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Rank	Score	AI predict score	Application	Publication	Examined/Granted
{{numberRank.rank}}	{{numberRank.score}}	{{numberRank.predict_score}}	{{numberRank.app_id}}	{{numberRank.pub_id}}	{{numberRank.exam_id}}

Search conditions

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{{ preCondition.lop }} {{preUserSearchSettings[preCondition.q][0]}} {{preUserSearchSettings[preCondition.q][1] ? '(' + preUserSearchSettings[preCondition.q][1] + ')' : ''}} {{preCondition.key}}

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Name {{userSearchSetting.title}}

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Period

{{op[0]}}

Latest published

Latest granted

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Created at : {{userSearchSetting.created_at | strftime('YYYY-MM-DD')}}
N hits on last searched : {{userSearchSetting.last_searched_n_hits}}

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{{query[1]}} Search | Delete

{{query[0]}}

Similar Keywords

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Input keywords or sentences

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Input keyword or class

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{{index+1}}

{{condition.nhits}}

{{index+1}}

N records
N clusters
N search words

{{condition.nhits}}

{{index+1}}

# of cited

# of citing

Max # of connection

End edge only

Remove citation sources

Exclude same with

Include same with

Get Citation Type Cited/Citing Auto

{{condition.nhits}}

+ Add filter conditions

Logical expression

フィルター行番号(L:N)を組み合わせて論理式を設定できます。例： (L:1 OR L:2) AND L:3

{{linesNhits}}

Predict model

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Predict model (Pre) :

Label type

Binary class Multi class Multi label

Targe features

{{feature.name}}

Custom Labels

{{labelName.name}} {{countLabelName(labelName.name)}} {{labelName.weight ? labelName.weight : '1.0'}}

{{labelName.name}} {{countLabelName(labelName.name)}} {{labelName.weight ? labelName.weight : '1.0'}}

Supervised labels Prediction labels Visualize Parameter tuning Option

Pupervised labels Prediction labels Visualize Parameter tuning Option

Pupervised labels Prediction labels Visualize Parameter tuning Option

Pupervised labels Prediction labels Visualize Parameter tuning Option

Pupervised labels Prediction labels Visualize Parameter tuning Option

Manual Update

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Enable semantic score

# of predict labels

Using data for tuning A part of train data Predict data

Ratio of tuning

Features for tuning

{{feature.name}}

C : Cost

Sigma : Kernel parameter

Prefer label(binary)

Evaluation

{{label.name}}

Best parameters
Accuracy : {{Math.floor((tuningBest.accuracy * 1000)) / 1000}} Precision : {{Math.floor((tuningBest.precision * 1000)) / 1000}} Recall : {{Math.floor((tuningBest.recall * 1000)) / 1000}} F-measure : {{Math.floor((tuningBest.fmeasure * 1000)) / 1000}} Targe features : {{t.supervised_label_sets[tuningBest.features]}} C: {{tuningBest.c}} Sigma: {{tuningBest.sigma}} Prefer label(binary) : {{tuningBest.force_zero}}

Accuracy : {{Math.floor((result.accuracy * 1000)) / 1000}} Precision : {{Math.floor((result.precision * 1000)) / 1000}} Recall : {{Math.floor((result.recall * 1000)) / 1000}} F-measure : {{Math.floor((result.fmeasure * 1000)) / 1000}} Targe features : {{t.supervised_label_sets[result.features]}} C: {{result.c}} Sigma: {{result.sigma}} Prefer label(binary) : {{result.force_zero}}

Query type

Query Empty query Query must be less than 1000 characters Already exists Invalid number Too many supervised labels Too many prediction labels

Label

{{label}}

Pupervised labels Import from file Save supervised labels Edit supervised labels Save supervised labels CSV DL Delete All Parameter Settings Manual

Prediction labels Import from file Save prediction labels Edit prediction labels Save prediction labels CSV DL Delete All Parameter Settings Manual

C : Cost

Sigma : Kernel parameter

Prefer label(binary)

{{label.name}}

Automatically parameters tuning

Target of import

{{label.name}}

# of import

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No supervised labels

{{trainPageStart+index+1}} App Pub Exam Text {{label.q}} {{label.q}} {{label.n ? label.n : ' '}} {{label}} {{label}} {{label.s.join(',')}} {{label.l}}

- Accuracy : {{Math.floor((trainAccuracy * 1000)) / 1000}} Precision : {{Math.floor((trainPrecision * 1000)) / 1000}} Recall : {{Math.floor((trainRecall * 1000)) / 1000}} F-measure : {{Math.floor((trainFMeasure * 1000)) / 1000}}

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No prediction labels

{{predictPageStart+index+1}} App Pub Exam {{label.q}} {{label.q}} {{label.n ? label.n : ' '}} {{Math.floor((label.ps * 1000)) / 1000}} {{Math.floor((label.ps * 1000)) / 1000}} {{label.pcs.join(',')}} {{label.pl}} {{label}} {{label}} {{label.s.join(',')}} {{label.l}}

Accuracy : {{Math.floor((accuracy * 1000)) / 1000}} ({{correct}} / {{total}}) Precision : {{Math.floor((precision * 1000)) / 1000}} Recall : {{Math.floor((recall * 1000)) / 1000}} F-measure : {{Math.floor((FMeasure * 1000)) / 1000}}

{{labelName.name}} : {{Math.floor((predictStats[labelName.name].correct / predictStats[labelName.name].total) * 1000) / 1000}} ({{predictStats[labelName.name].correct}} / {{predictStats[labelName.name].total}})

Train Label filter

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Custom Label filter

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Custom Pre Label filter

{{label}}

名寄せ出願人名

原作者名: 文部科学省科学技術・学術政策研究所

タイトル：NISTEP企業名辞書
URL：http://www.nistep.go.jp/research/scisip/data-and-information-infrastructure

JST/RISTEX 社会問題キーワード

出典：社会俯瞰調査2018 (JST-RISTEX)

URL：https://www.jst.go.jp/ristex/pdf/unit/unit_tamenteki_2018.pdf

WIPO 技術分類

出典 : WIPO, IPC - Technology Concordance Table

URL : http://www.wipo.int/meetings/en/doc_details.jsp?doc_id=117672

NISTEP 技術分類

出典 : 文部科学省　科学技術・学術政策研究所、科学技術指標2017、調査資料-261、2017年8月

URL : http://www.nistep.go.jp/sti_indicator/2017/RM261_47.html

JPO 審査部技術分野

出典 : 審査第一部～第四部の担当技術の概要　（平成30年4月23日版）

URL : http://warp.da.ndl.go.jp/info:ndljp/pid/11239397/www.jpo.go.jp/torikumi/t_torikumi/pdf/sinsagai/bunya.pdf

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