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Patent Agency Ranking
公开(公告)号：US20210241179A1
公开(公告)日：2021-08-05
申请号：US16777686
申请日：2020-01-30
Applicant: salesforce.com, inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
Abstract: An online system performs predictions for real-time tasks and near real-time tasks that need to be performed by a deadline. A client device receives a real-time machine learning based model associated with a measure of accuracy. If the client device determines that a task can be performed using predictions having less than the specified measure of accuracy, the client device uses the real-time machine learning based model. If the client device determines that a higher level of accuracy of results is required, the client device sends a request to an online system. The online system provides a prediction along with a string representing a rationale for the prediction.
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公开(公告)号：US11790278B2
公开(公告)日：2023-10-17
申请号：US16778925
申请日：2020-01-31
Applicant: salesforce.com, inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
IPC: G06N20/20 , G06N5/045 , G06N20/00 , G06F18/243 , G06F18/2134
CPC classification number: G06N20/20 , G06F18/2134 , G06F18/24323 , G06N5/045 , G06N20/00
Abstract: An online system performs predictions for real-time tasks and near real-time tasks that need to be performed by a deadline. A client device receives a real-time machine learning based model associated with a measure of accuracy. If the client device determines that a task can be performed using predictions having less than the specified measure of accuracy, the client device uses the real-time machine learning based model. If the client device determines that a higher level of accuracy of results is required, the client device sends a request to an online system. The online system provides a prediction along with a string representing a rationale for the prediction.
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公开(公告)号：US11651291B2
公开(公告)日：2023-05-16
申请号：US16777686
申请日：2020-01-30
Applicant: salesforce.com, inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
Abstract: An online system performs predictions for real-time tasks and near real-time tasks that need to be performed by a deadline. A client device receives a real-time machine learning based model associated with a measure of accuracy. If the client device determines that a task can be performed using predictions having less than the specified measure of accuracy, the client device uses the real-time machine learning based model. If the client device determines that a higher level of accuracy of results is required, the client device sends a request to an online system. The online system provides a prediction along with a string representing a rationale for the prediction.
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公开(公告)号：US20210241047A1
公开(公告)日：2021-08-05
申请号：US16778925
申请日：2020-01-31
Applicant: salesforce.com, inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
Abstract: An online system performs predictions for real-time tasks and near real-time tasks that need to be performed by a deadline. A client device receives a real-time machine learning based model associated with a measure of accuracy. If the client device determines that a task can be performed using predictions having less than the specified measure of accuracy, the client device uses the real-time machine learning based model. If the client device determines that a higher level of accuracy of results is required, the client device sends a request to an online system. The online system provides a prediction along with a string representing a rationale for the prediction.
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公开(公告)号：US20190236460A1
公开(公告)日：2019-08-01
申请号：US15882134
申请日：2018-01-29
Applicant: salesforce.com, inc.
Inventor： Arun Kumar Jagota , Dmytro Kudriavtsev , Rakesh Ganapathi Karanth
CPC classification number: G06N5/025 , G06F16/951 , G06N20/00
Abstract: A training dataset having training instances is determined. Each training instance comprises first and second records and a second record and a label indicate whether there is a match between the first and second records. A matching score vector is determined for each such training instance, and comprises components storing match scores for extracted features from field values in the first and second records. Based on matching score vectors and a match objective function, match score thresholds are determined for the extracted features. Match rule(s) each of which comprises predicate(s) are generated. Each predicate makes a predication on whether two records match by comparing a match score derived from the two records against a match score threshold.
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