An electronic device is described which has at least one input interface to receive at least one item of a sequence of items. The electronic device is able to communicate with a server, the server storing a neural network and a process which generates item embeddings of the neural network. The electronic device has a memory storing a copy of the neural network and a plurality of item embeddings of the neural network. In the case when there is unavailability at the electronic device of a corresponding item embedding corresponding to the received at least one item, the electronic device triggers transfer of the corresponding item embedding from the server to the electronic device. A processor at the electronic device predicts at least one candidate next item in the sequence by processing the corresponding item embedding with the copy of the neural network and the plurality of item embeddings.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing thereon: data indicative of at least part of a neural network and a plurality of item embeddings of the neural network; and computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising: receiving, from at least one input interface, data indicative of at least one item of a sequence of items; initiating transfer of an item embedding corresponding to the received at least one item from a server storing the neural network when the corresponding item embedding is not available at the electronic device; accessing item embeddings for a plurality of candidate next items and item embeddings for one or more other preceding items when available; and predicting at least one candidate next item in the sequence of items based on said accessing, the at least part of the neural network, and the corresponding item embedding. 2. The electronic device of claim 1 wherein the plurality of item embeddings stored at the electronic device is fewer than a number of item embeddings stored at the server. 3. The electronic device of claim 1 wherein an item embedding is a plurality of learned weights representing an item of the sequence of items in a form that can be processed by units of a neural network. 4. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising retrieving an output vector at an output layer of the neural network by transforming input to the output layer, with an encoding matrix formed from the plurality of item embeddings stored in the memory, wherein the output vector corresponds to the at least one candidate next item. 5. The electronic device of claim 1 whe further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising triggering transfer of the corresponding item embedding from the server by sending a request to the server, the request comprising the received at least one item or an identifier of the received at least one item. 6. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising controlling a number of item embeddings of the at least part of the neural network by replacing or deleting individual ones of the item embeddings such that a maximum number of item embeddings remains below a threshold value. 7. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising controlling a number of item embeddings of the at least part of the neural network by using a least recently used cache. 8. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising accessing data about historical sequences of items and triggering transfer of item embeddings to the electronic device from the server based at least on the data. 9. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising using smoothed frequency of item use data to trigger transfer of embeddings to the electronic device from the server. 10. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising ensuring that a specified plurality of item embeddings are retained in the memory at the electronic device. 11. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising accessing the data about historical sequences of items from a dynamic n-gram language model at the electronic device. 12. The electronic device of claim 1 further comprising computer-readable instructions that, when executed by the at least one processor, cause the electronic device to performs operations comprising displaying the candidate next item for input to the electronic device. 13. A server comprising: a neural network configured to predict at least one candidate next item in a sequence of items using item embeddings of the neural network; a processor; and a memory communicatively coupled to processor, the memory storing thereon computer-readable instructions that, when executed by the processor, cause the server to performs operations comprising: generating item embeddings of the neural network; and sending one or more of the item embeddings to an electronic device when triggered by the electronic device storing thereon a copy of at least part of the neural network. 14. The server of claim 13 further comprising computer-readable instructions that, when executed by the processor, cause the server to performs operations comprising generating the item embeddings of the neural network by looking up the item embeddings in a store of item embeddings stored at the server. 15. The server of claim 13 further comprising computer-readable instructions that, when executed by the processor, cause the server to performs operations comprising: generating an item embedding corresponding to an item of a sequence of items received from the electronic device, and sending the generated item embedding to the electronic device. 16. The server of claim 13 further comprising computer-readable instructions that, when executed by the processor, cause the server to performs operations comprising sending one or more of the item embeddings to the electronic device according to predictions of item embeddings that the electronic device is predicted to need. 17. The server of claim 13 further comprising computer-readable instructions that, when executed by the processor, cause the server to performs operations comprising predicting items for which item embeddings will be needed at the electronic device, using item use data. 18. The server of claim 13 wherein a plurality of item embeddings is stored at the server and wherein a number of item embeddings stored at the server is greater than a threshold number of item embeddings permitted at the electronic device. 19. The server of claim 13 wherein an item embedding is a plurality of learned weights representing an item of the sequence of items as a real-valued vector. 20. A method comprising: receiving, at an input interface of a computing device, at least one item of a sequence of items; communicating, by the computing device over a communications network, with a server storing a neural network and configured to implement a process which generates item embeddings of the neural network; storing, at the computing device, a copy of at least part of the neural network and a plurality of item embeddings of the neural network; triggering, by the computing device, transfer of the corresponding item embedding from the server to the computing device when there is unavailability at the computing device of a corresponding item embedding corresponding to the received at least one item; predicting, by the computing device, at least one candidate next item in the sequence by processing the corresponding item embedding and item embeddings for one or more other preceding items when available, with the copy of at least part of the neural network and using item embeddings for a plurality of candidate next items retrieved from the memory; and inputting the candidate next item to the computing device.