-
公开(公告)号：US20230004364A1
公开(公告)日：2023-01-05
申请号：US17940831
申请日：2022-09-08
Applicant: X Development LLC
Inventor： Qianyu Zhang
Abstract: Techniques are described herein for training a machine learning model and using the trained machine learning model to more accurately determine alignments between matching/corresponding nodes of predecessor and successor graphs representing predecessor and successor source code snippets. A method includes: obtaining a first abstract syntax tree that represents a predecessor source code snippet and a second abstract syntax tree that represents a successor source code snippet; determining a mapping across the first and second abstract syntax trees; obtaining a first control-flow graph that represents the predecessor source code snippet and a second control-flow graph that represents the successor source code snippet; aligning blocks in the first control-flow graph with blocks in the second control-flow graph; and applying the aligned blocks as inputs across a trained machine learning model to generate an alignment of nodes in the first abstract syntax tree with nodes in the second abstract syntax tree.
-
公开(公告)号：US20220121427A1
公开(公告)日：2022-04-21
申请号：US17563881
申请日：2021-12-28
Applicant: X Development LLC
Inventor： Georgios Evangelopoulos , Olivia Hatalsky , Bin Ni , Qianyu Zhang
Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.
-
公开(公告)号：US20220019410A1
公开(公告)日：2022-01-20
申请号：US16946982
申请日：2020-07-14
Applicant: X Development LLC
Inventor： Catalina Codruta Cangea , Qianyu Zhang
Abstract: Implementations are described herein for training and using machine learning to determine mappings between matching nodes of graphs representing predecessor source code snippets and graphs representing successor source code snippets. In various implementations, first and second graphs may be obtained, wherein the first graph represents a predecessor source code snippet and the second graph represents a successor source code snippet. The first graph and the second graph may be applied as inputs across a trained machine learning model to generate node similarity measures between individual nodes of the first graph and nodes of the second graph. Based on the node similarity measures, a mapping may be determined across the first and second graphs between pairs of matching nodes.
-
公开(公告)号：US20210192321A1
公开(公告)日：2021-06-24
申请号：US16776285
申请日：2020-01-29
Applicant: X Development LLC
Inventor： Qianyu Zhang
Abstract: Implementations are described herein for learning and utilizing mappings between source code changes and regions of latent space associated with code change intents that motivated those source code changes. In various implementations, data indicative of a change made to source code snippet may be applied as input across a machine learning model to generate a new source code change embedding in a latent space. Reference source code change embedding(s) may be identified in the latent space based on distance(s) between the reference source code change embedding(s) and the new source code change embedding in the latent space. Based on the identified reference embedding(s), code change intent(s) may be identified. Association(s) may be created between the source code snippet and the code change intent(s).
-
公开(公告)号：US20240099703A1
公开(公告)日：2024-03-28
申请号：US18373179
申请日：2023-09-26
Applicant: X Development LLC
Inventor： Alexander Martin Zoellner , Daniel Edward Rosenfeld , Ashley Quinn Swartz , John Paul Issa , Joseph Hollis Sargent , Ningrui Li , Phillip Yee , Ulrich Niemann , Qianyu Zhang
IPC: A61B8/00
CPC classification number: A61B8/587
Abstract: This disclosure describes a system, method, and non-transitory computer readable media for an ultrasound probe configured to capture ultrasound images of an examination region. The system includes a first set of one or more sensors coupled to the ultrasound probe and configured to estimate a first positional information associated with the ultrasound probe. The system includes a second set of one or more sensors coupled to the ultrasound probe and configured to capture electromagnetic force (EMF) measurements in the examination region to estimate a second positional information associated with the ultrasound probe. The second positional information is used to calibrate the first set of one or more sensors. The system includes a controller configured to use at least one of (i) the first positional information, or (ii) the second positional information to generate a reconstruction of the examination region based on ultrasound images captured by the ultrasound probe.
-
Patent Agency Ranking
公开(公告)号：US20230004366A1
公开(公告)日：2023-01-05
申请号：US17901128
申请日：2022-09-01
Applicant: X Development LLC
Inventor： Qianyu Zhang , Bin Ni , Rishabh Singh , Olivia Hatalsky
Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.
-
公开(公告)号：US20220188081A1
公开(公告)日：2022-06-16
申请号：US17123768
申请日：2020-12-16
Applicant: X Development LLC
Inventor： Bin Ni , Owen Lewis , Qianyu Zhang
Abstract: Implementations are described herein for leveraging prior source code transformations to facilitate automatic creation and/or recommendation of tools for automating aspects of source code transformations captured in real time. In various implementations, a transformation made by a programmer to a source code snipped may be captured in a source code editor application in real time. Based on the transformation and the intent, one or more candidate source code transformations may be identified from one or more repositories of prior source code transformations made by one or more other programmers. The source code editor application may be caused to provide output indicative of a tool that is operable to automate one or more edits associated with both the transformation made by the programmer to the source code snippet and with one or more of the candidate source code transformations.
-
公开(公告)号：US20210011694A1
公开(公告)日：2021-01-14
申请号：US16506161
申请日：2019-07-09
Applicant: X Development LLC
Inventor： Bin Ni , Zhiqiang Yuan , Qianyu Zhang
Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. In various implementations, one or more components of one or more generative adversarial networks, such as a generator machine learning model, may be trained to generate “synthetically-naturalistic” source code that can be used as a translation of source code in an unfamiliar language. In some implementations, a discriminator machine learning model may be employed to aid in training the generator machine learning model, e.g., by being trained to discriminate between human-generated (“genuine”) and machine-generated (“synthetic”) source code.
-
公开(公告)号：US20220236971A1
公开(公告)日：2022-07-28
申请号：US17159524
申请日：2021-01-27
Applicant: X Development LLC
Inventor： Qianyu Zhang , Bin Ni , Rishabh Singh , Olivia Hatalsky
Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.
-
公开(公告)号：US20220066752A1
公开(公告)日：2022-03-03
申请号：US17009306
申请日：2020-09-01
Applicant: X Development LLC
Inventor： Qianyu Zhang
Abstract: Techniques are described herein for training a machine learning model and using the trained machine learning model to more accurately determine alignments between matching/corresponding nodes of predecessor and successor graphs representing predecessor and successor source code snippets. A method includes: obtaining a first abstract syntax tree that represents a predecessor source code snippet and a second abstract syntax tree that represents a successor source code snippet; determining a mapping across the first and second abstract syntax trees; obtaining a first control-flow graph that represents the predecessor source code snippet and a second control-flow graph that represents the successor source code snippet; aligning blocks in the first control-flow graph with blocks in the second control-flow graph; and applying the aligned blocks as inputs across a trained machine learning model to generate an alignment of nodes in the first abstract syntax tree with nodes in the second abstract syntax tree.
-
-
-
-
-
-
-
-
-
Search Results
16
records
(took 0.015 seconds)
