WIP: spec: update in parallel steps to resolve or reject in enqueued …

…tasks

spec.bs

@@ -639,14 +639,19 @@ If the handwriting recognizer wasn't able to recognize anything, {{HandwritingDr
 <div algorithm="handwriting-drawing-get-prediction">
   When {{HandwritingDrawing/getPrediction()}} is invoked:
 
+  1. If `this`.[=HandwritingDrawing/recognizer=].[=HandwritingRecognizer/active=] isn't true, return a [=/a promise rejected with=] {{"InvalidStateError"}} {{DOMException}}. 
+  1. If `this`.[=HandwritingDrawing/strokes=] is empty, return a [=a promise resolved with=] a new empty [=list=].
+  1. <a lt="convert">Convert `this` drawing into a format suitable</a> for [=handwriting recognizer=].
   1. Let |p| be a new Promise, run the following step [=in parallel=]
-    1. If `this`.[=HandwritingDrawing/recognizer=].[=HandwritingRecognizer/active=] isn't true, reject |p| with a new {{DOMException}} whose name is {{"InvalidStateError"}} and abort.
-    1. If `this`.[=HandwritingDrawing/strokes=] is empty, resolve |p| with a new empty [=list=].
-    1. Let |result| be a list.
-    1. <a lt="convert">Convert `this` drawing into a format suitable</a> for [=handwriting recognizer=].
-    1. Send the converted drawing to [=handwriting recognizer=], and retrieves its prediction.
-    1. <a lt="convert">Convert the retrieved predictions into </a> {{HandwritingPrediction}}, then [=list/append=] the converted predictions to |result|.
-    1. Resolve |p| with |result|.
+    1. Send the converted drawing to [=handwriting recognizer=].
+    1. Wait for [=handwriting recognizer=] to return its predictions.
+    1. Queue a global task, perform the following steps:
+      1. Let |result| be a list.
+      1. [=list/For each=] returned prediction |pred|:
+        1. <a lt="convert">Convert |pred| it into </a> {{HandwritingPrediction}} |idl_pred|.
+        1. [=list/Append=] |idl_pred| to |result|.
+      1. Resolve |p| with |result|.
+  1. Return |p|
 </div>
 
 <h3 id="handwriting-prediction">{{HandwritingPrediction}} attributes</h3>
@@ -795,4 +800,4 @@ Below are some types of recognizer implementations, and their associated risks:
 
 However, we aren't aware of any recognizer implementations that falls within this type. But we recommend using privacy protection for these models, or use a fresh / clean state for each session.
 
-**Cost of fingerprinting**: the fingerprinting solution need to craft and curate a set of handwriting drawings (adversarial samples) to exploit differences across models. The cost of generating these samples may be high, but it's safe to assume a motivated party can obtain such samples.
+**Cost of fingerprinting**: the fingerprinting solution need to craft and curate a set of handwriting drawings (adversarial samples) to exploit differences across models. The cost of generating these samples may be high, but it's safe to assume a motivated party can obtain such samples.