用状态的栅格地图（占用栅格）的生成过程，与后续风险评估存在逻辑上的前后依存关系。
3. 风险预测：在时间域内对“2秒内碰撞风险”进行预测。时间窗口为定值（2秒），具有实质性限定作用。
4. 双阈值决策：
   • 若碰撞风险超过第一阈值，则触发“减速”。
   • 若碰撞风险超过第二阈值，则“规划绕行路径”。 上述为分级处置逻辑，但权利要求文字未明确二者的相对大小关系及是否存在并行触发或优先级覆盖（该点为解释上的潜在歧义，见下文四）。
5. 自适应调参：栅格分辨率与上述阈值均“随海拔自适应调整”。该特征属于在线自适应策略的明确限制，但未限定具体函数或规则，构成结果性功能限定，范围相对宽泛。

三、技术特征间的功能耦合与链路

• 传感-建图-预测-决策-执行的闭环结构：前向深度相机提供深度信息；处理器生成占用栅格并预测2秒内碰撞风险；根据风险阈值分级触发制动控制模块或进行路径规划；自适应机制以海拔为输入调整栅格分辨率与阈值，从而影响预测与决策的灵敏度。
• 姿态传感器的作用虽未在文字中被显式绑定至某一算法步骤，但通常用于稳定建图坐标系与风险预测（例如姿态补偿、外参转换）。从权利要求角度，其为构成要素，必须具备，但未对其具体数据融合方式加以限定。

四、关键限定对保护范围的约束效果

• “前向深度相机”：对传感器的朝向和类型（具备深度测量能力）形成限定，不覆盖仅有二维前视相机或非前向布置的方案。
• “2秒内碰撞风险”：对预测时间域形成刚性限制，不涵盖仅预测1秒或5秒的系统。
• “双阈值分级响应”：将控制策略限定为至少两级处置，分别对应减速与绕行规划。但权利要求未明确：
  • 第一与第二阈值的大小关系；
  • 当超过第二阈值时，是否同时/优先触发减速；
  • “规划绕行路径”是否必须与后续“执行绕行”相结合（文字仅限定“规划”，未限定“执行”）。 上述未明事项构成边界解释上的潜在不确定性，可能影响侵权比对与稳定性评价。
• “随海拔自适应调整”：对参数调节的触发因素（海拔）作出明确限定，不覆盖仅随速度、能见度或其他环境因素自适应但与海拔无关的方案。

五、开放性与非限定项

• 用语“包括”使得权利要求具有开放性，允许系统含有其他附加模块（如后向避障、侧向雷达、GPS模块、路径跟踪控制器等），不影响落入保护范围。但缺少任何一项必要技术特征（如前向深度相机或制动控制模块）则不构成落入范围。
• 权利要求未限定具体的算法实现细节（如风险函数形式、路径规划算法类型、阈值计算公式、自适应策略的数学模型），因此在这些方面保护范围较宽，只要实现了相同的功能与逻辑关系，即可能构成等同或落入字面范围。

六、术语与可能的歧义点（从审查与解释角度提示）

• “碰撞风险”的度量方式、判定标准未清晰界定，可能引发清楚性与可实施性解释争议；但作为装置权利要求，若说明书对其有明确实现，可满足支持与清楚性要求。
• “阈值”及“第一/第二阈值”的相互关系未明，建议在说明书或从属权利要求中阐明二者的大小、优先级逻辑以及超过第二阈值时是否同时减速。
• “规划绕行路径”未与“执行绕行”或“跟踪控制”绑定，严格解释下仅要求具备“规划”能力，未必要求路径跟踪执行模块。
• “姿态传感器”的具体类型与输出参数未确定（如是否包括陀螺仪、加速度计、磁力计或气压计/高度计），但其存在为必要构件。

七、简要结论（保护范围要点） 该独立权利要求保护一类具备前向深度感知、占用栅格建图、2秒时域风险预测，并依据海拔进行参数自适应的无人机避障系统。其控制策略采用至少两级阈值分级响应，在较低风险情形触发减速，在更高风险情形触发绕行路径规划。权利要求以功能性语言限定处理器行为，但对具体实现算法保持开放，因而在相应功能链条成立的前提下，保护范围覆盖多种具体实现方案。上述特征（尤其是“2秒预测”“双阈值分级决策”“随海拔自适应调整”与“前向深度相机+占用栅格”的组合）构成本权利要求的核心限定。