diff --git a/index.bs b/index.bs
index 0f9aeaec..ddb5c667 100644
--- a/index.bs
+++ b/index.bs
@@ -313,7 +313,7 @@ noise suppression using Recurrent Neural Network such as [[RNNoise]] for
 suppressing background dynamic noise like baby cry or dog barking to improve 
 audio experiences in video conferences.
 
-### Detecting fake video ### {#usecase-detecting-fake-video}
+### Detecting Fake Video ### {#usecase-detecting-fake-video}
 
 A user is exposed to realistic fake videos generated by ‘deepfake’ on the web. 
 The fake video can swap the speaker’s face into the president’s face to incite 
@@ -355,15 +355,24 @@ fully-connected layers with it.
 
 ### Performance Adaptation ### {#usecase-perf-adapt}
 
-A web application developer has a concern about performance of her DNN model on
-mobile devices. She has confirmed that it may run too slow on mobile devices
-which do not have GPU acceleration. To address this issue, her web application
-refers to the WebNN API to confirm whether acceleration is available or not, so
-that the application can display the warning for devices without acceleration.
-
-After several weeks, she has developed a tiny DNN model that can even run on
-CPU. In order to accommodate CPU execution, she modifies the application
-so that the application loads the tiny model in the case of CPU-only devices.
+A web application developer has a concern about performance of her DNN model.
+The model needs to run on both a mobile device with a low power CPU as well as
+on a laptop with a powerful CPU, GPU and a dedicated AI accelerator.
+
+She has confirmed that the model may run too slow on the mobile device which does
+not have GPU acceleration. To address this issue, her web application refers to
+the WebNN API to confirm whether acceleration is available or not, so that the
+application can display a warning for devices without acceleration. After several
+weeks, she has developed a tiny DNN model that can even run on a CPU. In order to
+accommodate CPU execution, she modifies the application so that the application
+loads the tiny model in the case of CPU-only devices.
+
+When executing the DNN model on a laptop with a more powerful CPU, GPU and a
+dedicated AI accelerator, she wants to use the execution device that minimizes
+the inference time. To address this issue, she runs the model on each execution
+device and measures the inference time for each test run. This information helps
+her release a web application that provides the best possible user experience
+given available hardware.
 
 ### Operation Level Execution ### {#usecase-op-level-exec}