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公开(公告)号:US20240160726A1
公开(公告)日:2024-05-16
申请号:US18418502
申请日:2024-01-22
发明人: Justin Horowitz , Melissa Podrazka , Sameer Sharma
CPC分类号: G06F21/552 , G06F18/2193 , G06F21/577 , G06N3/045
摘要: A resource conservation system, including a determination processor may be provided. The determination processor may identify a characterization output that characterizes a plurality of data structures. The characterization output may be based on plurality of inputs. The inputs may be processed through a plurality, or cascade, of artificial intelligence models both in sequence and in parallel. A numerical value may be identified for each data structure. The value may identify a degree of certainty that the determination processor accurately characterized each data structure. When the degree is above a threshold, the determination processor may identify a subset of inputs that most contributed to the characterization output. The determination processor may execute an equation to identify a subset of inputs that most contributed to the output. The equation may involve inputs and/or outputs of each of the cascade of models. Identified inputs may be ranked based on contribution to the outcome.
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公开(公告)号:US11928209B2
公开(公告)日:2024-03-12
申请号:US17541428
申请日:2021-12-03
发明人: Justin Horowitz , Melissa Podrazka , Sameer Sharma
CPC分类号: G06F21/552 , G06F18/2193 , G06F21/577 , G06N3/045
摘要: A resource conservation system, including a determination processor may be provided. The determination processor may identify a characterization output that characterizes a plurality of data structures. The characterization output may be based on plurality of inputs. The inputs may be processed through a plurality, or cascade, of artificial intelligence models both in sequence and in parallel. A numerical value may be identified for each data structure. The value may identify a degree of certainty that the determination processor accurately characterized each data structure. When the degree is above a threshold, the determination processor may identify a subset of inputs that most contributed to the characterization output. The determination processor may execute an equation to identify a subset of inputs that most contributed to the output. The equation may involve inputs and/or outputs of each of the cascade of models. Identified inputs may be ranked based on contribution to the outcome.
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公开(公告)号:US12118079B2
公开(公告)日:2024-10-15
申请号:US18418502
申请日:2024-01-22
发明人: Justin Horowitz , Melissa Podrazka , Sameer Sharma
CPC分类号: G06F21/552 , G06F18/2193 , G06F21/577 , G06N3/045
摘要: A resource conservation system, including a determination processor may be provided. The determination processor may identify a characterization output that characterizes a plurality of data structures. The characterization output may be based on plurality of inputs. The inputs may be processed through a plurality, or cascade, of artificial intelligence models both in sequence and in parallel. A numerical value may be identified for each data structure. The value may identify a degree of certainty that the determination processor accurately characterized each data structure. When the degree is above a threshold, the determination processor may identify a subset of inputs that most contributed to the characterization output. The determination processor may execute an equation to identify a subset of inputs that most contributed to the output. The equation may involve inputs and/or outputs of each of the cascade of models. Identified inputs may be ranked based on contribution to the outcome.
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公开(公告)号:US20230177150A1
公开(公告)日:2023-06-08
申请号:US17541428
申请日:2021-12-03
发明人: Justin Horowitz , Melissa Podrazka , Sameer Sharma
CPC分类号: G06F21/552 , G06F21/577 , G06K9/6265 , G06N3/0454
摘要: A resource conservation system, including a determination processor may be provided. The determination processor may identify a characterization output that characterizes a plurality of data structures. The characterization output may be based on plurality of inputs. The inputs may be processed through a plurality, or cascade, of artificial intelligence models both in sequence and in parallel. A numerical value may be identified for each data structure. The value may identify a degree of certainty that the determination processor accurately characterized each data structure. When the degree is above a threshold, the determination processor may identify a subset of inputs that most contributed to the characterization output. The determination processor may execute an equation to identify a subset of inputs that most contributed to the output. The equation may involve inputs and/or outputs of each of the cascade of models. Identified inputs may be ranked based on contribution to the outcome.
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