和英特許翻訳メモ

便利そうな表現、疑問、謎、その他メモ書き。思いつきで書いてます。
拾った用例は必ずしも典型例、模範例ではありません。

嵌合

2024-10-04 18:17:11 | ChatGPT
Translate: 第1部材は第1方向にスライドして第2部材に篏合する。
ChatGPT:
ChatGPT

The first member slides in the first direction and fits into the second member.

あなた:
How about "The first member slides in the first direction and mates with the second member"?
ChatGPT:
ChatGPT

Yes, "The first member slides in the first direction and mates with the second member" is also accurate. Using "mates with" is technically correct and often used in engineering contexts to describe parts that join or fit together. This phrasing is suitable if you want to emphasize the precise alignment or fit between the two members.

あなた:
How about "The first member slides in the first direction to mate with the second member"?
ChatGPT:
ChatGPT

Yes, "The first member slides in the first direction to mate with the second member" is an excellent choice. This phrasing clearly indicates that the purpose of sliding is to achieve the mating or fitting action, making it both natural and precise in technical contexts.

あなた:
Which is correct, 嵌合 or 篏合?
ChatGPT:
ChatGPT

The correct and more commonly used term is 嵌合. Both characters can convey the meaning of "fitting together" or "mating," but 嵌合 is the standard and widely accepted form in technical and engineering contexts.

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に優れた、において優れた

2024-10-04 14:58:44 | ChatGPT

Translate: 本発明は絶縁性能に優れた電線を提供する。

ChatGPT:
ChatGPT

This invention provides an electric wire with excellent insulation performance.

あなた:
How about, "This invention provides an electric wire excellent in insulation performance"?
ChatGPT:
ChatGPT

The phrase "This invention provides an electric wire excellent in insulation performance" is understandable but sounds slightly unnatural. A smoother option would be:

"This invention provides an electric wire with excellent insulation performance."

The phrase "with excellent insulation performance" is more idiomatic in English and maintains the intended technical clarity.

あなた:
Translate: 本発明は絶縁性能において優れた電線を提供する。
ChatGPT:
ChatGPT

This invention provides an electric wire that is excellent in insulation performance.

あなた:
How about "This invention provides an electric wire excellent in insulation performance"?
ChatGPT:
ChatGPT

While "This invention provides an electric wire excellent in insulation performance" is understandable, a more natural-sounding translation would be:

"This invention provides an electric wire with excellent insulation performance."

Using "with" here is more idiomatic and technically precise in English.

あなた:
How about "This invention provides an electric wire having excellent insulation performance"?
ChatGPT:
ChatGPT

Yes, "This invention provides an electric wire having excellent insulation performance" is also correct and sounds natural. Using "having" instead of "with" is slightly more formal but still clear and accurate in technical contexts.

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字面翻訳では食えない

2024-10-04 12:54:43 | ChatGPT
Translate この装置は、情報を画素値に変換して、変換した画素値を含むデータを生成する。
ChatGPT:
ChatGPT

This device converts information into pixel values and generates data containing the converted pixel values.

あなた:
In the translation, does the "converted pixel values" accurately convey the meaning of 変換した画素値? Remember that the 画素値 is something that has been obtained by converting the 情報, and that the 画素値 itself is not converted.
ChatGPT:
ChatGPT

You're absolutely correct; in this context, "converted pixel values" might imply that the pixel values themselves have undergone conversion, which is not accurate. A more precise translation would be:

"This device converts information into pixel values and generates data that includes the pixel values obtained through the conversion."

This phrasing clarifies that the pixel values result from the conversion process, rather than being converted themselves.

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把握

2024-10-04 11:13:11 | ChatGPT
Translate: サーバは移動体の位置情報を正確に把握する。
ChatGPT:
ChatGPT

The server accurately identifies the location information of the mobile unit.

あなた:
How about the following? 1. The server accurately grasps the location information of the mobile unit.
ChatGPT:
ChatGPT

Your sentence is close, though "grasp" is less commonly used in technical contexts. A more natural phrasing might be:

  1. The server accurately obtains the location information of the mobile unit.
  2. The server precisely identifies the location information of the mobile unit.

Both of these capture the intended meaning and feel more suitable for technical descriptions.

あなた:
How about the following?
2. The server accurately monitors the location information of the mobile unit.
3. The server accurately learns the location information of the mobile unit.
4. The server accurately knows the location information of the mobile unit.
5. The server accurately keeps track of the location information of the mobile unit.
6. The server accurately ascertains the location information of the mobile unit.
7. The server accurately learns the location information of the mobile unit.
ChatGPT:
ChatGPT

Here’s a breakdown of each option, with some suggestions based on phrasing and technical tone:

  1. The server accurately monitors the location information of the mobile unit.

    • This is clear and suggests continuous tracking, which may be suitable if the server actively follows location changes over time.
  2. The server accurately learns the location information of the mobile unit.

    • "Learns" can imply a process of acquisition or adaptation, which is fine in AI contexts but less standard for simple tracking. For general tracking, "obtains" or "identifies" might be clearer.
  3. The server accurately knows the location information of the mobile unit.

    • "Knows" is less technical and implies a static understanding. For a more technical tone, "has" or "possesses" could replace "knows."
  4. The server accurately keeps track of the location information of the mobile unit.

    • This is suitable if you want to emphasize continuous tracking and accuracy.
  5. The server accurately ascertains the location information of the mobile unit.

    • "Ascertains" works well in a technical context, especially if the server is determining or verifying the location.
  6. The server accurately learns the location information of the mobile unit.

    • Same as option 2; for an ongoing or dynamic context, "learns" works in machine learning but may seem less direct for general tracking.

In general, Options 1, 4, and 5 feel the most technically precise for describing server actions involving location data tracking and verification.

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病変らしさ

2024-10-04 10:13:27 | 英語特許散策

US2024119588(HITACHI HIGH TECH CORP [JP])
(ii) Classification Unit 12
【0047】
  (ii)識別部12

[0063] As shown in FIG. , the classification unit 12 calculates a value of a likelihood of the object to be detected (a likelihood of a tumor, a likelihood of a lesion, or the like) using the feature FAi (a matrix f) by the feature extractor A obtained by the feature extraction unit 11 according to Formula (2) by a logistic regression processing,
識別部12は、図5に示すように、前記特徴抽出部11で求めた特徴抽出器Aの特徴量FAi(行列f)を用いて、ロジスティック回帰処理にて、式(2)により、検出すべき物体らしさ(腫瘍らしさ、病変らしさ等)の値を算出して、

and determines whether an object (for example, a tissue or a cell) in the input image A 1 is an object to be detected (a tumor or the like) or the object other than the object to be detected (a non-tumor or the like).
入力画像A1内の物体(例えば、組織・細胞)が検出すべき物体(腫瘍等)か検出すべき物体以外(非腫瘍等)かを判定する。

US2022133214(NEC CORP [JP])
[0038] Next, with reference to the flowchart in FIG. 3, the whole operation in the present example embodiment is detailedly described.
次に、図3のフローチャートを参照して本実施の形態の全体の動作について詳細に説明する。

First, the lesion area specifying unit 11 extracts a lesion area from a medical image (step A 1 ).
まず、病変領域特定部11は、医用画像から、病変領域を抽出する(ステップA1)。

Specifically, the lesion area specifying unit 11 scans all the pixels of the input medical image and calculates the score indicating the lesion-likeness in each pixel.
具体的には、病変領域特定部11は、入力された医用画像の全ピクセルをスキャンし、各ピクセルの病変らしさを示すスコアを算出する。

US2021271914(FUJIFILM CORP [JP])
The correction unit 44 changes the class that is not capable of originally existing in the disease names other than IPF in the classification map 220 to a class that has the highest probability indicating the likelihood of a class among the classes that can originally exist in the disease names other than IPF.
【0116】
  修正部44は、分類マップ220における本来はIPF以外の疾患名には存在し得ないクラスを、本来はIPF以外の疾患名に存在し得るクラスのうち、クラスらしさを表す確率が最も高いクラスに置き替える。

That is, the correction unit 44 extracts a lesion that can exist only in IPF among the classes of the classification map 220 illustrated in FIG. 6 and changes the extracted lesion to a lesion that has the highest probability indicating the likelihood of a lesion among the lesions that can exist in the diseases other than IPF.
すなわち、修正部44は、図6に示す分類マップ220のクラスのうち、IPFにのみ存在し得る病変を抽出し、IPF以外の病気に存在し得る病変のうち、病変らしさを表す確率が最も高い病変に抽出した病変を置き替える。

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当ブログの例文について

本ブログの「特許英語散策」等題した部分では、英語の例文を管理人の独断と偏見で収集し、適宜訳文・訳語を記載しています。 訳文等は原則として対応日本語公報をそのまま写したものです。私個人のコメント部分は(大抵)”*”を付しています。 訳語は多数の翻訳者の長年の努力の結晶ですが、誤訳、転記ミスもあると思いますのでご注意ください。