| CPC A01H 1/04 (2013.01) [A01H 1/02 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01)] |
| AS A RESULT OF REEXAMINATION, IT HAS BEEN DETERMINED THAT: |
| The patentability of claims 1-18 is confirmed. |
| New claims 19-23 are added and determined to be patentable. |
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1. A method for use in identifying crosses for use in plant breeding, the method comprising:
accessing a data structure representative of multiple parents;
identifying a set of potential crosses, each potential cross in the set of potential crosses including at least two of the multiple parents included in the data structure;
selecting, by at least one computing device, a subgroup of potential crosses, from the set of potential crosses, based on one or more thresholds associated with population prediction scores for the set of potential crosses, each population prediction score associated with a prediction of commercial success for the associated potential cross within the set of potential crosses;
selecting, by the at least one computing device, multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses;
filtering, by the at least one computing device, the target crosses based on at least one rule, the at least one rule defining at least one threshold for at least one characteristic and/or trait of at least one of: the multiple target crosses, one of the multiple parents included in the target crosses, and a parental line of the target crosses;
selecting, by the at least one computing device, ones of the filtered target crosses based on risk associated with the selected one of the filtered target crosses; and
including a plant in a growing space of a breeding pipeline, the plant derived from at least one of the selected ones of the filtered target crosses.
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[ 19. A method for use in identifying crosses for use in plant breeding, the method comprising:
accessing a data structure representative of multiple parents;
identifying a set of potential crosses, each potential cross in the set of potential crosses including at least two of the multiple parents included in the data structure;
selecting, by at least one computing device, a subgroup of potential crosses, from the set of potential crosses, based on population prediction scores for the set of potential crosses satisfying one or more thresholds, each population prediction score indicating a prediction of commercial success for the associated potential cross within the set of potential crosses;
selecting, by the at least one computing device, multiple target crosses from the subgroup of potential crosses, based on genetic relatedness between the parents included in the subgroup of potential crosses;
filtering, by the at least one computing device, the target crosses based on at least one rule, the at least one rule defining at least one threshold for at least one of: stalk lodging, root lodging, Goss Wilt, parental similarity, and/or a difference between expected relative maturity (ERM) between the two parents for the target crosses;
selecting, by the at least one computing device, ones of the filtered target crosses based on risk of parental age, root lodging, stalk lodging, and/or Goss Wilt susceptibility associated with the selected one of the filtered target crosses; and
including a plant in a growing space of a breeding pipeline, the plant derived from at least one of the selected ones of the filtered target crosses.]
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[ 20. The method of claim 19, wherein the multiple parents are each maize parents; and
wherein including a plant in a growing space includes including a maize plant in the growing space.]
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[ 21. The method of claim 19, wherein selecting multiple target crosses from the subgroup of potential crosses includes:
clustering all of the parents of the subgroup of potential crosses, based on the genetic relatedness of the parents included in the subgroup of potential crosses; and
selecting the multiple target crosses based on the clustering of the parents.]
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[ 22. The method of claim 21, wherein clustering all of the parents of the subgroup of potential crosses includes determining a distance metric for each of the potential crosses, based on relatedness of the parents, through the following:
 and
 wherein sij is the similarity between ith and jth parents, and lij is the ijth cross entry of a Laplacian matrix.]
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[ 23. The method of claim 22, wherein clustering all of the parents of the subgroup of potential crosses is further based on:
 and
 wherein L is the Laplacian matrix, created from the similarity distance , and is the normalized Laplacian that is normalized by a diagonal matrix D.]
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