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We're the blowjob machine people. Our company holds the record for the highest amount of money raised in crowdfunding for any male sex toy. In 2014 on Indiegogo, we crowdfunded the Autoblow 2 which, over time, raised more than $381,000 from more than 3300 backers. During the last 4 years, we gained traction in our niche and sold the Autoblow 2 and its  improved versions (Autoblow 2+ and Autoblow 2+XT) to more than 200,000 men around the world. The video below shows the product on tv shows, on many news websites, and even in an 8 page profile in Playboy Magazine. 

11 Item(s)

Autoblow A.I. is the first sex toy to use artificial intelligence to enhance the realism of a solo sexual experience. Virtually every other male sex toy that strokes, including our own Autoblow 2, strokes only in complete strokes from the top to the bottom of the penis. But full top to bottom stroking of the penis is only one of many possible oral sex techniques. The new Autoblow's techniques are far more advanced.

Our team used the power of machine learning to analyze a large dataset of oral sex videos to discover for the first time the actual movements that occur during blowjobs. We then turned the results of the research into Autoblow A.I's 10 blowjob experiences.

Our goal was to discover how much of the receiver's penis was inside of the giver's mouth at every moment, during the entire act. The result of our data ingestion efforts was 1 terabyte of images containing oral sex performed upon men, each tagged with a time within the oral sex film from which they were saved and an approximation of how much penis was inserted how deeply into the oral sex giver's mouth.

To obtain our dataset, we watched 8,333 minutes (almost 6 full days) of oral sex videos at 50% speed and manually moved a drawing of a head on a drawing of a penis to indicate the actions occurring in the films. The computer captured 30 screenshots of the video per second and captured the location data of the head using our input method. A quality controller then re-watched the recordings and eliminated ones that were inaccurate.

After data collection, we normalized the data at 16 frames per second. We fragmented the data into chunks and then applied K means algorithm on the mouth position data to discover the 16 most used mouth locations along the penis, during blowjobs. We then trained a model that classified sequences of head movements into clusters. After discarding some movements that were too passive for recreating into our machine, we used the head/mouth movements we discovered to create our product's modes.

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