Abstract: A hinge area and processes of forming the hinge area are described. The hinge area is part of cover of a laptop computer. A material used for the cover is of a certain thickness. Stamping and forming a shape that defines the hinge area is performed on the material. The top of the shape is cut to provide for sides of the hinge area. Squeeze forging operations are performed to thicken the sides, and CNC machining is further performed on the sides.

Abstract: An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgement prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.

Abstract: An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgement prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.

Abstract: An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgment prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.