Inspiration

As busy college students, we have become quite reliant on tools such as Google Calendar in order to plan our day. Even then, sometimes it becomes a chore to open up my Google Calendar and set-up an appointment every-time someone mentions a meeting or an event. With this chrome extension, the process of setting up events within Google Calendar becomes streamlined and accessible. Any user would be able to skim the most important details of their future meeting by highlighting relevant text. This is done through the user of NLP libraries and text queries. This then brings the user to a Google Calendar window where all of the relevant fields have already been filled in.

How we built it

This was mainly built upon Node.js framework. It was developed with the goal of keeping all of our process browser side and really took advantage of the flexibility of JavaScript. Our project also uses standard front-end tools such as html and css.

Challenges we ran into

Originally, we had trouble deciding on which natural language processing libraries we would use in order to identify important event details such as Event Title and Start Time. We wanted to use SpaCy which was a nlp python library that would have required the use of client-side processing and would have required transcompiling between Python and JavaScript that just did not seem feasible for the duration of the Hackathon. In the end, we were able to decide on sherlockjs for our nlp libraries. While less robust, it served our purposes well. This also required us to use our own methodology for identifying locations as sherlockjs did not have that capability.

Accomplishments that we're proud of

We are particularly proud of our effort to also identify location in our event parameters. In the very few similar apps to our extension, none of them attempted to identify the specific location of meeting places. This was also an attempt to cover for the fact that our nlp libraries did not also account for location.

What's next for QuickCal

QuickCal has the potential to be a very robust scheduling app that would help many college students stay on top of meetings and scheduling. In order to help achieve this goal, we would love to use more robust natural language processing libraries in order to more accurately identify event details to leave as little work for the user as possible.

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