Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for user interface generation for transacting goods are disclosed. In one aspect, a method includes receiving order data, user data, and other data. The method further includes determining, for a particular user, an order confidence score that corresponds to a likelihood that the particular user ordered a particular good from a particular vendor on a particular day. The method further includes receiving vendor-type mapping data. The method further includes determining a good-type confidence score that corresponds to the likelihood that the particular good or the particular vendor is associated with a particular type of good. The method further includes determining a composite confidence score. The method further includes storing the composite confidence score, data identifying the particular user, data identifying the particular type of good, and data identifying the particular date.

Abstract: A power transmitter is provided that can include a microwave cavity resonant at a desired operating frequency, a hexagonal mesh top to leak evanescent fields out of the cavity, and a plurality of orthogonal monopole feeds with 90 degrees phase differences creating circularly polarized waves. The power transmitter can be configured to transmit energy to a wireless device implanted in an animal passing through the evanescent fields. Implantable devices are also described which can receive wireless energy from the power transmitter and stimulate the animals (e.g., optogenetic or electrical stimulation).

Abstract: Methods, systems, and apparatus for receiving a particular set of user data; obtaining a predictive model that estimates a likelihood of a user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices; providing the particular set of user data to the predictive model; obtaining an indication of a likelihood of the user to order a food item; based on the indication of a likelihood of the user to order a food item, determining whether to output a notification on the user device inviting the user to order a food item; and in response to determining to output a notification on the user device inviting the user to order a food item, selectively outputting a notification on the user device inviting the user to order a food item.

Abstract: A power transmitter is provided that can include a microwave cavity resonant at a desired operating frequency, a hexagonal mesh top to leak evanescent fields out of the cavity, and a plurality of orthogonal monopole feeds with 90 degrees phase differences creating circularly polarized waves. The power transmitter can be configured to transmit energy to a wireless device implanted in an animal passing through the evanescent fields. Implantable devices are also described which can receive wireless energy from the power transmitter and stimulate the animals (e.g., optogenetic or electrical stimulation).

Abstract: The present disclosure relates to a method that includes determining a location of a delivery agent associated with a delivery route. The method includes determining that the location has satisfied one or more proximity criteria, and in response to determining that the location has satisfied the one or more proximity criteria, the method provides an initial alternative delivery user interface to a device of a user. The alternative delivery user interface includes a control for initiating an alternative delivery destination option. An indication is received that the control for initiating the alternative delivery destination option has been selected, and one or more candidate alternative delivery destinations are determined based at least on the location of the delivery agent and a location associated with the user.

Abstract: Methods, systems, and apparatus for receiving a particular set of user data; obtaining a predictive model that estimates a likelihood of a user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices; providing the particular set of user data to the predictive model; obtaining an indication of a likelihood of the user to order a food item; based on the indication of a likelihood of the user to order a food item, determining whether to output a notification on the user device inviting the user to order a food item; and in response to determining to output a notification on the user device inviting the user to order a food item, selectively outputting a notification on the user device inviting the user to order a food item.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for user interface generation for transacting goods are disclosed. In one aspect, a method includes receiving order data, user data, and other data. The method further includes determining, for a particular user, an order confidence score that corresponds to a likelihood that the particular user ordered a particular good from a particular vendor on a particular day. The method further includes receiving vendor-type mapping data. The method further includes determining a good-type confidence score that corresponds to the likelihood that the particular good or the particular vendor is associated with a particular type of good. The method further includes determining a composite confidence score. The method further includes storing the composite confidence score, data identifying the particular user, data identifying the particular type of good, and data identifying the particular date.

Abstract: Methods, systems, and apparatus for receiving a particular set of user data; obtaining a predictive model that estimates a likelihood of a user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices; providing the particular set of user data to the predictive model; obtaining an indication of a likelihood of the user to order a food item; based on the indication of a likelihood of the user to order a food item, determining whether to output a notification on the user device inviting the user to order a food item; and in response to determining to output a notification on the user device inviting the user to order a food item, selectively outputting a notification on the user device inviting the user to order a food item.