# US 12,001,796 B2 · Methods and systems for personal recipe generation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,796 B2 - Document ID: US12001796B2 - Status: Granted - Kind: B2 - Application number: 17/540,335 - Filed: 2021-12-02 - Published: 2022-06-02 - Granted: 2024-06-04 - Earliest claimed: 2020-11-30 - Assignees: Kpn Innovations, LLC. - Applicants: Kpn Innovations, LLC. - Inventors: Kenneth Neumann - Web page: https://patentgrep.com/patent/US12001796B2 ## CPC - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06F 40/237: Lexical tools - G06F 40/30: Semantic analysis - G16H 20/60: relating to nutrition control, e.g. diets - G06F 40/279: Recognition of textual entities - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A system for informing cooking style decisions, the system including a computing device designed and configured to receive a personal recipe relating to a user. The computing device also designed and configured to select a cooking style, including locating in a recipe database a set of cooking styles associated with the personal recipe and selecting the cooking style from the set of cooking styles as a function of the user's biological extraction. Additionally, the computing device is designed and configured to incorporate the cooking style into the personal recipe of the user. ## Claims ### Claim 1 A system for informing cooking style decisions, the system comprising a computing device, the computing device designed and configured to: identify a nutrient anomaly relating to a user; determine a target profile utilizing the nutrient anomaly; generate a personal recipe relating to the user, wherein generating the personal recipe comprises: locating, in a lexicon of ingredients, wherein the lexicon associates a first ingredient identifier to a first ingredient impact and a first semantic unit, wherein the lexicon associates a second ingredient identifier to a second ingredient impact and a second semantic unit, and wherein the first and second semantic unit has a first and second semantic meaning; comparing the first ingredient impact to the target profile; comparing the second ingredient impact to the first ingredient impact and the target profile; and generating the personal recipe as a function of the first ingredient impact, the second ingredient impact, and the target profile, wherein generating the personal recipe further comprises: receiving a corpus of documents from at least an expert; extracting one or more textual elements from the corpus of documents; receiving information on an allergy of the user; and updating the personal recipe by substituting an ingredient of the personal recipe with an alternative ingredient as a function of the allergy of the user; select a cooking style, wherein selecting the cooking style comprises: locating, in a recipe database, a cooking style table comprising a set of cooking styles associated with the personal recipe; and selecting the cooking style from the set of cooking styles as a function of the target profile; and integrate the cooking style into the personal recipe of the user. ### Claim 2 (depends on 1) The system of claim 1, wherein selecting the cooking style further comprises: associating each of the set of cooking styles with an ingredient identifier of a plurality of ingredient identifiers, wherein the personal recipe includes the plurality of ingredient identifiers; and selecting a preferred cooking style for each ingredient identifier of the plurality of ingredient identifiers, if the ingredient identifier has more than one associated cooking style of the set of cooking styles. ### Claim 3 (depends on 1) The system of claim 1, wherein the computing device is further configured to: receive a user preference from the user; and select a cooking style from the set of cooking styles as a function of the user preference. ### Claim 4 (depends on 3) The system of claim 3, wherein the user preference indicates a user dislike. ### Claim 5 (depends on 1) The system of claim 1, wherein selecting the cooking style from the set of cooking styles further comprises generating a machine learning process, wherein the machine learning process utilizes a user biological extraction as an input and outputs the cooking style. ### Claim 6 (depends on 5) The system of claim 5, wherein the machine learning process utilizes a user preference relating to the user as an input. ### Claim 7 (depends on 5) The system of claim 5, wherein the computing device is further configured to train the machine learning process as a function of training data, wherein the training data contains a plurality of data entries containing biological extraction data as inputs correlated to cooking styles as outputs. ### Claim 8 (depends on 1) The system of claim 1, wherein selecting the cooking style further comprises selecting a preparation technique from a preparation table in the recipe database as a function of a user biological extraction. ### Claim 9 (depends on 3) The system of claim 3, wherein the user preference includes a user diet, wherein the user diet is a diet to which the user is currently complying. ### Claim 10 (depends on 1) The system of claim 1, wherein generating the personal recipe further comprises updating the personal recipe by incorporating an additional ingredient therein that improves the first ingredient impact by increasing bioavailability of a first ingredient associated with the first ingredient impact. ### Claim 11 A method for informing cooking style decisions, the method comprising: identifying, by a computing device, a nutrient anomaly relating to a user; determining, by the computing device, a target profile utilizing the nutrient anomaly; generating, by the computing device, a personal recipe relating to the user, wherein generating the personal recipe comprises: locating, in a lexicon of ingredients, wherein the lexicon associates a first ingredient identifier to a first ingredient impact and a first semantic unit, wherein the lexicon associates a second ingredient identifier to a second ingredient impact and a second semantic unit, and wherein the first and second semantic unit has a first and second semantic meaning; comparing the first ingredient impact to the target profile; comparing the second ingredient impact to the first ingredient impact and the target profile; and generating the personal recipe as a function of the first ingredient impact, the second ingredient impact, and the target profile, wherein generating the personal recipe further comprises: receiving a corpus of documents from at least an expert; extracting one or more textual elements from the corpus of documents; receiving information on an allergy of the user; and updating the personal recipe by substituting an ingredient of the personal recipe with an alternative ingredient as a function of the allergy of the user; selecting, by the computing device, a cooking style, wherein selecting the cooking style comprises: locating in a recipe database a cooking style table comprising a set of cooking styles associated with the personal recipe; and selecting the cooking style from the set of cooking styles as a function of the target profile; and incorporating, by the computing device, the cooking style into the personal recipe of the user. ### Claim 12 (depends on 11) The method of claim 11, wherein selecting the cooking style further comprises: associating each of the set of cooking styles with an ingredient identifier of a plurality of ingredient identifiers, wherein the personal recipe includes the plurality of ingredient identifiers; and selecting a preferred cooking style for each ingredient identifier of the plurality of ingredient identifiers, if the ingredient identifier has more than one associated cooking style of the set of cooking styles. ### Claim 13 (depends on 11) The method of claim 11, further comprising: receiving, by the computing device, a user preference from the user; and selecting a cooking style from the set of cooking styles as a function of the user preference. ### Claim 14 (depends on 13) The method of claim 13, wherein the user preference indicates a user dislike. ### Claim 15 (depends on 11) The method of claim 11, wherein selecting the cooking style from the set of cooking styles further comprises generating a machine learning process, wherein the machine learning process utilizes a user biological extraction as an input and outputs the cooking style. ### Claim 16 (depends on 15) The method of claim 15, wherein the machine learning process utilizes a user preference relating to the user as an input. ### Claim 17 (depends on 15) The method of claim 15, further comprising training the machine learning process as a function of training data, wherein the training data contains a plurality of data entries containing biological extraction data as inputs correlated to cooking styles as outputs. ### Claim 18 (depends on 11) The method of claim 11, wherein selecting the cooking style from the set of cooking styles further comprises selecting a preparation technique from a preparation table in the recipe database as a function of a user biological extraction. ### Claim 19 (depends on 13) The method of claim 13, wherein the user preference includes a user diet, wherein the user diet is a diet to which the user is currently complying. ### Claim 20 (depends on 11) The method of claim 11, wherein generating the personal recipe further comprises updating the personal recipe by incorporating an additional ingredient therein that improves the first ingredient impact by increasing bioavailability of a first ingredient associated with the first ingredient impact.