# US 2024/0197263 A1 · Systems and methods of generating a food compatibility datum > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0197263 A1 - Document ID: US20240197263A1 - Status: Published application - Kind: A1 - Application number: 18/591,331 - Filed: 2024-02-29 - Published: 2024-06-20 - Earliest claimed: 2020-08-31 - Assignees: Kpn Innovations, LLC. - Applicants: KPN Innovations, LLC - Inventors: Kenneth Neumann - Web page: https://patentgrep.com/patent/US20240197263A1 ## CPC - A61B 5/7267: {involving training the classification device} - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/14546: {for measuring analytes not otherwise provided for, e.g. ions, cytochromes} - A61B 5/48: {Other medical applications} - A61B 5/7246: {using correlation, e.g. template matching or determination of similarity} - A61B 5/7435: {Displaying user selection data, e.g. icons in a graphical user interface} - A61B 90/39: {Markers, e.g. radio-opaque or breast lesions markers} - G06N 20/00: Machine learning - G16H 20/60: relating to nutrition control, e.g. diets - G16H 40/63: for local operation - 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 - A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Described herein are systems and methods of generating a food compatibility datum. In some embodiments, a system may include a computing device configured to receive a plurality of physiological extractions of a subject, wherein the plurality of physiological extractions comprises at least an inflammation metric; receive a plurality of alimentary element consumption data wherein each alimentary element consumption datum of the plurality of plurality of alimentary element consumption data describes a consumption of the subject prior to a physiological extraction of the plurality of physiological extractions; generate a plurality of alimentary element compatibility data; identify an inflammatory alimentary element as a function of the plurality of alimentary element compatibility data; pair a medical professional with the subject as a function of the inflammatory alimentary element; and display the inflammatory alimentary element using a user interface at a display device. ## Claims ### Claim 1 A system for generating a food compatibility datum, wherein the system comprises a computing device configured to: receive a plurality of physiological extractions of a subject, wherein the plurality of physiological extractions comprises at least an inflammation metric; receive a plurality of alimentary element consumption data wherein each alimentary element consumption datum of the plurality of plurality of alimentary element consumption data describes a consumption of the subject prior to a physiological extraction of the plurality of physiological extractions; generate a plurality of alimentary element compatibility data, wherein each alimentary element compatibility datum of the plurality of alimentary element compatibility data is associated with at least an alimentary element consumption datum of the plurality of alimentary element consumption data, wherein generating the plurality of alimentary element compatibility data comprises: training an alimentary element compatibility machine learning model on a training dataset including a plurality of example physiological extractions and a plurality of example alimentary element consumption data as inputs correlated to a plurality of example alimentary element compatibility data as outputs; and generating an alimentary element compatibility datum as a function of the at least an inflammation metric and the plurality of alimentary element consumption data using the trained alimentary element compatibility machine learning model; identify an inflammatory alimentary element as a function of the plurality of alimentary element compatibility data; pair a medical professional with the subject as a function of the inflammatory alimentary element; and display the inflammatory alimentary element using a user interface at a display device. ### Claim 2 (depends on 1) The system of claim 1, wherein receiving the plurality of alimentary element consumption data comprises tracking the subject's historical consumptions of one or more alimentary elements with a set of predetermined alimentary elements. ### Claim 3 (depends on 1) The system of claim 1, wherein: the alimentary element compatibility machine learning model comprises a classifier trained to classify the plurality of alimentary element consumption data into a plurality of categories representing different severity of an inflammatory response; and identifying the inflammatory alimentary element comprises selecting an instance of alimentary element consumption data within inflammatory element consumption data, where the inflammatory element consumption data describes consumption of the subject prior to a physiological extraction which alimentary element compatibility machine learning model categorizes as above a predetermined threshold of an inflammatory effect. ### Claim 4 (depends on 1) The system of claim 1, wherein identifying the inflammatory alimentary element comprises: generating an alimentary element compatibility representation by categorizing the plurality of alimentary element consumption data according to the plurality of alimentary element compatibility data. ### Claim 5 (depends on 4) The system of claim 4, wherein the computing device is configured to transmit to a remote device operated by the subject the alimentary element compatibility representation. ### Claim 6 (depends on 5) The system of claim 5, wherein the computing device is configured to receive from the remote device operated by the subject an alimentary element annotation. ### Claim 7 (depends on 5) The system of claim 5, wherein the computing device is configured to receive from the remote device operated by the subject an alimentary element selection. ### Claim 8 (depends on 5) The system of claim 5, wherein the computing device is configured to: update the alimentary element compatibility representation as a function of a subsequent alimentary element compatibility datum; and transmit to the remote device operated by the subject the updated alimentary element compatibility representation. ### Claim 9 (depends on 1) The system of claim 1, wherein the computing device is configured to: continuously receive physiological extractions of a subject; and identify a subset of the continuously received physiological extractions as a function of alimentary element consumption timing. ### Claim 10 (depends on 1) The system of claim 1, wherein pairing the medical professional with the subject comprises: identifying the medial professional with experience providing guidance as to health effects of alimentary elements of a category including the inflammatory alimentary element. ### Claim 11 A method of generating a food compatibility datum, wherein the method comprises: using at least a processor, receiving a plurality of physiological extractions of a subject, wherein the plurality of physiological extractions comprises at least an inflammation metric; using the at least a processor, receiving a plurality of alimentary element consumption data wherein each alimentary element consumption datum of the plurality of plurality of alimentary element consumption data describes a consumption of the subject prior to a physiological extraction of the plurality of physiological extractions; using the at least a processor, generating a plurality of alimentary element compatibility data, wherein each alimentary element compatibility datum of the plurality of alimentary element compatibility data is associated with at least an alimentary element consumption datum of the plurality of alimentary element consumption data, wherein generating the plurality of alimentary element compatibility data comprises: training an alimentary element compatibility machine learning model on a training dataset including a plurality of example physiological extractions and a plurality of example alimentary element consumption data as inputs correlated to a plurality of example alimentary element compatibility data as outputs; and generating an alimentary element compatibility datum as a function of the at least an inflammation metric and the plurality of alimentary element consumption data using the trained alimentary element compatibility machine learning model; using the at least a processor, identifying an inflammatory alimentary element as a function of the plurality of alimentary element compatibility data; using the at least a processor, pairing a medical professional with the subject as a function of the inflammatory alimentary element; and using the at least a processor, displaying the inflammatory alimentary element using a user interface at a display device. ### Claim 12 (depends on 11) The method of claim 11, wherein receiving the plurality of alimentary element consumption data comprises tracking the subject's historical consumptions of one or more alimentary elements with a set of predetermined alimentary elements. ### Claim 13 (depends on 11) The method of claim 11, wherein: the alimentary element compatibility machine learning model comprises a classifier trained to classify the plurality of alimentary element consumption data into a plurality of categories representing different severity of an inflammatory response; and identifying the inflammatory alimentary element comprises selecting an instance of alimentary element consumption data within inflammatory element consumption data, where the inflammatory element consumption data describes consumption of the subject prior to a physiological extraction which alimentary element compatibility machine learning model categorizes as above a predetermined threshold of an inflammatory effect. ### Claim 14 (depends on 11) The method of claim 11, wherein identifying the inflammatory alimentary element comprises: generating an alimentary element compatibility representation by categorizing the plurality of alimentary element consumption data according to the plurality of alimentary element compatibility data. ### Claim 15 (depends on 14) The method of claim 14, further comprises: using the at least a processor, transmitting to a remote device operated by the subject the alimentary element compatibility representation. ### Claim 16 (depends on 15) The method of claim 15, further comprises: using the at least a processor, receiving from the remote device operated by the subject an alimentary element annotation. ### Claim 17 (depends on 15) The method of claim 15, further comprises: using the at least a processor, receiving from the remote device operated by the subject an alimentary element selection. ### Claim 18 (depends on 15) The method of claim 15, further comprises: using the at least a processor, updating the alimentary element compatibility representation as a function of a subsequent alimentary element compatibility datum; and using the at least a processor, transmitting to the remote device operated by the subject the updated alimentary element compatibility representation. ### Claim 19 (depends on 11) The method of claim 11, further comprises: using the at least a processor, continuously receiving physiological extractions of a subject; and using the at least a processor, identifying a subset of the continuously received physiological extractions as a function of alimentary element consumption timing. ### Claim 20 (depends on 11) The method of claim 11, wherein pairing the medical professional with the subject comprises: identifying the medial professional with experience providing guidance as to health effects of alimentary elements of a category including the inflammatory alimentary element.