はじめに
対話型AIプラットフォーム
チャットボットの概要
自然言語処理(NLP)
ボットの概念と用語
クイックスタートガイド
プラットフォームへのアクセス
ボットビルダーの操作
リリースノート
最新バージョン(英語)
以前のバージョン(英語)
廃止機能(英語)
コンセプト
設計
ストーリーボード
ダイアログタスク
ダイアログタスクとは
ダイアログビルダー
ノードタイプ
インテントノード
ダイアログノード
エンティティノード
フォームノード
確認ノード
ロジックノード
ボットアクションノード
サービスノード
Webhookノード
スクリプトノード
グループノード
エージェント転送ノード
ユーザープロンプト
音声通話プロパティ
イベント ハンドラー
ナレッジグラフ
ナレッジグラフの抽出
ナレッジグラフの構築
ボットにナレッジグラフを追加
グラフの作成
ナレッジグラフの構築
既存のソースからFAQを構築
通知タスク
スモールトーク
デジタルスキル
デジタルフォーム
デジタルビュー
デジタルビューとは
パネル
ウィジェット
トレーニング
トレーニングとは
機械学習
機械学習とは
モデル検証
ファンダメンタルミーニング
ナレッジグラフ
示唆
ランキングおよび解決
NLPの詳細設定
NLPのガイドライン
インテリジェンス
インテリジェンスとは
コンテキスト
コンテキストインテント
割り込み
複数インテントの検出
エンティティの変更
デフォルトの会話
センチメント管理
トーン分析
テストとデバッグ
ボットと会話
発話テスト
バッチテスト
会話テスト
デプロイ
チャネル
公開
分析
ボットの分析
NLPメトリクス
会話フロー
Usage Metrics
封じ込め測定
カスタムダッシュボード
カスタムダッシュボードとは
メタタグ
カスタムダッシュボードとウィジェット
LLM and Generative AI
Introduction
LLM Integration
Kore.ai XO GPT Module
Prompts & Requests Library
Co-Pilot Features
Dynamic Conversations Features
ユニバーサルボット
ユニバーサルボットとは
ユニバーサルボットの定義
ユニバーサルボットの作成
ユニバーサルボットのトレーニング
ユニバーサルボットのカスタマイズ
他言語の有効化
ストア
プラントと使用
Overview
Usage Plans
Support Plans
Invoices
管理
ボット認証
複数言語対応ボット
個人を特定できる情報の編集
ボット変数の使用
IVRのシステム連携
一般設定
ボット管理
ハウツー
会話スキルの設計
バンキングボットを作成
バンキングボット – 資金の振り替え
バンキングボット – 残高を更新
ナレッジグラフを構築
スマートアラートの予約方法
Integrations
Actions
Actions Overview
Asana
Configure
Templates
Azure OpenAI
Configure
Templates
BambooHR
Configure
Templates
Bitly
Configure
Templates
Confluence
Configure
Templates
DHL
Configure
Templates
Freshdesk
Configure
Templates
Freshservice
Configure
Templates
Google Maps
Configure
Templates
Here
Configure
Templates
HubSpot
Configure
Templates
JIRA
Configure
Templates
Microsoft Graph
Configure
Templates
Open AI
Configure
Templates
Salesforce
Configure
Templates
ServiceNow
Configure
Templates
Stripe
Configure
Templates
Shopify
Configure
Templates
Twilio
Configure
Templates
Zendesk
Configure
Templates
Agents
Agent Transfer Overview
Custom (BotKit)
Drift
Genesys
Intercom
NiceInContact
NiceInContact(User Hub)
Salesforce
ServiceNow
Configure Tokyo and Lower versions
Configure Utah and Higher versions
Unblu
External NLU Adapters
Overview
Dialogflow Engine
Test and Debug
デジタルスキルの設計
デジタルフォームの設定方法
デジタルビューの設定方法
データテーブルのデータの追加方法
データテーブルのデータの更新方法
Add Data from Digital Forms
ボットのトレーニング
示唆の使用方法
インテントとエンティティのパターンの使用方法
コンテキスト切り替えの管理方法
ボットのデプロイ
エージェント転送の設定方法
ボット関数の使用方法
コンテンツ変数の使用方法
グローバル変数の使用方法
ボットの分析
カスタムダッシュボードの作成方法
カスタムタグを使ってフィルタリング
Data
Overview
Data Table
Table Views
App Definitions
Data as Service
Build a Travel Planning Assistant
Travel Assistant Overview
Create a Travel Virtual Assistant
Design Conversation Skills
Create an ‘Update Booking’ Task
Create a Change Flight Task
Build a Knowledge Graph
Schedule a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Train the Assistant
Use Traits
Use Patterns
Manage Context Switching
Deploy the Assistant
Use Bot Functions
Use Content Variables
Use Global Variables
Use Web SDK
Build a Banking Assistant
Migrate External Bots
Google Dialogflow Bot
APIs & SDKs
API Reference
API Introduction
Rate Limits
API List
koreUtil Libraries
SDK Reference
SDK Introduction
Web SDK
How the Web SDK Works
SDK Security
SDK Registration
Web Socket Connect and RTM
Tutorials
Widget SDK Tutorial
Web SDK Tutorial
BotKit SDK
BotKit SDK Deployment Guide
Installing the BotKit SDK
Using the BotKit SDK
SDK Events
SDK Functions
Tutorials
BotKit - Blue Prism
BotKit - Flight Search Sample VA
BotKit - Agent Transfer
  1. ホーム
  2. Docs
  3. Virtual Assistants
  4. What's New
  5. What’s New

What’s New

Learn about the new features and enhancements included in Kore.ai Experience Optimization Platform v10.2, released on April 27, 2024.

Key features and enhancements included in this release are summarized below.

LLM and Generative AI

Custom LLM Integration Support for Rephrase Dialog Responses

Rephrase Dialog Responses now supports Custom LLMs in addition to commercial LLMs. This allows platform users to use the rephrasing feature with their own custom-trained language models and create customized prompts tailored to their specific use cases, models, and linguistic contexts, providing greater flexibility and control over the rephrasing process and conversational experiences. Learn more.

NLP

New Pre-trained MPNet Few-shot Model for Intent Detection

The few-shot model now supports the Pre-trained MPNet embedding model for intent detection. The Pre-trained MPNet model is the advanced version of MPNet. It has been pre-trained and fine-tuned for superior accuracy and precision in intent identification compared to MPNet. Learn more.

Advantages of using the Pre-trained MPNet Model:

  • Understanding Negations: Pre-trained models get negations like “not” or “don’t” easily, unlike MPNet, which might mix things up. So, when you say, “I want to transfer funds,” and “I do not want to transfer funds,” pre-trained models know the difference, making them better at understanding what you really mean.
  • Clear Intent Differentiation: Pre-trained models are great at telling apart similar things, which can be tricky for MPNet. For example, if you say, “I want to unblock a card,” or “I want to block a card,” pre-trained models can confidently tell the difference between wanting to block or unblock a card, unlike MPNet, which might get confused.
  • Staying on Topic: Pre-trained models are good at sticking to the topic at hand. So, if you talk about increasing your credit card limit, they won’t start suggesting things unrelated to credit cards, unlike MPNet, which might go off track.
  • Easier Training: Pre-trained models need less training data compared to MPNet. This means the model can learn the same things with fewer examples, making the training process faster and simpler.

Channels

Support for Thread Handling for Virtual Assistants in Slack Channels

The Platform now offers native support for threaded conversations in the Slack channel. Users can initiate a new thread from any message within a Slack channel or direct message group.

Additionally, the platform provides extended functionality for developers. It can automatically create a new thread whenever a user @mentions the virtual assistant in a Slack channel. This behavior is configurable, giving developers control over this feature.

Digital Forms

  • Enable the “Off-the-Record Information” Flag for Digital Forms: On a digital form, when the field’s “Off the record” flag is enabled, the field data is cleared at the end of the user session and not stored in databases or logs.
  • Digital Forms Date Picker Supports Japanese: The digital form’s Date Picker now supports the Japanese language if the bot language is Japanese.

What’s New

Learn about the new features and enhancements included in Kore.ai Experience Optimization Platform v10.2, released on April 27, 2024.

Key features and enhancements included in this release are summarized below.

LLM and Generative AI

Custom LLM Integration Support for Rephrase Dialog Responses

Rephrase Dialog Responses now supports Custom LLMs in addition to commercial LLMs. This allows platform users to use the rephrasing feature with their own custom-trained language models and create customized prompts tailored to their specific use cases, models, and linguistic contexts, providing greater flexibility and control over the rephrasing process and conversational experiences. Learn more.

NLP

New Pre-trained MPNet Few-shot Model for Intent Detection

The few-shot model now supports the Pre-trained MPNet embedding model for intent detection. The Pre-trained MPNet model is the advanced version of MPNet. It has been pre-trained and fine-tuned for superior accuracy and precision in intent identification compared to MPNet. Learn more.

Advantages of using the Pre-trained MPNet Model:

  • Understanding Negations: Pre-trained models get negations like “not” or “don’t” easily, unlike MPNet, which might mix things up. So, when you say, “I want to transfer funds,” and “I do not want to transfer funds,” pre-trained models know the difference, making them better at understanding what you really mean.
  • Clear Intent Differentiation: Pre-trained models are great at telling apart similar things, which can be tricky for MPNet. For example, if you say, “I want to unblock a card,” or “I want to block a card,” pre-trained models can confidently tell the difference between wanting to block or unblock a card, unlike MPNet, which might get confused.
  • Staying on Topic: Pre-trained models are good at sticking to the topic at hand. So, if you talk about increasing your credit card limit, they won’t start suggesting things unrelated to credit cards, unlike MPNet, which might go off track.
  • Easier Training: Pre-trained models need less training data compared to MPNet. This means the model can learn the same things with fewer examples, making the training process faster and simpler.

Channels

Support for Thread Handling for Virtual Assistants in Slack Channels

The Platform now offers native support for threaded conversations in the Slack channel. Users can initiate a new thread from any message within a Slack channel or direct message group.

Additionally, the platform provides extended functionality for developers. It can automatically create a new thread whenever a user @mentions the virtual assistant in a Slack channel. This behavior is configurable, giving developers control over this feature.

Digital Forms

  • Enable the “Off-the-Record Information” Flag for Digital Forms: On a digital form, when the field’s “Off the record” flag is enabled, the field data is cleared at the end of the user session and not stored in databases or logs.
  • Digital Forms Date Picker Supports Japanese: The digital form’s Date Picker now supports the Japanese language if the bot language is Japanese.
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