Using analytics to determine dining venue based on group preferences

This invention helps groups of people choose a restaurant that satisfies everyone's diverse tastes. It works by creating individual flavor profiles for each person, which numerically rate their preferences for things like savory, sweet, or salty, often by analyzing food inputs using a deep-learning system. These individual profiles are then combined, typically by averaging, to form a group preference profile that can also include non-food factors like cost or distance, and this group profile is matched to suitable dining venues. The claims narrow the individual flavor profiles to include at least five specific types: savory, salty, sweet, and bitter.

Why it matters: Filed before the widespread availability of powerful, pre-trained AI models and cloud-based machine learning platforms. The core challenge of accurately determining flavor profiles from diverse inputs using deep learning is significantly more feasible and less resource-intensive today.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedDeep-learning neural network, software logic

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