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公开(公告)号：US20220290989A1
公开(公告)日：2022-09-15
申请号：US17200023
申请日：2021-03-12
Applicant: X Development LLC
Inventor： Grigory Bronevetsky , Charlotte Leroy , Bin Ni , Hongxu Ma , Gengchen Mai
Abstract: Implementations are described herein for leveraging teleconnections and location embeddings to predict geospatial measures for a geographic location of interest. In various implementations, a plurality of reference geographic locations may be identified that are disparate from a geographic location of interest and influence a geospatial measure in the geographic location of interest. One or more features may be extracted from each of the plurality of reference geographic locations. The extracted features and a location embedding generated for the geographic location of interest may be encoded into a joint embedding. A sequence encoder may be applied to the joint embedding to generate encoded data indicative of the predicted geospatial measure.
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公开(公告)号：US20220261231A1
公开(公告)日：2022-08-18
申请号：US17176730
申请日：2021-02-16
Applicant: X Development LLC
Inventor： Owen Lewis , Bin Ni
Abstract: Implementations are described herein for building and/or applying a library of transformation templates to automate migration of source code. In various implementations, pre-migration and post-migration versions of source code that exist prior to and after migration of the source code may be analyzed. Based on the analysis, one or more transformations made to the pre-migration version of the source code to yield the post-migration version of the source code may be identified. A library of transformation templates that are applicable subsequently to automate migration of new source code may be built. In some implementations, for one or more of the transformations, a plurality of candidate transformation templates may be generated with different permutations of tokens being replaced with placeholders. One of the plurality of candidate transformation templates may be selected for inclusion in the library based on one or more criteria.
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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.
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公开(公告)号：US11152785B1
公开(公告)日：2021-10-19
申请号：US16573183
申请日：2019-09-17
Applicant: X Development LLC
Inventor： Phillip E. Stahlfeld , Bin Ni
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a neural network to predict locations of feeders in an electrical power grid. One of the methods includes training a generative adversarial network comprising a generator and a discriminator; and generating, by the generator, from input images, output images with feeder metadata that represents predicted locations of feeder assets, including receiving by the generator a first input image and generating by the generator a corresponding first output image with first feeder data that identifies one or more feeder assets and their respective locations, wherein the one or more feeder assets had not been identified in any input to the generator.
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公开(公告)号：US20210304011A1
公开(公告)日：2021-09-30
申请号：US16829107
申请日：2020-03-25
Applicant: X Development LLC
Inventor： Sarah Ann Laszlo , Matthew Sibigtroth , Bin Ni
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying one or more regions of a brain of a biological organism that are predicted to be functionally-specialized for performing a task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in the brain of the biological organism; identifying a plurality of sub-graphs of the synaptic connectivity graph; determining, for each sub-graph of the plurality of sub-graphs, a performance measure characterizing a performance of a neural network having a neural network architecture that is specified by the sub-graph in accomplishing the task; and determining, based on the performance measures, that one or more sub-graphs of the plurality of sub-graphs correspond to regions of the brain of the biological organism that are predicted to be functionally-specialized for performing the task.
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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.
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公开(公告)号：US11481210B2
公开(公告)日：2022-10-25
申请号：US17136968
申请日：2020-12-29
Applicant: X Development LLC
Inventor： Rishabh Singh , David Andre , Bin Ni , Owen Lewis
Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming. A pre-migration version of a source code file may be processed based on the conditioned autoregressive language model, and a post-migration version may be generated based on output generated based on the conditioned autoregressive model.
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公开(公告)号：US20220292330A1
公开(公告)日：2022-09-15
申请号：US17200097
申请日：2021-03-12
Applicant: X Development LLC
Inventor： Hongxu Ma , Gengchen Mai , Bin Ni
Abstract: Implementations are described herein for generating location embeddings that capture spatial dependence and heterogeneity of data, making the embeddings suitable for downstream statistical analysis and/or machine learning processing. In various implementations, a position coordinate for a geographic location of interest may be processed using a spatial dependence encoder to generate a first location embedding that captures spatial dependence of geospatial measure(s) for the geographic location of interest. The position coordinate may also be processed using a spatial heterogeneity encoder to generate a second location embedding that captures spatial heterogeneity of the geospatial measure(s) for the geographic location. A combined embedding corresponding to the geographic location may be generated based on the first and second location embeddings. The combined embedding may be processed using a function to determine a prediction for one or more of the geospatial measures of the geographic location of interest.
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公开(公告)号：US20220206785A1
公开(公告)日：2022-06-30
申请号：US17136968
申请日：2020-12-29
Applicant: X Development LLC
Inventor： Rishabh Singh , David Andre , Bin Ni , Owen Lewis
Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming. A pre-migration version of a source code file may be processed based on the conditioned autoregressive language model, and a post-migration version may be generated based on output generated based on the conditioned autoregressive model.
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30.发明申请公开(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.
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