Merge pull request #7 from jinruishao/patch-1

Update quickstart-protected-material.md

Author: Court72

articles/ai-services/content-safety/quickstart-protected-material.md

@@ -1,7 +1,7 @@
 ---
-title: "Quickstart: Detect protected material (preview)"
+title: "Quickstart: Protected Material for Text"
 titleSuffix: Azure AI services
-description: Learn how to detect protected material generated by large language models and mitigate risk with Azure AI Content Safety.
+description: Learn how to Protected Material for Text generated by large language models and mitigate risk with Azure AI Content Safety.
 services: ai-services
 author: PatrickFarley
 manager: nitinme
@@ -11,13 +11,46 @@ ms.date: 10/30/2023
 ms.author: pafarley
 ---
 
-# Quickstart: Detect protected material (preview)
+# Quickstart: Protected Material Detection for Text
 
-Protected material text describes language that matches known text content (for example, song lyrics, articles, recipes, selected web content). This feature can be used to identify and block known text content from being displayed in language model output (English content only). For more information, see [Protected material concepts](./concepts/protected-material.md).
+The "Protected Material for Text" feature provides a robust solution for managing risks associated with GenAI-generated content (English content only). By detecting and preventing the display of protected material, organizations can ensure compliance with intellectual property laws, maintain content originality, and protect their reputations. Protected material refers to content that matches known text from copyrighted sources, such as song lyrics, articles, recipes, or other selected web content. 
+The key objectives of the "Protected Material Detection for Text" feature for GenAI-generated content are:
+
+- To detect and prevent the display of protected content generated by AI models.
+- To enable organizations to manage risks associated with AI-generated content.
+- To ensure that AI-generated content complies with legal, ethical, and content policy guidelines.
 
 For more information on protected material detection, see the [Protected material detection concept page](./concepts/protected-material.md). For API input limits, see the [Input requirements](./overview.md#input-requirements) section of the Overview. 
 
 
+
+## User scenarios
+### 1. Content generation platforms for creative writing
+- Scenario: A content generation platform that uses GenAI for creative writing (e.g., blog posts, stories, marketing copy) integrates the "Protected Material for Text" feature to prevent the generation of content that closely matches known copyrighted material.
+- User: Platform administrators and content creators.
+- Action: The platform uses Azure AI Content Safety to scan AI-generated content before it is provided to users. If the generated text matches protected material, the content is flagged and either blocked or revised.
+- Outcome: The platform avoids potential copyright infringements and ensures that all generated content is original and compliant with intellectual property laws.
+### 2. Automated social media content creation
+- Scenario: A digital marketing agency uses GenAI to automate social media content creation. The agency integrates the "Protected Material for Text" feature to avoid publishing AI-generated content that includes copyrighted text, such as song lyrics or excerpts from books.
+- User: Digital marketers and social media managers.
+- Action: The agency employs Azure AI Content Safety to check all AI-generated social media content for matches against a database of protected material. Content that matches is flagged for revision or blocked from posting.
+- Outcome: The agency maintains compliance with copyright laws and avoids reputational risks associated with posting unauthorized content.
+### 3. AI-assisted news writing
+- Scenario: A news outlet uses GenAI to assist journalists in drafting articles and reports. To ensure the content does not unintentionally replicate protected news articles or other copyrighted material, the outlet uses the "Protected Material for Text" feature.
+- User: Journalists, editors, and compliance officers.
+- Action: The news outlet integrates Azure AI Content Safety into its content creation workflow. AI-generated drafts are automatically scanned for protected content before submission for editorial review.
+- Outcome: The news outlet prevents accidental copyright violations and maintains the integrity and originality of its reporting.
+### 4. E-learning platforms using AI for content generation
+- Scenario: An e-learning platform employs GenAI to generate educational content, such as summaries, quizzes, and explanatory text. The platform uses the "Protected Material for Text" feature to ensure the generated content does not include protected material from textbooks, articles, or academic papers.
+- User: Educational content creators and compliance officers.
+- Action: The platform integrates the feature to scan AI-generated educational materials. If any content matches known protected academic material, it is flagged for revision or automatically removed.
+- Outcome: The platform maintains educational content quality and complies with copyright laws, avoiding the use of protected material in AI-generated learning resources.
+### 5. AI-powered recipe generators
+- Scenario: A food and recipe website uses GenAI to generate new recipes based on user preferences. To avoid generating content that matches protected recipes from famous cookbooks or websites, the website integrates the "Protected Material for Text" feature.
+- User: Content managers and platform administrators.
+- Action: The website uses Azure AI Content Safety to check AI-generated recipes against a database of known protected content. If a generated recipe matches a protected one, it is flagged and revised or blocked.
+- Outcome: The website ensures that all AI-generated recipes are original, reducing the risk of copyright infringement.
+
 ## Prerequisites
 
 * An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/) 
@@ -36,7 +69,7 @@ The following section walks through a sample request with cURL. Paste the comman
     > See [Input requirements](./overview.md#input-requirements) for maximum text length limitations. Protected material detection is meant to be run on LLM completions, not user prompts.
 
 ```shell
-curl --location --request POST '<endpoint>/contentsafety/text:detectProtectedMaterial?api-version=2023-10-15-preview' \
+curl --location --request POST '<endpoint>/contentsafety/text:detectProtectedMaterial?api-version=2024-09-01' \
 --header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
 --header 'Content-Type: application/json' \
 --data-raw '{
@@ -47,7 +80,7 @@ The below fields must be included in the url:
 
 | Name      |Required  |  Description | Type   |
 | :------- |-------- |:--------------- | ------ |
-| **API Version** |Required |This is the API version to be checked. The current version is: api-version=2023-10-15-preview. Example: `<endpoint>/contentsafety/text:detectProtectedMaterial?api-version=2023-10-15-preview` |String |
+| **API Version** |Required |This is the API version to be checked. The current version is: api-version=2024-09-01. Example: `<endpoint>/contentsafety/text:detectProtectedMaterial?api-version=2024-09-01` |String |
 
 The parameters in the request body are defined in this table:
 
@@ -95,4 +128,3 @@ If you want to clean up and remove an Azure AI services subscription, you can de
 
 * [Protected material detection concepts](./concepts/protected-material.md)
 * Configure filters for each category and test on datasets using [Content Safety Studio](studio-quickstart.md), export the code and deploy.
-