Resumen de: CN121171653A
本发明公开了一种基于多任务协同学习的社区独居老人风险识别方法,包括:收集社区独居老人多源异构数据集并进行预处理;对预处理后的数据进行数据特征提取;将提取的数据特征作为预训练的多任务协同的专家网络的输入;利用基于时间注意力机制的定制化多任务门控单元网络根据任务特性选择不同的专家并分配专家权重;通过选择出的专家网络对输入数据进行异常判定;融合各个专家网络的输出,得到融合特征;通过任务塔根据融合特征判断对应具体任务的风险评分;通过模糊综合评价方法对风险评估结果进行综合权衡,得到最终综合评估值。本发明实现了多源数据的协调工作,解决了高风险时段漏判、低风险时段误判的问题,提升了风险行为预测准确度。
Resumen de: US2025385871A1
A system for controlling network traffic or responding to communication channel impairment. The system includes a number of circuits configured to perform classification using a number of artificial intelligence models trained to provide an inference related to one class and an artificial intelligence model trained to provide an inference related to several classes. Models are connected within an architecture providing for selective execution of one or more of the individual models. Classification results are used to perform actions to affect flow of information in a communications system.
Nº publicación: US2025384340A1 18/12/2025
Solicitante:
KK TOSHIBA [JP]
TOSHIBA ELECTRONIC DEVICES & STORAGE CORP [JP]
KABUSHIKI KAISHA TOSHIBA,
TOSHIBA ELECTRONIC DEVICES & STORAGE CORPORATION
Resumen de: US2025384340A1
An information processing device includes a processing unit including a hardware processor. The hardware processor calculates plural exogenous-noise-estimation values corresponding to plural variables for each of one or more pieces of result data including plural result values respectively corresponding to the plural variables based on the result data and a structural-causal-model representing a causal-relationship of the plural variables. Each of the exogenous-noise-estimation values represents an estimation value of influence by an exogenous-noise different from influences from the plural variables on corresponding variables among the plural variables. The hardware processor generates a contribution-degree representing an influence-magnitude by the exogenous-noise given to a source variable as one of two variables to a target variable that is another variable for each combination of the two variables in the plural variables for the result data based on the structural-causal model and the plural exogenous-noise-estimation values for each result data.