公开(公告)号：US20230409577A1

公开(公告)日：2023-12-21

申请号：US17842132

申请日：2022-06-16

Applicant: X Development LLC

Inventor： David Andre

IPC: G06F16/2455 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F16/24556 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

Abstract: Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.

公开(公告)号：US20230359789A1

公开(公告)日：2023-11-09

申请号：US18142472

申请日：2023-05-02

Applicant: X Development LLC

Inventor： David Andre ,  Rishabh Singh ,  Rebecca Radkoff ,  Yu-Ann Madan ,  Nisarg Vyas ,  Jayendra Parmar ,  Falak Shah ,  Shaili Trivedi

IPC: G06F30/27 ,  G10L15/183

CPC classification number: G06F30/27 ,  G10L15/183

Abstract: As opposed to a rigid approach, implementations disclosed herein utilize a flexible approach in automatically determining an action set to utilize in attempting performance of a task that is requested by natural language input of a user. The approach is flexible at least in that embedding technique(s) and/or action model(s), that are utilized in generating action set(s) from which the action set to utilize is determined, are at least selectively varied. Put another way, implementations leverage a framework via which different embedding technique(s) and/or different action model(s) can at least selectively be utilized in generating different candidate action sets for given NL input of a user. Further, one of those action sets can be selected for actual use in attempting real-world performance of a given task reflected by the given NL input. The selection can be based on a suitability metric for the selected action set and/or other considerations.

公开(公告)号：US20230342167A1

公开(公告)日：2023-10-26

申请号：US17726258

申请日：2022-04-21

Applicant: X Development LLC

Inventor： Rebecca Radkoff ,  David Andre

IPC: G06F9/455 ,  G06F40/30 ,  G06F3/0482

CPC classification number: G06F9/45529 ,  G06F40/30 ,  G06F3/0482

Abstract: Disclosed implementations relate to automating semantically-similar computing tasks across multiple contexts. In various implementations, an initial natural language input and a first plurality of actions performed using a first computer application may be used to generate a first task embedding and a first action embedding in action embedding space. An association between the first task embedding and first action embedding may be stored. Later, subsequent natural language input may be used to generate a second task embedding that is then matched to the first task embedding. Based on the stored association, the first action embedding may be identified and processed using a selected domain model to select actions to be performed using a second computer application. The selected domain model may be trained to translate between an action space of the second computer application and the action embedding space.

公开(公告)号：US11706111B1

公开(公告)日：2023-07-18

申请号：US17732957

申请日：2022-04-29

Applicant: X Development LLC

Inventor： John Michael Stivoric ,  David Andre ,  Ryan Butterfoss ,  Rebecca Radkoff ,  Salil Vijaykumar Pradhan ,  Grace Taixi Brentano ,  Lam Thanh Nguyen

IPC: H04L43/065 ,  H04L41/0604 ,  H04L41/12 ,  H04L43/0817

CPC classification number: H04L43/065 ,  H04L41/0627 ,  H04L41/12 ,  H04L43/0817

Abstract: Implementations are directed to improving network anti-fragility. In some aspects, a method includes receiving parameter data from a network of nodes, the parameter data comprising attributes, policies, and action spaces for each node in the network of nodes; configuring one or more interruptive events on one or more nodes included in the network of nodes; determining a first action of each node in the network of nodes in response to the one or more interruptive events; determining a first performance metric, for each node, that corresponds to the first action, wherein the first performance matric is determined based on at least a first reward value associated with the first action; continuously updating the first action in an iterative process to obtain a final action, wherein a performance metric corresponding to the final action satisfies a performance threshold, and transmitting the final action for each node to the network of nodes.

公开(公告)号：US20250131366A1

公开(公告)日：2025-04-24

申请号：US18926132

申请日：2024-10-24

Applicant: X Development LLC

Inventor： Lam Thanh Nguyen ,  Grace Taixi Brentano ,  Sze Man Lee ,  Karush Suri ,  Anikait Singh ,  Salil Vijaykumar Pradhan ,  David Andre ,  Gearoid Murphy

IPC: G06Q10/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating actions for a supply chain network. One of the methods includes receiving a request to generate an action in a supply chain network for a particular product based on current state information; providing a request to an action model to generate a respective probability distribution for one or more actions for one or more products; receiving, from the action model, the respective probability distributions for the one or more products; determining, for each product, a binned action from the respective probability distribution; providing a request to a sequence model to generate a respective correction for the one or more binned actions; and receiving, from the sequence model, the respective correction for the respective binned action.

公开(公告)号：US20250028995A1

公开(公告)日：2025-01-23

申请号：US18224889

申请日：2023-07-21

Applicant: X Development LLC

Inventor： Rishabh Singh ,  David Andre ,  Garrett Raymond Honke ,  Falak Shah ,  Nisarg Vyas ,  Jayendra Parmar ,  Brian M. Rosen ,  Shaili Trivedi

IPC: G06N20/00

Abstract: Disclosed implementations relate to adding “bottleneck” models to machine learning pipelines that already apply domain models to translate and/or transfer representations of high-level semantic concepts between domains. In various implementations, an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment may be processed based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition. The first embedding may be processed based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding. In various implementations, the second embedding may be processed in various ways to train one or more of the bottleneck model(s) based on various different auxiliary loss functions.

公开(公告)号：US20240330743A1

公开(公告)日：2024-10-03

申请号：US18129416

申请日：2023-03-31

Applicant: X Development LLC

Inventor： David Andre ,  Grace Taixi Brentano ,  Lam Thanh Nguyen ,  Salil Vijaykumar Pradhan ,  Peter Michael Aronow

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating synthetic training data representing network disruptions. One of the methods includes obtaining data representing one or more first travel time distributions between at the at least two entities in the supply chain network. Synthetic network disruption data is generated including sampling from one or more second travel time distributions corresponding respectively to one or more simulated network disruptions. A second dataset having the synthetic network disruption data is generated, and a network policy agent is trained using the second dataset.

公开(公告)号：US20240311749A1

公开(公告)日：2024-09-19

申请号：US18122057

申请日：2023-03-15

Applicant: X Development LLC

Inventor： Grace Taixi Brentano ,  Salil Vijaykumar Pradhan ,  Rebecca Radkoff ,  David Andre ,  Lam Thanh Nguyen ,  Sze Man Lee ,  Gearoid Murphy

IPC: G06Q10/0835 ,  G06N3/0475 ,  G06N3/092 ,  G06N3/094

CPC classification number: G06Q10/08355 ,  G06N3/0475 ,  G06N3/092 ,  G06N3/094

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating alternative networks. One of the methods includes receiving supply chain data representing a first supply chain network having nodes and links, receiving map data, providing the map data and the supply chain data as input to a generative process that is configured to generate one or more second supply chain networks, receiving, as output from the generative process, a second supply chain network, performing a supply chain simulation on the second supply chain network generated by the generative model, and computing a performance metric for the second supply chain network based on performing the simulation.

公开(公告)号：US20240311377A1

公开(公告)日：2024-09-19

申请号：US18673222

申请日：2024-05-23

Applicant: X Development LLC

Inventor： David Andre

IPC: G06F16/2455 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F16/24556 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

Abstract: Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.

公开(公告)号：US12013859B2

公开(公告)日：2024-06-18

申请号：US17842132

申请日：2022-06-16

Applicant: X Development LLC

Inventor： David Andre

IPC: G06F16/2455 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F16/24556 ,  G06F16/248 ,  G06F40/30 ,  G06F40/40

Abstract: Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.