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公开(公告)号：US20240346362A1
公开(公告)日：2024-10-17
申请号：US18135043
申请日：2023-04-14
Applicant: X Development LLC
Inventor： David Andre , Garrett Raymond Honke
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: Disclosed implementations relate to preserving individuals' semantic privacy while facilitating automation of tasks across a population of individuals. In various implementations, data indicative of an observed set of interactions between a user and a computing device may be recorded and used to simulate multiple different synthetic sets of interactions between the user and the computing device. Each synthetic set may include a variation of the observed set of interactions at a different level of abstraction. User feedback may be obtained about each of the multiple different sets. Based on the user feedback, one of the multiple different synthetic sets of interactions may be selected and used to train a machine learning model.
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公开(公告)号：US11861263B1
公开(公告)日：2024-01-02
申请号：US17846351
申请日：2022-06-22
Applicant: X Development LLC
Inventor： Thomas Hunt , David Andre , Nisarg Vyas , Rebecca Radkoff , Rishabh Singh
IPC: G06F3/0481 , G06F3/16 , G06F3/0484 , G10L15/22
CPC classification number: G06F3/167 , G06F3/0481 , G06F3/0484 , G10L15/22
Abstract: This specification is generally directed to techniques for robust natural language (NL) based control of computer applications. In many implementations, the NL control is at least selectively interactive in that the user feedback input is solicited, and received, in resolving action(s), resolving action set(s), generating domain specific knowledge, and/or in providing feedback on implemented action set(s). The user feedback input can be utilized in further training of machine learning model(s) utilized in the NL based control of the computer applications.
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公开(公告)号：US20230409677A1
公开(公告)日：2023-12-21
申请号：US17841022
申请日：2022-06-15
Applicant: X Development LLC
Inventor： David Andre , Yu-Ann Madan
CPC classification number: G06K9/6277 , G06K9/6252 , G06K9/6215 , G06N7/005
Abstract: Disclosed implementations relate to automatically generating and providing guidance for navigating HCIs to carry out semantically equivalent/similar computing tasks across different computer applications. In various implementations, a domain of a first computer application that is operable using a first HCI may be used to select a domain model that translates between an action space of the first computer application and another space. Based on the selected domain model, a domain-agnostic action embedding—representing actions performed previously using a second HCI of a second computer application to perform a semantic task—may be processed to generate probability distribution(s) over actions in the action space of the first computer application. Based on the probability distribution(s), actions may be identified that are performable using the first computer application—these actions may be used to generate guidance for navigating the first HCI to perform the semantic task.
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公开(公告)号：US20230144113A1
公开(公告)日：2023-05-11
申请号：US17984185
申请日：2022-11-09
Applicant: X Development LLC
Inventor： Salil Vijaykumar Pradhan , Grigory Bronevetsky , Ryan Butterfoss , Rebecca Radkoff , David Andre , Randolph Preston McAfee , John Michael Stivoric , Grace Taixi Brentano , Sze Man Lee
CPC classification number: G06Q10/08345 , G06Q10/067 , G06Q30/0611
Abstract: Methods and systems including receiving a plurality of shipping bids from a plurality of shipping entities, each entity having goods to ship from locations to destinations, wherein each bid represents an option to ship goods at a shipping price, and wherein each bid comprises a plurality of shipping parameters; receiving a plurality of carrier bids from a plurality of carrier entities, each entity transporting the goods, wherein each bid represents an option to transport the goods at a price, and wherein each bid comprises a plurality of carrier parameters; performing a matching process to generate a plurality of pair-wise partial matches, wherein each match associates a shipping and carrier bid at a modified price, wherein the modified price is based on a deviation between the parameters; providing information representing the matches to the shipping and carrier entities; and generating training data representing which matches were exercised.
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公开(公告)号：US20220405489A1
公开(公告)日：2022-12-22
申请号：US17706844
申请日：2022-03-29
Applicant: X Development LLC
Inventor： Rebecca Radkoff , David Andre
Abstract: Implementations are described herein for formulating natural language descriptions based on temporal sequences of digital images. In various implementations, a natural language input may be analyzed. Based on the analysis, a semantic scope to be imposed on a natural language description that is to be formulated based on a temporal sequence of digital images may be determined. The temporal sequence of digital images may be processed based on one or more machine learning models to identify one or more candidate features that fall within the semantic scope. One or more other features that fall outside of the semantic scope may be disregarded. The natural language description may be formulated to describe one or more of the candidate features.
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Patent Agency Ranking
公开(公告)号：US11487522B1
公开(公告)日：2022-11-01
申请号：US17318687
申请日：2021-05-12
Applicant: X Development LLC
Inventor： Rishabh Singh , Nisarg Vyas , Jayendra Parmar , Dhara Kotecha , Artem Goncharuk , David Andre
Abstract: Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).
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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：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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公开(公告)号：US20250117589A1
公开(公告)日：2025-04-10
申请号：US18830758
申请日：2024-09-11
Applicant: X DEVELOPMENT LLC
Inventor： Julia Black Ling , Alberto Camacho Martinez , David Andre , Christopher Hahn
IPC: G06F40/30
Abstract: An inverse design system combines a large language model (LLM) with a task-specific optimizer, which includes a search function, a forward model, and a comparator. The LLM adjusts parameters of the optimizer's components in response to a design scenario. Then the optimizer processes the design scenario to produce design candidates. Optionally, the LLM learns from the design candidates in an iterative process. A stochastic predictive modeling system combines an LLM with input distributions and a forward model. The LLM adjusts one or more of the input distributions and/or the forward model in response to a forecast scenario. Then the forward model processes a sampling of the input distributions to produce a forward distribution. Optionally, the LLM informs the sampling process. Optionally, the LLM learns from the forward distribution.
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公开(公告)号：US20230188940A1
公开(公告)日：2023-06-15
申请号：US17726647
申请日：2022-04-22
Applicant: X DEVELOPMENT LLC
Inventor： David Andre , Erich Karl Nachbar
IPC: H04W4/029 , G01S5/02 , H04W52/36 , H04B17/318
CPC classification number: H04W4/029 , G01S5/0284 , H04W52/367 , H04B17/318
Abstract: The technology enables locating asset tracking tags based on a ramped sequence of signals from one or more beacon tracking tags. The sequence includes at least one minimum power signal and at least one maximum power signal. Each signal in the sequence has a tag identifier and an initial signal strength value. Each beacon signal in the ramped sequence is associated with the time at which that beacon signal was received by a reader. Each beacon signal is also associated with a received signal strength at reception. A location of the beacon tracking tag is estimated according to the signals in the sequence based on the difference between the initial and received signal strengths. A position of the reader device is identified based on the beacon tag's location. An asset tracking tag location is identified based on the reader's location and packets received by the reader from the asset tag.
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