★★★ ＜第 23 回知的財産翻訳検定試験【第 11 回英文和訳】＞ ★★★
≪１級課題 -電気・電子工学-≫【解答にあたっての注意】
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４．課題は３題あります。それぞれの課題の指示に従い、３題すべて解答してください。

問１） 次の英文は、穿孔用ロボットの発明の「従来技術」に関する説明です。
日本語に訳してください。
For many years now, technologies have been developed andused to rehabilitate damaged water and sewage conduits. The
various technologies basically consist of re-lining the insidewalls of existing underground conduits to rehabilitate theunderground conduits.
The service entrances of the existing conduit are
initially plugged with a water plug carrying a position marker
with a plug setting robot controlled by an operator looking
through a video camera connected to the plug setting robot.
Thereafter the existing conduit is re-lined internally with aflexible tubular liner impregnated with a curable resin which
is cured in place with recirculating heated water after
insertion in the underground conduit. Once the resin is cured
and the re-lining of the existing conduit is done, the waterplug can be located with a detection and drilling robot using
the position marker of the water plug. Once the center of thewater plug is located, the water plug is drilled out using adrill mounted on the robot which is operated by the operator.
The drill mounted on the robot typically consists of amain housing attached to the front portion of the robot that
includes a power unit and an actuator for controlling a drillinghead positioned at the front of the main housing. The drillinghead includes a drill bit extending perpendicular to the1drilling head and to the longitudinal axis of the underground
conduit.

次の英文は、３Ｄプリンタに関連する技術に関する実施例の説明です。
一対の符号(Ａ）、（Ｂ）、及び（Ｃ）で囲まれた部分を日本語に訳して下さい。
問２）

This embodiment relates to a system for surface finishingof rapid-manufactured three-dimensional (3D) objects by vaporsmoothing.
(A)
3D objects built by rapid manufacturing techniques
generally exhibit "stair-step" appearances, particularly atcurved or angled exterior surfaces. The stair stepping effectis caused by the layering of cross-sectional shapes that havesquare-edge profiles, and is more pronounced as layer
thicknesses increase. While the stair stepping effect generally
does not affect the strengths of the 3D objects, it may
significantly diminish the desired aesthetic qualities.
(A)
FIG. 1 is a perspective view of a vapor smoothing surface
finishing system 10 according to an embodiment of the present
invention. FIG. 2 is a front view thereof.
Vapor chamber 18 includes bottom heater 26 and sideheaters 28 and 29.
(B)
Primary cooling coils 30 are connected to refrigeration
compressor 22, and are located near the top of vapor chamber
18 to create a vapor ceiling for containing vapor in vapor
chamber 18. In order to operate vapor chamber 18 for vapor
smoothing an object, solvent 32 is provided at the bottom ofvapor chamber 18. Solvent 32 has a fluid level that is lower
than the elevation of plate 34, so that any object that may be
inadvertently dropped inside vapor chamber 18 does not fall intosolvent 32.

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(B)Heaters 26, 28 and 29 are operated to form a vapor fromsolvent 32 in vapor chamber 18, which fills vapor chamber 18
below the vapor ceiling formed by primary cooling coils 30.
Drying chamber 20 is located within cabinet housinginterior 16 adjacent to vapor chamber 18.
(C)
Drying chamber 20 is provided to allow an object that has
been exposed to vapor in vapor chamber 18 to outgas for a periodof time until the object is ready for either another exposurein vapor chamber 18 or removal from system 10.
In operation, sliding door 14 and sliding door 21 are
opened to suspend an object in vapor chamber 18 in order to besmoothed at its surface by exposure to the vapor therein.
After the object has been exposed in vapor chamber 18,
the object is raised out of vapor chamber 18 and moved to drying
chamber 20. When the object is removed from vapor chamber 18,
it is preferable to cover vapor chamber with sliding door 21,
as a precaution to ensure that no further vapors escape vapor
chamber 18. The object may be suspended in drying chamber 20
for as long as is necessary to dry and re-harden the surfaceof the object.
(C)
Secondary cooling coils 36 are provided near the top of
cabinet housing 12 to cool drying chamber to a desired
temperature, and to ensure that all vapors are contained within
cabinet housing 12.
The processes of exposing an object in vapor chamber 18 and in
drying chamber 20 may be repeated as many times as desired, toobtain a desired surface finish of the object. When the vapor
smoothing process is complete, the "stair-step" features thatare typically present in rapid manufactured objects will besignificantly
reducedoreliminated.

34問３） 次のクレームは、いわゆる人工知能（ＡＩ）技術を用いた文字認識技術に関するものです。クレーム部分を日本語に訳して下さい。クレームはその字句通りに訳していただいて結構ですが、必要に応じて、以下の実施例、及び図１～図２の記載を参照してください。なお、以下の実施例は、クレーム記載の事項の全てをサポートしたものではなく、その概要を示したものです。
1. A method for classifying a machine printed text and ahandwritten text in an input, comprising:defining a perspective for an auto-encoder, wherein theperspective comprises an inpainting perspective, wherein theinpainting perspective is a function of a hidden representationof the input and a reconstruction of the input, wherein the5hidden representation is based on an encoding function
comprising a first non-linear function of an encoding layer
weight of the input and an encoding layer bias thereof, wherein
the reconstruction of the input is based on a decoding function
comprising a second non-linear function of a decoding layer
weight of the input and a decoding layer bias thereof;
receiving the input for the auto-encoder, wherein theinput comprises a document comprising the machine printed text
and the handwritten text;
performing an encoding on the input using an auto-encoder
to generate a classifier;applying the classifier on the input; andgenerating an output that classifies the machine printed
text and the handwritten text in the input based on theclassifier in accordance with the perspective.
(実施例)
FIG. 1 illustrates an example system 100 of the present
disclosure. The system 100 may include an input 102, a deep learning
machine 106, an output or a classified input 108 and an opticalcharacterrecognition(OCR)

readerorintelligent

characterrecognition (ICR) reader 114. The input 102 may be a document thathas an unknown mixture of text 104.
The deep learning machine 106 may encode the input 102 andgenerate classifiers that can be applied to the input 102. The deeplearning machine 106 may then generate a classified input or output
108 that classifies the machine printed text 116 and the handwritten
text 112 in the input 102. The output 108 may then be forwarded tothe OCR/ICR reader 114 for automatic processing (e.g., scanning andreading the text using an appropriate character reader based onmachine printed text or handwritten text).
As shown in FIG. 2, the deep learning machine 106 may include
a plurality of different processing layers. The processing layers
may include an input layer 202, an auto-encoder layer 204, aclassifier layer 206 and an output layer or layers 208 and/or 210.
For example, based on a selected perspective that defines how the6deep learning machine 106 will process the input 102, the deeplearning machine 106 may have a single output layer 208 or two output
layers 208 and 210. The input layer 202 may receive the input 102
and extract the connected components from the input 102.
The auto encoder may be considered a one-hidden-layer neuralnetwork and its objective is to reconstruct the input 102 using its
hidden representations or activations so that the reconstruction
error is as small as possible. The auto-encoder takes the input 102and puts the input 102 through an encoding function to get the encodingof the input 102 and then the auto-encoder decodes the encoding
through a decoding function to recover an approximation of theoriginal input 102. The auto-encoder layer 204 may apply a backward
propagation algorithm to perform the above process and to generate
the classifiers in the classifier layer 206.
The auto-encoder may be used to generate the classifier layer
206. The classifier layer 206 may be used to classify each text asmachine printed text or handwritten text. The labels may be applied
to each location of the sliding window as it moves along within theinput 102. This process may be repeated until all the contents within
the input 102 have been classified as a machine printed text or ahandwritten text.
The deep learning machine 106 may then generate a single output
layer 208 or two output layers 208 and 210 depending on a perspectivethat is selected for the auto-encoder. The perspectives may include
an inpainting perspective or a classification perspective. The
inpainting

perspectivemayrequiregeneratingaseparate

classification model within the deep learning machine 106 for themachine printed text and the handwritten text, thus, creating twooutput layers 208 and 210.

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