公开(公告)号：US12072888B1

公开(公告)日：2024-08-27

申请号：US18316624

申请日：2023-05-12

申请人： SAP SE

发明人： Robert Lasch ,  Thomas Legler ,  Norman May ,  Kai-Uwe Sattler ,  Bernhard Scheirle

IPC分类号： G06F16/00 ,  G06F16/22 ,  G06F16/2455 ,  G06F16/25

CPC分类号： G06F16/2456 ,  G06F16/2282 ,  G06F16/252

摘要： Embodiments of the present disclosure include techniques for cooperative memory management. In one embodiment, a database management system includes a memory manager for managing a memory buffer. The memory buffer is configured to allocate memory to table data and temporary data dynamically. In some embodiments, table data memory allocations are reduced and temporary data memory allocations are increased to process queries. Temporary data memory allocations may be increased to store temporary data associated with one or more portions of a query. The memory buffer reallocates table data memory allocations and temporary data memory allocations as needed to more efficiently use available memory.

公开(公告)号：US12069074B2

公开(公告)日：2024-08-20

申请号：US17342785

申请日：2021-06-09

申请人： Arista Networks, Inc.

发明人： Keith Amidon ,  David Pearson ,  Jeff Polakow ,  Matthew Park ,  Gary Golomb

IPC分类号： H04L9/40

CPC分类号： H04L63/1425 ,  H04L63/1416

摘要： An automated framework provides security monitoring and analysis in a network by autonomously detecting actual and potential threats to the network. In response to detection of a threat, the framework instantiates a Situation to provide directed monitoring of the threat. The Situation invokes specific skills based on the state of the Situation to monitor network traffic for activity specific to the threat that instantiated the Situation. As data is collected, additional skills may be invoked based on the additional data to collect new data, and previously invoked skills may be terminated depending on the additional data to avoid collecting information that is no-longer relevant.

公开(公告)号：US12067370B2

公开(公告)日：2024-08-20

申请号：US17342114

申请日：2021-06-08

申请人： SAP SE

发明人： Tetyana Chernenko ,  Anton Snitko ,  Jens Scharnbacher ,  Michail Vasiltschenko

IPC分类号： G06F40/30 ,  G06F16/2458 ,  G06F40/274 ,  G06F40/44 ,  G06F40/55 ,  G06F40/58

CPC分类号： G06F40/58 ,  G06F16/2468 ,  G06F40/274 ,  G06F40/55

摘要： Translation capability for language processing determines an existence of an abbreviation, followed by non-exact matching to map the abbreviation to the original full term. A received string in a source language is provided as input to a translation service. Translation proposals in a different target language are received back. A ruleset (considering factors, e.g., camel case format, the presence of a concluding period, and/or consecutive consonants) is applied to generate abbreviation candidates from the translation proposals. Non-exact matching (referencing e.g., a comparison metric) may then be used to map the abbreviation candidates to text strings of their original full terms. A mapping of the abbreviation to the text string of the original full term is stored in a translation database comprising linguistic data. Embodiments leverage existing resources (e.g., translation service, non-exact matching) to reduce effort and expense of accurately identifying abbreviations and then mapping them to their full original terms.

公开(公告)号：US12056110B2

公开(公告)日：2024-08-06

申请号：US17828536

申请日：2022-05-31

申请人： Arista Networks, Inc.

发明人： Pierre-Alexandre Rene Veyry ,  Aran Sean Liam Nolan ,  Vlad Serban Hanciuta

IPC分类号： G06F16/00 ,  G06F16/23 ,  G06F16/2458 ,  G06F16/27

CPC分类号： G06F16/2358 ,  G06F16/2477 ,  G06F16/27

摘要： A method for managing streams includes obtaining, by a state processing module in a coordination point, a notification for a standing query, and in response to the notification: identifying a storage location in the coordination point associated with a stream of the standing query, initiating a subscription to the standing query using the storage location, reserving a data buffer for the standing query, replicating, based on the standing query, data of the data stream using the storage location, updating the data buffer using the replicated data or a reference to the replicated data to obtain an updated data buffer, and servicing the standing query using the updated data buffer.

公开(公告)号：US12052288B2

公开(公告)日：2024-07-30

申请号：US17689378

申请日：2022-03-08

申请人： Arista Networks, Inc.

发明人： John William French

IPC分类号： H04L9/40 ,  H04L101/622

CPC分类号： H04L63/20 ,  H04L63/0876 ,  H04L63/102 ,  H04L2101/622

摘要： In general, embodiments relate to a network device, including network device hardware including a processor; and memory comprising instructions which, when executed by the processor, performs a method for creating segment mapping in a network. The method includes entering a fallback mode in response to detecting a fallback scenario, determining, based on the fallback mode, a segment identification (ID) for a client device of the network, wherein the segment ID identifies a segment of the network including a client device, obtaining an Internet Protocol (IP) address to segment ID mapping, wherein the client device is associated with the IP address, and processing at least one packet from the client device using the IP address to segment ID mapping.

公开(公告)号：US12050628B1

公开(公告)日：2024-07-30

申请号：US18348143

申请日：2023-07-06

申请人： Business Objects Software Ltd.

发明人： Paul O'Hara ,  Ying Wu ,  Malte Christian Kaufmann

IPC分类号： G06F16/28 ,  G06F16/23

CPC分类号： G06F16/285 ,  G06F16/2365

摘要： Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.

公开(公告)号：US12045662B2

公开(公告)日：2024-07-23

申请号：US17394109

申请日：2021-08-04

申请人： SAP SE

发明人： Himanshu Joshi ,  Hiren Shah

IPC分类号： G06F21/00 ,  G06F9/50 ,  G06F16/22 ,  G06F21/78

CPC分类号： G06F9/505 ,  G06F16/22 ,  G06F21/78

摘要： Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request to execute a task for re-encrypting a set of data associated with an application that has been encrypted with a first encryption key. The task is for re-encrypting the set of data using a second encryption key. The program further determines an amount of work to complete the task. The program also divides the task into a set of subtasks based on the amount of work. The program further assigns each subtask in the set of subtasks to a node in a plurality of nodes for execution of the subtask. The plurality of nodes are configured to implement the application.

公开(公告)号：US12045259B2

公开(公告)日：2024-07-23

申请号：US17347336

申请日：2021-06-14

申请人： SAP SE

发明人： Abhishek Chaturvedi ,  Jehil Vora ,  Surendranath Gopinathan ,  Nithya Rajagopalan ,  Sunil Gornalle ,  Jeremiah Reeves ,  Erik Scheithauer ,  Girija B ,  Samhith Bharadwaj

IPC分类号： G06F16/00 ,  G06F7/08 ,  G06F16/28

CPC分类号： G06F16/285 ,  G06F7/08

摘要： Some embodiments provide a program that determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The program further sorts values of the first attribute of the plurality of data objects. The program also sorts values of the second attribute of the plurality of data objects. The program further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The program also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The program further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.

公开(公告)号：US11893469B2

公开(公告)日：2024-02-06

申请号：US16882036

申请日：2020-05-22

申请人： Microsoft Technology Licensing, LLC

发明人： Andy Wagner ,  Tiyasa Mitra ,  Marc Tremblay

IPC分类号： G06F17/00 ,  G06N3/02 ,  G06N20/00 ,  G06F7/24 ,  G06F17/18 ,  G06N3/084 ,  G06F18/211 ,  G06F18/214

CPC分类号： G06N3/02 ,  G06F7/24 ,  G06F17/18 ,  G06F18/211 ,  G06F18/214 ,  G06N3/084 ,  G06N20/00

摘要： Embodiments of the present disclosure include systems and methods for training transformer models using position masking. In some embodiments, a set of data for training a transformer model is received. The set of data includes a sequence of tokens and a set of position values. Each position value in the set of position values represents a position of a token in the sequence of tokens relative to other tokens in the sequence of tokens. A subset of the set of position values in the set of data is selected. Each position value in the subset of the set of position values is replaced with a second defined value to form a second set of defined values. The transformer model is trained using the set of data.

公开(公告)号：US11886983B2

公开(公告)日：2024-01-30

申请号：US17002478

申请日：2020-08-25

申请人： Microsoft Technology Licensing, LLC

发明人： Andy Wagner ,  Tiyasa Mitra ,  Marc Tremblay

IPC分类号： G06N3/063 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084

CPC分类号： G06N3/063 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084

摘要： Embodiments of the present disclosure include systems and methods for reducing hardware resource utilization by residual neural networks. In some embodiments, a first matrix is received at a layer included in a neural network. The first matrix is compressed to produce a second matrix. The second matrix has a reduced dimensionality relative to a dimensionality of the first matrix. The second matrix is processed through a network block in the layer included in the neural network. The processed second matrix is expanded to produce a third matrix. The third matrix has a dimensionality that is equal to a dimensionality of the first matrix. The third matrix is added to the first matrix to produce a fourth matrix.