# US 12,014,834 B2 · Methods and systems for generating a vibrant compatbility plan using artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,014,834 B2 - Document ID: US12014834B2 - Status: Granted - Kind: B2 - Application number: 18/115,849 - Filed: 2023-03-01 - Published: 2023-06-29 - Granted: 2024-06-18 - Earliest claimed: 2019-08-05 - Assignees: Kpn Innovations, LLC. - Applicants: Kpn Innovations, LLC. - Inventors: Kenneth Neumann - Web page: https://patentgrep.com/patent/US12014834B2 ## CPC - G16H 50/30: for calculating health indices; for individual health risk assessment - G06F 16/24578: {using ranking} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N 5/04: Inference or reasoning models - G16H 20/60: relating to nutrition control, e.g. diets - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06N 3/045: Combinations of networks - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system for generating a vibrant compatibility plan using artificial intelligence. The system includes at least a server wherein the at least a server is designed and configured to receive at least a composition datum from a user client device wherein the at least a composition datum includes at least an element of user body data and at least an element of desired dietary state data. At least a server is configured to select at least a correlated dataset. At least a server is configured to create at least an unsupervised machine-learning model including at least a hierarchical clustering model to output at least a compatible food element. At least a server is configured to generate at least a vibrant compatibility plan wherein the at least a vibrant compatibility plan further comprises a plurality of compatible food elements each containing at least a food element compatibility index value score as a function of the at least a hierarchical clustering model. ## Claims ### Claim 1 An apparatus for generating a vibrant compatibility plan using artificial intelligence, the system comprising: at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: receive at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein: the at least a composition datum comprises: at least an element of user body data; and at least an element of desired dietary state data; and the at least a user conclusive label comprises at least a first food element; select at least a correlated dataset containing a plurality of data entries as a function of the at least a composition datum, wherein the at least a correlated dataset comprises: at least a datum of body data; and at least a correlated second food element; extract at least a physiological trait from the at least a composition datum; match the at least a physiological trait to the at least a correlated dataset containing at least an element of the at least a physiological trait; generate a vibrant compatibility plan containing a multiple second food elements as a function of the at least a physiological trait, wherein generating the vibrant compatibility plan comprises: receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of second food elements; training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and determining the vibrant compatibility plan as a function of the at least a physiological trait using the trained machine-learning model; compare each second food element of the multiple second food elements with the at least a first food element, wherein comparing each second food element of the multiple second food elements with the at least a first food element comprises: generating at least a food element compatibility index value score as a function of each comparison; and display the vibrant compatibility plan, through a graphical user interface (GUI) of the user client device of the comparison. ### Claim 2 (depends on 1) The system of claim 1, wherein the at least a first food element comprises: an incompatible food element, wherein the incompatible food element is generated as a function of at least a conclusive label neutralizer. ### Claim 3 (depends on 1) The system of claim 1, wherein the second food element comprises a compatible food element. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least a processor is further configured to generate a desired dietary state as a function of the at least a user conclusive label. ### Claim 5 (depends on 4) The system of claim 4, wherein the at least a user dietary response comprises a user preference for the desired dietary state. ### Claim 6 (depends on 1) The system of claim 1, wherein the user dietary response comprises a shot-term dietary response, a chronic dietary response, and a lifelong dietary response. ### Claim 7 (depends on 1) The system of claim 1, wherein selecting the at least a correlated dataset comprises: receiving at least a document from an expert knowledge database; identifying a plurality of significant categories of the body data using a language processing module; and selecting the at least a correlated dataset as a function of the plurality of significant categories of the body data. ### Claim 8 (depends on 1) The system of claim 1, wherein generating the vibrant compatibility plan comprises: creating an unsupervised machine-learning model as a function of the at least a composition datum and the at least a correlated dataset, wherein creating the unsupervised machine-learning model comprises generating a hierarchical clustering model configured to output at least a second food element. ### Claim 9 (depends on 1) The system of claim 1, wherein comparing each second food element of the multiple second food elements with the at least a first food element comprises: ranking the second food element and the at least a first food element as a function of the at least a food element compatibility index value. ### Claim 10 (depends on 1) The system of claim 1, wherein generating the at least a food element compatibility index score comprises: calculating the at least a food element compatibility index score as a function of the at least a composition datum and at least a food element profile; and correlating the at least a food element compatibility index score with the multiple second food elements. ### Claim 11 A method of generating a vibrant compatibility plan using artificial intelligence, the method comprising: receiving, by at least a processor, at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein: the at least a composition datum comprises: at least an element of user body data; and at least an element of desired dietary state data; and the at least a user conclusive label contains at least a first food element; selecting, by the at least a processor, at least a correlated dataset containing a plurality of data entries as a function of the at least a composition datum, wherein the at least a correlated dataset comprises: at least a datum of body data; and at least a correlated second food element; extracting, by the at least a processor, at least a physiological trait from the at least a composition datum; matching, by the at least a processor, the at least a physiological trait to at least a correlated dataset containing at least an element of the at least a physiological trait; generating, by the at least a processor, a vibrant compatibility plan containing a plurality of second food elements as a function of the at least a physiological trait, wherein generating the vibrant compatibility plan comprises: receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of second food elements; training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; determining the vibrant compatibility plan as a function of the at least a physiological trait using the trained machine-learning model; comparing, by the at least a processor, each second food element of the multiple second food elements with the at least a first food element, wherein comparing each second food elements of the plurality of second food elements with the at least a first food element comprises: generating at least a food element compatibility index value score as a function of each comparison; and displaying, by the user client device, the at least a vibrant compatibility plan through a graphical user interface (GUI) of the comparison. ### Claim 12 (depends on 11) The method of claim 11, wherein the at least a first food element comprises: an incompatible food element, wherein the incompatible food element is generated as a function of at least a conclusive label neutralizer. ### Claim 13 (depends on 11) The method of claim 11, wherein the second food element comprises a compatible food element. ### Claim 14 (depends on 11) The method of claim 11, wherein the at least a processor is further configured to generate a desired dietary state as a function of the at least a user conclusive label. ### Claim 15 (depends on 14) The method of claim 14, wherein the at least a user dietary response comprises a user preference for the desired dietary state. ### Claim 16 (depends on 11) The method of claim 11, wherein the user dietary response comprises a shot-term dietary response, a chronic dietary response, and a lifelong dietary response. ### Claim 17 (depends on 11) The method of claim 11, wherein selecting the at least a correlated dataset comprises: receiving at least a document from an expert knowledge database; identifying a plurality of significant categories of the body data using a language processing module; and selecting the at least a correlated dataset as a function of the plurality of significant categories of the body data. ### Claim 18 (depends on 11) The method of claim 11, wherein generating the vibrant compatibility plan comprises: creating an unsupervised machine-learning model as a function of the at least a composition datum and the at least a correlated dataset, wherein creating the unsupervised machine-learning model comprises: generating a hierarchical clustering model configured to output at least a second food element. ### Claim 19 (depends on 11) The method of claim 11, wherein comparing each second food element of the multiple second food elements with the at least a first food element comprises: ranking the second food element and the at least a first food element as a function of the at least a food element compatibility index value. ### Claim 20 (depends on 11) The method of claim 11, wherein generating the at least a food element compatibility index score comprises: calculating the at least a food element compatibility index score as a function of the at least a composition datum and at least a food element profile; and correlating the at least a food element compatibility index score with the multiple second food elements.