专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明公开

US20230153841A1 METHOD AND APPARATUS FOR DETERMINING DATA LINKAGE CONFIDENCE LEVELS 审中-公开
公开(公告)号：US20230153841A1
公开(公告)日：2023-05-18
申请号：US18156990
申请日：2023-01-19
申请人： Walmart Apollo, LLC
发明人： Mridul Jain , Saigopal Thota , Xun Luan , Gajendra Alias Nishad Kamat
IPC分类号： G06Q30/0201 , G06F16/901 , G06N20/10 , G06N20/20 , G06F17/18 , G06F18/214
CPC分类号： G06Q30/0201 , G06F16/9024 , G06N20/10 , G06N20/20 , G06F17/18 , G06F18/2148
摘要： This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

2. 发明申请

US20230068831A1 OPTIMIZING SCANS USING QUERY PLANNING ON BATCH DATA 有权
公开(公告)号：US20230068831A1
公开(公告)日：2023-03-02
申请号：US17412106
申请日：2021-08-25
申请人： Walmart Apollo, LLC
发明人： Mridul Jain , Saigopal Thota , Rewati Mahendra Ovalekar , Sébastien Jean-Maurice Olivier Péhu , Saumya Agarwal , Sai Kiran Reddy Malikireddy , Gajendra Alias Nishad Kamat , Mitesh Sinha
IPC分类号： G06F16/2453 , G06F16/21
摘要： A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: bundling multiple registered queries of a dataset using a scheduling technique, wherein the dataset is homogenous in schema; running a single table scan of the dataset to process the multiple registered queries of the dataset in parallel; and generating a respective output responsive to each of the multiple registered queries. Other embodiments are disclosed.

3. 发明申请

US20210240506A1 TRAVERSING A LARGE CONNECTED COMPONENT ON A DISTRIBUTED FILE-BASED DATA STRUCTURE 有权
公开(公告)号：US20210240506A1
公开(公告)日：2021-08-05
申请号：US16777834
申请日：2020-01-30
申请人： Walmart Apollo, LLC
发明人： Saigopal Thota , Mridul Jain , Albin Kuruvilla , Pruthvi Raj Eranti , Antriksh Shah
IPC分类号： G06F9/455 , G06F9/38 , G06F16/907
摘要： A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied. Each iteration can include processing a set of input nodes at the multiple processing nodes using the set of criteria to generate first data at the multiple processing nodes, determining a set of output nodes such that each output node of the set of output nodes is one hop from a respective input node of the set of input nodes, consolidating the first data from the multiple processing nodes to a first processing node of the multiple processing nodes, processing the first data at the first processing node; and assigning the set of input nodes for a subsequent iteration of the one or more iterations based on the set of output nodes when the stopping condition is not satisfied. The acts further can include outputting second data based on the first data received and processed at the first processing node during the one or more iterations. Other embodiments are disclosed.

4. 发明公开

US20240232921A1 AUTOMATICALLY MEASURING QUALITY SCORES FOR CONNECTED COMPONENTS USING MACHINE LEARNING MODELS 审中-公开
公开(公告)号：US20240232921A1
公开(公告)日：2024-07-11
申请号：US18607164
申请日：2024-03-15
申请人： WALMART APOLLO, LLC
发明人： Mridul Jain , Saigopal Thota , Ashraful Arefeen , Antriksh Akshesh Shah , Albin Kuruvilla , Gajendra Aliaas Nishad Kamat , Rijul Magu , Neil Mohan Reddy Palleti
IPC分类号： G06Q30/0204 , G06Q30/0241 , G06Q30/0242
CPC分类号： G06Q30/0204 , G06Q30/0244 , G06Q30/0276
摘要： A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: creating links between nodes to form connected components based on linkage scores exceeding a predetermined threshold; generating, by using a relaxed blocking criteria, an initial labeled set, wherein the relaxed blocking criteria is configured to prevent loss of data signals; tuning the relaxed blocking criteria by relaxing and tightening a precision threshold associated with the connected components; and generating a quality score for the connected components, wherein the quality score comprises an objective metric to identify a true graph of each user. Other embodiments are described.

5. 发明授权

US11556370B2 Traversing a large connected component on a distributed file-based data structure 有权
公开(公告)号：US11556370B2
公开(公告)日：2023-01-17
申请号：US16777834
申请日：2020-01-30
申请人： Walmart Apollo, LLC
发明人： Saigopal Thota , Mridul Jain , Albin Kuruvilla , Pruthvi Raj Eranti , Antriksh Shah
IPC分类号： G06F9/455 , G06F9/38 , G06F16/907
摘要： A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied. Each iteration can include processing a set of input nodes at the multiple processing nodes using the set of criteria to generate first data at the multiple processing nodes, determining a set of output nodes such that each output node of the set of output nodes is one hop from a respective input node of the set of input nodes, consolidating the first data from the multiple processing nodes to a first processing node of the multiple processing nodes, processing the first data at the first processing node; and assigning the set of input nodes for a subsequent iteration of the one or more iterations based on the set of output nodes when the stopping condition is not satisfied. The acts further can include outputting second data based on the first data received and processed at the first processing node during the one or more iterations. Other embodiments are disclosed.

6. 发明公开

US20240256878A1 DEEP LEARNING ENTITY MATCHING SYSTEM USING WEAK SUPERVISION 审中-公开
公开(公告)号：US20240256878A1
公开(公告)日：2024-08-01
申请号：US18103559
申请日：2023-01-31
申请人： Walmart Apollo, LLC
发明人： Neil Palleti , Antriksh Shah , Ashraful Arefeen , Saigopal Thota , Sreenaadh Sreekumar , Mridul Jain , Nishad Kamat , Rijul Magu
IPC分类号： G06N3/0895 , G06N5/01
CPC分类号： G06N3/0895 , G06N5/01
摘要： A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: generating pairs of identities from a plurality of sources; for each respective pair of identities of the pairs of identities: determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities; generating, using a connected component algorithm, clusters each containing identities representing a respective user; and generating a respective user profile for the respective user for each cluster. Other embodiments are disclosed.

7. 发明公开

US20230145505A1 TRAVERSING AN ADJACENCY LIST ON DISTRIBUTED PROCESSORS 审中-公开
公开(公告)号：US20230145505A1
公开(公告)日：2023-05-11
申请号：US18096273
申请日：2023-01-12
申请人： Walmart Apollo, LLC
发明人： Saigopal Thota , Mridul Jain , Albin Kuruvilla , Pruthvi Raj Eranti , Antriksh Shah
IPC分类号： G06F9/455 , G06F9/38 , G06F16/907
CPC分类号： G06F9/45558 , G06F9/3891 , G06F16/907 , G06F2009/45575 , G06F2009/4557
摘要： A distributed system including multiple processors associated with non-transitory computer-readable media storing computing instructions. The computing instructions, when collectively executed on the multiple processors, cause the multiple processors collectively to perform certain acts. The acts can include executing multiple iterations until a stopping condition is satisfied, by, for each of the multiple iterations: (i) processing a set of input nodes at the multiple processors using a set of criteria to generate first data at the multiple processors, wherein the set of input nodes is different at each of the multiple iterations; (ii) determining a list of output nodes using adjacency rows of an adjacency list at different ones of the multiple processors, such that each output node of the list of output nodes is one hop from a respective input node of the set of the input nodes; and (iii) updating the set of the input nodes for a subsequent iteration of the multiple iterations based on the list of output nodes when the stopping condition is not satisfied. The acts also can include outputting second data based at least in part on the first data. Other embodiments are disclosed.

8. 发明授权

US11604942B2 Method and apparatus for determining data linkage confidence levels 有权
公开(公告)号：US11604942B2
公开(公告)日：2023-03-14
申请号：US16263625
申请日：2019-01-31
申请人： Walmart Apollo, LLC
发明人： Mridul Jain , Saigopal Thota , Xun Luan , Gajendra Alias Nishad Kamat
IPC分类号： G06F9/44 , G06K9/62 , G06F16/901 , G06Q30/0201 , G06N20/10 , G06N20/20 , G06F17/18
摘要： This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

9. 发明授权

US11501185B2 System and method for real-time modeling inference pipeline 有权
公开(公告)号：US11501185B2
公开(公告)日：2022-11-15
申请号：US16262830
申请日：2019-01-30
申请人： Walmart Apollo, LLC
发明人： Mridul Jain , Gajendra Alias Nishad Kamat , Pawan Gupta , Saurabh Agrawal
IPC分类号： G06Q30/02 , G06N5/04 , G06N20/00 , H04L43/08 , G06F30/20
摘要： Systems and methods of real-time modeling pipeline inferencing are disclosed. At least one model configured to calculate at least one metric from one or more features is deployed. A model inferencing pipeline configured to extract the one or more features from a customer-specific data pipeline is implemented for the at least one mode. The model inferencing pipeline is generated using a training data set extracted from a cross-customer data pipeline. The at least one metric is calculated using the one or more features extracted from the customer-specific data pipeline.

10. 发明申请

US20200242487A1 SYSTEM AND METHOD FOR REAL-TIME MODELING INFERENCE PIPELINE 审中-公开
公开(公告)号：US20200242487A1
公开(公告)日：2020-07-30
申请号：US16262830
申请日：2019-01-30
申请人： Walmart Apollo, LLC
发明人： Mridul Jain , Gajendra Alias Nishad Kamat , Pawan Gupta , Saurabh Agrawal
IPC分类号： G06N5/04 , G06N20/00 , G06F17/50 , G06Q10/06 , H04L12/26
摘要： Systems and methods of real-time modeling pipeline inferencing are disclosed. At least one model configured to calculate at least one metric from one or more features is deployed. A model inferencing pipeline configured to extract the one or more features from a customer-specific data pipeline is implemented for the at least one mode. The model inferencing pipeline is generated using a training data set extracted from a cross-customer data pipeline. The at least one metric is calculated using the one or more features extracted from the customer-specific data pipeline.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式