公开(公告)号：US11281631B2

公开(公告)日：2022-03-22

申请号：US16927264

申请日：2020-07-13

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat ,  Andrew Fikes ,  Yasushi Saito

IPC分类号： G06F16/182 ,  G06F16/22 ,  G06F9/50 ,  G06F16/13 ,  H04L67/1001 ,  H04L67/1004 ,  H04L67/1029

摘要： A method of accessing data includes storing a table that includes a plurality of tablets corresponding to distinct non-overlapping table portions. Respective pluralities of tablet access objects and application objects are stored in a plurality of servers. A distinct application object and distinct tablet are associated with each tablet access object. Each application object corresponds to a distinct instantiation of an application associated with the table. The tablet access objects and associated application objects are redistributed among the servers in accordance with a first load-balancing criterion. A first request directed to a respective tablet is received from a client. In response, the tablet access object associated with the respective tablet is used to perform a data access operation on the respective tablet, and the application object associated with the respective tablet is used to perform an additional computational operation to produce a result to be returned to the client.

公开(公告)号：US11275743B2

公开(公告)日：2022-03-15

申请号：US15799939

申请日：2017-10-31

申请人： Google LLC

发明人： Robert C. Pike ,  Sean Quinlan ,  Sean M. Dorward ,  Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F16/18 ,  G06F16/2455 ,  G06F16/28 ,  G06F16/2458 ,  G06F11/14

摘要： Systems and methods for analyzing input data records are provided in which a master process initiates a plurality of concurrent first processes each of which comprises, for each data record in at least a subset of a plurality of input data records, creating a parsed representation of the data record and independently applying a procedural language query to the parsed representation to extract one or more values. A respective emit operator is applied to at least one of the extracted one or more values thereby adding corresponding information to a respective intermediate data structure. The respective emit operator implements one of a predefined set of statistical information processing functions. The master process also initiates a plurality of second processes each of which aggregates information from a corresponding subset of intermediate data structures to produce aggregated data that is, in turn, combined to produce output data.

公开(公告)号：US11366797B2

公开(公告)日：2022-06-21

申请号：US17134862

申请日：2020-12-28

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F16/22 ,  G06F16/23 ,  G06F16/2453 ,  G06F9/48 ,  G06F9/54

摘要： A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US11935634B2

公开(公告)日：2024-03-19

申请号：US15690721

申请日：2017-08-30

申请人： Google LLC

发明人： Alexander Mossin ,  Alvin Rajkomar ,  Eyal Oren ,  James Wilson ,  James Wexler ,  Patrik Sundberg ,  Andrew Dai ,  Yingwei Cui ,  Gregory Corrado ,  Hector Yee ,  Jacob Marcus ,  Jeffrey Dean ,  Benjamin Irvine ,  Kai Chen ,  Kun Zhang ,  Michaela Hardt ,  Xiaomi Sun ,  Nissan Hajaj ,  Peter Junteng Liu ,  Quoc Le ,  Xiaobing Liu ,  Yi Zhang

IPC分类号： G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  G16H50/20

CPC分类号： G16H10/60 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G16H15/00 ,  G16H50/30 ,  G16H50/20

摘要： A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

公开(公告)号：US20230385262A1

公开(公告)日：2023-11-30

申请号：US18137695

申请日：2023-04-21

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F16/22 ,  G06F16/23 ,  G06F16/2453 ,  G06F9/48 ,  G06F9/54

CPC分类号： G06F16/2282 ,  G06F16/2379 ,  G06F16/24532 ,  G06F9/4881 ,  G06F9/54

摘要： A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US20200341950A1

公开(公告)日：2020-10-29

申请号：US16927264

申请日：2020-07-13

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat ,  Andrew Fikes ,  Yasushi Saito

IPC分类号： G06F16/182 ,  G06F16/22 ,  G06F9/50 ,  G06F16/13 ,  H04L29/08

摘要： A method of accessing data includes storing a table that includes a plurality of tablets corresponding to distinct non-overlapping table portions. Respective pluralities of tablet access objects and application objects are stored in a plurality of servers. A distinct application object and distinct tablet are associated with each tablet access object. Each application object corresponds to a distinct instantiation of an application associated with the table. The tablet access objects and associated application objects are redistributed among the servers in accordance with a first load-balancing criterion. A first request directed to a respective tablet is received from a client. In response, the tablet access object associated with the respective tablet is used to perform a data access operation on the respective tablet, and the application object associated with the respective tablet is used to perform an additional computational operation to produce a result to be returned to the client.

公开(公告)号：US20190272264A1

公开(公告)日：2019-09-05

申请号：US16417126

申请日：2019-05-20

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F16/22 ,  G06F16/2453 ,  G06F9/54 ,  G06F9/48 ,  G06F16/23

摘要： A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US20210117401A1

公开(公告)日：2021-04-22

申请号：US17134862

申请日：2020-12-28

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F16/22 ,  G06F9/48 ,  G06F16/2453 ,  G06F16/23 ,  G06F9/54

摘要： A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US10885285B2

公开(公告)日：2021-01-05

申请号：US16116833

申请日：2018-08-29

申请人： Google LLC

发明人： Franz Josef Och ,  Jeffrey Dean ,  Thorsten Brants ,  Alexander Mark Franz ,  Jay Ponte ,  Peng Xu ,  Sha-Mayn Teh ,  Jeffrey Chin ,  Ignacio E. Thayer ,  Anton Carver ,  Daniel Rosart ,  John S. Hawkins ,  Karel Driesen

IPC分类号： G06F40/58 ,  G06F40/44 ,  G06F40/47 ,  G06F40/49

摘要： Systems, methods, and apparatus for accessing distributed models in automated machine processing, including using large language models in machine translation, speech recognition and other applications.

公开(公告)号：US10296500B2

公开(公告)日：2019-05-21

申请号：US15479228

申请日：2017-04-04

申请人： Google LLC

发明人： Jeffrey Dean ,  Sanjay Ghemawat

IPC分类号： G06F9/38 ,  G06F16/22 ,  G06F16/23 ,  G06F16/2453 ,  G06F17/30 ,  G06F9/48 ,  G06F9/54

摘要： A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.