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公开(公告)号：US20200265196A1
公开(公告)日：2020-08-20
申请号：US16790917
申请日：2020-02-14
Applicant: Google LLC
Inventor： Sujith Ravi , Zornitsa Kozareva , Chinnadhurai Sankar
Abstract: Systems and methods are provided to pre-train projection networks for use as transferable natural language representation generators. In particular, example pre-training schemes described herein enable learning of transferable deep neural projection representations over randomized locality sensitive hashing (LSH) projections, thereby surmounting the need to store any embedding matrices because the projections can be dynamically computed at inference time.
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公开(公告)号：US11410044B2
公开(公告)日：2022-08-09
申请号：US16605702
申请日：2018-05-21
Applicant: Google LLC
Inventor： Sujith Ravi , Gaurav Menghani , Prabhu Kaliamoorthi , Yicheng Fan
Abstract: The present disclosure provides an application development platform and associated software development kits (“SDKs”) that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.
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公开(公告)号：US11238058B2
公开(公告)日：2022-02-01
申请号：US17086564
申请日：2020-11-02
Applicant: Google LLC
Inventor： Marc Alexander Najork , Sujith Ravi , Michael Bendersky , Peter Shao-sen Young , Timothy Youngjin Sohn , Mingyang Zhang , Thomas Nelson , Xuanhui Wang
IPC: G06F16/248 , G06F16/2455 , G06F16/951 , G06F16/38
Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate identification of additional trigger-terms for a structured information card. In one aspect, the method includes actions of accessing data associated with a template for presenting structured information, wherein the accessed data references (i) a label term and (ii) a value. Other actions may include obtaining a candidate label term, identifying one or more entities that are associated with the label term, identifying one or more of the entities that are associated with the candidate label term, and for each particular entity of the one or more entities that are associated with the candidate label term, associating, with the candidate label term, (i) a label term that is associated with the particular entity, and (ii) the value associated with the label term.
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公开(公告)号：US20180336472A1
公开(公告)日：2018-11-22
申请号：US15983441
申请日：2018-05-18
Applicant: Google LLC
Inventor： Sujith Ravi
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a projection neural network. In one aspect, a projection neural network is configured to receive a projection network input and to generate a projection network output from the projection network input. The projection neural network includes a sequence of one or more projection layers. Each projection layer has multiple projection layer parameters, and is configured to receive a layer input, apply multiple projection layer functions to the layer input, and generate a layer output by applying the projection layer parameters for the projection layer to the projection function outputs.
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公开(公告)号：US11934791B2
公开(公告)日：2024-03-19
申请号：US17878631
申请日：2022-08-01
Applicant: Google LLC
Inventor： Sujith Ravi , Zornitsa Kozareva
IPC: G06F40/30 , G06F40/253 , G06N3/04 , G06N3/084
CPC classification number: G06F40/30 , G06F40/253 , G06N3/04 , G06N3/084
Abstract: The present disclosure provides projection neural networks and example applications thereof. In particular, the present disclosure provides a number of different architectures for projection neural networks, including two example architectures which can be referred to as: Self-Governing Neural Networks (SGNNs) and Projection Sequence Networks (ProSeqoNets). Each projection neural network can include one or more projection layers that project an input into a different space. For example, each projection layer can use a set of projection functions to project the input into a bit-space, thereby greatly reducing the dimensionality of the input and enabling computation with lower resource usage. As such, the projection neural networks provided herein are highly useful for on-device inference in resource-constrained devices. For example, the provided SGNN and ProSeqoNet architectures are particularly beneficial for on-device inference such as, for example, solving natural language understanding tasks on-device.
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Patent Agency Ranking
公开(公告)号：US20220188133A1
公开(公告)日：2022-06-16
申请号：US17687316
申请日：2022-03-04
Applicant: Google LLC
Inventor： Robin Dua , Andrew Tomkins , Sujith Ravi
Abstract: Implementations are directed to facilitating user device and/or agent device actions during a communication session. An interactive communications system provides outputs, as outlined below, that are tailored to enhance the functionality of the communication session, reduce the number of dialog “turns” of the communications session and/or the number of user inputs to devices involved in the session, and/or otherwise mitigate consumption of network and/or hardware resources during the communication session. In various implementations, the communication session involves user device(s) of a user, agent device(s) of an agent, and the interactive communications system. The interactive communications system can analyze various communications from the user device(s) and/or agent device(s) during a communication session in which the user (via the user device(s)) directs various communications to the agent, and in which the agent (via the agent device(s)) optionally directs various communications to the user. The interactive communications system provides action performance element(s) and/or other output(s) that are each specific to a corresponding current intent and corresponding current action of the communication session.
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公开(公告)号：US11138476B2
公开(公告)日：2021-10-05
申请号：US16368561
申请日：2019-03-28
Applicant: Google LLC
Inventor： Robin Dua , Sujith Ravi
Abstract: A method includes identifying images associated with a user, where the image is identified as at least one of captured by a user device associated with the user, stored on the user device associated with the user, and stored in cloud storage associated with the user. The method also includes for each of the images, determining one or more labels, wherein the one or more labels are based on at least one of metadata and a primary annotation. The method also includes generating a mapping of the one or more labels to one or more confidence scores, wherein the one or more confidence scores indicate an extent to which the one or more labels apply to corresponding images. The method also includes interacting with the user to obtain identifying information that is used to categorize one or more of the images.
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公开(公告)号：US10430464B1
公开(公告)日：2019-10-01
申请号：US15849880
申请日：2017-12-21
Applicant: GOOGLE LLC
Inventor： Sujith Ravi , Qiming Diao
IPC: G06F17/30 , G06F16/901 , G06N20/00
Abstract: Systems and methods for adding labels to a graph are disclosed. One system includes a plurality of computing devices including processors and memory storing an input graph generated based on a source data set, where an edge represents a similarity measure between two nodes in the input graph, the input graph being distributed across the plurality of computing devices, and some of the nodes are seed nodes associated with one or more training labels from a set of labels, each training label having an associated original weight. The memory may also store instructions that, when executed by the processors, cause the plurality of distributed computing devices to propagate the training labels through the input graph using a sparsity approximation for label propagation, resulting in learned weights for respective node and label pairs, and automatically update the source data set using node and label pairs selected based on the learned weights.
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公开(公告)号：US10248889B2
公开(公告)日：2019-04-02
申请号：US15839739
申请日：2017-12-12
Applicant: Google LLC
Inventor： Robin Dua , Sujith Ravi
Abstract: A method includes identifying images associated with a user, where the image is identified as at least one of captured by a user device associated with the user, stored on the user device associated with the user, and stored in cloud storage associated with the user. The method also includes for each of the images, determining one or more labels, wherein the one or more labels are based on at least one of metadata and a primary annotation. The method also includes generating a mapping of the one or more labels to one or more confidence scores, wherein the one or more confidence scores indicate an extent to which the one or more labels apply to corresponding images. The method also includes interacting with the user to obtain identifying information that is used to categorize one or more of the images.
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公开(公告)号：US12112134B2
公开(公告)日：2024-10-08
申请号：US17582206
申请日：2022-01-24
Applicant: Google LLC
Inventor： Dana Movshovitz-Attias , John Patrick McGregor, Jr. , Gaurav Nemade , Sujith Ravi , Jeongwoo Ko , Dora Demszky
IPC: G06F40/289 , G06F40/30 , G06N20/00
CPC classification number: G06F40/289 , G06F40/30 , G06N20/00
Abstract: The technology relates to methods for detecting and classifying emotions in textual communication, and using this information to suggest graphical indicia such as emoji, stickers or GIFs to a user. Two main types of models are fully supervised models and few-shot models. In addition to fully supervised and few-shot models, other types of models focusing on the back-end (server) side or client (on-device) side may also be employed. Server-side models are larger-scale models that can enable higher degrees of accuracy, such as for use cases where models can be hosted on cloud servers where computational and storage resources are relatively abundant. On-device models are smaller-scale models, which enable use on resource-constrained devices such as mobile phones, smart watches or other wearables (e.g., head mounted displays), in-home devices, embedded devices, etc.
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