Methods and systems for identifying compatible meal options

a technology of compatible meal options and methods, applied in the field of artificial intelligence, can solve the problems of complex analysis of multiple user demands and requirements, difficult and accurate identification of compatible meal options,

Active Publication Date: 2021-04-22
KPN INNOVATIONS LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Accurate identification of compatible meal options can be challenging.
Analyzing multiple user demands and requirements can be complex.
Further, this can be complicated by large quantities of data to be analyzed to locate and identify compatible meal options.

Method used

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  • Methods and systems for identifying compatible meal options
  • Methods and systems for identifying compatible meal options
  • Methods and systems for identifying compatible meal options

Examples

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exemplary embodiment 200

[0084]Referring now to FIG. 2, an exemplary embodiment 200 of biological marker database 116 is illustrated. Biological marker database 116 may be implemented as any data structure suitable for use as clustering database 120 as described above in reference to FIG. 1. Biological marker database 116 may store one or more biological markers 112. One or more tables contained within biological marker database 116 may include microbiome sample table 204; microbiome sample table 204 may store one or more biological marker 112 relating to the microbiome. For instance and without limitation, microbiome sample table 204 may include results reflecting levels of a particular bacterial strain such as quantities of Bifidobacterium found in a user's gastrointestinal tract. One or more tables contained within biological marker database 116 may include fluid sample table 208; fluid sample table 208 may store one or more biological marker 112 obtained from a fluid sample. For instance and without lim...

exemplary embodiment 300

[0085]Referring now to FIG. 3, an exemplary embodiment 300 of body analysis module 108 is illustrated. Body analysis module 108 may be implemented as any software and / or hardware module. Body analysis module 108 receives a user biological marker 112 containing a plurality of user body measurements. Body analysis module 108 may receive a user biological marker 112 from user client device 156. This may be performed utilizing any network methodology as described herein. Body analysis module 108 may receive a user biological marker 112 from biological marker database 116 as described above in more detail in reference to FIG. 2. For instance and without limitation, body analysis module 108 may receive from biological marker database 116a biological marker 112 such as a saliva sample analyzed for multiple user body measurements such as progesterone level, testosterone level, estrogen level, heavy metal toxicity, cortisol measurement, and thyroid level. In yet another non-limiting example,...

exemplary embodiment 400

[0093]Referring now to FIG. 4, an exemplary embodiment 400 of clustering database 120 is illustrated. Clustering database 120 may be implemented as any data structure as described above. Clustering database 120 may store one or more clustering datasets, which may be organized according to datapoints contained within each dataset. One or more tables contained within clustering database 120 may include microbiome cluster table 404; microbiome cluster table 404 may include one or more clustering datasets related to the microbiome body dimension. One or more tables contained within clustering database 120 may include epigenetic cluster table 408; epigenetic cluster table 408 may include one or more datasets related to the epigenetic body dimension. One or more tables contained within clustering database 120 may include genetic cluster table 412; genetic cluster table 412 may include one or more datasets related to genetic body dimension. One or more tables contained within clustering da...

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Abstract

A system for identifying compatible meal options. The system includes a body analysis module configured to receive a user biological marker, select a clustering dataset from a clustering database, generate a hierarchical clustering algorithm and assign a plurality of user body measurements to a first classified dataset cluster. The system includes a food analysis module configured to select a food training set from a food database, generate using a supervised machine-learning process a food model, generate a food tolerance instruction set, and display on a graphical user interface the food tolerance instruction set. The system includes a menu generator module configured to select a menu training set from a menu database, generate using a supervised machine-learning process a menu model that produces an output containing a plurality of menu options, and display on a graphical user interface the plurality of menu options. The system includes a local selector module configured to receive a plurality of meal option inputs from a meal preparer device, generate a k-nearest neighbors algorithm, identify a plurality of compatible meal options, and display the plurality of compatible meal options on a graphical user interface.

Description

FIELD OF THE INVENTION[0001]The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to methods and systems for identifying compatible meal options.BACKGROUND[0002]Accurate identification of compatible meal options can be challenging. Analyzing multiple user demands and requirements can be complex. Further, this can be complicated by large quantities of data to be analyzed to locate and identify compatible meal options.SUMMARY OF THE DISCLOSURE[0003]In an aspect, a system for identifying compatible meal options. The system includes a processor wherein the processor further comprises a body analysis module wherein the body analysis module is further configured to receive a user biological marker wherein the user biological marker contains a plurality of user body measurements; select a clustering dataset from a clustering database wherein the clustering dataset further comprises a plurality of unclassified datap...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06N5/04G06F16/28G06N20/00G06F16/25
CPCG06N5/04G06F16/252G06N20/00G06F16/285G16H20/60G06F16/906G16H50/70G16H50/20G16H10/40G16H10/20G16H40/67Y02A90/10G06N5/01
Inventor NEUMANN, KENNETH
Owner KPN INNOVATIONS LLC
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