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US-20260127979-A1 - RECIPE GENERATION WITH MACHINE LEARNING AND SYNCHRONIZED RECIPE USE WITH CONNECTED KITCHEN APPLIANCES

US20260127979A1US 20260127979 A1US20260127979 A1US 20260127979A1US-20260127979-A1

Abstract

In a system in which a recipe is stored on a recipe framework, a method includes a recipe program presenting recipe information to a user using a device interface on a first device and/or an appliance interface of a first appliance; tracking user interactions with the recipe program via the device or appliance interface; monitoring progress and state of the recipe; and maintaining in the recipe framework, a version of the progress and state of the recipe. Responsive to the user switching to a second device or appliance while the recipe is in progress, presenting recipe information on the second device or appliance based on the version of the progress and state of the recipe maintained in the recipe framework, where the second device or appliance obtains the version of the progress and state from the recipe framework. The recipe was generated by one or more machine-learning algorithms.

Inventors

  • Timothy James Redfern
  • Benjamin Harris
  • Graham O’SULLIVAN
  • Adam Bermingham

Assignees

  • ADAPTICS LIMITED

Dates

Publication Date
20260507
Application Date
20230927

Claims (20)

  1. 1 - 33 . (canceled)
  2. 34 . A method, in a system in which a recipe is stored on a recipe framework, the method comprising, by a user having one or more devices and one or more appliances: a recipe program presenting recipe information to the user using a device interface on a first of said one or more devices and/or on an appliance interface of a first appliance of said one or more appliances; tracking interactions of the user with the recipe program via the device interface or the appliance interface; monitoring progress and state of the recipe; based on said monitoring, maintaining in said recipe framework, a version of the progress and state of the recipe; and while the recipe is in progress, and in response to the user switching to a second device of said one or more devices and/or to a second appliance of said one or more appliances, presenting recipe information on the second device and/or on the second appliance based on the version of the progress and state of the recipe maintained in the recipe framework, wherein the second device or second appliance obtains the version of the progress and state from the recipe framework.
  3. 35 . The method of claim 34 , wherein the version of the progress and state of the recipe maintained in the recipe framework is a true version of the progress and state of the recipe, and wherein, if there is a discrepancy between versions of the progress and state of the recipe, the progress and state maintained by the recipe framework will govern.
  4. 36 . The method of claim 35 , wherein the recipe framework is accessible via one or more interfaces, and wherein a device or appliance obtains the true version of the progress and state from the recipe framework via said one or more interfaces.
  5. 37 . The method of claim 36 , wherein the true version of the progress and state of the recipe is based on received streams of events and/or state data from the one or more devices and/or the one or more appliances, wherein state data from an appliance includes information about a current state of the appliance.
  6. 38 . The method of claim 34 , wherein the recipe comprises a list of one or more ingredients and a list of recipe steps, wherein the state of the progress and state of the recipe comprises information about which recipe step or steps have been completed.
  7. 39 . The method of claim 34 , wherein, when the user selects the recipe, the recipe framework determines which one or more appliances to use for the recipe based on (i) information about appliances available to the user, and (ii) user data maintained by the recipe framework, the user data including appliance data.
  8. 40 . The method of claim 34 , further comprising performing one or more of the following acts before steps and/or ingredients of the recipe are determined: (i) calibration; (ii) recipe scaling; (iii) ingredient substitutions; (iv) nutritional information determination; (v) recommendations; and (vi) capability resolution.
  9. 41 . The method of claim 40 , wherein the recipe determines which of the one or more appliances are to be used, and wherein a determination of which of the one or more appliances are to be used is made after the acts of the method are performed.
  10. 42 . The method of claim 34 , wherein the one or more devices are selected from: a personal computer, a cell phone, a tablet computer, a desktop computer, a TV, a smartwatch, a voice assistant, or an appliance UI; and wherein the one or more appliances are selected from: cooking and food preparation appliances.
  11. 43 . The method of claim 34 , wherein the recipe was generated by one or more machine-learning algorithms.
  12. 44 . The method of claim 43 , wherein the recipe is a connected recipe that was generated based on an initial recipe.
  13. 45 . The method of claim 44 , wherein the initial recipe was a structured recipe, including initial recipe step data, and/or initial recipe ingredient data, and/or initial recipe appliance data.
  14. 46 . The method of claim 45 , wherein the connected recipe is a structured recipe and includes: connected recipe step data and/or connected recipe ingredient data, and/or connected recipe appliance data.
  15. 47 . The method of claim 46 , wherein the connected recipe step data and/or connected recipe ingredient data was determined by the one or more machine-learning algorithms based on the initial recipe step data, and/or initial recipe ingredient data, and using a knowledge graph of culinary processes, ingredients, and measurement units.
  16. 48 . The method of claim 46 , wherein the one or more machine-learning algorithms comprise a machine learning (ML) pipeline, wherein the ML pipeline generates said connected recipe step data and/or said connected recipe ingredient data, and/or said connected recipe appliance data.
  17. 49 . The method of claim 48 , wherein said ML pipeline includes a first model that recognizes culinary techniques and maps them to a knowledge graph of capabilities that an appliance can fulfill to annotate the connected recipe with capability events.
  18. 50 . The method of claim 49 , wherein the first model finds appliance-related parameters, wherein the appliance-related parameters comprise one or more of temperature, speed, time, and/or power.
  19. 51 . The method of claim 49 , wherein said ML pipeline further includes: a third model that maps ambiguous capabilities to the knowledge graph of capabilities.
  20. 52 . The method of claim 48 , wherein said ML pipeline further includes: a second model for relation classifications to determine which parameters relate to which capabilities.

Description

RELATED AND/OR INCORPORATED PATENTS AND PATENT APPLICATIONS This application claims the benefit of U.S. provisional patent applications (i) U.S. Provisional patent application No. 63/410,340, filed Sep. 27, 2022, for “SYNCHRONIZED RECIPE USE WITH CONNECTED KITCHEN APPLIANCES,” and (ii) 63/527,435, filed Jul. 18, 2023, for “RECIPE GENERATION WITH MACHINE LEARNING AND SYNCHRONIZED RECIPE USE WITH CONNECTED KITCHEN APPLIANCES,” the entire contents of which are hereby fully incorporated herein by reference for all purposes. The entire contents of U.S. Pat. No. 11,631,010, issued Apr. 18, 2023, are hereby fully incorporated herein by reference for all purposes. COPYRIGHT STATEMENT This patent document contains material subject to copyright protection. The copyright owner has no objection to the reproduction of this patent document or any related materials in the files of the United States Patent and Trademark Office, but otherwise reserves all copyrights whatsoever. APPENDICES This application includes the following appendices, which are part of this application: Appendix A: Example of Schema.org recipe in JSON-LD format;Appendix B: Examples of Step Extraction; andAppendix C: Example connected (smart) recipe. FIELD OF THE INVENTION Aspects of this invention relate to improving user experiences with connected kitchen appliances. More specifically, aspects of the invention relate to using machine learning (ML) to generate recipes usable on connected kitchen appliances. BACKGROUND Home chefs are now offered many innovative kitchen appliances with multiple cooking functions and time-saving features. However, it is not always easy for the home user to understand how to use these appliances beyond the limited range of sample recipes developed and distributed by the manufacturer. Connecting these appliances to home networks/the internet is a popular option with both consumers and manufacturers, with the potential to link recipe discovery, ingredient supply, and cooking in a seamless guided journey. However, there is a tendency that connected appliances each exist in a separate ecosystem with their user interface, mobile app, and recipes. One of the biggest problems facing the owners and manufacturers of connected kitchen appliances is the availability of relevant recipe content. Home chefs want the freedom to discover recipes from many sources, with the confidence to know how to make the best use of their appliances and the convenience of applying them to a connected guided cooking flow. Kitchen appliance manufacturers want to avoid developing and testing recipes for every appliance they sell. It is an object hereof to provide recipes usable with connected kitchen appliances. SUMMARY Aspects of the present invention are specified in the claims and the below description. Preferred embodiments are particularly specified in the dependent claims and the description of various embodiments. In some aspects, embodiments provide a machine-learning pipeline that allows recipes as human-readable text to be processed to recognize cooking entities from a curated knowledge graph. Once imported, recipes are annotated with machine-readable information: for example, ingredients and appliance instructions, allowing the recipe to connect the user with automated ingredient and appliance features and other smart algorithms. The recipe pipeline may fulfill the needs of a cross-brand connected kitchen platform designed to assist users with recipe discovery, recipe customization, ingredient management, following recipes, and controlling automated appliances. When using the platform, recipes may be submitted to the machine learning pipeline, allowing ingredient, appliance, and algorithm features to be used with recipes chosen by the user at runtime. The platform may also represent user actions and store user activity history centrally. Representing the user with a centrally stored profile allows user actions to be synchronized between mobile devices, appliances, and other recipe clients, affording flexibility for the user. The digital recipe knowledge graph allows the implementation of smart kitchen algorithms allowing the platform to apply culinary expertise automatically to adapt recipes to achieve the best results. Examples of these algorithms include appliance capability resolution, recipe scaling, ingredient substitutions, calibration of appliances, recipes, and ingredients, algorithms using nutritional information, and recipe recommendation. In some other aspects, embodiments provide a knowledge graph of culinary processes, ingredients, and measurement units. Appliance capabilities may be mapped to the knowledge graph, and the machine learning pipeline may be trained to annotate recipes via the knowledge graph. In some other embodiments, the machine learning pipeline may generate ingredient lists, step descriptions, and step metadata (e.g., ingredients and appliance data) required for guided cooking. In the case of appliance step data,