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That's a great idea!
To make automated reviews, we need to figure out what kind of reviews you want to automate.
For example:
* Product reviews: Analyzing customer feedback to identify common themes and sentiment.
* Code reviews: Using tools to automatically check for coding errors and style inconsistencies.
* Academic paper reviews: Summarizing key findings and identifying potential areas for improvement.
Tell me more about the type of automated reviews you have in mind, and I can give you more specific information and examples.

Identifying People Expressions in Google Meets Calls
This is a complex task with several challenges:
* Technical Limitations: Google Meets doesn't currently offer an API to directly access facial expressions of participants.
* Privacy Concerns: Analyzing facial expressions raises significant privacy issues. Users should have control over whether their expressions are being tracked and used.
* Accuracy: Even with access to facial data, accurately interpreting expressions can be difficult due to variations in lighting, angles, and individual differences.
Possible Approaches (with limitations):
* User-Submitted Data: Participants could manually indicate their emotions during the call, which could be collected and analyzed. This relies on user honesty and may not capture subtle expressions.
* Third-Party Tools: Some external tools might analyze video feeds and attempt to detect expressions. However, their accuracy and privacy practices should be carefully evaluated.
* Future Developments: Google or other companies might develop features that allow for more ethical and accurate expression analysis in the future.
It's important to remember that facial expressions are just one aspect of communication, and relying solely on them can be misleading.

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