Cues, Replication, and Tech competitions- The story of a revolutionizing app!

Prologue!

We won! Thomas led our team in this nail-biting competition of app development; we had finally managed to find a place at the top. At this point, we were coming out from the five year long pandemic. We wanted to change the way messaging was done using conventional apps, so we invested in developing Cues.  

In transit!

Cues was my brainchild. In a bid to understand how language could be integrated with familiar emotions, I started correlating both. Although once I had hit a roadblock, I had to turn to my friend Thomas, and along came Raj and Balakrishna (BK). Cues’ sole purpose is to understand the language being typed into the app and decipher the emotions to supplement the conversation with the correct emoticon by an automated procedure. 

Me: “Hello, just go get me a beer, BK!” The tone of the same was more of ordering him to do something, the first time we tried, it wasn’t working.

Me: “Don’t you dare speak to me like that, Tom.” Cues put an anger emoticon right beside my typed sentence.

Me: “Hey, Gayatri, I will be late tonight! Sorry!” I had a sad smile conjugated with a sorrowful face.

We quickly realized that Cues could decipher distinct human emotions, but not the ones we were trying to talk about otherwise. We first tried to integrate into it automated processing of words and symbols. So, we said, “Hey BK, go get me the beer from the fridge!” And even if we did not get the emoticons at my end, when I pressed send, it had the message and the emoticons once BK received the message. The complete statement bore the emoticons of a beer and a refrigerator, allowing him to understand the exact order. Now, this was happening in transit. The use of in transit fumbled us initially. Were we happy with that? We developed a β version of Cues into the uncertainties. We released it to both our friends and some ordinary users to get a grading on it. The use of in transit fumbled us initially.

The fruit it bore!

A few days after its roll-out, people started responding back. Thomas’s inbox was filled with thousands of emails every day about how it was slowly able to understand a wide range of emotions. Someone wrote that in a house that recently had a baby, when the mother was sending out pictures, the receivers received a large sticker of a baby along with the image. That obviously fumbled us a bit, because we had not wholly integrated the library of words and correlated pictures. We had to figure out what it was doing in transit again. When we tried sending someone’s photos, after getting this response, we realized that it was filtering a range of pictures from the cloud closely matching the original image sent. So basically, any words or photographs whose relevant emoticon was not added in our library were being taken up from the cloud, used by someone else. All the conversations using Cues were stored in the cloud. So it had easy access to the library from where it could pick up Cues. The name was worth it. 

The Win!

When we finally developed Cues to its entirety, it was able to process multiple languages we had fed in the library other than English. Cues was also using appropriate emoticons based on that language. It could use Cues cloud storage to gain access to any kind of language and emotions;. However, user data was secured from us. The messaging AI could use the data to integrate new systems, but only for the languages, we fed. This was the version we sent to the tech competition, where it was selected to top the charts.

The future is here!

Five years from its original release date, it had become the most widely used messaging app. We all knew it would also be obsolete one day, and hence our strive to develop further and better continued. We had become rich, true, but it was never about the money. It was always about user experience. Our next goal was to recognize emotions through voice patterns at the beginning of the decade, which honestly seemed a little too far-fetched.