Generative group-based meal planning system and method

Patent metadata

Field	Value
Publication number	US-2017193853-A1
Application number	US-201614988357-A
Country	US
Kind code	A1
Filing date	Jan 5, 2016
Priority date	Jan 5, 2016
Publication date	Jul 6, 2017
Grant date	—

Abstract

Embodiments provide a generative group-based meal planning system and method for the creation of candidate meal plans based upon a pre-selected list of ingredients. The meal planning system can create parent meal plans based upon one or more recipes having one or more of the pre-selected ingredients. Child meal plans can be created by the random crossing of the recipes contained in the parent meal plans. The child meal plans can be scored against a genetic algorithm, such as a fitness function, which takes into consideration cost of ingredients, waste, flavor compatibility, preparation time, and ingredient shelf life. The meal planning system can utilize a cognitive system with natural language processing abilities to generate new recipes based off of waste or flavor compatibility. The child meal plans having the highest fitness score can be used as the parent meal plans in the next iteration of analysis. After a pre-determined number of iterations, a candidate meal plan can be output by the system.

First claim

What is claimed is:

1 . A computer implemented method in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a generative group-based meal planning system, the method comprising:
receiving, by the meal planning system, a request to generate a candidate meal plan, wherein one or more ingredients are identified to be incorporated into one or more recipes contained in the candidate meal plan, wherein the number of needed recipes to be included in the candidate meal plan is pre-determined; importing, by the meal planning system, one or more recipes incorporating the one or more identified ingredients; generating, by the meal planning system, one or more parent meal plans containing the pre-determined number of recipes, wherein the parent meal plan recipes are randomly selected from the imported recipes incorporating the one or more identified ingredients; generating, by the meal planning system, one or more child meal plans, wherein the child meal plan recipes are selected through the random crossing of recipes between the one or more parent meal plans; determining, by the meal planning system, a fitness score for the one or more child meal plans through the utilization of a genetic algorithm; wherein the steps of generating one or more parent meal plans, generating one or more child meal plans, and determining the fitness score for the one or more child meal plans are repeated for a pre-determined amount of iterations, wherein the one or more child meal plans with the highest fitness scores are used as the next generation of one or more parent meal plans; and outputting, by the meal planning system, the candidate meal plan having the pre-determined number of recipes, wherein the candidate meal plan is selected from the child meal plan having the highest fitness score.

2 . The method as recited in claim 1 , wherein in addition to identifying one or more ingredients to be incorporated, one or more ingredients to be excluded from the one or more recipes contained in the candidate meal plan are further identified.

3 . The method as recited in claim 1 , wherein the genetic algorithm comprises a fitness function that considers cost of ingredients, shelf life of ingredients, flavor compatibility of ingredients, waste, and preparation time as factors in determining the fitness score.

4 . The method as recited in claim 3 , wherein the meal planning system is connected to a database containing cost of ingredient information.

5 . The method as recited in claim 3 , wherein the meal planning system is connected to a database containing shelf life of ingredient information.

6 . The method as recited in claim 3 , wherein the meal planning system is connected to a database containing flavor compatibility of ingredient information.

7 . The method as recited in claim 3 , wherein the meal planning system comprises a cognitive system having natural language processing capabilities.

8 . The method as recited in claim 7 , further comprising:
caching, through the meal planning system, waste ingredients in one or more recipes identified during the determination of the fitness score; creating, through the cognitive system, one or more new recipes based upon the waste ingredients cached; and incorporating, by the meal planning system, the one or more new recipes based upon the waste ingredients into the next generation of one or more parent meal plans.

9 . The method as recited in claim 7 , further comprising:
identifying, through the cognitive system, one or more identified ingredients as having flavor compatibility; creating, through the cognitive system, one or more new recipes based upon the compatible ingredients; and incorporating, by the meal planning system, the one or more new recipes based upon the compatible ingredients into the first or next generation of one or more parent meal plans.

10 . The method as recited in claim 9 , wherein the fitness function is configured to award greater weight to recipes having greater cost efficiency of ingredients, thereby causing the cognitive system to create the one or more new recipes prioritizing cost of ingredients over flavor compatibility.

11 . The method as recited in claim 1 , further comprising the step of:
verifying the one or more recipes in the candidate meal plan against shelf life of ingredient information.

12 . The method as recited in claim 11 , further comprising the step of:
reorganizing the one or more recipes in the candidate meal plan to maximize the shelf life of the ingredients.

13 . A computer program product for generative group-based meal planning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
receive a request to generate a candidate meal plan, wherein one or more ingredients are identified to be incorporated into one or more recipes contained in the candidate meal plan, wherein the number of needed recipes to be included in the candidate meal plan is pre-determined; import one or more recipes incorporating the one or more identified ingredients; generate one or more parent meal plans containing the pre-determined number of recipes, wherein the parent meal plan recipes are randomly selected from the identified recipes incorporating the one or more imported ingredients; generate one or more child meal plans, wherein the child meal plan recipes are selected through the random crossing of recipes between the one or more parent meal plans; determine a fitness score for the one or more child meal plans through the utilization of a genetic algorithm; and output the candidate meal plan having the chosen number of recipes, wherein the candidate meal plan is selected from the child meal plan having the highest fitness score; wherein generating one or more parent meal plans, generating one or more child meal plans, and determining the fitness score for the one or more child meal plans are repeated for a pre-determined number of iterations, wherein the one or more child meal plans with the highest fitness scores are used as the next generation of one or more parent meal plans.

14 . The computer program product as recited in claim 13 , wherein the processor is further configured to identify one or more ingredients to be excluded from the one or more recipes contained in the candidate meal plan.

15 . The computer program product as recited in claim 13 , wherein the genetic algorithm comprises a fitness function that considers cost of ingredients, shelf life of ingredients, flavor compatibility of ingredients, waste, and preparation time as factors in determining the fitness score.

16 . The computer program product as recited in claim 15 , wherein the processor is connected to a database containing flavor compatibility of ingredient information.

17 . The computer program product as recited in claim 13 , further comprising a cognitive system having natural language processing capabilities.

18 . The computer program product as recited in claim 17 , wherein the processor is configured to cache waste ingredients in one or more recipes identified during the determination of the fitness score;
the cognitive system is configured to create one or more new recipes based upon the waste ingredients cached; and the processor is further configured to incorporate the one or more new recipes based upon the waste ingredients int

Assignees

• IBM

Inventors

• Byron Donna K
• Dimascio Carmine M
• Pinel Florian
• Winkler Timothy P

Classifications

• G06N20/00

  Machine learning · CPC title

• G09B19/0092 Primary

  Nutrition · CPC title