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The app communicates with a central server to generate meal recommendations based on information gathered by the server. The server could be constantly updated with new information from data collected from websites of local grocery stores, including nutritional and pricing information. The server also would have a second database containing recipes of meals.<\/p>\n
The server would then use an AI model trained on common user preferences and recommended nutritional data to determine what recipes are most applicable for each user based on location, available products and their costs and what they have eaten recently to support a balanced diet. Using the vast amount of data collected the website could also be used to find pricing and availability for individual products between stores.<\/p>\n
The app would also have an element of crowdsourcing for grocery stores where pricing and stock information is not readily available online or inaccurate, where users can submit new information about available produce or flag inaccurate information. Stores that have a track record of inaccurate information would be flagged so users know not to completely trust the data presented for those stores. Users will also be able to add locations with no web presence such as farmers markets. New recipes can also be suggested by users and voted on by other users.<\/p>\n","protected":false},"excerpt":{"rendered":"
The app communicates with a central server to generate meal recommendations based on information gathered by the server. The server could be constantly updated with new information from data collected from websites of local grocery stores, including nutritional and pricing information. The server also would have a second database containing recipes of meals. The server… Continue reading