Merge pull request #283509 from laujan/jp-updates-preview-release

JP updates preview release

Author: Stacyrch140

articles/ai-services/document-intelligence/concept-accuracy-confidence.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 07/11/2024
+ms.date: 08/07/2024
 ms.author: lajanuar
 ---
 
@@ -134,4 +134,4 @@ Variances in the visual structure of your documents affect the accuracy of your
 ## Next step
 
 > [!div class="nextstepaction"]
-> [Learn to create custom models](quickstarts/try-document-intelligence-studio.md#custom-models)
+> [Learn more about custom models](concept-custom.md)

articles/ai-services/document-intelligence/concept-custom-classifier.md

@@ -6,7 +6,7 @@ author: vkurpad
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 07/09/2024
+ms.date: 08/07/2024
 ms.author: lajanuar
 ms.custom:
   - references_regions
@@ -31,7 +31,7 @@ monikerRange: '>=doc-intel-3.1.0'
 
 > [!IMPORTANT]
 >
-> * The `2024-02-29-preview` API, custom classification model won't split documents by default during the analyzing process.
+> * The `2024-07-31-preview` API, custom classification model won't split documents by default during the analyzing process.
 > * You need to explicitly set the ``splitMode`` property to auto to preserve the behavior from previous releases. The default for `splitMode` is `none`.
 > * If your input file contains multiple documents, you need to enable splitting by setting the ``splitMode`` to ``auto``.
 
@@ -59,7 +59,7 @@ Custom classification models can analyze a single- or multi-file documents to id
 
 ✔️ The maximum allowed number of classes is `500`. The maximum allowed number of document samples per class is `100`.
 
-The model classifies each page of the input document to one of the classes in the labeled dataset. To set the threshold for your application, use the confidence score from the response.
+The model classifies each page of the input document, unless specified, to one of the classes in the labeled dataset. You can specify the page numbers to analyze in the input document as well. To set the threshold for your application, use the confidence score from the response. 
 ### Incremental training
 
 With custom models, you need to maintain access to the training dataset to update your classifier with new samples for an existing class, or add new classes. Classifier models now support incremental training where you can reference an existing classifier and append new samples for an existing class or add new classes with samples. Incremental training enables scenarios where data retention is a challenge and the classifier needs to be updated to align with changing business needs. Incremental training is supported with models trained with API version `2024-02-29-preview` and later.
@@ -146,7 +146,7 @@ The classifier attempts to assign each document to one of the classes, if you ex
 
 ## Training a model
 
-Custom classification models are supported by **v4.0:2024-02-29-preview** and **v3.1:2023-07-31 (GA)** APIs. [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier. Follow the [how to guide](how-to-guides/build-a-custom-classifier.md) to get started.
+Custom classification models are supported by **v4.0: 2024-02-29-preview, 2024-07-31-preview** and **v3.1: 2023-07-31 (GA)** APIs. [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier. Follow the [how to guide](how-to-guides/build-a-custom-classifier.md) to get started.
 
 When using the REST API, if you organize your documents by folders, you can use the `azureBlobSource` property of the request to train a classification model.
 
@@ -250,7 +250,7 @@ Alternatively, if you have a flat list of files or only plan to use a few select
 ```
 
 As an example, the file list `car-maint.jsonl` contains the following files.
-
+ 
 ```json
 {"file":"classifier/car-maint/Commercial Motor Vehicle - Adatum.pdf"}
 {"file":"classifier/car-maint/Commercial Motor Vehicle - Fincher.pdf"}
@@ -259,6 +259,90 @@ As an example, the file list `car-maint.jsonl` contains the following files.
 {"file":"classifier/car-maint/Commercial Motor Vehicle - Trey.pdf"}
 ```
 
+::: moniker range=">=doc-intel-4.0.0"
+## Overwriting a model
+
+> [!NOTE]
+> Starting with the `2024-07-31-preview` API, custom classification models support overwriting a model in-place.
+
+You can now update the custom classification in-place. Directly overwriting the model would lose you the ability to compare model quality before deciding to replace the existing model. Model overwriting is allowed when the `allowOverwrite` property is explicitly specified in the request body. It's impossible to recover the overwritten, original model once this action is performed.
+
+```json
+
+
+{
+  "classifierId": "existingClassifierName",
+  "allowOverwrite": true,  // Default=false
+  ...
+}
+
+```
+
+## Copy a model
+
+> [!NOTE]
+> Starting with the `2024-07-31-preview` API, custom clasification models support copying a model to and from any of the follwing regions:
+> * **East US**
+> * **West US2**
+> * **West Europe**
+>
+> Use the [**REST API**](/rest/api/aiservices/operation-groups?view=rest-aiservices-2024-07-31-preview&preserve-view=true) or [**Document Intelligence Studio**](https://documentintelligence.ai.azure.com/studio/document-classifier/projects) to copy a model to another region.
+
+### Generate Copy authorization request
+
+The following HTTP request gets copy authorization from your target resource. You need to enter the endpoint and key of your target resource as headers.
+
+```http
+POST https://myendpoint.cognitiveservices.azure.com/documentintelligence/documentClassifiers:authorizeCopy?api-version=2024-07-31-preview
+Ocp-Apim-Subscription-Key: {<your-key>}
+```
+
+Request body
+
+```json
+{
+  "classifierId": "targetClassifier",
+  "description": "Target classifier description"
+}
+```
+
+You receive a `200` response code with response body that contains the JSON payload required to initiate the copy.
+
+```json
+{
+  "targetResourceId": "/subscriptions/targetSub/resourceGroups/targetRG/providers/Microsoft.CognitiveServices/accounts/targetService",
+  "targetResourceRegion": "targetResourceRegion",
+  "targetClassifierId": "targetClassifier",
+  "targetClassifierLocation": "https://targetEndpoint.cognitiveservices.azure.com/documentintelligence/documentClassifiers/targetClassifier",
+  "accessToken": "accessToken",
+  "expirationDateTime": "timestamp"
+}
+```
+
+### Start Copy operation
+
+The following HTTP request starts the copy operation on the source resource. You need to enter the endpoint and key of your source resource as the url and header. Notice that the request URL contains the classifier ID of the source classifier you want to copy.
+
+```http
+POST {endpoint}/documentintelligence/documentClassifiers/{classifierId}:copyTo?api-version=2024-07-31-preview
+Ocp-Apim-Subscription-Key: {<your-key>}
+```
+
+The body of your request is the response from the previous step.
+
+```json
+{
+  "targetResourceId": "/subscriptions/targetSub/resourceGroups/targetRG/providers/Microsoft.CognitiveServices/accounts/targetService",
+  "targetResourceRegion": "targetResourceRegion",
+  "targetClassifierId": "targetClassifier",
+  "targetClassifierLocation": "https://targetEndpoint.cognitiveservices.azure.com/documentintelligence/documentClassifiers/targetClassifier",
+  "accessToken": "accessToken",
+  "expirationDateTime": "timestamp"
+}
+```
+
+:::moniker-end
+
 ## Model response
 
 Analyze an input file with the document classification model.
@@ -269,6 +353,8 @@ Analyze an input file with the document classification model.
 https://{endpoint}/documentintelligence/documentClassifiers/{classifier}:analyze?api-version=2024-02-29-preview
 ```
 
+Starting with the `2024-07-31-preview` API, you can specify pages to analyze from the input document using the `pages` query parameter in the request.
+
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"

articles/ai-services/document-intelligence/concept-layout.md

@@ -5,8 +5,6 @@ description: Extract text, tables, selections, titles, section headings, page he
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 08/07/2024
 ms.author: lajanuar
@@ -602,7 +600,7 @@ Here are a few factors to consider when using the Document Intelligence bale ext
 
 * Do your tables span multiple pages? If so, to avoid having to label all the pages, split the PDF into pages before sending it to Document Intelligence. After the analysis, post-process the pages to a single table.
 
-* Refer to [Labeling as tables](quickstarts/try-document-intelligence-studio.md#labeling-as-tables) if you're creating custom models. Dynamic tables have a variable number of rows for each column. Fixed tables have a constant number of rows for each column.
+* Refer to [Tabular fields](concept-custom-label.md#tabular-fields) if you're creating custom models. Dynamic tables have a variable number of rows for each column. Fixed tables have a constant number of rows for each column.
 
 > [!NOTE]
 >
@@ -840,7 +838,7 @@ Learn how to accelerate your business processes by automating text extraction wi
 Figures (charts, images) in documents play a crucial role in complementing and enhancing the textual content, providing visual representations that aid in the understanding of complex information. The figures object detected by the Layout model has key properties like `boundingRegions` (the spatial locations of the figure on the document pages, including the page number and the polygon coordinates that outline the figure's boundary), `spans` (details the text spans related to the figure, specifying their offsets and lengths within the document's text. This connection helps in associating the figure with its relevant textual context), `elements` (the identifiers for text elements or paragraphs within the document that are related to or describe the figure) and `caption` if there's any.
 
 When *output=figures* is specified during the initial analyze operation, the service generates cropped images for all detected figures that can be accessed via `/analyeResults/{resultId}/figures/{figureId}`.
-`FigureId` will be included in each figure object, following an undocumented convention of `{pageNumber}.{figureIndex}` where `figureIndex` resets to one per page.
+`FigureId` is included in each figure object, following an undocumented convention of `{pageNumber}.{figureIndex}` where `figureIndex` resets to one per page.
 
 > [!NOTE]
 > Starting with *2024-07-31-preview*, the bounding regions for figures and tables cover only the core content and exclude associated caption and footnotes.

articles/ai-services/document-intelligence/concept-model-overview.md

@@ -5,10 +5,8 @@ description: Document processing models for OCR, document layout, invoices, iden
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
-ms.date: 07/09/2024
+ms.date: 08/07/2024
 ms.author: lajanuar
 ---
 
@@ -327,7 +325,7 @@ Custom models can be broadly classified into two types. Custom classification mo
 
 Custom document models analyze and extract data from forms and documents specific to your business. They recognize form fields within your distinct content and extract key-value pairs and table data. You only need one example of the form type to get started.
 
-Version v3.0 and later custom models support signature detection in custom template (form) and cross-page tables in both template and neural models. [Signature detection](quickstarts/try-document-intelligence-studio.md#signature-detection) looks for the presence of a signature, not the identity of the person who signs the document. If the model returns **unsigned** for signature detection, the model didn't find a signature in the defined field.
+Version v3.0 and later custom models support signature detection in custom template (form) and cross-page tables in both template and neural models. [Signature detection](concept-custom-template.md#model-capabilities) looks for the presence of a signature, not the identity of the person who signs the document. If the model returns **unsigned** for signature detection, the model didn't find a signature in the defined field.
 
 ***Sample custom template processed using [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/customform/projects)***:
 

articles/ai-services/document-intelligence/concept-read.md

@@ -152,9 +152,6 @@ GET /documentModels/prebuilt-read/analyzeResults/{resultId}/pdf
 Content-Type: application/pdf
 ```
 
-## Pricing
-
-
 ### Pages
 
 The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle indicating if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown:
@@ -185,6 +182,7 @@ The pages collection is a list of pages within the document. Each page is repres
     }
 ]
 ```
+
 ::: moniker-end
 
 ::: moniker range="doc-intel-3.1.0"