US 11,693,630 B2
Multi-lingual code generation with zero-shot inference
Colin Bruce Clement, Seattle, WA (US); Shuai Lu, Beijing (CN); Neelakantan Sundaresan, Bellevue, WA (US); Alexey Svyatkovskiy, Bellevue, WA (US); and Duyu Tang, Beijing (CN)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC., Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC., Redmond, WA (US)
Filed on Nov. 1, 2022, as Appl. No. 17/978,627.
Application 17/978,627 is a continuation of application No. 17/140,091, filed on Jan. 3, 2021, granted, now 11,513,774.
Prior Publication US 2023/0048186 A1, Feb. 16, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 8/33 (2018.01); G06F 8/41 (2018.01); G06N 3/088 (2023.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01)
CPC G06F 8/33 (2013.01) [G06F 8/44 (2013.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
a memory that stores one or more programs that are configured to be executed by the one or more processors, the one or more programs including instructions to perform actions that:
receive a source code program;
extract a file-level context from the source code program;
extract a local context from the source code program at a designated position in the source code program;
obtain a deep learning model to generate a source code candidate to complete one or more partially-formed lines of source code in the source code program;
generate the source code candidate to complete the one or more partially-formed lines of source code of the source code program at the designated position in the source code program from the deep learning model, wherein the deep learning model is given the file-level context and the local context, wherein the file-level context and the local context are written in a programming language not observed by the deep learning model during training of the deep learning model; and
provide the source code candidate to the source code program.