Apple set to roll out AI powered app store review summaries to streamline user experience

tech.hindustantimes.com:

Apple has recently rolled out its personal intelligence system, known as Apple Intelligence, to select users in the United States, particularly those with the iPhone 15 Pro and iPhone 16 models. This system introduces various AI-driven features, including the capability to summarise texts and notifications. Now, Apple is reportedly extending this summarization feature to the App Store, a primary platform for iOS users to download applications and games.

A recent report indicates that Apple is developing a new feature to generate summaries of App Store reviews, concentrating on common customer feedback for each app. These summaries will update automatically as new reviews are submitted, ensuring users receive timely information.

Also read: 6G is here? China Mobile unveils baseband prototype for Sub7GHz frequency band development

While Apple has not disclosed the exact format of these summaries, they are expected to appear on the app's page, likely alongside the app description and screenshots. This approach resembles the AI-generated review summaries that Google introduced on its Play Store earlier this year.

Also read: OPPO rolls out cross country warranty services for select smartphones: Check eligible devices

It appears that Apple will generate these summaries on its servers rather than relying on local processing on user devices. Developers will have the opportunity to flag any summaries they find inaccurate, which could enhance the reliability of the information provided.

The summaries will become visible once an app accumulates a specified number of reviews, with the feature initially rolling out in selected countries and regions. Although no official launch date has been announced, the feature's presence in the App Store API suggests it could launch soon.

Also read: Apple Intelligence is here: Watch the top features in action (Video)

The primary aim of this initiative is to assist users in quickly identifying apps that may not meet expectations. If successful, this