DreamDigger: Subconscious Shelf Price Oracle

Inspired by the manipulative pricing tactics in e-commerce and the exploration of subconscious desires in 'Nightfall' and 'Inception,' DreamDigger is a low-cost, individual-implementable self-checkout solution for brick-and-mortar retailers. The core concept is to leverage subtle physiological data (e.g., pupil dilation, micro-expressions, galvanic skin response – obtainable through readily available webcams and basic sensors) captured while a customer browses products. This data, processed by a simple machine learning model, would aim to identify moments of heightened interest or perceived value. Instead of a traditional price display, the system would 'suggest' an optimal price point at checkout, tailored to the individual's subconscious receptiveness at that moment. This isn't about predatory pricing, but rather about understanding the customer's emotional 'buy-in' and presenting a price that feels serendipitous, akin to finding a hidden gem. The 'Inception' element comes into play with the idea of planting a suggestion (the price) into the subconscious. Retailers could use this for impulse buy sections or to dynamically adjust pricing on high-margin items. The niche lies in its focus on psychological pricing at the point of sale, moving beyond static price tags. Implementation is feasible with open-source ML libraries and affordable hardware. High earning potential stems from increased sales conversion rates and improved customer perceived value.