Augmented reality and blockchain technology for decision augmentation systems and methods using contextual filtering and personalized program generation

1. A method, comprising:

receiving any of an image, a video stream, and contextual data from a mobile device;

evaluating any of the image, the video stream, and the contextual data for target food or beverage content by:

determining ingredient and nutritional components of the target food or beverage content; and

applying an augmented reality overlay to the target food or beverage content based on the ingredient and nutritional components;

wherein a personalized plan is generated by: obtaining input from empirical evidence-based lifestyle and nutritional programs; and

selecting a dietary program for the user that is based on information comprising genetics, biomarkers, profile, activities, background, clinical data, and combinations thereof;

converting lifestyle programs, dietary plans, nutrition plans, and empirical evidence-based programs into rule sets; and

creating a ruleset stack by merging a plurality of any of lifestyle programs, dietary plans, nutrition plans, and empirical evidence-based programs, wherein the ruleset stack is further configured based on any of goals, biometrics, biomarkers, genetics, demographics, lifestyle and combinations thereof.

2. The method according to claim 1 , wherein the target food or beverage content comprises any of restaurant menus, ingredient lists, beverages, food products, groceries, supplements, medications, food labels, and combinations thereof.

3. The method according to claim 2 , further comprising generating a dynamic adherence score for any of the target food or beverage content wherein the dynamic adherence score is indicative of how well an item adheres to the personalized plan established by considering for the item any of required time lapse, periodicity, quantity, sequence, food and activity logs, sensors, external data sources, user context, and combinations thereof.

4. The method according to claim 3 , wherein the augmented reality overlay comprises any of an icon, text, a number applied proximately to, or over, a portion of the target food or beverage content so as to highlight or obscure items included in the target food or beverage content that do or do not comply with the personalized plan established for the user.

5. The method according to claim 1 , further comprising:

receiving biometric or user-generated feedback; and

updating the personalized plan established for the user based on the feedback.

6. The method according to claim 5 , wherein the biometric or user-generated feedback comprises any of relations and specific sequences of foods, activities, symptoms, and outcomes.

7. The method according to claim 1 , further comprising matching the image, video stream or contextual data to markers and data in a database, wherein the image or the video stream captures any of a restaurant menu, an ingredient list, a beverage, a food product, a grocery item, a supplement, a medication or a food label and data in the database when a match is found, wherein the image or the video stream is converted into an augmented reality enabled item or image.

8. The method according to claim 6 , further comprising:

receiving a request for any of a recommended image, markers, and data options overlay; and

calculating and displaying an adherence score that is indicative of how well at least one item adheres to the personalized plan established for the user.

9. The method according to claim 6 , further comprising converting the image or video stream into an augmented reality enabled image process pipeline that allows for any of image correction, marker creation, and links for each element to the ingredient and nutritional components using a multi-model, multi-ontology, multi-label deep neural network (mLOM).

10. The method according to claim 9 , wherein the mLOM is used to generate specific ingredient, substance and nutritional components of items in the target food or beverage content that are indicative of nutritional values.

11. The method according to claim 1 , further comprising prioritizing rulesets in the ruleset stack according to medical needs.

12. The method according to claim 1 , wherein at least a portion of the lifestyle programs, dietary plans, nutrition plans, and empirical evidence-based programs selected for use are obtained from a database based on a comparison of the user to a plurality of other users with respect to any of goals, biometrics, biomarkers, genetics, demographics, lifestyle and combinations thereof.

13. The method according to claim 1 , further comprising updating the personalized plan using empirical feedback gathered from the user or from biometric measuring devices.

14. The method according to claim 13 , wherein the empirical feedback is processed using multivariate causation discovery to find patterns and sequences that best predict one or more desired outcomes, lab test results, environment and personal information based on one or more selected rulesets.

15. The method according to claim 14 , further comprising generating a merged program or dietary plan for the user or a group of users based on multiple applied rulesets for the user or the group of users that also have individual ruleset based programs.

16. The method according to claim 15 , wherein the personalized plan is updated using updated rulesets, empirical feedback, and active and passive feedback obtained from biometric feedback devices utilized by the user.